diff --git a/.gitignore b/.gitignore
index 1a2f21e6..4b3f4e76 100755
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,5 @@
 build/
+docs/examples/circle/*
 docs/examples/frenchman-flat/*
 docs/examples/quickstart/*
 docs/examples/twri/*
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 5ed51110..d6907419 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -10,6 +10,7 @@ repos:
       exclude: ^test/__compare__|^docs/examples/data
     - id: check-yaml
     - id: check-added-large-files
+      args: ['--maxkb=2048']
     - id: check-toml
 - repo: https://github.com/astral-sh/ruff-pre-commit
   rev: v0.8.2
diff --git a/docs/_toc.yml b/docs/_toc.yml
index 0addf3cb..24531176 100644
--- a/docs/_toc.yml
+++ b/docs/_toc.yml
@@ -10,6 +10,8 @@ parts:
         title: TWRI
       - file: examples/frenchman-flat
         title: Frenchman Flat
+      - file: examples/circle
+        title: Circle
 
   - caption: Developer Notes
     chapters:
diff --git a/docs/examples/circle.ipynb b/docs/examples/circle.ipynb
new file mode 100644
index 00000000..4b76fbb4
--- /dev/null
+++ b/docs/examples/circle.ipynb
@@ -0,0 +1,1470 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "957a2dbf",
+   "metadata": {},
+   "source": [
+    "# Circle\n",
+    "\n",
+    "DISV vertex grid with xugrid\n",
+    "\n",
+    "This example models steady-state groundwater flow in a circular domain\n",
+    "using DISV (vertex-based) discretization.  DISV allows arbitrary polygon\n",
+    "cells, which is useful for curvilinear or unstructured domains where a\n",
+    "regular structured grid would introduce excessive staircase error.\n",
+    "\n",
+    "Circle example:\n",
+    "* build a `Disv` package from an existing GRB file\n",
+    "* construct an xugrid `Ugrid2d` mesh from the discretization\n",
+    "* convert MODFLOW head and budget output to `UgridDataArray` for plotting\n",
+    "* overlay quiver vectors on an unstructured mesh using `assign_edge_coords()`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1c097fa",
+   "metadata": {},
+   "source": [
+    "### Import dependencies."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2341b301",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:19:56.650840Z",
+     "iopub.status.busy": "2026-03-06T20:19:56.650604Z",
+     "iopub.status.idle": "2026-03-06T20:19:56.656923Z",
+     "shell.execute_reply": "2026-03-06T20:19:56.656246Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from pathlib import Path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5d450fdd",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:19:56.658649Z",
+     "iopub.status.busy": "2026-03-06T20:19:56.658533Z",
+     "iopub.status.idle": "2026-03-06T20:19:57.716402Z",
+     "shell.execute_reply": "2026-03-06T20:19:57.715448Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import xarray as xr\n",
+    "import xugrid as xu\n",
+    "from flopy.mf6.utils.binarygrid_util import MfGrdFile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "21253d03",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:19:57.718482Z",
+     "iopub.status.busy": "2026-03-06T20:19:57.718210Z",
+     "iopub.status.idle": "2026-03-06T20:19:57.863355Z",
+     "shell.execute_reply": "2026-03-06T20:19:57.862622Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mjreno/.clone/dev/pyphoenix-project/flopy4/mf6/spec.py:14: FutureWarning: The modflow_devtools.dfns API is experimental and may change or be removed in future versions without following normal deprecation procedures. Use at your own risk. To suppress this warning, use:\n",
+      "  warnings.filterwarnings('ignore', message='.*modflow_devtools.dfns.*experimental.*')\n",
+      "  from modflow_devtools.dfns.schema.block import block_sort_key\n"
+     ]
+    }
+   ],
+   "source": [
+    "import flopy4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e8d45ee",
+   "metadata": {},
+   "source": [
+    "### Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bb703932",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:19:57.865260Z",
+     "iopub.status.busy": "2026-03-06T20:19:57.865140Z",
+     "iopub.status.idle": "2026-03-06T20:19:57.867680Z",
+     "shell.execute_reply": "2026-03-06T20:19:57.866912Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    CIRCLE_ROOT = Path(__file__).parent\n",
+    "except NameError:\n",
+    "    CIRCLE_ROOT = Path.cwd()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4ae10e5",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "source": [
+    "### Define plot function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b603e045",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:19:57.869190Z",
+     "iopub.status.busy": "2026-03-06T20:19:57.869071Z",
+     "iopub.status.idle": "2026-03-06T20:19:57.872974Z",
+     "shell.execute_reply": "2026-03-06T20:19:57.872226Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def plot_head_ugrid(head, cbc, workspace):\n",
+    "    \"\"\"Plot head and flow vectors using xugrid on a DISV (vertex) grid.\n",
+    "\n",
+    "    `gwf.output.head` returns a `UgridDataArray` for DISV models.\n",
+    "    `gwf.output.budget` returns a `UgridDataset` keyed by face-flow term.\n",
+    "\n",
+    "    To overlay quiver vectors on an unstructured mesh:\n",
+    "    1. Assemble `u` and `v` (face-normal flow) into a `UgridDataset`.\n",
+    "    2. Call `.ugrid.assign_edge_coords()` to attach edge-centre coordinates\n",
+    "        (`mesh2d_edge_x`, `mesh2d_edge_y`) as xarray coordinates.\n",
+    "    3. Call `.plot.quiver()` using those coordinates as positional arguments.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    cbc_grid = cbc[\"flow-horizontal-face-x\"].grid\n",
+    "    ds = xu.UgridDataset(grids=cbc_grid)\n",
+    "    ds[\"u\"] = cbc[\"flow-horizontal-face-x\"]\n",
+    "    ds[\"v\"] = cbc[\"flow-horizontal-face-y\"]\n",
+    "\n",
+    "    # Visualize the results\n",
+    "    ds = ds.ugrid.assign_edge_coords()\n",
+    "    fig, ax = plt.subplots()\n",
+    "    head.isel(time=0, layer=0).compute().ugrid.plot(ax=ax)\n",
+    "    ds.isel(time=0, layer=0).plot.quiver(\n",
+    "        x=\"mesh2d_edge_x\", y=\"mesh2d_edge_y\", u=\"u\", v=\"v\", color=\"white\"\n",
+    "    )\n",
+    "    ax.set_aspect(1)\n",
+    "    ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha=\"right\")\n",
+    "    plt.savefig(workspace / \"head_ugrid.png\", dpi=1200, bbox_inches=\"tight\")\n",
+    "    if not os.environ.get(\"PYTEST_CURRENT_TEST\"):\n",
+    "        plt.show()\n",
+    "    plt.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cff3ac1b",
+   "metadata": {},
+   "source": [
+    "### Timing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "0f1e60fd",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:19:57.874604Z",
+     "iopub.status.busy": "2026-03-06T20:19:57.874481Z",
+     "iopub.status.idle": "2026-03-06T20:19:57.877203Z",
+     "shell.execute_reply": "2026-03-06T20:19:57.876350Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# One steady-state stress period of length 1.0 day with a single time step.\n",
+    "time = flopy4.mf6.utils.time.Time(perlen=[1.0], nstp=[1], tsmult=[1.0], time_units=\"days\")\n",
+    "nper = time.nper"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d282b60",
+   "metadata": {},
+   "source": [
+    "### Load from GRB"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "4d20de6f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:19:57.878608Z",
+     "iopub.status.busy": "2026-03-06T20:19:57.878499Z",
+     "iopub.status.idle": "2026-03-06T20:19:57.883068Z",
+     "shell.execute_reply": "2026-03-06T20:19:57.882336Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Processing binary grid file: /home/mjreno/.clone/dev/pyphoenix-project/docs/examples/data/circle/disv.disv.grb\n",
+      "  File contains data for NCELLS with shape \n",
+      "  File contains data for NLAY with shape \n",
+      "  File contains data for NCPL with shape \n",
+      "  File contains data for NVERT with shape \n",
+      "  File contains data for NJAVERT with shape \n",
+      "  File contains data for NJA with shape \n",
+      "  File contains data for XORIGIN with shape \n",
+      "  File contains data for YORIGIN with shape \n",
+      "  File contains data for ANGROT with shape \n",
+      "  File contains data for TOP with shape (216,)\n",
+      "  File contains data for BOTM with shape (432,)\n",
+      "  File contains data for VERTICES with shape (127, 2)\n",
+      "  File contains data for CELLX with shape (216,)\n",
+      "  File contains data for CELLY with shape (216,)\n",
+      "  File contains data for IAVERT with shape (217,)\n",
+      "  File contains data for JAVERT with shape (864,)\n",
+      "  File contains data for IA with shape (433,)\n",
+      "  File contains data for JA with shape (2088,)\n",
+      "  File contains data for IDOMAIN with shape (432,)\n",
+      "  File contains data for ICELLTYPE with shape (432,)\n",
+      "Attempting to read 20 records from /home/mjreno/.clone/dev/pyphoenix-project/docs/examples/data/circle/disv.disv.grb\n",
+      "  Reading NCELLS\n",
+      "  NCELLS = 432\n",
+      "  Reading NLAY\n",
+      "  NLAY = 2\n",
+      "  Reading NCPL\n",
+      "  NCPL = 216\n",
+      "  Reading NVERT\n",
+      "  NVERT = 127\n",
+      "  Reading NJAVERT\n",
+      "  NJAVERT = 864\n",
+      "  Reading NJA\n",
+      "  NJA = 2088\n",
+      "  Reading XORIGIN\n",
+      "  XORIGIN = 0.0\n",
+      "  Reading YORIGIN\n",
+      "  YORIGIN = 0.0\n",
+      "  Reading ANGROT\n",
+      "  ANGROT = 0.0\n",
+      "  Reading TOP\n",
+      "  TOP: min = 10.0 max = 10.0\n",
+      "  Reading BOTM\n",
+      "  BOTM: min = 0.0 max = 5.0\n",
+      "  Reading VERTICES\n",
+      "  VERTICES: min = -1000.0 max = 1000.0\n",
+      "  Reading CELLX\n",
+      "  CELLX: min = -936.6044097425392 max = 936.6044097425392\n",
+      "  Reading CELLY\n",
+      "  CELLY: min = -940.3874981601984 max = 940.3874981601984\n",
+      "  Reading IAVERT\n",
+      "  IAVERT: min = 1 max = 865\n",
+      "  Reading JAVERT\n",
+      "  JAVERT: min = 1 max = 127\n",
+      "  Reading IA\n",
+      "  IA: min = 1 max = 2089\n",
+      "  Reading JA\n",
+      "  JA: min = 1 max = 432\n",
+      "  Reading IDOMAIN\n",
+      "  IDOMAIN: min = 1 max = 1\n",
+      "  Reading ICELLTYPE\n",
+      "  ICELLTYPE: min = 0 max = 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load an existing GRB (binary grid) file to get the DISV geometry.\n",
+    "# A GRB records vertex coordinates, cell connectivity, and grid metadata\n",
+    "# written by MODFLOW 6 after it has processed the DISV package.  Using it\n",
+    "# as the source avoids duplicating the geometry in Python.\n",
+    "grb_fpth = CIRCLE_ROOT / \"data\" / \"circle\" / \"disv.disv.grb\"\n",
+    "grb_obj = MfGrdFile(grb_fpth, verbose=True)\n",
+    "idomain = grb_obj.idomain\n",
+    "xorigin = grb_obj.xorigin\n",
+    "yorigin = grb_obj.yorigin\n",
+    "angrot = grb_obj.angrot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ad52cfa7",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:19:57.884471Z",
+     "iopub.status.busy": "2026-03-06T20:19:57.884344Z",
+     "iopub.status.idle": "2026-03-06T20:19:57.887958Z",
+     "shell.execute_reply": "2026-03-06T20:19:57.887079Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "returning iverts from /home/mjreno/.clone/dev/pyphoenix-project/docs/examples/data/circle/disv.disv.grb\n",
+      "returning verts from /home/mjreno/.clone/dev/pyphoenix-project/docs/examples/data/circle/disv.disv.grb\n",
+      "returning cell centers from /home/mjreno/.clone/dev/pyphoenix-project/docs/examples/data/circle/disv.disv.grb\n"
+     ]
+    }
+   ],
+   "source": [
+    "nlay, ncpl = int(grb_obj.nlay), int(grb_obj.ncpl)\n",
+    "top = np.ravel(grb_obj.top)\n",
+    "botm = grb_obj.bot\n",
+    "botm.shape = (nlay, ncpl)\n",
+    "vertices, cell2d = grb_obj.cell2d"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "447fb53c",
+   "metadata": {},
+   "source": [
+    "### DISV and Grid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "34afe80f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:19:57.889332Z",
+     "iopub.status.busy": "2026-03-06T20:19:57.889225Z",
+     "iopub.status.idle": "2026-03-06T20:19:57.894212Z",
+     "shell.execute_reply": "2026-03-06T20:19:57.893561Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# `Disv` holds the vertex-based discretization.  The grid origin and rotation\n",
+    "# can be set via `xorigin`/`yorigin`/`angrot`.  A CRS string (e.g. \"EPSG:26911\")\n",
+    "# can be attached for georeferencing.  All are passed to `Disv` at construction time.\n",
+    "disv = flopy4.mf6.gwf.disv.Disv(\n",
+    "    xorigin=573309.700,\n",
+    "    yorigin=4102552.000,\n",
+    "    crs=\"EPSG:26911\",\n",
+    "    length_units=\"meters\",\n",
+    "    nlay=nlay,\n",
+    "    ncpl=ncpl,\n",
+    "    nvert=len(vertices),\n",
+    "    top=top,\n",
+    "    botm=botm,\n",
+    "    idomain=idomain.reshape(nlay, ncpl),\n",
+    "    iv=np.array([v[0] for v in vertices], dtype=int),\n",
+    "    xv=np.array([v[1] for v in vertices], dtype=float),\n",
+    "    yv=np.array([v[2] for v in vertices], dtype=float),\n",
+    "    cell2ddata=flopy4.mf6.gwf.disv.Disv.grid_to_disv_cell2d(cell2d),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ef9949c1",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:19:57.895581Z",
+     "iopub.status.busy": "2026-03-06T20:19:57.895472Z",
+     "iopub.status.idle": "2026-03-06T20:19:57.925888Z",
+     "shell.execute_reply": "2026-03-06T20:19:57.925285Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Build the xugrid Ugrid2d mesh from the Disv package.\n",
+    "# `disv.to_grid()` returns a `VertexGrid`; `.ugrid` converts it to an\n",
+    "# `xu.Ugrid2d` object suitable for xugrid operations.\n",
+    "grid = disv.to_grid().ugrid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "658bd5a7",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:19:57.927700Z",
+     "iopub.status.busy": "2026-03-06T20:19:57.927576Z",
+     "iopub.status.idle": "2026-03-06T20:19:57.930231Z",
+     "shell.execute_reply": "2026-03-06T20:19:57.929601Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# `dims` captures array shapes needed by packages that pre-allocate xarray storage.\n",
+    "dims = {\"nper\": nper, \"nlay\": nlay, \"ncpl\": ncpl, \"nvert\": len(vertices), \"nodes\": nlay * ncpl}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "45f3cd43",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:19:57.931793Z",
+     "iopub.status.busy": "2026-03-06T20:19:57.931674Z",
+     "iopub.status.idle": "2026-03-06T20:19:57.935660Z",
+     "shell.execute_reply": "2026-03-06T20:19:57.934923Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Wrap idomain as a UgridDataArray so xugrid knows the cell topology.\n",
+    "idomain = xu.UgridDataArray(\n",
+    "    xr.DataArray(\n",
+    "        idomain.reshape(nlay, ncpl),\n",
+    "        coords={\"layer\": [l + 1 for l in range(nlay)]},\n",
+    "        dims=[\"layer\", grid.face_dimension],\n",
+    "    ),\n",
+    "    grid=grid,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22ce736e",
+   "metadata": {},
+   "source": [
+    "### Plot Grid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "6b74c791",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:19:57.936987Z",
+     "iopub.status.busy": "2026-03-06T20:19:57.936877Z",
+     "iopub.status.idle": "2026-03-06T20:19:57.939580Z",
+     "shell.execute_reply": "2026-03-06T20:19:57.938800Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create workspace\n",
+    "workspace = CIRCLE_ROOT / \"circle\" / \"list\"\n",
+    "workspace.mkdir(parents=True, exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "ef43c0bd",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:19:57.940968Z",
+     "iopub.status.busy": "2026-03-06T20:19:57.940856Z",
+     "iopub.status.idle": "2026-03-06T20:19:59.054062Z",
+     "shell.execute_reply": "2026-03-06T20:19:59.053247Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_77971/1750207287.py:5: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
+      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha=\"right\")\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot the grid\n",
+    "fig, ax = plt.subplots()\n",
+    "xu.plot.line(grid, ax=ax)\n",
+    "ax.set_aspect(1)\n",
+    "ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha=\"right\")\n",
+    "plt.savefig(workspace / \"grid.png\", dpi=1200, bbox_inches=\"tight\")\n",
+    "if not os.environ.get(\"PYTEST_CURRENT_TEST\"):\n",
+    "    plt.show()\n",
+    "plt.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43ea6fa9",
+   "metadata": {},
+   "source": [
+    "### Packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "da55090c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:19:59.056800Z",
+     "iopub.status.busy": "2026-03-06T20:19:59.056674Z",
+     "iopub.status.idle": "2026-03-06T20:19:59.059355Z",
+     "shell.execute_reply": "2026-03-06T20:19:59.058762Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Initial conditions: uniform starting head of 0.0 m.\n",
+    "ic = flopy4.mf6.gwf.Ic(strt=0.0, dims=dims)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "b90b3603",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:19:59.061448Z",
+     "iopub.status.busy": "2026-03-06T20:19:59.061321Z",
+     "iopub.status.idle": "2026-03-06T20:19:59.066801Z",
+     "shell.execute_reply": "2026-03-06T20:19:59.066136Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Node-property flow: isotropic, uniform conductivity; saves specific-discharge\n",
+    "# for vector plotting.\n",
+    "icelltype = xu.full_like(idomain, 0)\n",
+    "k = xu.full_like(idomain, 1.0, dtype=float)\n",
+    "k33 = k.copy()\n",
+    "npf = flopy4.mf6.gwf.Npf(\n",
+    "    # save_specific_discharge=True,\n",
+    "    icelltype=icelltype.values.ravel(),\n",
+    "    k=k.values.ravel(),\n",
+    "    k33=k33.values.ravel(),\n",
+    "    save_flows=True,\n",
+    "    dims=dims,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "da167fb9",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:19:59.068169Z",
+     "iopub.status.busy": "2026-03-06T20:19:59.068059Z",
+     "iopub.status.idle": "2026-03-06T20:19:59.070796Z",
+     "shell.execute_reply": "2026-03-06T20:19:59.070063Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Storage: steady-state for this single period.\n",
+    "sto = flopy4.mf6.gwf.Sto(\n",
+    "    ss=1.0e-5,\n",
+    "    sy=0.15,\n",
+    "    steady_state=[True],\n",
+    "    iconvert=0,\n",
+    "    dims=dims,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "28f4bed9",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:19:59.072326Z",
+     "iopub.status.busy": "2026-03-06T20:19:59.072210Z",
+     "iopub.status.idle": "2026-03-06T20:19:59.078121Z",
+     "shell.execute_reply": "2026-03-06T20:19:59.077508Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Constant head boundary on the outer ring of cells: `binary_dilation` with\n",
+    "# `border_value=True` identifies all cells that touch the domain boundary.\n",
+    "chd_head = {}\n",
+    "chd_location = xu.zeros_like(idomain.sel(layer=2), dtype=bool).ugrid.binary_dilation(\n",
+    "    border_value=True\n",
+    ")\n",
+    "for i in np.where(chd_location)[0]:\n",
+    "    chd_head[(1, int(i))] = 1.0\n",
+    "chd = flopy4.mf6.gwf.Chd(\n",
+    "    head={\"*\": chd_head},\n",
+    "    print_input=True,\n",
+    "    print_flows=True,\n",
+    "    save_flows=True,\n",
+    "    dims=dims,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "fb9ec303",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:19:59.079504Z",
+     "iopub.status.busy": "2026-03-06T20:19:59.079388Z",
+     "iopub.status.idle": "2026-03-06T20:19:59.082274Z",
+     "shell.execute_reply": "2026-03-06T20:19:59.081586Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Recharge: uniform rate applied to every cell in the top layer.\n",
+    "rch = flopy4.mf6.gwf.Rch(recharge={\"*\": {(0, j): 0.001 for j in range(ncpl)}}, dims=dims)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "184415fb",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:19:59.083810Z",
+     "iopub.status.busy": "2026-03-06T20:19:59.083695Z",
+     "iopub.status.idle": "2026-03-06T20:19:59.086654Z",
+     "shell.execute_reply": "2026-03-06T20:19:59.085895Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Output control: write heads and budget to binary files.\n",
+    "oc = flopy4.mf6.gwf.Oc(\n",
+    "    budget_file=\"gwf.bud\",\n",
+    "    head_file=\"gwf.hds\",\n",
+    "    save_head={0: \"all\"},\n",
+    "    save_budget={0: \"all\"},\n",
+    "    dims=dims,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9dbd9c6",
+   "metadata": {},
+   "source": [
+    "### Plot CHD"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "49316de9",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:19:59.088575Z",
+     "iopub.status.busy": "2026-03-06T20:19:59.088456Z",
+     "iopub.status.idle": "2026-03-06T20:20:00.474511Z",
+     "shell.execute_reply": "2026-03-06T20:20:00.473777Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_77971/1692941532.py:6: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
+      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha=\"right\")\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "constant_head = xu.full_like(idomain.sel(layer=2), 1.0, dtype=float).where(chd_location)\n",
+    "fig, ax = plt.subplots()\n",
+    "constant_head.ugrid.plot(ax=ax)\n",
+    "xu.plot.line(grid, ax=ax, color=\"black\")\n",
+    "ax.set_aspect(1)\n",
+    "ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha=\"right\")\n",
+    "plt.savefig(workspace / \"chd.png\", dpi=1200, bbox_inches=\"tight\")\n",
+    "if not os.environ.get(\"PYTEST_CURRENT_TEST\"):\n",
+    "    plt.show()\n",
+    "plt.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f4d35a2",
+   "metadata": {},
+   "source": [
+    "### Flow Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "a276fb91",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:20:00.476446Z",
+     "iopub.status.busy": "2026-03-06T20:20:00.476306Z",
+     "iopub.status.idle": "2026-03-06T20:20:00.482010Z",
+     "shell.execute_reply": "2026-03-06T20:20:00.481414Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Flow model: assemble GWF model from all packages defined above.\n",
+    "gwf = flopy4.mf6.gwf.Gwf(\n",
+    "    # save_flows=True,\n",
+    "    dis=disv,\n",
+    "    ic=ic,\n",
+    "    npf=npf,\n",
+    "    sto=sto,\n",
+    "    chd=[chd],\n",
+    "    rch=[rch],\n",
+    "    oc=oc,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "24bdb5a7",
+   "metadata": {},
+   "source": [
+    "### NetCDF mesh2d output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "71aac22d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:20:00.483504Z",
+     "iopub.status.busy": "2026-03-06T20:20:00.483387Z",
+     "iopub.status.idle": "2026-03-06T20:20:00.485851Z",
+     "shell.execute_reply": "2026-03-06T20:20:00.485248Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# When MF6_EXTENDED is set, also write a mesh2d NetCDF output file.\n",
+    "if os.getenv(\"MF6_EXTENDED\"):\n",
+    "    gwf.netcdf_mesh2d_file = Path(\"circle.nc\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3bb70aff",
+   "metadata": {},
+   "source": [
+    "### Solution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "9807ef7d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:20:00.487256Z",
+     "iopub.status.busy": "2026-03-06T20:20:00.487140Z",
+     "iopub.status.idle": "2026-03-06T20:20:00.490316Z",
+     "shell.execute_reply": "2026-03-06T20:20:00.489551Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Solver: conjugate-gradient suitable for the symmetric SPD system.\n",
+    "ims = flopy4.mf6.Ims(\n",
+    "    print_option=\"summary\",\n",
+    "    outer_dvclose=1.0e-4,\n",
+    "    outer_maximum=500,\n",
+    "    under_relaxation=None,\n",
+    "    inner_dvclose=1.0e-4,\n",
+    "    inner_rclose=0.001,\n",
+    "    inner_maximum=100,\n",
+    "    linear_acceleration=\"cg\",\n",
+    "    reordering_method=None,\n",
+    "    relaxation_factor=0.97,\n",
+    "    models=[\"gwf\"],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fec8b01b",
+   "metadata": {},
+   "source": [
+    "### TDIS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "0f25551a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:20:00.491747Z",
+     "iopub.status.busy": "2026-03-06T20:20:00.491624Z",
+     "iopub.status.idle": "2026-03-06T20:20:00.494405Z",
+     "shell.execute_reply": "2026-03-06T20:20:00.493768Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "tdis = flopy4.mf6.simulation.Tdis.from_time(time)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0f03021",
+   "metadata": {},
+   "source": [
+    "### Write and run — list-based inputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "446ee0b4",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:20:00.495804Z",
+     "iopub.status.busy": "2026-03-06T20:20:00.495688Z",
+     "iopub.status.idle": "2026-03-06T20:20:00.502563Z",
+     "shell.execute_reply": "2026-03-06T20:20:00.501867Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create simulation\n",
+    "sim = flopy4.mf6.simulation.Simulation(\n",
+    "    name=\"circle\",\n",
+    "    tdis=tdis,\n",
+    "    models={\"gwf\": gwf},\n",
+    "    solutions={\"ims\": ims},\n",
+    "    workspace=workspace,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "f40eb909",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:20:00.503925Z",
+     "iopub.status.busy": "2026-03-06T20:20:00.503796Z",
+     "iopub.status.idle": "2026-03-06T20:20:00.669240Z",
+     "shell.execute_reply": "2026-03-06T20:20:00.668141Z"
+    },
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "running: mf6\n",
+      "stdout:\n",
+      "                               MODFLOW 6 EXTENDED\n",
+      "                U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL\n",
+      "                  VERSION 6.8.0.dev0 (preliminary) 02/06/2026\n",
+      "                               ***DEVELOP MODE***\n",
+      "\n",
+      "        MODFLOW 6 compiled Feb 25 2026 15:19:23 with GCC version 13.3.0\n",
+      "\n",
+      "This software is preliminary or provisional and is subject to \n",
+      "revision. It is being provided to meet the need for timely best \n",
+      "science. The software has not received final approval by the U.S. \n",
+      "Geological Survey (USGS). No warranty, expressed or implied, is made \n",
+      "by the USGS or the U.S. Government as to the functionality of the \n",
+      "software and related material nor shall the fact of release \n",
+      "constitute any such warranty. The software is provided on the \n",
+      "condition that neither the USGS nor the U.S. Government shall be held \n",
+      "liable for any damages resulting from the authorized or unauthorized \n",
+      "use of the software.\n",
+      "\n",
+      "\n",
+      " MODFLOW runs in SEQUENTIAL mode\n",
+      "\n",
+      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2026/03/06 15:20:00\n",
+      "\n",
+      " Writing simulation list file: mfsim.lst\n",
+      " Using Simulation name file: mfsim.nam\n",
+      "\n",
+      "    Solving:  Stress period:     1    Time step:     1\n",
+      "\n",
+      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2026/03/06 15:20:00\n",
+      " Elapsed run time:  0.016 Seconds\n",
+      "\n",
+      " Normal termination of simulation.\n",
+      "\n",
+      "stderr:\n",
+      "\n",
+      "returncode: 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Write input files and run the simulation\n",
+    "sim.write()\n",
+    "sim.run(verbose=True)  # assumes the ``mf6`` executable is available on your PATH."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb856228",
+   "metadata": {},
+   "source": [
+    "### Read results and plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "ffa402bb",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:20:00.671104Z",
+     "iopub.status.busy": "2026-03-06T20:20:00.670979Z",
+     "iopub.status.idle": "2026-03-06T20:20:00.676428Z",
+     "shell.execute_reply": "2026-03-06T20:20:00.675650Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# head object from netcdf output — `UgridDataArray` backed by the mesh2d output file.\n",
+    "head = gwf.output.head"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "6dc857ca",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:20:00.678386Z",
+     "iopub.status.busy": "2026-03-06T20:20:00.678268Z",
+     "iopub.status.idle": "2026-03-06T20:20:00.693484Z",
+     "shell.execute_reply": "2026-03-06T20:20:00.692792Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# budget object — `UgridDataset` with one variable per face-flow term.\n",
+    "cbc = gwf.output.budget"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "ca71e22d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:20:00.695283Z",
+     "iopub.status.busy": "2026-03-06T20:20:00.695173Z",
+     "iopub.status.idle": "2026-03-06T20:20:02.252396Z",
+     "shell.execute_reply": "2026-03-06T20:20:02.251407Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_77971/559833197.py:27: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
+      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha=\"right\")\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot results\n",
+    "plot_head_ugrid(head, cbc, workspace)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "185b8494",
+   "metadata": {},
+   "source": [
+    "### NetCDF input — layered mesh (list-based CHD)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "880b6126",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:20:02.255066Z",
+     "iopub.status.busy": "2026-03-06T20:20:02.254931Z",
+     "iopub.status.idle": "2026-03-06T20:20:02.257422Z",
+     "shell.execute_reply": "2026-03-06T20:20:02.256947Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Re-run with the DISV packages written to a layered-mesh NetCDF file.\n",
+    "# `MF6_EXTENDED=1` is required to run MODFLOW with NetCDF input.\n",
+    "workspace = CIRCLE_ROOT / \"circle\" / \"netcdf\"\n",
+    "workspace.mkdir(parents=True, exist_ok=True)\n",
+    "sim.workspace = workspace"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "c043766d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:20:02.259088Z",
+     "iopub.status.busy": "2026-03-06T20:20:02.258967Z",
+     "iopub.status.idle": "2026-03-06T20:20:02.261545Z",
+     "shell.execute_reply": "2026-03-06T20:20:02.260975Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "nc_fpth = workspace / \"circle.input.nc\"\n",
+    "gwf.netcdf_file = nc_fpth"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "f6c377d3",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:20:02.263202Z",
+     "iopub.status.busy": "2026-03-06T20:20:02.263082Z",
+     "iopub.status.idle": "2026-03-06T20:20:02.326786Z",
+     "shell.execute_reply": "2026-03-06T20:20:02.325879Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Here, grid and time info is passed to the `NetCDFModel' constructor\n",
+    "# so that coordinate and mesh data is written to the NetCDF file.\n",
+    "nc_model = flopy4.mf6.netcdf.NetCDFModel.from_model(\n",
+    "    gwf, mesh=\"layered\", grid=disv.to_grid(), time=time\n",
+    ")\n",
+    "nc_model.to_netcdf(nc_fpth)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "1f5869a8",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:20:02.328849Z",
+     "iopub.status.busy": "2026-03-06T20:20:02.328718Z",
+     "iopub.status.idle": "2026-03-06T20:20:02.400527Z",
+     "shell.execute_reply": "2026-03-06T20:20:02.399665Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "with flopy4.mf6.write_context.WriteContext(use_netcdf=True):\n",
+    "    sim.write()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "941dab32",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:20:02.402324Z",
+     "iopub.status.busy": "2026-03-06T20:20:02.402185Z",
+     "iopub.status.idle": "2026-03-06T20:20:03.895259Z",
+     "shell.execute_reply": "2026-03-06T20:20:03.894120Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "running: mf6\n",
+      "stdout:\n",
+      "                               MODFLOW 6 EXTENDED\n",
+      "                U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL\n",
+      "                  VERSION 6.8.0.dev0 (preliminary) 02/06/2026\n",
+      "                               ***DEVELOP MODE***\n",
+      "\n",
+      "        MODFLOW 6 compiled Feb 25 2026 15:19:23 with GCC version 13.3.0\n",
+      "\n",
+      "This software is preliminary or provisional and is subject to \n",
+      "revision. It is being provided to meet the need for timely best \n",
+      "science. The software has not received final approval by the U.S. \n",
+      "Geological Survey (USGS). No warranty, expressed or implied, is made \n",
+      "by the USGS or the U.S. Government as to the functionality of the \n",
+      "software and related material nor shall the fact of release \n",
+      "constitute any such warranty. The software is provided on the \n",
+      "condition that neither the USGS nor the U.S. Government shall be held \n",
+      "liable for any damages resulting from the authorized or unauthorized \n",
+      "use of the software.\n",
+      "\n",
+      "\n",
+      " MODFLOW runs in SEQUENTIAL mode\n",
+      "\n",
+      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2026/03/06 15:20:02\n",
+      "\n",
+      " Writing simulation list file: mfsim.lst\n",
+      " Using Simulation name file: mfsim.nam\n",
+      "\n",
+      "    Solving:  Stress period:     1    Time step:     1\n",
+      "\n",
+      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2026/03/06 15:20:02\n",
+      " Elapsed run time:  0.018 Seconds\n",
+      "\n",
+      " Normal termination of simulation.\n",
+      "\n",
+      "stderr:\n",
+      "\n",
+      "returncode: 0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_77971/559833197.py:27: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
+      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha=\"right\")\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if os.getenv(\"MF6_EXTENDED\"):\n",
+    "    sim.run(verbose=True)\n",
+    "\n",
+    "    # head object from netcdf output — `UgridDataArray` backed by the mesh2d output file.\n",
+    "    head = gwf.output.head\n",
+    "\n",
+    "    # budget object — `UgridDataset` with one variable per face-flow term.\n",
+    "    cbc = gwf.output.budget\n",
+    "\n",
+    "    # plot results\n",
+    "    plot_head_ugrid(head, cbc, workspace)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "060ff868",
+   "metadata": {},
+   "source": [
+    "### Array-based CHD (Chdg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "449cf5fd",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:20:03.897568Z",
+     "iopub.status.busy": "2026-03-06T20:20:03.897434Z",
+     "iopub.status.idle": "2026-03-06T20:20:03.900535Z",
+     "shell.execute_reply": "2026-03-06T20:20:03.899791Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Switch from the list-based `Chd` to the array-based `Chdg` and re-run\n",
+    "# with NetCDF input.  Head output is not requested here (no head_file in OC).\n",
+    "GRID_NODATA = np.full((nlay, ncpl), flopy4.mf6.constants.FILL_DNODATA, dtype=float)\n",
+    "head = np.repeat(np.expand_dims(GRID_NODATA, axis=0), repeats=nper, axis=0)\n",
+    "for i in np.where(chd_location)[0]:\n",
+    "    head[0, 1, i] = 1.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "38d08e4e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:20:03.902078Z",
+     "iopub.status.busy": "2026-03-06T20:20:03.901951Z",
+     "iopub.status.idle": "2026-03-06T20:20:03.905356Z",
+     "shell.execute_reply": "2026-03-06T20:20:03.904530Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "chdg = flopy4.mf6.gwf.Chdg(\n",
+    "    print_input=True,\n",
+    "    print_flows=True,\n",
+    "    save_flows=True,\n",
+    "    head=head.reshape(nper, nlay * ncpl),\n",
+    "    dims=dims,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "bd822a01",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:20:03.906955Z",
+     "iopub.status.busy": "2026-03-06T20:20:03.906835Z",
+     "iopub.status.idle": "2026-03-06T20:20:03.921765Z",
+     "shell.execute_reply": "2026-03-06T20:20:03.921038Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# update chd to chdg\n",
+    "gwf.chd.remove(chd)\n",
+    "gwf.chd = [chdg]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "764eb7e2",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:20:03.923290Z",
+     "iopub.status.busy": "2026-03-06T20:20:03.923174Z",
+     "iopub.status.idle": "2026-03-06T20:20:03.925703Z",
+     "shell.execute_reply": "2026-03-06T20:20:03.925088Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "workspace = CIRCLE_ROOT / \"circle\" / \"array\"\n",
+    "workspace.mkdir(parents=True, exist_ok=True)\n",
+    "sim.workspace = workspace"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "43d5b7e7",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:20:03.927196Z",
+     "iopub.status.busy": "2026-03-06T20:20:03.927079Z",
+     "iopub.status.idle": "2026-03-06T20:20:03.929498Z",
+     "shell.execute_reply": "2026-03-06T20:20:03.928727Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "nc_fpth = workspace / \"circle.input.nc\"\n",
+    "gwf.netcdf_file = nc_fpth"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "43bbab2c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:20:03.930960Z",
+     "iopub.status.busy": "2026-03-06T20:20:03.930846Z",
+     "iopub.status.idle": "2026-03-06T20:20:03.987393Z",
+     "shell.execute_reply": "2026-03-06T20:20:03.986556Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Again, with grid and time info\n",
+    "nc_model = flopy4.mf6.netcdf.NetCDFModel.from_model(\n",
+    "    gwf, mesh=\"layered\", grid=disv.to_grid(), time=time\n",
+    ")\n",
+    "nc_model.to_netcdf(nc_fpth)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "6f4f3252",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:20:03.989460Z",
+     "iopub.status.busy": "2026-03-06T20:20:03.989337Z",
+     "iopub.status.idle": "2026-03-06T20:20:04.057218Z",
+     "shell.execute_reply": "2026-03-06T20:20:04.056425Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "with flopy4.mf6.write_context.WriteContext(use_netcdf=True):\n",
+    "    sim.write()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "67679361",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:20:04.059210Z",
+     "iopub.status.busy": "2026-03-06T20:20:04.059062Z",
+     "iopub.status.idle": "2026-03-06T20:20:05.655069Z",
+     "shell.execute_reply": "2026-03-06T20:20:05.654004Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "running: mf6\n",
+      "stdout:\n",
+      "                               MODFLOW 6 EXTENDED\n",
+      "                U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL\n",
+      "                  VERSION 6.8.0.dev0 (preliminary) 02/06/2026\n",
+      "                               ***DEVELOP MODE***\n",
+      "\n",
+      "        MODFLOW 6 compiled Feb 25 2026 15:19:23 with GCC version 13.3.0\n",
+      "\n",
+      "This software is preliminary or provisional and is subject to \n",
+      "revision. It is being provided to meet the need for timely best \n",
+      "science. The software has not received final approval by the U.S. \n",
+      "Geological Survey (USGS). No warranty, expressed or implied, is made \n",
+      "by the USGS or the U.S. Government as to the functionality of the \n",
+      "software and related material nor shall the fact of release \n",
+      "constitute any such warranty. The software is provided on the \n",
+      "condition that neither the USGS nor the U.S. Government shall be held \n",
+      "liable for any damages resulting from the authorized or unauthorized \n",
+      "use of the software.\n",
+      "\n",
+      "\n",
+      " MODFLOW runs in SEQUENTIAL mode\n",
+      "\n",
+      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2026/03/06 15:20:04\n",
+      "\n",
+      " Writing simulation list file: mfsim.lst\n",
+      " Using Simulation name file: mfsim.nam\n",
+      "\n",
+      "    Solving:  Stress period:     1    Time step:     1\n",
+      "\n",
+      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2026/03/06 15:20:04\n",
+      " Elapsed run time:  0.018 Seconds\n",
+      "\n",
+      " Normal termination of simulation.\n",
+      "\n",
+      "stderr:\n",
+      "\n",
+      "returncode: 0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_77971/559833197.py:27: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
+      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha=\"right\")\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if os.getenv(\"MF6_EXTENDED\"):\n",
+    "    sim.run(verbose=True)\n",
+    "\n",
+    "    # Load head results — `UgridDataArray` backed by the NetCDF mesh2d output file.\n",
+    "    head = flopy4.mf6.utils.open_hds(\n",
+    "        workspace / gwf.netcdf_mesh2d_file,\n",
+    "        workspace / f\"{gwf.name}.disv.grb\",\n",
+    "    )\n",
+    "\n",
+    "    # budget object — `UgridDataset` with one variable per face-flow term.\n",
+    "    cbc = gwf.output.budget\n",
+    "\n",
+    "    # plot results\n",
+    "    plot_head_ugrid(head, cbc, workspace)"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-all",
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/circle.py b/docs/examples/circle.py
new file mode 100644
index 00000000..eb940c86
--- /dev/null
+++ b/docs/examples/circle.py
@@ -0,0 +1,373 @@
+# # Circle
+#
+# DISV vertex grid with xugrid
+#
+# This example models steady-state groundwater flow in a circular domain
+# using DISV (vertex-based) discretization.  DISV allows arbitrary polygon
+# cells, which is useful for curvilinear or unstructured domains where a
+# regular structured grid would introduce excessive staircase error.
+#
+# Circle example:
+# * build a `Disv` package from an existing GRB file
+# * construct an xugrid `Ugrid2d` mesh from the discretization
+# * convert MODFLOW head and budget output to `UgridDataArray` for plotting
+# * overlay quiver vectors on an unstructured mesh using `assign_edge_coords()`
+
+# ### Import dependencies.
+
+import os
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+import xarray as xr
+import xugrid as xu
+from flopy.mf6.utils.binarygrid_util import MfGrdFile
+
+import flopy4
+
+# ### Setup
+
+try:
+    CIRCLE_ROOT = Path(__file__).parent
+except NameError:
+    CIRCLE_ROOT = Path.cwd()
+
+# ### Define plot function
+
+
+def plot_head_ugrid(head, cbc, workspace):
+    """Plot head and flow vectors using xugrid on a DISV (vertex) grid.
+
+    `gwf.output.head` returns a `UgridDataArray` for DISV models.
+    `gwf.output.budget` returns a `UgridDataset` keyed by face-flow term.
+
+    To overlay quiver vectors on an unstructured mesh:
+    1. Assemble `u` and `v` (face-normal flow) into a `UgridDataset`.
+    2. Call `.ugrid.assign_edge_coords()` to attach edge-centre coordinates
+        (`mesh2d_edge_x`, `mesh2d_edge_y`) as xarray coordinates.
+    3. Call `.plot.quiver()` using those coordinates as positional arguments.
+    """
+
+    cbc_grid = cbc["flow-horizontal-face-x"].grid
+    ds = xu.UgridDataset(grids=cbc_grid)
+    ds["u"] = cbc["flow-horizontal-face-x"]
+    ds["v"] = cbc["flow-horizontal-face-y"]
+
+    # Visualize the results
+    ds = ds.ugrid.assign_edge_coords()
+    fig, ax = plt.subplots()
+    head.isel(time=0, layer=0).compute().ugrid.plot(ax=ax)
+    ds.isel(time=0, layer=0).plot.quiver(
+        x="mesh2d_edge_x", y="mesh2d_edge_y", u="u", v="v", color="white"
+    )
+    ax.set_aspect(1)
+    ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha="right")
+    plt.savefig(workspace / "head_ugrid.png", dpi=1200, bbox_inches="tight")
+    if not os.environ.get("PYTEST_CURRENT_TEST"):
+        plt.show()
+    plt.close()
+
+
+# ### Timing
+
+# One steady-state stress period of length 1.0 day with a single time step.
+time = flopy4.mf6.utils.time.Time(perlen=[1.0], nstp=[1], tsmult=[1.0], time_units="days")
+nper = time.nper
+
+# ### Load from GRB
+
+# Load an existing GRB (binary grid) file to get the DISV geometry.
+# A GRB records vertex coordinates, cell connectivity, and grid metadata
+# written by MODFLOW 6 after it has processed the DISV package.  Using it
+# as the source avoids duplicating the geometry in Python.
+grb_fpth = CIRCLE_ROOT / "data" / "circle" / "disv.disv.grb"
+grb_obj = MfGrdFile(grb_fpth, verbose=True)
+idomain = grb_obj.idomain
+xorigin = grb_obj.xorigin
+yorigin = grb_obj.yorigin
+angrot = grb_obj.angrot
+
+nlay, ncpl = int(grb_obj.nlay), int(grb_obj.ncpl)
+top = np.ravel(grb_obj.top)
+botm = grb_obj.bot
+botm.shape = (nlay, ncpl)
+vertices, cell2d = grb_obj.cell2d
+
+# ### DISV and Grid
+
+# `Disv` holds the vertex-based discretization.  The grid origin and rotation
+# can be set via `xorigin`/`yorigin`/`angrot`.  A CRS string (e.g. "EPSG:26911")
+# can be attached for georeferencing.  All are passed to `Disv` at construction time.
+disv = flopy4.mf6.gwf.disv.Disv(
+    xorigin=573309.700,
+    yorigin=4102552.000,
+    crs="EPSG:26911",
+    length_units="meters",
+    nlay=nlay,
+    ncpl=ncpl,
+    nvert=len(vertices),
+    top=top,
+    botm=botm,
+    idomain=idomain.reshape(nlay, ncpl),
+    iv=np.array([v[0] for v in vertices], dtype=int),
+    xv=np.array([v[1] for v in vertices], dtype=float),
+    yv=np.array([v[2] for v in vertices], dtype=float),
+    cell2ddata=flopy4.mf6.gwf.disv.Disv.grid_to_disv_cell2d(cell2d),
+)
+
+# Build the xugrid Ugrid2d mesh from the Disv package.
+# `disv.to_grid()` returns a `VertexGrid`; `.ugrid` converts it to an
+# `xu.Ugrid2d` object suitable for xugrid operations.
+grid = disv.to_grid().ugrid
+
+# `dims` captures array shapes needed by packages that pre-allocate xarray storage.
+dims = {"nper": nper, "nlay": nlay, "ncpl": ncpl, "nvert": len(vertices), "nodes": nlay * ncpl}
+
+# Wrap idomain as a UgridDataArray so xugrid knows the cell topology.
+idomain = xu.UgridDataArray(
+    xr.DataArray(
+        idomain.reshape(nlay, ncpl),
+        coords={"layer": [l + 1 for l in range(nlay)]},
+        dims=["layer", grid.face_dimension],
+    ),
+    grid=grid,
+)
+
+# ### Plot Grid
+
+# Create workspace
+workspace = CIRCLE_ROOT / "circle" / "list"
+workspace.mkdir(parents=True, exist_ok=True)
+
+# plot the grid
+fig, ax = plt.subplots()
+xu.plot.line(grid, ax=ax)
+ax.set_aspect(1)
+ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha="right")
+plt.savefig(workspace / "grid.png", dpi=1200, bbox_inches="tight")
+if not os.environ.get("PYTEST_CURRENT_TEST"):
+    plt.show()
+plt.close()
+
+# ### Packages
+
+# Initial conditions: uniform starting head of 0.0 m.
+ic = flopy4.mf6.gwf.Ic(strt=0.0, dims=dims)
+
+# Node-property flow: isotropic, uniform conductivity; saves specific-discharge
+# for vector plotting.
+icelltype = xu.full_like(idomain, 0)
+k = xu.full_like(idomain, 1.0, dtype=float)
+k33 = k.copy()
+npf = flopy4.mf6.gwf.Npf(
+    # save_specific_discharge=True,
+    icelltype=icelltype.values.ravel(),
+    k=k.values.ravel(),
+    k33=k33.values.ravel(),
+    save_flows=True,
+    dims=dims,
+)
+
+# Storage: steady-state for this single period.
+sto = flopy4.mf6.gwf.Sto(
+    ss=1.0e-5,
+    sy=0.15,
+    steady_state=[True],
+    iconvert=0,
+    dims=dims,
+)
+
+# Constant head boundary on the outer ring of cells: `binary_dilation` with
+# `border_value=True` identifies all cells that touch the domain boundary.
+chd_head = {}
+chd_location = xu.zeros_like(idomain.sel(layer=2), dtype=bool).ugrid.binary_dilation(
+    border_value=True
+)
+for i in np.where(chd_location)[0]:
+    chd_head[(1, int(i))] = 1.0
+chd = flopy4.mf6.gwf.Chd(
+    head={"*": chd_head},
+    print_input=True,
+    print_flows=True,
+    save_flows=True,
+    dims=dims,
+)
+
+# Recharge: uniform rate applied to every cell in the top layer.
+rch = flopy4.mf6.gwf.Rch(recharge={"*": {(0, j): 0.001 for j in range(ncpl)}}, dims=dims)
+
+# Output control: write heads and budget to binary files.
+oc = flopy4.mf6.gwf.Oc(
+    budget_file="gwf.bud",
+    head_file="gwf.hds",
+    save_head={0: "all"},
+    save_budget={0: "all"},
+    dims=dims,
+)
+
+# ### Plot CHD
+
+constant_head = xu.full_like(idomain.sel(layer=2), 1.0, dtype=float).where(chd_location)
+fig, ax = plt.subplots()
+constant_head.ugrid.plot(ax=ax)
+xu.plot.line(grid, ax=ax, color="black")
+ax.set_aspect(1)
+ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha="right")
+plt.savefig(workspace / "chd.png", dpi=1200, bbox_inches="tight")
+if not os.environ.get("PYTEST_CURRENT_TEST"):
+    plt.show()
+plt.close()
+
+# ### Flow Model
+
+# Flow model: assemble GWF model from all packages defined above.
+gwf = flopy4.mf6.gwf.Gwf(
+    # save_flows=True,
+    dis=disv,
+    ic=ic,
+    npf=npf,
+    sto=sto,
+    chd=[chd],
+    rch=[rch],
+    oc=oc,
+)
+
+# ### NetCDF mesh2d output
+
+# When MF6_EXTENDED is set, also write a mesh2d NetCDF output file.
+if os.getenv("MF6_EXTENDED"):
+    gwf.netcdf_mesh2d_file = Path("circle.nc")
+
+# ### Solution
+
+# Solver: conjugate-gradient suitable for the symmetric SPD system.
+ims = flopy4.mf6.Ims(
+    print_option="summary",
+    outer_dvclose=1.0e-4,
+    outer_maximum=500,
+    under_relaxation=None,
+    inner_dvclose=1.0e-4,
+    inner_rclose=0.001,
+    inner_maximum=100,
+    linear_acceleration="cg",
+    reordering_method=None,
+    relaxation_factor=0.97,
+    models=["gwf"],
+)
+
+# ### TDIS
+
+tdis = flopy4.mf6.simulation.Tdis.from_time(time)
+
+# ### Write and run — list-based inputs
+
+# Create simulation
+sim = flopy4.mf6.simulation.Simulation(
+    name="circle",
+    tdis=tdis,
+    models={"gwf": gwf},
+    solutions={"ims": ims},
+    workspace=workspace,
+)
+
+# Write input files and run the simulation
+sim.write()
+sim.run(verbose=True)  # assumes the ``mf6`` executable is available on your PATH.
+
+
+# ### Read results and plot
+
+# head object from netcdf output — `UgridDataArray` backed by the mesh2d output file.
+head = gwf.output.head
+
+# budget object — `UgridDataset` with one variable per face-flow term.
+cbc = gwf.output.budget
+
+# plot results
+plot_head_ugrid(head, cbc, workspace)
+
+# ### NetCDF input — layered mesh (list-based CHD)
+
+# Re-run with the DISV packages written to a layered-mesh NetCDF file.
+# `MF6_EXTENDED=1` is required to run MODFLOW with NetCDF input.
+workspace = CIRCLE_ROOT / "circle" / "netcdf"
+workspace.mkdir(parents=True, exist_ok=True)
+sim.workspace = workspace
+
+nc_fpth = workspace / "circle.input.nc"
+gwf.netcdf_file = nc_fpth
+
+# Here, grid and time info is passed to the `NetCDFModel' constructor
+# so that coordinate and mesh data is written to the NetCDF file.
+nc_model = flopy4.mf6.netcdf.NetCDFModel.from_model(
+    gwf, mesh="layered", grid=disv.to_grid(), time=time
+)
+nc_model.to_netcdf(nc_fpth)
+
+with flopy4.mf6.write_context.WriteContext(use_netcdf=True):
+    sim.write()
+
+if os.getenv("MF6_EXTENDED"):
+    sim.run(verbose=True)
+
+    # head object from netcdf output — `UgridDataArray` backed by the mesh2d output file.
+    head = gwf.output.head
+
+    # budget object — `UgridDataset` with one variable per face-flow term.
+    cbc = gwf.output.budget
+
+    # plot results
+    plot_head_ugrid(head, cbc, workspace)
+
+# ### Array-based CHD (Chdg)
+
+# Switch from the list-based `Chd` to the array-based `Chdg` and re-run
+# with NetCDF input.  Head output is not requested here (no head_file in OC).
+GRID_NODATA = np.full((nlay, ncpl), flopy4.mf6.constants.FILL_DNODATA, dtype=float)
+head = np.repeat(np.expand_dims(GRID_NODATA, axis=0), repeats=nper, axis=0)
+for i in np.where(chd_location)[0]:
+    head[0, 1, i] = 1.0
+
+chdg = flopy4.mf6.gwf.Chdg(
+    print_input=True,
+    print_flows=True,
+    save_flows=True,
+    head=head.reshape(nper, nlay * ncpl),
+    dims=dims,
+)
+
+# update chd to chdg
+gwf.chd.remove(chd)
+gwf.chd = [chdg]
+
+workspace = CIRCLE_ROOT / "circle" / "array"
+workspace.mkdir(parents=True, exist_ok=True)
+sim.workspace = workspace
+
+nc_fpth = workspace / "circle.input.nc"
+gwf.netcdf_file = nc_fpth
+
+# Again, with grid and time info
+nc_model = flopy4.mf6.netcdf.NetCDFModel.from_model(
+    gwf, mesh="layered", grid=disv.to_grid(), time=time
+)
+nc_model.to_netcdf(nc_fpth)
+
+with flopy4.mf6.write_context.WriteContext(use_netcdf=True):
+    sim.write()
+
+if os.getenv("MF6_EXTENDED"):
+    sim.run(verbose=True)
+
+    # Load head results — `UgridDataArray` backed by the NetCDF mesh2d output file.
+    head = flopy4.mf6.utils.open_hds(
+        workspace / gwf.netcdf_mesh2d_file,
+        workspace / f"{gwf.name}.disv.grb",
+    )
+
+    # budget object — `UgridDataset` with one variable per face-flow term.
+    cbc = gwf.output.budget
+
+    # plot results
+    plot_head_ugrid(head, cbc, workspace)
diff --git a/docs/examples/data/circle/disv.disv.grb b/docs/examples/data/circle/disv.disv.grb
new file mode 100644
index 00000000..841400d0
Binary files /dev/null and b/docs/examples/data/circle/disv.disv.grb differ
diff --git a/docs/examples/frenchman-flat.ipynb b/docs/examples/frenchman-flat.ipynb
index c6cab690..4e5d132a 100644
--- a/docs/examples/frenchman-flat.ipynb
+++ b/docs/examples/frenchman-flat.ipynb
@@ -41,10 +41,10 @@
    "id": "130eb946",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:09:14.170665Z",
-     "iopub.status.busy": "2026-03-04T20:09:14.170544Z",
-     "iopub.status.idle": "2026-03-04T20:09:14.174556Z",
-     "shell.execute_reply": "2026-03-04T20:09:14.173770Z"
+     "iopub.execute_input": "2026-03-06T20:20:22.094884Z",
+     "iopub.status.busy": "2026-03-06T20:20:22.094756Z",
+     "iopub.status.idle": "2026-03-06T20:20:22.101530Z",
+     "shell.execute_reply": "2026-03-06T20:20:22.100470Z"
     }
    },
    "outputs": [],
@@ -59,10 +59,10 @@
    "id": "082b831c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:09:14.176167Z",
-     "iopub.status.busy": "2026-03-04T20:09:14.176051Z",
-     "iopub.status.idle": "2026-03-04T20:09:14.272601Z",
-     "shell.execute_reply": "2026-03-04T20:09:14.272140Z"
+     "iopub.execute_input": "2026-03-06T20:20:22.104044Z",
+     "iopub.status.busy": "2026-03-06T20:20:22.103888Z",
+     "iopub.status.idle": "2026-03-06T20:20:22.228636Z",
+     "shell.execute_reply": "2026-03-06T20:20:22.227579Z"
     }
    },
    "outputs": [],
@@ -76,13 +76,23 @@
    "id": "db86bf03",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:09:14.276692Z",
-     "iopub.status.busy": "2026-03-04T20:09:14.276478Z",
-     "iopub.status.idle": "2026-03-04T20:09:15.384923Z",
-     "shell.execute_reply": "2026-03-04T20:09:15.384194Z"
+     "iopub.execute_input": "2026-03-06T20:20:22.230785Z",
+     "iopub.status.busy": "2026-03-06T20:20:22.230577Z",
+     "iopub.status.idle": "2026-03-06T20:20:23.345278Z",
+     "shell.execute_reply": "2026-03-06T20:20:23.344405Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mjreno/.clone/dev/pyphoenix-project/flopy4/mf6/spec.py:14: FutureWarning: The modflow_devtools.dfns API is experimental and may change or be removed in future versions without following normal deprecation procedures. Use at your own risk. To suppress this warning, use:\n",
+      "  warnings.filterwarnings('ignore', message='.*modflow_devtools.dfns.*experimental.*')\n",
+      "  from modflow_devtools.dfns.schema.block import block_sort_key\n"
+     ]
+    }
+   ],
    "source": [
     "import flopy4"
    ]
@@ -101,10 +111,10 @@
    "id": "59a970d6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:09:15.387255Z",
-     "iopub.status.busy": "2026-03-04T20:09:15.386969Z",
-     "iopub.status.idle": "2026-03-04T20:09:15.389664Z",
-     "shell.execute_reply": "2026-03-04T20:09:15.389035Z"
+     "iopub.execute_input": "2026-03-06T20:20:23.347669Z",
+     "iopub.status.busy": "2026-03-06T20:20:23.347382Z",
+     "iopub.status.idle": "2026-03-06T20:20:23.350353Z",
+     "shell.execute_reply": "2026-03-06T20:20:23.349599Z"
     }
    },
    "outputs": [],
@@ -122,7 +132,7 @@
     "lines_to_next_cell": 2
    },
    "source": [
-    "### Define plot function"
+    "### Define plot functions"
    ]
   },
   {
@@ -131,10 +141,10 @@
    "id": "9356fc39",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:09:15.391219Z",
-     "iopub.status.busy": "2026-03-04T20:09:15.391111Z",
-     "iopub.status.idle": "2026-03-04T20:09:15.394033Z",
-     "shell.execute_reply": "2026-03-04T20:09:15.393403Z"
+     "iopub.execute_input": "2026-03-06T20:20:23.352515Z",
+     "iopub.status.busy": "2026-03-06T20:20:23.352391Z",
+     "iopub.status.idle": "2026-03-06T20:20:23.355343Z",
+     "shell.execute_reply": "2026-03-06T20:20:23.354640Z"
     }
    },
    "outputs": [],
@@ -150,32 +160,94 @@
     "    plt.ylabel(\"y\")\n",
     "    plt.grid(True)\n",
     "    plt.savefig(workspace / \"head.png\", dpi=300, bbox_inches=\"tight\")\n",
-    "    plt.show()\n",
+    "    if not os.environ.get(\"PYTEST_CURRENT_TEST\"):\n",
+    "        plt.show()\n",
     "    plt.close()"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "55bf34f8",
-   "metadata": {},
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "0f3d811b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:20:23.357072Z",
+     "iopub.status.busy": "2026-03-06T20:20:23.356955Z",
+     "iopub.status.idle": "2026-03-06T20:20:23.362348Z",
+     "shell.execute_reply": "2026-03-06T20:20:23.361425Z"
+    }
+   },
+   "outputs": [],
    "source": [
-    "### Timing"
+    "def plot_head_ugrid(head, cbc, grid, workspace):\n",
+    "    \"\"\"Plot head and flow vectors using xugrid on a DIS (structured) grid.\n",
+    "\n",
+    "    Even though the model uses DIS discretization, we can wrap the 2-D\n",
+    "    (y, x) head slice in a ``xu.UgridDataArray`` by flattening it to a\n",
+    "    face dimension whose topology is defined by ``grid.ugrid``.  This lets\n",
+    "    us use xugrid's plotting API without converting the model to DISV.\n",
+    "\n",
+    "    Flow vectors come from the CBC budget terms:\n",
+    "    * ``u = flow-right-face``  (positive = eastward / +x direction)\n",
+    "    * ``v = -flow-front-face`` (negated because MODFLOW's \"front\" face is\n",
+    "      the south face; positive \"front\" flow is southward, so we negate to\n",
+    "      get the northward (+y) component for a conventional quiver plot)\n",
+    "    \"\"\"\n",
+    "    import matplotlib.pyplot as plt\n",
+    "    import xarray as xr\n",
+    "    import xugrid as xu\n",
+    "\n",
+    "    ugrid = grid.ugrid\n",
+    "    facedim = ugrid.face_dimension\n",
+    "\n",
+    "    # Select first timestep and first layer; flatten (y, x) -> face dimension\n",
+    "    h = head.isel(time=0, layer=0).compute()\n",
+    "    head_uda = xu.UgridDataArray(\n",
+    "        xr.DataArray(h.values.ravel(), dims=[facedim], name=\"head\"),\n",
+    "        grid=ugrid,\n",
+    "    )\n",
+    "\n",
+    "    # Flow vectors: u = flow-right-face (+x/east), v = -flow-front-face (+y/north)\n",
+    "    u = cbc[\"flow-right-face\"].isel(time=0, layer=0).compute()\n",
+    "    v = -cbc[\"flow-front-face\"].isel(time=0, layer=0).compute()\n",
+    "    ds = xu.UgridDataset(grids=ugrid)\n",
+    "    ds[\"u\"] = xu.UgridDataArray(\n",
+    "        xr.DataArray(u.values.ravel(), dims=[facedim], name=\"u\"), grid=ugrid\n",
+    "    )\n",
+    "    ds[\"v\"] = xu.UgridDataArray(\n",
+    "        xr.DataArray(v.values.ravel(), dims=[facedim], name=\"v\"), grid=ugrid\n",
+    "    )\n",
+    "    ds = ds.ugrid.assign_face_coords()\n",
+    "\n",
+    "    fig, ax = plt.subplots(figsize=(10, 8))\n",
+    "    head_uda.ugrid.plot(ax=ax)\n",
+    "    xu.plot.line(ugrid, ax=ax, color=\"white\", linewidth=0.1)\n",
+    "    ds.plot.quiver(x=\"mesh2d_face_x\", y=\"mesh2d_face_y\", u=\"u\", v=\"v\", color=\"black\")\n",
+    "    ax.set_aspect(1)\n",
+    "    ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha=\"right\")\n",
+    "    ax.set_title(\"Head with flow vectors (layer 1, time 0)\")\n",
+    "    plt.savefig(workspace / \"head_ugrid.png\", dpi=300, bbox_inches=\"tight\")\n",
+    "    if not os.environ.get(\"PYTEST_CURRENT_TEST\"):\n",
+    "        plt.show()\n",
+    "    plt.close()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "8c18e438",
+   "execution_count": 7,
+   "id": "5c83aa90",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:09:15.395577Z",
-     "iopub.status.busy": "2026-03-04T20:09:15.395460Z",
-     "iopub.status.idle": "2026-03-04T20:09:15.399358Z",
-     "shell.execute_reply": "2026-03-04T20:09:15.398666Z"
+     "iopub.execute_input": "2026-03-06T20:20:23.364361Z",
+     "iopub.status.busy": "2026-03-06T20:20:23.364235Z",
+     "iopub.status.idle": "2026-03-06T20:20:23.368431Z",
+     "shell.execute_reply": "2026-03-06T20:20:23.367619Z"
     }
    },
    "outputs": [],
    "source": [
+    "# # Timing\n",
+    "#\n",
     "# 33 transient stress periods matching the original pumping schedule,\n",
     "# with 15 time steps per period and a 1.1× geometric time-step multiplier.\n",
     "time = flopy4.mf6.utils.time.Time(\n",
@@ -289,14 +361,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "be78c1cc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:09:15.401117Z",
-     "iopub.status.busy": "2026-03-04T20:09:15.400997Z",
-     "iopub.status.idle": "2026-03-04T20:09:15.403589Z",
-     "shell.execute_reply": "2026-03-04T20:09:15.402870Z"
+     "iopub.execute_input": "2026-03-06T20:20:23.369789Z",
+     "iopub.status.busy": "2026-03-06T20:20:23.369673Z",
+     "iopub.status.idle": "2026-03-06T20:20:23.372157Z",
+     "shell.execute_reply": "2026-03-06T20:20:23.371430Z"
     }
    },
    "outputs": [],
@@ -314,14 +386,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "e4541547",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:09:15.405465Z",
-     "iopub.status.busy": "2026-03-04T20:09:15.405339Z",
-     "iopub.status.idle": "2026-03-04T20:09:15.435095Z",
-     "shell.execute_reply": "2026-03-04T20:09:15.434305Z"
+     "iopub.execute_input": "2026-03-06T20:20:23.373675Z",
+     "iopub.status.busy": "2026-03-06T20:20:23.373563Z",
+     "iopub.status.idle": "2026-03-06T20:20:23.390386Z",
+     "shell.execute_reply": "2026-03-06T20:20:23.389494Z"
     }
    },
    "outputs": [],
@@ -570,14 +642,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "443d1daf",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:09:15.437022Z",
-     "iopub.status.busy": "2026-03-04T20:09:15.436897Z",
-     "iopub.status.idle": "2026-03-04T20:09:15.443946Z",
-     "shell.execute_reply": "2026-03-04T20:09:15.443207Z"
+     "iopub.execute_input": "2026-03-06T20:20:23.392366Z",
+     "iopub.status.busy": "2026-03-06T20:20:23.392247Z",
+     "iopub.status.idle": "2026-03-06T20:20:23.399179Z",
+     "shell.execute_reply": "2026-03-06T20:20:23.398427Z"
     }
    },
    "outputs": [],
@@ -588,14 +660,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "a668afe9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:09:15.445715Z",
-     "iopub.status.busy": "2026-03-04T20:09:15.445594Z",
-     "iopub.status.idle": "2026-03-04T20:09:15.448774Z",
-     "shell.execute_reply": "2026-03-04T20:09:15.447919Z"
+     "iopub.execute_input": "2026-03-06T20:20:23.401074Z",
+     "iopub.status.busy": "2026-03-06T20:20:23.400955Z",
+     "iopub.status.idle": "2026-03-06T20:20:23.403938Z",
+     "shell.execute_reply": "2026-03-06T20:20:23.403234Z"
     }
    },
    "outputs": [],
@@ -606,14 +678,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "3b902cc1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:09:15.450422Z",
-     "iopub.status.busy": "2026-03-04T20:09:15.450310Z",
-     "iopub.status.idle": "2026-03-04T20:09:15.470988Z",
-     "shell.execute_reply": "2026-03-04T20:09:15.470392Z"
+     "iopub.execute_input": "2026-03-06T20:20:23.405499Z",
+     "iopub.status.busy": "2026-03-06T20:20:23.405389Z",
+     "iopub.status.idle": "2026-03-06T20:20:23.428077Z",
+     "shell.execute_reply": "2026-03-06T20:20:23.427272Z"
     }
    },
    "outputs": [],
@@ -640,14 +712,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "c48e5d06",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:09:15.472419Z",
-     "iopub.status.busy": "2026-03-04T20:09:15.472306Z",
-     "iopub.status.idle": "2026-03-04T20:09:15.476082Z",
-     "shell.execute_reply": "2026-03-04T20:09:15.475347Z"
+     "iopub.execute_input": "2026-03-06T20:20:23.429955Z",
+     "iopub.status.busy": "2026-03-06T20:20:23.429826Z",
+     "iopub.status.idle": "2026-03-06T20:20:23.433914Z",
+     "shell.execute_reply": "2026-03-06T20:20:23.433117Z"
     }
    },
    "outputs": [],
@@ -663,14 +735,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "94b0a67b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:09:15.477761Z",
-     "iopub.status.busy": "2026-03-04T20:09:15.477650Z",
-     "iopub.status.idle": "2026-03-04T20:09:15.492079Z",
-     "shell.execute_reply": "2026-03-04T20:09:15.491339Z"
+     "iopub.execute_input": "2026-03-06T20:20:23.435524Z",
+     "iopub.status.busy": "2026-03-06T20:20:23.435413Z",
+     "iopub.status.idle": "2026-03-06T20:20:23.450026Z",
+     "shell.execute_reply": "2026-03-06T20:20:23.449403Z"
     }
    },
    "outputs": [],
@@ -687,14 +759,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "60997da5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:09:15.493355Z",
-     "iopub.status.busy": "2026-03-04T20:09:15.493242Z",
-     "iopub.status.idle": "2026-03-04T20:09:15.496693Z",
-     "shell.execute_reply": "2026-03-04T20:09:15.496044Z"
+     "iopub.execute_input": "2026-03-06T20:20:23.452058Z",
+     "iopub.status.busy": "2026-03-06T20:20:23.451946Z",
+     "iopub.status.idle": "2026-03-06T20:20:23.455698Z",
+     "shell.execute_reply": "2026-03-06T20:20:23.454822Z"
     }
    },
    "outputs": [],
@@ -720,14 +792,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "id": "7aee5258",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:09:15.498393Z",
-     "iopub.status.busy": "2026-03-04T20:09:15.498166Z",
-     "iopub.status.idle": "2026-03-04T20:09:15.529887Z",
-     "shell.execute_reply": "2026-03-04T20:09:15.529089Z"
+     "iopub.execute_input": "2026-03-06T20:20:23.457253Z",
+     "iopub.status.busy": "2026-03-06T20:20:23.457144Z",
+     "iopub.status.idle": "2026-03-06T20:20:23.488372Z",
+     "shell.execute_reply": "2026-03-06T20:20:23.487561Z"
     }
    },
    "outputs": [],
@@ -764,14 +836,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "id": "66fc2063",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:09:15.531979Z",
-     "iopub.status.busy": "2026-03-04T20:09:15.531859Z",
-     "iopub.status.idle": "2026-03-04T20:09:15.554106Z",
-     "shell.execute_reply": "2026-03-04T20:09:15.553182Z"
+     "iopub.execute_input": "2026-03-06T20:20:23.491082Z",
+     "iopub.status.busy": "2026-03-06T20:20:23.490961Z",
+     "iopub.status.idle": "2026-03-06T20:20:23.513763Z",
+     "shell.execute_reply": "2026-03-06T20:20:23.512977Z"
     }
    },
    "outputs": [],
@@ -835,14 +907,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "id": "646a91dd",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:09:15.556481Z",
-     "iopub.status.busy": "2026-03-04T20:09:15.556359Z",
-     "iopub.status.idle": "2026-03-04T20:09:15.575715Z",
-     "shell.execute_reply": "2026-03-04T20:09:15.574777Z"
+     "iopub.execute_input": "2026-03-06T20:20:23.515730Z",
+     "iopub.status.busy": "2026-03-06T20:20:23.515609Z",
+     "iopub.status.idle": "2026-03-06T20:20:23.533774Z",
+     "shell.execute_reply": "2026-03-06T20:20:23.532848Z"
     }
    },
    "outputs": [],
@@ -894,14 +966,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "id": "6d424c90",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:09:15.577841Z",
-     "iopub.status.busy": "2026-03-04T20:09:15.577721Z",
-     "iopub.status.idle": "2026-03-04T20:09:15.584989Z",
-     "shell.execute_reply": "2026-03-04T20:09:15.584067Z"
+     "iopub.execute_input": "2026-03-06T20:20:23.536049Z",
+     "iopub.status.busy": "2026-03-06T20:20:23.535921Z",
+     "iopub.status.idle": "2026-03-06T20:20:23.540424Z",
+     "shell.execute_reply": "2026-03-06T20:20:23.539839Z"
     }
    },
    "outputs": [],
@@ -952,14 +1024,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "id": "661ef945",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:09:15.587058Z",
-     "iopub.status.busy": "2026-03-04T20:09:15.586937Z",
-     "iopub.status.idle": "2026-03-04T20:09:15.595566Z",
-     "shell.execute_reply": "2026-03-04T20:09:15.594652Z"
+     "iopub.execute_input": "2026-03-06T20:20:23.541941Z",
+     "iopub.status.busy": "2026-03-06T20:20:23.541827Z",
+     "iopub.status.idle": "2026-03-06T20:20:23.551011Z",
+     "shell.execute_reply": "2026-03-06T20:20:23.550281Z"
     }
    },
    "outputs": [],
@@ -986,14 +1058,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "id": "06ea8a44",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:09:15.597178Z",
-     "iopub.status.busy": "2026-03-04T20:09:15.597061Z",
-     "iopub.status.idle": "2026-03-04T20:09:15.600056Z",
-     "shell.execute_reply": "2026-03-04T20:09:15.599302Z"
+     "iopub.execute_input": "2026-03-06T20:20:23.552501Z",
+     "iopub.status.busy": "2026-03-06T20:20:23.552385Z",
+     "iopub.status.idle": "2026-03-06T20:20:23.555797Z",
+     "shell.execute_reply": "2026-03-06T20:20:23.554903Z"
     }
    },
    "outputs": [],
@@ -1030,14 +1102,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "id": "1f6033ea",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:09:15.601728Z",
-     "iopub.status.busy": "2026-03-04T20:09:15.601613Z",
-     "iopub.status.idle": "2026-03-04T20:09:15.604564Z",
-     "shell.execute_reply": "2026-03-04T20:09:15.603789Z"
+     "iopub.execute_input": "2026-03-06T20:20:23.557271Z",
+     "iopub.status.busy": "2026-03-06T20:20:23.557163Z",
+     "iopub.status.idle": "2026-03-06T20:20:23.560063Z",
+     "shell.execute_reply": "2026-03-06T20:20:23.559349Z"
     }
    },
    "outputs": [],
@@ -1055,14 +1127,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "id": "a8cb6913",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:09:15.606268Z",
-     "iopub.status.busy": "2026-03-04T20:09:15.606132Z",
-     "iopub.status.idle": "2026-03-04T20:09:15.608560Z",
-     "shell.execute_reply": "2026-03-04T20:09:15.607954Z"
+     "iopub.execute_input": "2026-03-06T20:20:23.561533Z",
+     "iopub.status.busy": "2026-03-06T20:20:23.561424Z",
+     "iopub.status.idle": "2026-03-06T20:20:23.563973Z",
+     "shell.execute_reply": "2026-03-06T20:20:23.563248Z"
     }
    },
    "outputs": [],
@@ -1082,14 +1154,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "id": "1c744d64",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:09:15.610120Z",
-     "iopub.status.busy": "2026-03-04T20:09:15.610011Z",
-     "iopub.status.idle": "2026-03-04T20:09:15.616886Z",
-     "shell.execute_reply": "2026-03-04T20:09:15.616263Z"
+     "iopub.execute_input": "2026-03-06T20:20:23.565382Z",
+     "iopub.status.busy": "2026-03-06T20:20:23.565272Z",
+     "iopub.status.idle": "2026-03-06T20:20:23.572277Z",
+     "shell.execute_reply": "2026-03-06T20:20:23.571593Z"
     }
    },
    "outputs": [],
@@ -1106,14 +1178,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "id": "13e2fe77",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:09:15.618588Z",
-     "iopub.status.busy": "2026-03-04T20:09:15.618475Z",
-     "iopub.status.idle": "2026-03-04T20:09:56.802144Z",
-     "shell.execute_reply": "2026-03-04T20:09:56.801057Z"
+     "iopub.execute_input": "2026-03-06T20:20:23.573729Z",
+     "iopub.status.busy": "2026-03-06T20:20:23.573620Z",
+     "iopub.status.idle": "2026-03-06T20:21:04.384376Z",
+     "shell.execute_reply": "2026-03-06T20:21:04.383463Z"
     }
    },
    "outputs": [
@@ -1150,7 +1222,7 @@
       "\n",
       " MODFLOW runs in SEQUENTIAL mode\n",
       "\n",
-      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2026/03/04 15:09:15\n",
+      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2026/03/06 15:20:23\n",
       "\n",
       " Writing simulation list file: mfsim.lst\n",
       " Using Simulation name file: mfsim.nam\n",
@@ -1651,8 +1723,8 @@
       "    Solving:  Stress period:    33    Time step:    14\n",
       "    Solving:  Stress period:    33    Time step:    15\n",
       "\n",
-      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2026/03/04 15:09:56\n",
-      " Elapsed run time: 40.993 Seconds\n",
+      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2026/03/06 15:21:04\n",
+      " Elapsed run time: 40.612 Seconds\n",
       "\n",
       " Normal termination of simulation.\n",
       "\n",
@@ -1663,6 +1735,7 @@
     }
    ],
    "source": [
+    "# run verbose only this time\n",
     "sim.write()\n",
     "sim.run(verbose=True)"
    ]
@@ -1677,18 +1750,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "id": "06f8070b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:09:56.804141Z",
-     "iopub.status.busy": "2026-03-04T20:09:56.804004Z",
-     "iopub.status.idle": "2026-03-04T20:09:56.860788Z",
-     "shell.execute_reply": "2026-03-04T20:09:56.860092Z"
+     "iopub.execute_input": "2026-03-06T20:21:04.386313Z",
+     "iopub.status.busy": "2026-03-06T20:21:04.386154Z",
+     "iopub.status.idle": "2026-03-06T20:21:04.493910Z",
+     "shell.execute_reply": "2026-03-06T20:21:04.492805Z"
     }
    },
    "outputs": [],
    "source": [
+    "# Load head results\n",
     "head = flopy4.mf6.utils.open_hds(\n",
     "    workspace / \"ff.hds\",\n",
     "    workspace / \"ff.dis.grb\",\n",
@@ -1696,39 +1770,83 @@
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "934f8127",
-   "metadata": {},
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "33cc1f3c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:21:04.496401Z",
+     "iopub.status.busy": "2026-03-06T20:21:04.496280Z",
+     "iopub.status.idle": "2026-03-06T20:21:04.658205Z",
+     "shell.execute_reply": "2026-03-06T20:21:04.657494Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mjreno/.clone/dev/pyphoenix-project/flopy4/mf6/utils/cbc_reader.py:147: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  return xr.merge([cbc])\n",
+      "/home/mjreno/.clone/dev/pyphoenix-project/flopy4/mf6/utils/cbc_reader.py:147: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  return xr.merge([cbc])\n"
+     ]
+    }
+   ],
    "source": [
-    "### Plot head results"
+    "# Load budget results\n",
+    "cbc = flopy4.mf6.utils.open_cbc(\n",
+    "    workspace / \"ff.cbc\",\n",
+    "    workspace / \"ff.dis.grb\",\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "id": "ee3e6b4d",
+   "execution_count": 28,
+   "id": "b96fc6c9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:09:56.862589Z",
-     "iopub.status.busy": "2026-03-04T20:09:56.862466Z",
-     "iopub.status.idle": "2026-03-04T20:09:57.270875Z",
-     "shell.execute_reply": "2026-03-04T20:09:57.269744Z"
+     "iopub.execute_input": "2026-03-06T20:21:04.660096Z",
+     "iopub.status.busy": "2026-03-06T20:21:04.659975Z",
+     "iopub.status.idle": "2026-03-06T20:21:05.713443Z",
+     "shell.execute_reply": "2026-03-06T20:21:05.712529Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 2 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_78070/3549307819.py:46: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
+      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha=\"right\")\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "plot_head(head, workspace)"
+    "# Plot head results\n",
+    "plot_head(head, workspace)\n",
+    "plot_head_ugrid(head, cbc, grid, workspace)"
    ]
   },
   {
@@ -1751,14 +1869,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 29,
    "id": "a21bc0c8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:09:57.272682Z",
-     "iopub.status.busy": "2026-03-04T20:09:57.272558Z",
-     "iopub.status.idle": "2026-03-04T20:09:57.275564Z",
-     "shell.execute_reply": "2026-03-04T20:09:57.274789Z"
+     "iopub.execute_input": "2026-03-06T20:21:05.715953Z",
+     "iopub.status.busy": "2026-03-06T20:21:05.715821Z",
+     "iopub.status.idle": "2026-03-06T20:21:05.718760Z",
+     "shell.execute_reply": "2026-03-06T20:21:05.717962Z"
     }
    },
    "outputs": [],
@@ -1771,14 +1889,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 30,
    "id": "e87dac4e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:09:57.277242Z",
-     "iopub.status.busy": "2026-03-04T20:09:57.277124Z",
-     "iopub.status.idle": "2026-03-04T20:09:57.279625Z",
-     "shell.execute_reply": "2026-03-04T20:09:57.278739Z"
+     "iopub.execute_input": "2026-03-06T20:21:05.720256Z",
+     "iopub.status.busy": "2026-03-06T20:21:05.720105Z",
+     "iopub.status.idle": "2026-03-06T20:21:05.722401Z",
+     "shell.execute_reply": "2026-03-06T20:21:05.721797Z"
     }
    },
    "outputs": [],
@@ -1789,32 +1907,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 31,
    "id": "a6485d96",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:09:57.281081Z",
-     "iopub.status.busy": "2026-03-04T20:09:57.280966Z",
-     "iopub.status.idle": "2026-03-04T20:09:57.375124Z",
-     "shell.execute_reply": "2026-03-04T20:09:57.374128Z"
+     "iopub.execute_input": "2026-03-06T20:21:05.723821Z",
+     "iopub.status.busy": "2026-03-06T20:21:05.723707Z",
+     "iopub.status.idle": "2026-03-06T20:21:05.802388Z",
+     "shell.execute_reply": "2026-03-06T20:21:05.801339Z"
     }
    },
    "outputs": [],
    "source": [
+    "# Here, grid and time info is passed to the `NetCDFModel' constructor\n",
+    "# so that coordinate and mesh data is written to the NetCDF file.\n",
     "nc_model = flopy4.mf6.netcdf.NetCDFModel.from_model(gwf, mesh=\"layered\", grid=grid, time=time)\n",
     "nc_model.to_netcdf(nc_fpth)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 32,
    "id": "7ae091a4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:09:57.376766Z",
-     "iopub.status.busy": "2026-03-04T20:09:57.376628Z",
-     "iopub.status.idle": "2026-03-04T20:09:57.482185Z",
-     "shell.execute_reply": "2026-03-04T20:09:57.481456Z"
+     "iopub.execute_input": "2026-03-06T20:21:05.804209Z",
+     "iopub.status.busy": "2026-03-06T20:21:05.804066Z",
+     "iopub.status.idle": "2026-03-06T20:21:05.907224Z",
+     "shell.execute_reply": "2026-03-06T20:21:05.906364Z"
     }
    },
    "outputs": [],
@@ -1825,2068 +1945,536 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 33,
    "id": "89f79547",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:09:57.484259Z",
-     "iopub.status.busy": "2026-03-04T20:09:57.484131Z",
-     "iopub.status.idle": "2026-03-04T20:10:37.624019Z",
-     "shell.execute_reply": "2026-03-04T20:10:37.623185Z"
+     "iopub.execute_input": "2026-03-06T20:21:05.909391Z",
+     "iopub.status.busy": "2026-03-06T20:21:05.909274Z",
+     "iopub.status.idle": "2026-03-06T20:21:47.376333Z",
+     "shell.execute_reply": "2026-03-06T20:21:47.375675Z"
     }
    },
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "running: mf6\n"
+      "/home/mjreno/.clone/dev/pyphoenix-project/flopy4/mf6/utils/cbc_reader.py:147: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  return xr.merge([cbc])\n",
+      "/home/mjreno/.clone/dev/pyphoenix-project/flopy4/mf6/utils/cbc_reader.py:147: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  return xr.merge([cbc])\n"
      ]
     },
     {
-     "name": "stdout",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "stdout:\n",
-      "                               MODFLOW 6 EXTENDED\n",
-      "                U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL\n",
-      "                  VERSION 6.8.0.dev0 (preliminary) 02/06/2026\n",
-      "                               ***DEVELOP MODE***\n",
-      "\n",
-      "        MODFLOW 6 compiled Feb 25 2026 15:19:23 with GCC version 13.3.0\n",
-      "\n",
-      "This software is preliminary or provisional and is subject to \n",
-      "revision. It is being provided to meet the need for timely best \n",
-      "science. The software has not received final approval by the U.S. \n",
-      "Geological Survey (USGS). No warranty, expressed or implied, is made \n",
-      "by the USGS or the U.S. Government as to the functionality of the \n",
-      "software and related material nor shall the fact of release \n",
-      "constitute any such warranty. The software is provided on the \n",
-      "condition that neither the USGS nor the U.S. Government shall be held \n",
-      "liable for any damages resulting from the authorized or unauthorized \n",
-      "use of the software.\n",
-      "\n",
-      "\n",
-      " MODFLOW runs in SEQUENTIAL mode\n",
-      "\n",
-      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2026/03/04 15:09:57\n",
-      "\n",
-      " Writing simulation list file: mfsim.lst\n",
-      " Using Simulation name file: mfsim.nam\n",
-      "\n",
-      "    Solving:  Stress period:     1    Time step:     1\n",
-      "    Solving:  Stress period:     1    Time step:     2\n",
-      "    Solving:  Stress period:     1    Time step:     3\n",
-      "    Solving:  Stress period:     1    Time step:     4\n",
-      "    Solving:  Stress period:     1    Time step:     5\n",
-      "    Solving:  Stress period:     1    Time step:     6\n",
-      "    Solving:  Stress period:     1    Time step:     7\n",
-      "    Solving:  Stress period:     1    Time step:     8\n",
-      "    Solving:  Stress period:     1    Time step:     9\n",
-      "    Solving:  Stress period:     1    Time step:    10\n",
-      "    Solving:  Stress period:     1    Time step:    11\n",
-      "    Solving:  Stress period:     1    Time step:    12\n",
-      "    Solving:  Stress period:     1    Time step:    13\n",
-      "    Solving:  Stress period:     1    Time step:    14\n",
-      "    Solving:  Stress period:     1    Time step:    15\n",
-      "    Solving:  Stress period:     2    Time step:     1\n",
-      "    Solving:  Stress period:     2    Time step:     2\n",
-      "    Solving:  Stress period:     2    Time step:     3\n",
-      "    Solving:  Stress period:     2    Time step:     4\n",
-      "    Solving:  Stress period:     2    Time step:     5\n",
-      "    Solving:  Stress period:     2    Time step:     6\n",
-      "    Solving:  Stress period:     2    Time step:     7\n",
-      "    Solving:  Stress period:     2    Time step:     8\n",
-      "    Solving:  Stress period:     2    Time step:     9\n",
-      "    Solving:  Stress period:     2    Time step:    10\n",
-      "    Solving:  Stress period:     2    Time step:    11\n",
-      "    Solving:  Stress period:     2    Time step:    12\n",
-      "    Solving:  Stress period:     2    Time step:    13\n",
-      "    Solving:  Stress period:     2    Time step:    14\n",
-      "    Solving:  Stress period:     2    Time step:    15\n",
-      "    Solving:  Stress period:     3    Time step:     1\n",
-      "    Solving:  Stress period:     3    Time step:     2\n",
-      "    Solving:  Stress period:     3    Time step:     3\n",
-      "    Solving:  Stress period:     3    Time step:     4\n",
-      "    Solving:  Stress period:     3    Time step:     5\n",
-      "    Solving:  Stress period:     3    Time step:     6\n",
-      "    Solving:  Stress period:     3    Time step:     7\n",
-      "    Solving:  Stress period:     3    Time step:     8\n",
-      "    Solving:  Stress period:     3    Time step:     9\n",
-      "    Solving:  Stress period:     3    Time step:    10\n",
-      "    Solving:  Stress period:     3    Time step:    11\n",
-      "    Solving:  Stress period:     3    Time step:    12\n",
-      "    Solving:  Stress period:     3    Time step:    13\n",
-      "    Solving:  Stress period:     3    Time step:    14\n",
-      "    Solving:  Stress period:     3    Time step:    15\n",
-      "    Solving:  Stress period:     4    Time step:     1\n",
-      "    Solving:  Stress period:     4    Time step:     2\n",
-      "    Solving:  Stress period:     4    Time step:     3\n",
-      "    Solving:  Stress period:     4    Time step:     4\n",
-      "    Solving:  Stress period:     4    Time step:     5\n",
-      "    Solving:  Stress period:     4    Time step:     6\n",
-      "    Solving:  Stress period:     4    Time step:     7\n",
-      "    Solving:  Stress period:     4    Time step:     8\n",
-      "    Solving:  Stress period:     4    Time step:     9\n",
-      "    Solving:  Stress period:     4    Time step:    10\n",
-      "    Solving:  Stress period:     4    Time step:    11\n",
-      "    Solving:  Stress period:     4    Time step:    12\n",
-      "    Solving:  Stress period:     4    Time step:    13\n",
-      "    Solving:  Stress period:     4    Time step:    14\n",
-      "    Solving:  Stress period:     4    Time step:    15\n",
-      "    Solving:  Stress period:     5    Time step:     1\n",
-      "    Solving:  Stress period:     5    Time step:     2\n",
-      "    Solving:  Stress period:     5    Time step:     3\n",
-      "    Solving:  Stress period:     5    Time step:     4\n",
-      "    Solving:  Stress period:     5    Time step:     5\n",
-      "    Solving:  Stress period:     5    Time step:     6\n",
-      "    Solving:  Stress period:     5    Time step:     7\n",
-      "    Solving:  Stress period:     5    Time step:     8\n",
-      "    Solving:  Stress period:     5    Time step:     9\n",
-      "    Solving:  Stress period:     5    Time step:    10\n",
-      "    Solving:  Stress period:     5    Time step:    11\n",
-      "    Solving:  Stress period:     5    Time step:    12\n",
-      "    Solving:  Stress period:     5    Time step:    13\n",
-      "    Solving:  Stress period:     5    Time step:    14\n",
-      "    Solving:  Stress period:     5    Time step:    15\n",
-      "    Solving:  Stress period:     6    Time step:     1\n",
-      "    Solving:  Stress period:     6    Time step:     2\n",
-      "    Solving:  Stress period:     6    Time step:     3\n",
-      "    Solving:  Stress period:     6    Time step:     4\n",
-      "    Solving:  Stress period:     6    Time step:     5\n",
-      "    Solving:  Stress period:     6    Time step:     6\n",
-      "    Solving:  Stress period:     6    Time step:     7\n",
-      "    Solving:  Stress period:     6    Time step:     8\n",
-      "    Solving:  Stress period:     6    Time step:     9\n",
-      "    Solving:  Stress period:     6    Time step:    10\n",
-      "    Solving:  Stress period:     6    Time step:    11\n",
-      "    Solving:  Stress period:     6    Time step:    12\n",
-      "    Solving:  Stress period:     6    Time step:    13\n",
-      "    Solving:  Stress period:     6    Time step:    14\n",
-      "    Solving:  Stress period:     6    Time step:    15\n",
-      "    Solving:  Stress period:     7    Time step:     1\n",
-      "    Solving:  Stress period:     7    Time step:     2\n",
-      "    Solving:  Stress period:     7    Time step:     3\n",
-      "    Solving:  Stress period:     7    Time step:     4\n",
-      "    Solving:  Stress period:     7    Time step:     5\n",
-      "    Solving:  Stress period:     7    Time step:     6\n",
-      "    Solving:  Stress period:     7    Time step:     7\n",
-      "    Solving:  Stress period:     7    Time step:     8\n",
-      "    Solving:  Stress period:     7    Time step:     9\n",
-      "    Solving:  Stress period:     7    Time step:    10\n",
-      "    Solving:  Stress period:     7    Time step:    11\n",
-      "    Solving:  Stress period:     7    Time step:    12\n",
-      "    Solving:  Stress period:     7    Time step:    13\n",
-      "    Solving:  Stress period:     7    Time step:    14\n",
-      "    Solving:  Stress period:     7    Time step:    15\n",
-      "    Solving:  Stress period:     8    Time step:     1\n",
-      "    Solving:  Stress period:     8    Time step:     2\n",
-      "    Solving:  Stress period:     8    Time step:     3\n",
-      "    Solving:  Stress period:     8    Time step:     4\n",
-      "    Solving:  Stress period:     8    Time step:     5\n",
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-      "    Solving:  Stress period:     9    Time step:     1\n",
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-      "    Solving:  Stress period:    10    Time step:     1\n",
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-      "    Solving:  Stress period:    10    Time step:    11\n",
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-      "    Solving:  Stress period:    11    Time step:     1\n",
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-      "    Solving:  Stress period:    11    Time step:    11\n",
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-      "    Solving:  Stress period:    12    Time step:     1\n",
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-      "    Solving:  Stress period:    12    Time step:    11\n",
-      "    Solving:  Stress period:    12    Time step:    12\n",
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-      "    Solving:  Stress period:    12    Time step:    14\n",
-      "    Solving:  Stress period:    12    Time step:    15\n",
-      "    Solving:  Stress period:    13    Time step:     1\n",
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-      "    Solving:  Stress period:    13    Time step:    11\n",
-      "    Solving:  Stress period:    13    Time step:    12\n",
-      "    Solving:  Stress period:    13    Time step:    13\n",
-      "    Solving:  Stress period:    13    Time step:    14\n",
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-      "    Solving:  Stress period:    14    Time step:     1\n",
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-      "    Solving:  Stress period:    14    Time step:    10\n",
-      "    Solving:  Stress period:    14    Time step:    11\n",
-      "    Solving:  Stress period:    14    Time step:    12\n",
-      "    Solving:  Stress period:    14    Time step:    13\n",
-      "    Solving:  Stress period:    14    Time step:    14\n",
-      "    Solving:  Stress period:    14    Time step:    15\n",
-      "    Solving:  Stress period:    15    Time step:     1\n",
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-      "    Solving:  Stress period:    15    Time step:    10\n",
-      "    Solving:  Stress period:    15    Time step:    11\n",
-      "    Solving:  Stress period:    15    Time step:    12\n",
-      "    Solving:  Stress period:    15    Time step:    13\n",
-      "    Solving:  Stress period:    15    Time step:    14\n",
-      "    Solving:  Stress period:    15    Time step:    15\n",
-      "    Solving:  Stress period:    16    Time step:     1\n",
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-      "    Solving:  Stress period:    16    Time step:    10\n",
-      "    Solving:  Stress period:    16    Time step:    11\n",
-      "    Solving:  Stress period:    16    Time step:    12\n",
-      "    Solving:  Stress period:    16    Time step:    13\n",
-      "    Solving:  Stress period:    16    Time step:    14\n",
-      "    Solving:  Stress period:    16    Time step:    15\n",
-      "    Solving:  Stress period:    17    Time step:     1\n",
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-      "    Solving:  Stress period:    17    Time step:    10\n",
-      "    Solving:  Stress period:    17    Time step:    11\n",
-      "    Solving:  Stress period:    17    Time step:    12\n",
-      "    Solving:  Stress period:    17    Time step:    13\n",
-      "    Solving:  Stress period:    17    Time step:    14\n",
-      "    Solving:  Stress period:    17    Time step:    15\n",
-      "    Solving:  Stress period:    18    Time step:     1\n",
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-      "    Solving:  Stress period:    18    Time step:     8\n",
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-      "    Solving:  Stress period:    18    Time step:    10\n",
-      "    Solving:  Stress period:    18    Time step:    11\n",
-      "    Solving:  Stress period:    18    Time step:    12\n",
-      "    Solving:  Stress period:    18    Time step:    13\n",
-      "    Solving:  Stress period:    18    Time step:    14\n",
-      "    Solving:  Stress period:    18    Time step:    15\n",
-      "    Solving:  Stress period:    19    Time step:     1\n",
-      "    Solving:  Stress period:    19    Time step:     2\n",
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-      "    Solving:  Stress period:    19    Time step:     8\n",
-      "    Solving:  Stress period:    19    Time step:     9\n",
-      "    Solving:  Stress period:    19    Time step:    10\n",
-      "    Solving:  Stress period:    19    Time step:    11\n",
-      "    Solving:  Stress period:    19    Time step:    12\n",
-      "    Solving:  Stress period:    19    Time step:    13\n",
-      "    Solving:  Stress period:    19    Time step:    14\n",
-      "    Solving:  Stress period:    19    Time step:    15\n",
-      "    Solving:  Stress period:    20    Time step:     1\n",
-      "    Solving:  Stress period:    20    Time step:     2\n",
-      "    Solving:  Stress period:    20    Time step:     3\n",
-      "    Solving:  Stress period:    20    Time step:     4\n",
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-      "    Solving:  Stress period:    20    Time step:     6\n",
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-      "    Solving:  Stress period:    20    Time step:     8\n",
-      "    Solving:  Stress period:    20    Time step:     9\n",
-      "    Solving:  Stress period:    20    Time step:    10\n",
-      "    Solving:  Stress period:    20    Time step:    11\n",
-      "    Solving:  Stress period:    20    Time step:    12\n",
-      "    Solving:  Stress period:    20    Time step:    13\n",
-      "    Solving:  Stress period:    20    Time step:    14\n",
-      "    Solving:  Stress period:    20    Time step:    15\n",
-      "    Solving:  Stress period:    21    Time step:     1\n",
-      "    Solving:  Stress period:    21    Time step:     2\n",
-      "    Solving:  Stress period:    21    Time step:     3\n",
-      "    Solving:  Stress period:    21    Time step:     4\n",
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-      "    Solving:  Stress period:    21    Time step:     6\n",
-      "    Solving:  Stress period:    21    Time step:     7\n",
-      "    Solving:  Stress period:    21    Time step:     8\n",
-      "    Solving:  Stress period:    21    Time step:     9\n",
-      "    Solving:  Stress period:    21    Time step:    10\n",
-      "    Solving:  Stress period:    21    Time step:    11\n",
-      "    Solving:  Stress period:    21    Time step:    12\n",
-      "    Solving:  Stress period:    21    Time step:    13\n",
-      "    Solving:  Stress period:    21    Time step:    14\n",
-      "    Solving:  Stress period:    21    Time step:    15\n",
-      "    Solving:  Stress period:    22    Time step:     1\n",
-      "    Solving:  Stress period:    22    Time step:     2\n",
-      "    Solving:  Stress period:    22    Time step:     3\n",
-      "    Solving:  Stress period:    22    Time step:     4\n",
-      "    Solving:  Stress period:    22    Time step:     5\n",
-      "    Solving:  Stress period:    22    Time step:     6\n",
-      "    Solving:  Stress period:    22    Time step:     7\n",
-      "    Solving:  Stress period:    22    Time step:     8\n",
-      "    Solving:  Stress period:    22    Time step:     9\n",
-      "    Solving:  Stress period:    22    Time step:    10\n",
-      "    Solving:  Stress period:    22    Time step:    11\n",
-      "    Solving:  Stress period:    22    Time step:    12\n",
-      "    Solving:  Stress period:    22    Time step:    13\n",
-      "    Solving:  Stress period:    22    Time step:    14\n",
-      "    Solving:  Stress period:    22    Time step:    15\n",
-      "    Solving:  Stress period:    23    Time step:     1\n",
-      "    Solving:  Stress period:    23    Time step:     2\n",
-      "    Solving:  Stress period:    23    Time step:     3\n",
-      "    Solving:  Stress period:    23    Time step:     4\n",
-      "    Solving:  Stress period:    23    Time step:     5\n",
-      "    Solving:  Stress period:    23    Time step:     6\n",
-      "    Solving:  Stress period:    23    Time step:     7\n",
-      "    Solving:  Stress period:    23    Time step:     8\n",
-      "    Solving:  Stress period:    23    Time step:     9\n",
-      "    Solving:  Stress period:    23    Time step:    10\n",
-      "    Solving:  Stress period:    23    Time step:    11\n",
-      "    Solving:  Stress period:    23    Time step:    12\n",
-      "    Solving:  Stress period:    23    Time step:    13\n",
-      "    Solving:  Stress period:    23    Time step:    14\n",
-      "    Solving:  Stress period:    23    Time step:    15\n",
-      "    Solving:  Stress period:    24    Time step:     1\n",
-      "    Solving:  Stress period:    24    Time step:     2\n",
-      "    Solving:  Stress period:    24    Time step:     3\n",
-      "    Solving:  Stress period:    24    Time step:     4\n",
-      "    Solving:  Stress period:    24    Time step:     5\n",
-      "    Solving:  Stress period:    24    Time step:     6\n",
-      "    Solving:  Stress period:    24    Time step:     7\n",
-      "    Solving:  Stress period:    24    Time step:     8\n",
-      "    Solving:  Stress period:    24    Time step:     9\n",
-      "    Solving:  Stress period:    24    Time step:    10\n",
-      "    Solving:  Stress period:    24    Time step:    11\n",
-      "    Solving:  Stress period:    24    Time step:    12\n",
-      "    Solving:  Stress period:    24    Time step:    13\n",
-      "    Solving:  Stress period:    24    Time step:    14\n",
-      "    Solving:  Stress period:    24    Time step:    15\n",
-      "    Solving:  Stress period:    25    Time step:     1\n",
-      "    Solving:  Stress period:    25    Time step:     2\n",
-      "    Solving:  Stress period:    25    Time step:     3\n",
-      "    Solving:  Stress period:    25    Time step:     4\n",
-      "    Solving:  Stress period:    25    Time step:     5\n",
-      "    Solving:  Stress period:    25    Time step:     6\n",
-      "    Solving:  Stress period:    25    Time step:     7\n",
-      "    Solving:  Stress period:    25    Time step:     8\n",
-      "    Solving:  Stress period:    25    Time step:     9\n",
-      "    Solving:  Stress period:    25    Time step:    10\n",
-      "    Solving:  Stress period:    25    Time step:    11\n",
-      "    Solving:  Stress period:    25    Time step:    12\n",
-      "    Solving:  Stress period:    25    Time step:    13\n",
-      "    Solving:  Stress period:    25    Time step:    14\n",
-      "    Solving:  Stress period:    25    Time step:    15\n",
-      "    Solving:  Stress period:    26    Time step:     1\n",
-      "    Solving:  Stress period:    26    Time step:     2\n",
-      "    Solving:  Stress period:    26    Time step:     3\n",
-      "    Solving:  Stress period:    26    Time step:     4\n",
-      "    Solving:  Stress period:    26    Time step:     5\n",
-      "    Solving:  Stress period:    26    Time step:     6\n",
-      "    Solving:  Stress period:    26    Time step:     7\n",
-      "    Solving:  Stress period:    26    Time step:     8\n",
-      "    Solving:  Stress period:    26    Time step:     9\n",
-      "    Solving:  Stress period:    26    Time step:    10\n",
-      "    Solving:  Stress period:    26    Time step:    11\n",
-      "    Solving:  Stress period:    26    Time step:    12\n",
-      "    Solving:  Stress period:    26    Time step:    13\n",
-      "    Solving:  Stress period:    26    Time step:    14\n",
-      "    Solving:  Stress period:    26    Time step:    15\n",
-      "    Solving:  Stress period:    27    Time step:     1\n",
-      "    Solving:  Stress period:    27    Time step:     2\n",
-      "    Solving:  Stress period:    27    Time step:     3\n",
-      "    Solving:  Stress period:    27    Time step:     4\n",
-      "    Solving:  Stress period:    27    Time step:     5\n",
-      "    Solving:  Stress period:    27    Time step:     6\n",
-      "    Solving:  Stress period:    27    Time step:     7\n",
-      "    Solving:  Stress period:    27    Time step:     8\n",
-      "    Solving:  Stress period:    27    Time step:     9\n",
-      "    Solving:  Stress period:    27    Time step:    10\n",
-      "    Solving:  Stress period:    27    Time step:    11\n",
-      "    Solving:  Stress period:    27    Time step:    12\n",
-      "    Solving:  Stress period:    27    Time step:    13\n",
-      "    Solving:  Stress period:    27    Time step:    14\n",
-      "    Solving:  Stress period:    27    Time step:    15\n",
-      "    Solving:  Stress period:    28    Time step:     1\n",
-      "    Solving:  Stress period:    28    Time step:     2\n",
-      "    Solving:  Stress period:    28    Time step:     3\n",
-      "    Solving:  Stress period:    28    Time step:     4\n",
-      "    Solving:  Stress period:    28    Time step:     5\n",
-      "    Solving:  Stress period:    28    Time step:     6\n",
-      "    Solving:  Stress period:    28    Time step:     7\n",
-      "    Solving:  Stress period:    28    Time step:     8\n",
-      "    Solving:  Stress period:    28    Time step:     9\n",
-      "    Solving:  Stress period:    28    Time step:    10\n",
-      "    Solving:  Stress period:    28    Time step:    11\n",
-      "    Solving:  Stress period:    28    Time step:    12\n",
-      "    Solving:  Stress period:    28    Time step:    13\n",
-      "    Solving:  Stress period:    28    Time step:    14\n",
-      "    Solving:  Stress period:    28    Time step:    15\n",
-      "    Solving:  Stress period:    29    Time step:     1\n",
-      "    Solving:  Stress period:    29    Time step:     2\n",
-      "    Solving:  Stress period:    29    Time step:     3\n",
-      "    Solving:  Stress period:    29    Time step:     4\n",
-      "    Solving:  Stress period:    29    Time step:     5\n",
-      "    Solving:  Stress period:    29    Time step:     6\n",
-      "    Solving:  Stress period:    29    Time step:     7\n",
-      "    Solving:  Stress period:    29    Time step:     8\n",
-      "    Solving:  Stress period:    29    Time step:     9\n",
-      "    Solving:  Stress period:    29    Time step:    10\n",
-      "    Solving:  Stress period:    29    Time step:    11\n",
-      "    Solving:  Stress period:    29    Time step:    12\n",
-      "    Solving:  Stress period:    29    Time step:    13\n",
-      "    Solving:  Stress period:    29    Time step:    14\n",
-      "    Solving:  Stress period:    29    Time step:    15\n",
-      "    Solving:  Stress period:    30    Time step:     1\n",
-      "    Solving:  Stress period:    30    Time step:     2\n",
-      "    Solving:  Stress period:    30    Time step:     3\n",
-      "    Solving:  Stress period:    30    Time step:     4\n",
-      "    Solving:  Stress period:    30    Time step:     5\n",
-      "    Solving:  Stress period:    30    Time step:     6\n",
-      "    Solving:  Stress period:    30    Time step:     7\n",
-      "    Solving:  Stress period:    30    Time step:     8\n",
-      "    Solving:  Stress period:    30    Time step:     9\n",
-      "    Solving:  Stress period:    30    Time step:    10\n",
-      "    Solving:  Stress period:    30    Time step:    11\n",
-      "    Solving:  Stress period:    30    Time step:    12\n",
-      "    Solving:  Stress period:    30    Time step:    13\n",
-      "    Solving:  Stress period:    30    Time step:    14\n",
-      "    Solving:  Stress period:    30    Time step:    15\n",
-      "    Solving:  Stress period:    31    Time step:     1\n",
-      "    Solving:  Stress period:    31    Time step:     2\n",
-      "    Solving:  Stress period:    31    Time step:     3\n",
-      "    Solving:  Stress period:    31    Time step:     4\n",
-      "    Solving:  Stress period:    31    Time step:     5\n",
-      "    Solving:  Stress period:    31    Time step:     6\n",
-      "    Solving:  Stress period:    31    Time step:     7\n",
-      "    Solving:  Stress period:    31    Time step:     8\n",
-      "    Solving:  Stress period:    31    Time step:     9\n",
-      "    Solving:  Stress period:    31    Time step:    10\n",
-      "    Solving:  Stress period:    31    Time step:    11\n",
-      "    Solving:  Stress period:    31    Time step:    12\n",
-      "    Solving:  Stress period:    31    Time step:    13\n",
-      "    Solving:  Stress period:    31    Time step:    14\n",
-      "    Solving:  Stress period:    31    Time step:    15\n",
-      "    Solving:  Stress period:    32    Time step:     1\n",
-      "    Solving:  Stress period:    32    Time step:     2\n",
-      "    Solving:  Stress period:    32    Time step:     3\n",
-      "    Solving:  Stress period:    32    Time step:     4\n",
-      "    Solving:  Stress period:    32    Time step:     5\n",
-      "    Solving:  Stress period:    32    Time step:     6\n",
-      "    Solving:  Stress period:    32    Time step:     7\n",
-      "    Solving:  Stress period:    32    Time step:     8\n",
-      "    Solving:  Stress period:    32    Time step:     9\n",
-      "    Solving:  Stress period:    32    Time step:    10\n",
-      "    Solving:  Stress period:    32    Time step:    11\n",
-      "    Solving:  Stress period:    32    Time step:    12\n",
-      "    Solving:  Stress period:    32    Time step:    13\n",
-      "    Solving:  Stress period:    32    Time step:    14\n",
-      "    Solving:  Stress period:    32    Time step:    15\n",
-      "    Solving:  Stress period:    33    Time step:     1\n",
-      "    Solving:  Stress period:    33    Time step:     2\n",
-      "    Solving:  Stress period:    33    Time step:     3\n",
-      "    Solving:  Stress period:    33    Time step:     4\n",
-      "    Solving:  Stress period:    33    Time step:     5\n",
-      "    Solving:  Stress period:    33    Time step:     6\n",
-      "    Solving:  Stress period:    33    Time step:     7\n",
-      "    Solving:  Stress period:    33    Time step:     8\n",
-      "    Solving:  Stress period:    33    Time step:     9\n",
-      "    Solving:  Stress period:    33    Time step:    10\n",
-      "    Solving:  Stress period:    33    Time step:    11\n",
-      "    Solving:  Stress period:    33    Time step:    12\n",
-      "    Solving:  Stress period:    33    Time step:    13\n",
-      "    Solving:  Stress period:    33    Time step:    14\n",
-      "    Solving:  Stress period:    33    Time step:    15\n",
-      "\n",
-      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2026/03/04 15:10:37\n",
-      " Elapsed run time: 39.806 Seconds\n",
-      "\n",
-      " Normal termination of simulation.\n",
-      "\n",
-      "stderr:\n",
-      "\n",
-      "returncode: 0\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "if os.getenv(\"MF6_EXTENDED\"):\n",
-    "    sim.run(verbose=True)\n",
-    "\n",
-    "    # Load head results\n",
-    "    head = flopy4.mf6.utils.open_hds(\n",
-    "        workspace / \"ff.hds\",\n",
-    "        workspace / \"ff.dis.grb\",\n",
-    "    )\n",
-    "\n",
-    "    # Plot head results\n",
-    "    plot_head(head, workspace)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8ee3f813",
-   "metadata": {},
-   "source": [
-    "### Array-based NetCDF WEL packages + layered mesh NetCDF output"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "64005723",
-   "metadata": {},
-   "source": [
-    "Switch the three WEL packages from list-based to array-based (`Welg`),\n",
-    "combine with a layered-mesh NetCDF input file, and also request mesh2d\n",
-    "NetCDF output (`gwf.netcdf_mesh2d_file`).  `FILL_DNODATA` marks inactive\n",
-    "cells so MODFLOW ignores them for those stress periods."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "83518416",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-03-04T20:10:37.625785Z",
-     "iopub.status.busy": "2026-03-04T20:10:37.625660Z",
-     "iopub.status.idle": "2026-03-04T20:10:37.628372Z",
-     "shell.execute_reply": "2026-03-04T20:10:37.627738Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# update simulation with array based inputs\n",
-    "LAYER_NODATA = np.full((nrow, ncol), flopy4.mf6.constants.FILL_DNODATA, dtype=float)\n",
-    "GRID_NODATA = np.full((nlay, nrow, ncol), flopy4.mf6.constants.FILL_DNODATA, dtype=float)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "9ad711c3",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-03-04T20:10:37.630087Z",
-     "iopub.status.busy": "2026-03-04T20:10:37.629970Z",
-     "iopub.status.idle": "2026-03-04T20:10:37.646364Z",
-     "shell.execute_reply": "2026-03-04T20:10:37.645409Z"
-    },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [],
-   "source": [
-    "# Constant-rate pumping — array form of wel_crt.\n",
-    "q_crt = np.repeat(np.expand_dims(GRID_NODATA, axis=0), repeats=nper, axis=0)\n",
-    "q_crt[0, 1, 43, 43] = -30992.50\n",
-    "q_crt[1, 1, 43, 43] = -00000.0\n",
-    "q_crt[2, 1, 43, 43] = -30992.50\n",
-    "q_crt[3, 1, 43, 43] = -00000.0\n",
-    "q_crt[4, 1, 43, 43] = -30992.50\n",
-    "q_crt[5, 1, 43, 43] = -00000.0\n",
-    "welg_crt = flopy4.mf6.gwf.Welg(\n",
-    "    filename=\"ff.crt.welg\",\n",
-    "    q=q_crt,\n",
-    "    print_input=True,\n",
-    "    print_flows=True,\n",
-    "    save_flows=True,\n",
-    "    dims=dims,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "0a65da84",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-03-04T20:10:37.648032Z",
-     "iopub.status.busy": "2026-03-04T20:10:37.647910Z",
-     "iopub.status.idle": "2026-03-04T20:10:37.665434Z",
-     "shell.execute_reply": "2026-03-04T20:10:37.664483Z"
-    },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [],
-   "source": [
-    "# Leakage — array form of wel_leak.\n",
-    "q_leak = np.repeat(np.expand_dims(GRID_NODATA, axis=0), repeats=nper, axis=0)\n",
-    "q_leak[0, 1, 43, 43] = 1.0000000e-05\n",
-    "q_leak[7, 1, 43, 43] = 1.5000000e03\n",
-    "q_leak[8, 1, 43, 43] = 2.6500000e03\n",
-    "q_leak[9, 1, 43, 43] = 3.1500000e03\n",
-    "q_leak[10, 1, 43, 43] = 4.1000000e03\n",
-    "q_leak[11, 1, 43, 43] = 4.6500000e03\n",
-    "q_leak[12, 1, 43, 43] = 4.9500000e03\n",
-    "q_leak[13, 1, 43, 43] = 5.3000000e03\n",
-    "q_leak[14, 1, 43, 43] = 5.8000000e03\n",
-    "q_leak[16, 1, 43, 43] = 5.9000000e03\n",
-    "q_leak[17, 1, 43, 43] = 5.8000000e03\n",
-    "q_leak[19, 1, 43, 43] = 5.6000000e03\n",
-    "q_leak[20, 1, 43, 43] = 4.7000000e03\n",
-    "q_leak[22, 1, 43, 43] = 3.4000000e03\n",
-    "q_leak[23, 1, 43, 43] = 1.0000000e-05\n",
-    "welg_leak = flopy4.mf6.gwf.Welg(\n",
-    "    filename=\"ff.leak.welg\",\n",
-    "    q=q_leak,\n",
-    "    print_input=True,\n",
-    "    print_flows=True,\n",
-    "    save_flows=True,\n",
-    "    dims=dims,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "acfdd562",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-03-04T20:10:37.667477Z",
-     "iopub.status.busy": "2026-03-04T20:10:37.667353Z",
-     "iopub.status.idle": "2026-03-04T20:10:37.689951Z",
-     "shell.execute_reply": "2026-03-04T20:10:37.689179Z"
-    },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [],
-   "source": [
-    "# Sampling — array form of wel_sampleQ.\n",
-    "q_sampleQ = np.repeat(np.expand_dims(GRID_NODATA, axis=0), repeats=nper, axis=0)\n",
-    "q_sampleQ[0, 1, 43, 43] = -00000.0\n",
-    "q_sampleQ[22, 1, 43, 43] = -04981.90\n",
-    "q_sampleQ[23, 1, 43, 43] = -00000.0\n",
-    "q_sampleQ[24, 1, 43, 43] = -04059.83\n",
-    "q_sampleQ[25, 1, 43, 43] = -00000.0\n",
-    "q_sampleQ[26, 1, 43, 43] = -05678.75\n",
-    "q_sampleQ[27, 1, 43, 43] = -00000.0\n",
-    "q_sampleQ[28, 1, 43, 43] = -05755.75\n",
-    "q_sampleQ[29, 1, 43, 43] = -00000.0\n",
-    "q_sampleQ[30, 1, 43, 43] = -04117.58\n",
-    "q_sampleQ[31, 1, 43, 43] = -00000.0\n",
-    "welg_sampleQ = flopy4.mf6.gwf.Welg(\n",
-    "    filename=\"ff.sampleQ.welg\",\n",
-    "    q=q_sampleQ,\n",
-    "    print_input=True,\n",
-    "    print_flows=True,\n",
-    "    save_flows=True,\n",
-    "    dims=dims,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "dc5b49f5",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-03-04T20:10:37.691870Z",
-     "iopub.status.busy": "2026-03-04T20:10:37.691749Z",
-     "iopub.status.idle": "2026-03-04T20:10:37.694674Z",
-     "shell.execute_reply": "2026-03-04T20:10:37.694000Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Swap list-based WEL packages for their array-based equivalents.\n",
-    "del gwf.wel[0]\n",
-    "del gwf.wel[1]\n",
-    "del gwf.wel[2]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "ccf7a35e",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-03-04T20:10:37.696231Z",
-     "iopub.status.busy": "2026-03-04T20:10:37.696116Z",
-     "iopub.status.idle": "2026-03-04T20:10:37.709501Z",
-     "shell.execute_reply": "2026-03-04T20:10:37.708551Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Attach the array-based WEL packages.\n",
-    "gwf.wel = [welg_crt, welg_leak, welg_sampleQ]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "4bb06dbf",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-03-04T20:10:37.711250Z",
-     "iopub.status.busy": "2026-03-04T20:10:37.711133Z",
-     "iopub.status.idle": "2026-03-04T20:10:37.714016Z",
-     "shell.execute_reply": "2026-03-04T20:10:37.713369Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# create new workspace\n",
-    "workspace = FF_ROOT / \"frenchman-flat\" / \"netcdf_mesh\"\n",
-    "workspace.mkdir(parents=True, exist_ok=True)\n",
-    "sim.workspace = workspace"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "bece80da",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-03-04T20:10:37.715545Z",
-     "iopub.status.busy": "2026-03-04T20:10:37.715429Z",
-     "iopub.status.idle": "2026-03-04T20:10:37.717831Z",
-     "shell.execute_reply": "2026-03-04T20:10:37.717099Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "gwf.netcdf_mesh2d_file = Path(\"frenchman-flat.nc\")\n",
-    "gwf.netcdf_file = Path(\"frenchman-flat.input.nc\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "cb9866a5",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-03-04T20:10:37.719227Z",
-     "iopub.status.busy": "2026-03-04T20:10:37.719120Z",
-     "iopub.status.idle": "2026-03-04T20:10:37.949498Z",
-     "shell.execute_reply": "2026-03-04T20:10:37.948205Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "nc_model = flopy4.mf6.netcdf.NetCDFModel.from_model(gwf, mesh=\"layered\", grid=grid, time=time)\n",
-    "nc_model.to_netcdf(workspace / \"frenchman-flat.input.nc\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "7f90bba7",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-03-04T20:10:37.951986Z",
-     "iopub.status.busy": "2026-03-04T20:10:37.951837Z",
-     "iopub.status.idle": "2026-03-04T20:11:18.333661Z",
-     "shell.execute_reply": "2026-03-04T20:11:18.332447Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "running: mf6\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "stdout:\n",
-      "                               MODFLOW 6 EXTENDED\n",
-      "                U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL\n",
-      "                  VERSION 6.8.0.dev0 (preliminary) 02/06/2026\n",
-      "                               ***DEVELOP MODE***\n",
-      "\n",
-      "        MODFLOW 6 compiled Feb 25 2026 15:19:23 with GCC version 13.3.0\n",
-      "\n",
-      "This software is preliminary or provisional and is subject to \n",
-      "revision. It is being provided to meet the need for timely best \n",
-      "science. The software has not received final approval by the U.S. \n",
-      "Geological Survey (USGS). No warranty, expressed or implied, is made \n",
-      "by the USGS or the U.S. Government as to the functionality of the \n",
-      "software and related material nor shall the fact of release \n",
-      "constitute any such warranty. The software is provided on the \n",
-      "condition that neither the USGS nor the U.S. Government shall be held \n",
-      "liable for any damages resulting from the authorized or unauthorized \n",
-      "use of the software.\n",
-      "\n",
-      "\n",
-      " MODFLOW runs in SEQUENTIAL mode\n",
-      "\n",
-      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2026/03/04 15:10:37\n",
-      "\n",
-      " Writing simulation list file: mfsim.lst\n",
-      " Using Simulation name file: mfsim.nam\n",
-      "\n",
-      "    Solving:  Stress period:     1    Time step:     1\n",
-      "    Solving:  Stress period:     1    Time step:     2\n",
-      "    Solving:  Stress period:     1    Time step:     3\n",
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-      "    Solving:  Stress period:    17    Time step:    12\n",
-      "    Solving:  Stress period:    17    Time step:    13\n",
-      "    Solving:  Stress period:    17    Time step:    14\n",
-      "    Solving:  Stress period:    17    Time step:    15\n",
-      "    Solving:  Stress period:    18    Time step:     1\n",
-      "    Solving:  Stress period:    18    Time step:     2\n",
-      "    Solving:  Stress period:    18    Time step:     3\n",
-      "    Solving:  Stress period:    18    Time step:     4\n",
-      "    Solving:  Stress period:    18    Time step:     5\n",
-      "    Solving:  Stress period:    18    Time step:     6\n",
-      "    Solving:  Stress period:    18    Time step:     7\n",
-      "    Solving:  Stress period:    18    Time step:     8\n",
-      "    Solving:  Stress period:    18    Time step:     9\n",
-      "    Solving:  Stress period:    18    Time step:    10\n",
-      "    Solving:  Stress period:    18    Time step:    11\n",
-      "    Solving:  Stress period:    18    Time step:    12\n",
-      "    Solving:  Stress period:    18    Time step:    13\n",
-      "    Solving:  Stress period:    18    Time step:    14\n",
-      "    Solving:  Stress period:    18    Time step:    15\n",
-      "    Solving:  Stress period:    19    Time step:     1\n",
-      "    Solving:  Stress period:    19    Time step:     2\n",
-      "    Solving:  Stress period:    19    Time step:     3\n",
-      "    Solving:  Stress period:    19    Time step:     4\n",
-      "    Solving:  Stress period:    19    Time step:     5\n",
-      "    Solving:  Stress period:    19    Time step:     6\n",
-      "    Solving:  Stress period:    19    Time step:     7\n",
-      "    Solving:  Stress period:    19    Time step:     8\n",
-      "    Solving:  Stress period:    19    Time step:     9\n",
-      "    Solving:  Stress period:    19    Time step:    10\n",
-      "    Solving:  Stress period:    19    Time step:    11\n",
-      "    Solving:  Stress period:    19    Time step:    12\n",
-      "    Solving:  Stress period:    19    Time step:    13\n",
-      "    Solving:  Stress period:    19    Time step:    14\n",
-      "    Solving:  Stress period:    19    Time step:    15\n",
-      "    Solving:  Stress period:    20    Time step:     1\n",
-      "    Solving:  Stress period:    20    Time step:     2\n",
-      "    Solving:  Stress period:    20    Time step:     3\n",
-      "    Solving:  Stress period:    20    Time step:     4\n",
-      "    Solving:  Stress period:    20    Time step:     5\n",
-      "    Solving:  Stress period:    20    Time step:     6\n",
-      "    Solving:  Stress period:    20    Time step:     7\n",
-      "    Solving:  Stress period:    20    Time step:     8\n",
-      "    Solving:  Stress period:    20    Time step:     9\n",
-      "    Solving:  Stress period:    20    Time step:    10\n",
-      "    Solving:  Stress period:    20    Time step:    11\n",
-      "    Solving:  Stress period:    20    Time step:    12\n",
-      "    Solving:  Stress period:    20    Time step:    13\n",
-      "    Solving:  Stress period:    20    Time step:    14\n",
-      "    Solving:  Stress period:    20    Time step:    15\n",
-      "    Solving:  Stress period:    21    Time step:     1\n",
-      "    Solving:  Stress period:    21    Time step:     2\n",
-      "    Solving:  Stress period:    21    Time step:     3\n",
-      "    Solving:  Stress period:    21    Time step:     4\n",
-      "    Solving:  Stress period:    21    Time step:     5\n",
-      "    Solving:  Stress period:    21    Time step:     6\n",
-      "    Solving:  Stress period:    21    Time step:     7\n",
-      "    Solving:  Stress period:    21    Time step:     8\n",
-      "    Solving:  Stress period:    21    Time step:     9\n",
-      "    Solving:  Stress period:    21    Time step:    10\n",
-      "    Solving:  Stress period:    21    Time step:    11\n",
-      "    Solving:  Stress period:    21    Time step:    12\n",
-      "    Solving:  Stress period:    21    Time step:    13\n",
-      "    Solving:  Stress period:    21    Time step:    14\n",
-      "    Solving:  Stress period:    21    Time step:    15\n",
-      "    Solving:  Stress period:    22    Time step:     1\n",
-      "    Solving:  Stress period:    22    Time step:     2\n",
-      "    Solving:  Stress period:    22    Time step:     3\n",
-      "    Solving:  Stress period:    22    Time step:     4\n",
-      "    Solving:  Stress period:    22    Time step:     5\n",
-      "    Solving:  Stress period:    22    Time step:     6\n",
-      "    Solving:  Stress period:    22    Time step:     7\n",
-      "    Solving:  Stress period:    22    Time step:     8\n",
-      "    Solving:  Stress period:    22    Time step:     9\n",
-      "    Solving:  Stress period:    22    Time step:    10\n",
-      "    Solving:  Stress period:    22    Time step:    11\n",
-      "    Solving:  Stress period:    22    Time step:    12\n",
-      "    Solving:  Stress period:    22    Time step:    13\n",
-      "    Solving:  Stress period:    22    Time step:    14\n",
-      "    Solving:  Stress period:    22    Time step:    15\n",
-      "    Solving:  Stress period:    23    Time step:     1\n",
-      "    Solving:  Stress period:    23    Time step:     2\n",
-      "    Solving:  Stress period:    23    Time step:     3\n",
-      "    Solving:  Stress period:    23    Time step:     4\n",
-      "    Solving:  Stress period:    23    Time step:     5\n",
-      "    Solving:  Stress period:    23    Time step:     6\n",
-      "    Solving:  Stress period:    23    Time step:     7\n",
-      "    Solving:  Stress period:    23    Time step:     8\n",
-      "    Solving:  Stress period:    23    Time step:     9\n",
-      "    Solving:  Stress period:    23    Time step:    10\n",
-      "    Solving:  Stress period:    23    Time step:    11\n",
-      "    Solving:  Stress period:    23    Time step:    12\n",
-      "    Solving:  Stress period:    23    Time step:    13\n",
-      "    Solving:  Stress period:    23    Time step:    14\n",
-      "    Solving:  Stress period:    23    Time step:    15\n",
-      "    Solving:  Stress period:    24    Time step:     1\n",
-      "    Solving:  Stress period:    24    Time step:     2\n",
-      "    Solving:  Stress period:    24    Time step:     3\n",
-      "    Solving:  Stress period:    24    Time step:     4\n",
-      "    Solving:  Stress period:    24    Time step:     5\n",
-      "    Solving:  Stress period:    24    Time step:     6\n",
-      "    Solving:  Stress period:    24    Time step:     7\n",
-      "    Solving:  Stress period:    24    Time step:     8\n",
-      "    Solving:  Stress period:    24    Time step:     9\n",
-      "    Solving:  Stress period:    24    Time step:    10\n",
-      "    Solving:  Stress period:    24    Time step:    11\n",
-      "    Solving:  Stress period:    24    Time step:    12\n",
-      "    Solving:  Stress period:    24    Time step:    13\n",
-      "    Solving:  Stress period:    24    Time step:    14\n",
-      "    Solving:  Stress period:    24    Time step:    15\n",
-      "    Solving:  Stress period:    25    Time step:     1\n",
-      "    Solving:  Stress period:    25    Time step:     2\n",
-      "    Solving:  Stress period:    25    Time step:     3\n",
-      "    Solving:  Stress period:    25    Time step:     4\n",
-      "    Solving:  Stress period:    25    Time step:     5\n",
-      "    Solving:  Stress period:    25    Time step:     6\n",
-      "    Solving:  Stress period:    25    Time step:     7\n",
-      "    Solving:  Stress period:    25    Time step:     8\n",
-      "    Solving:  Stress period:    25    Time step:     9\n",
-      "    Solving:  Stress period:    25    Time step:    10\n",
-      "    Solving:  Stress period:    25    Time step:    11\n",
-      "    Solving:  Stress period:    25    Time step:    12\n",
-      "    Solving:  Stress period:    25    Time step:    13\n",
-      "    Solving:  Stress period:    25    Time step:    14\n",
-      "    Solving:  Stress period:    25    Time step:    15\n",
-      "    Solving:  Stress period:    26    Time step:     1\n",
-      "    Solving:  Stress period:    26    Time step:     2\n",
-      "    Solving:  Stress period:    26    Time step:     3\n",
-      "    Solving:  Stress period:    26    Time step:     4\n",
-      "    Solving:  Stress period:    26    Time step:     5\n",
-      "    Solving:  Stress period:    26    Time step:     6\n",
-      "    Solving:  Stress period:    26    Time step:     7\n",
-      "    Solving:  Stress period:    26    Time step:     8\n",
-      "    Solving:  Stress period:    26    Time step:     9\n",
-      "    Solving:  Stress period:    26    Time step:    10\n",
-      "    Solving:  Stress period:    26    Time step:    11\n",
-      "    Solving:  Stress period:    26    Time step:    12\n",
-      "    Solving:  Stress period:    26    Time step:    13\n",
-      "    Solving:  Stress period:    26    Time step:    14\n",
-      "    Solving:  Stress period:    26    Time step:    15\n",
-      "    Solving:  Stress period:    27    Time step:     1\n",
-      "    Solving:  Stress period:    27    Time step:     2\n",
-      "    Solving:  Stress period:    27    Time step:     3\n",
-      "    Solving:  Stress period:    27    Time step:     4\n",
-      "    Solving:  Stress period:    27    Time step:     5\n",
-      "    Solving:  Stress period:    27    Time step:     6\n",
-      "    Solving:  Stress period:    27    Time step:     7\n",
-      "    Solving:  Stress period:    27    Time step:     8\n",
-      "    Solving:  Stress period:    27    Time step:     9\n",
-      "    Solving:  Stress period:    27    Time step:    10\n",
-      "    Solving:  Stress period:    27    Time step:    11\n",
-      "    Solving:  Stress period:    27    Time step:    12\n",
-      "    Solving:  Stress period:    27    Time step:    13\n",
-      "    Solving:  Stress period:    27    Time step:    14\n",
-      "    Solving:  Stress period:    27    Time step:    15\n",
-      "    Solving:  Stress period:    28    Time step:     1\n",
-      "    Solving:  Stress period:    28    Time step:     2\n",
-      "    Solving:  Stress period:    28    Time step:     3\n",
-      "    Solving:  Stress period:    28    Time step:     4\n",
-      "    Solving:  Stress period:    28    Time step:     5\n",
-      "    Solving:  Stress period:    28    Time step:     6\n",
-      "    Solving:  Stress period:    28    Time step:     7\n",
-      "    Solving:  Stress period:    28    Time step:     8\n",
-      "    Solving:  Stress period:    28    Time step:     9\n",
-      "    Solving:  Stress period:    28    Time step:    10\n",
-      "    Solving:  Stress period:    28    Time step:    11\n",
-      "    Solving:  Stress period:    28    Time step:    12\n",
-      "    Solving:  Stress period:    28    Time step:    13\n",
-      "    Solving:  Stress period:    28    Time step:    14\n",
-      "    Solving:  Stress period:    28    Time step:    15\n",
-      "    Solving:  Stress period:    29    Time step:     1\n",
-      "    Solving:  Stress period:    29    Time step:     2\n",
-      "    Solving:  Stress period:    29    Time step:     3\n",
-      "    Solving:  Stress period:    29    Time step:     4\n",
-      "    Solving:  Stress period:    29    Time step:     5\n",
-      "    Solving:  Stress period:    29    Time step:     6\n",
-      "    Solving:  Stress period:    29    Time step:     7\n",
-      "    Solving:  Stress period:    29    Time step:     8\n",
-      "    Solving:  Stress period:    29    Time step:     9\n",
-      "    Solving:  Stress period:    29    Time step:    10\n",
-      "    Solving:  Stress period:    29    Time step:    11\n",
-      "    Solving:  Stress period:    29    Time step:    12\n",
-      "    Solving:  Stress period:    29    Time step:    13\n",
-      "    Solving:  Stress period:    29    Time step:    14\n",
-      "    Solving:  Stress period:    29    Time step:    15\n",
-      "    Solving:  Stress period:    30    Time step:     1\n",
-      "    Solving:  Stress period:    30    Time step:     2\n",
-      "    Solving:  Stress period:    30    Time step:     3\n",
-      "    Solving:  Stress period:    30    Time step:     4\n",
-      "    Solving:  Stress period:    30    Time step:     5\n",
-      "    Solving:  Stress period:    30    Time step:     6\n",
-      "    Solving:  Stress period:    30    Time step:     7\n",
-      "    Solving:  Stress period:    30    Time step:     8\n",
-      "    Solving:  Stress period:    30    Time step:     9\n",
-      "    Solving:  Stress period:    30    Time step:    10\n",
-      "    Solving:  Stress period:    30    Time step:    11\n",
-      "    Solving:  Stress period:    30    Time step:    12\n",
-      "    Solving:  Stress period:    30    Time step:    13\n",
-      "    Solving:  Stress period:    30    Time step:    14\n",
-      "    Solving:  Stress period:    30    Time step:    15\n",
-      "    Solving:  Stress period:    31    Time step:     1\n",
-      "    Solving:  Stress period:    31    Time step:     2\n",
-      "    Solving:  Stress period:    31    Time step:     3\n",
-      "    Solving:  Stress period:    31    Time step:     4\n",
-      "    Solving:  Stress period:    31    Time step:     5\n",
-      "    Solving:  Stress period:    31    Time step:     6\n",
-      "    Solving:  Stress period:    31    Time step:     7\n",
-      "    Solving:  Stress period:    31    Time step:     8\n",
-      "    Solving:  Stress period:    31    Time step:     9\n",
-      "    Solving:  Stress period:    31    Time step:    10\n",
-      "    Solving:  Stress period:    31    Time step:    11\n",
-      "    Solving:  Stress period:    31    Time step:    12\n",
-      "    Solving:  Stress period:    31    Time step:    13\n",
-      "    Solving:  Stress period:    31    Time step:    14\n",
-      "    Solving:  Stress period:    31    Time step:    15\n",
-      "    Solving:  Stress period:    32    Time step:     1\n",
-      "    Solving:  Stress period:    32    Time step:     2\n",
-      "    Solving:  Stress period:    32    Time step:     3\n",
-      "    Solving:  Stress period:    32    Time step:     4\n",
-      "    Solving:  Stress period:    32    Time step:     5\n",
-      "    Solving:  Stress period:    32    Time step:     6\n",
-      "    Solving:  Stress period:    32    Time step:     7\n",
-      "    Solving:  Stress period:    32    Time step:     8\n",
-      "    Solving:  Stress period:    32    Time step:     9\n",
-      "    Solving:  Stress period:    32    Time step:    10\n",
-      "    Solving:  Stress period:    32    Time step:    11\n",
-      "    Solving:  Stress period:    32    Time step:    12\n",
-      "    Solving:  Stress period:    32    Time step:    13\n",
-      "    Solving:  Stress period:    32    Time step:    14\n",
-      "    Solving:  Stress period:    32    Time step:    15\n",
-      "    Solving:  Stress period:    33    Time step:     1\n",
-      "    Solving:  Stress period:    33    Time step:     2\n",
-      "    Solving:  Stress period:    33    Time step:     3\n",
-      "    Solving:  Stress period:    33    Time step:     4\n",
-      "    Solving:  Stress period:    33    Time step:     5\n",
-      "    Solving:  Stress period:    33    Time step:     6\n",
-      "    Solving:  Stress period:    33    Time step:     7\n",
-      "    Solving:  Stress period:    33    Time step:     8\n",
-      "    Solving:  Stress period:    33    Time step:     9\n",
-      "    Solving:  Stress period:    33    Time step:    10\n",
-      "    Solving:  Stress period:    33    Time step:    11\n",
-      "    Solving:  Stress period:    33    Time step:    12\n",
-      "    Solving:  Stress period:    33    Time step:    13\n",
-      "    Solving:  Stress period:    33    Time step:    14\n",
-      "    Solving:  Stress period:    33    Time step:    15\n",
-      "\n",
-      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2026/03/04 15:11:18\n",
-      " Elapsed run time: 40.017 Seconds\n",
-      "\n",
-      " Normal termination of simulation.\n",
-      "\n",
-      "stderr:\n",
-      "\n",
-      "returncode: 0\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "with flopy4.mf6.write_context.WriteContext(use_netcdf=True):\n",
-    "    sim.write()\n",
-    "if os.getenv(\"MF6_EXTENDED\"):\n",
-    "    sim.run(verbose=True)\n",
-    "\n",
-    "    # Load head results\n",
-    "    head = flopy4.mf6.utils.open_hds(\n",
-    "        workspace / \"ff.hds\",\n",
-    "        workspace / \"ff.dis.grb\",\n",
-    "    )\n",
-    "\n",
-    "    # Plot head results\n",
-    "    plot_head(head, workspace)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "866b5667",
-   "metadata": {},
-   "source": [
-    "The mesh2d NetCDF written to `netcdf_mesh/frenchman-flat.input.nc` can be\n",
-    "loaded into QGIS as a mesh layer via **Layer -> Add Layer -> Add Mesh Layer**.\n",
-    "The screenshot below shows the field NPF K layer 7 overlaid on the variable-\n",
-    "resolution Frenchman Flat grid.  The mesh is properly geolocated because a\n",
-    "CRS user input string was provided on grid construction.\n",
-    "\n",
-    "![QGIS: Frenchman Flat K layer 7 input — layered mesh](images/ff.qgis.npf-k-layer7.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fd9d5418",
-   "metadata": {},
-   "source": [
-    "### NetCDF input — structured (no mesh)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "749fc15b",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-03-04T20:11:18.335607Z",
-     "iopub.status.busy": "2026-03-04T20:11:18.335484Z",
-     "iopub.status.idle": "2026-03-04T20:11:18.338446Z",
-     "shell.execute_reply": "2026-03-04T20:11:18.337652Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# `NetCDFModel.from_model(gwf, grid=grid, time=time)` (no `mesh` argument)\n",
-    "# writes a CF-convention structured NetCDF.  This is a simpler format than\n",
-    "# the layered-mesh variant and does not require a UGRID-capable MODFLOW build.\n",
-    "workspace = FF_ROOT / \"frenchman-flat\" / \"netcdf_structured\"\n",
-    "workspace.mkdir(parents=True, exist_ok=True)\n",
-    "sim.workspace = workspace"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "4adf29c8",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-03-04T20:11:18.339849Z",
-     "iopub.status.busy": "2026-03-04T20:11:18.339736Z",
-     "iopub.status.idle": "2026-03-04T20:11:18.342116Z",
-     "shell.execute_reply": "2026-03-04T20:11:18.341532Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "nc_fpth = workspace / \"frenchnam-flat.input.nc\"\n",
-    "gwf.netcdf_file = nc_fpth"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "9f7e8a2b",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-03-04T20:11:18.343413Z",
-     "iopub.status.busy": "2026-03-04T20:11:18.343299Z",
-     "iopub.status.idle": "2026-03-04T20:11:18.432732Z",
-     "shell.execute_reply": "2026-03-04T20:11:18.431276Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "nc_model = flopy4.mf6.netcdf.NetCDFModel.from_model(gwf, grid=grid, time=time)\n",
-    "nc_model.to_netcdf(nc_fpth)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "9a844c90",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-03-04T20:11:18.435630Z",
-     "iopub.status.busy": "2026-03-04T20:11:18.435379Z",
-     "iopub.status.idle": "2026-03-04T20:11:59.091654Z",
-     "shell.execute_reply": "2026-03-04T20:11:59.090769Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "running: mf6\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "stdout:\n",
-      "                               MODFLOW 6 EXTENDED\n",
-      "                U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL\n",
-      "                  VERSION 6.8.0.dev0 (preliminary) 02/06/2026\n",
-      "                               ***DEVELOP MODE***\n",
-      "\n",
-      "        MODFLOW 6 compiled Feb 25 2026 15:19:23 with GCC version 13.3.0\n",
-      "\n",
-      "This software is preliminary or provisional and is subject to \n",
-      "revision. It is being provided to meet the need for timely best \n",
-      "science. The software has not received final approval by the U.S. \n",
-      "Geological Survey (USGS). No warranty, expressed or implied, is made \n",
-      "by the USGS or the U.S. Government as to the functionality of the \n",
-      "software and related material nor shall the fact of release \n",
-      "constitute any such warranty. The software is provided on the \n",
-      "condition that neither the USGS nor the U.S. Government shall be held \n",
-      "liable for any damages resulting from the authorized or unauthorized \n",
-      "use of the software.\n",
-      "\n",
-      "\n",
-      " MODFLOW runs in SEQUENTIAL mode\n",
-      "\n",
-      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2026/03/04 15:11:18\n",
-      "\n",
-      " Writing simulation list file: mfsim.lst\n",
-      " Using Simulation name file: mfsim.nam\n",
-      "\n",
-      "    Solving:  Stress period:     1    Time step:     1\n",
-      "    Solving:  Stress period:     1    Time step:     2\n",
-      "    Solving:  Stress period:     1    Time step:     3\n",
-      "    Solving:  Stress period:     1    Time step:     4\n",
-      "    Solving:  Stress period:     1    Time step:     5\n",
-      "    Solving:  Stress period:     1    Time step:     6\n",
-      "    Solving:  Stress period:     1    Time step:     7\n",
-      "    Solving:  Stress period:     1    Time step:     8\n",
-      "    Solving:  Stress period:     1    Time step:     9\n",
-      "    Solving:  Stress period:     1    Time step:    10\n",
-      "    Solving:  Stress period:     1    Time step:    11\n",
-      "    Solving:  Stress period:     1    Time step:    12\n",
-      "    Solving:  Stress period:     1    Time step:    13\n",
-      "    Solving:  Stress period:     1    Time step:    14\n",
-      "    Solving:  Stress period:     1    Time step:    15\n",
-      "    Solving:  Stress period:     2    Time step:     1\n",
-      "    Solving:  Stress period:     2    Time step:     2\n",
-      "    Solving:  Stress period:     2    Time step:     3\n",
-      "    Solving:  Stress period:     2    Time step:     4\n",
-      "    Solving:  Stress period:     2    Time step:     5\n",
-      "    Solving:  Stress period:     2    Time step:     6\n",
-      "    Solving:  Stress period:     2    Time step:     7\n",
-      "    Solving:  Stress period:     2    Time step:     8\n",
-      "    Solving:  Stress period:     2    Time step:     9\n",
-      "    Solving:  Stress period:     2    Time step:    10\n",
-      "    Solving:  Stress period:     2    Time step:    11\n",
-      "    Solving:  Stress period:     2    Time step:    12\n",
-      "    Solving:  Stress period:     2    Time step:    13\n",
-      "    Solving:  Stress period:     2    Time step:    14\n",
-      "    Solving:  Stress period:     2    Time step:    15\n",
-      "    Solving:  Stress period:     3    Time step:     1\n",
-      "    Solving:  Stress period:     3    Time step:     2\n",
-      "    Solving:  Stress period:     3    Time step:     3\n",
-      "    Solving:  Stress period:     3    Time step:     4\n",
-      "    Solving:  Stress period:     3    Time step:     5\n",
-      "    Solving:  Stress period:     3    Time step:     6\n",
-      "    Solving:  Stress period:     3    Time step:     7\n",
-      "    Solving:  Stress period:     3    Time step:     8\n",
-      "    Solving:  Stress period:     3    Time step:     9\n",
-      "    Solving:  Stress period:     3    Time step:    10\n",
-      "    Solving:  Stress period:     3    Time step:    11\n",
-      "    Solving:  Stress period:     3    Time step:    12\n",
-      "    Solving:  Stress period:     3    Time step:    13\n",
-      "    Solving:  Stress period:     3    Time step:    14\n",
-      "    Solving:  Stress period:     3    Time step:    15\n",
-      "    Solving:  Stress period:     4    Time step:     1\n",
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-      "    Solving:  Stress period:     4    Time step:     6\n",
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-      "    Solving:  Stress period:     4    Time step:     9\n",
-      "    Solving:  Stress period:     4    Time step:    10\n",
-      "    Solving:  Stress period:     4    Time step:    11\n",
-      "    Solving:  Stress period:     4    Time step:    12\n",
-      "    Solving:  Stress period:     4    Time step:    13\n",
-      "    Solving:  Stress period:     4    Time step:    14\n",
-      "    Solving:  Stress period:     4    Time step:    15\n",
-      "    Solving:  Stress period:     5    Time step:     1\n",
-      "    Solving:  Stress period:     5    Time step:     2\n",
-      "    Solving:  Stress period:     5    Time step:     3\n",
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-      "    Solving:  Stress period:     5    Time step:     5\n",
-      "    Solving:  Stress period:     5    Time step:     6\n",
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-      "    Solving:  Stress period:     5    Time step:    10\n",
-      "    Solving:  Stress period:     5    Time step:    11\n",
-      "    Solving:  Stress period:     5    Time step:    12\n",
-      "    Solving:  Stress period:     5    Time step:    13\n",
-      "    Solving:  Stress period:     5    Time step:    14\n",
-      "    Solving:  Stress period:     5    Time step:    15\n",
-      "    Solving:  Stress period:     6    Time step:     1\n",
-      "    Solving:  Stress period:     6    Time step:     2\n",
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-      "    Solving:  Stress period:     6    Time step:     4\n",
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-      "    Solving:  Stress period:     6    Time step:     8\n",
-      "    Solving:  Stress period:     6    Time step:     9\n",
-      "    Solving:  Stress period:     6    Time step:    10\n",
-      "    Solving:  Stress period:     6    Time step:    11\n",
-      "    Solving:  Stress period:     6    Time step:    12\n",
-      "    Solving:  Stress period:     6    Time step:    13\n",
-      "    Solving:  Stress period:     6    Time step:    14\n",
-      "    Solving:  Stress period:     6    Time step:    15\n",
-      "    Solving:  Stress period:     7    Time step:     1\n",
-      "    Solving:  Stress period:     7    Time step:     2\n",
-      "    Solving:  Stress period:     7    Time step:     3\n",
-      "    Solving:  Stress period:     7    Time step:     4\n",
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-      "    Solving:  Stress period:     7    Time step:     6\n",
-      "    Solving:  Stress period:     7    Time step:     7\n",
-      "    Solving:  Stress period:     7    Time step:     8\n",
-      "    Solving:  Stress period:     7    Time step:     9\n",
-      "    Solving:  Stress period:     7    Time step:    10\n",
-      "    Solving:  Stress period:     7    Time step:    11\n",
-      "    Solving:  Stress period:     7    Time step:    12\n",
-      "    Solving:  Stress period:     7    Time step:    13\n",
-      "    Solving:  Stress period:     7    Time step:    14\n",
-      "    Solving:  Stress period:     7    Time step:    15\n",
-      "    Solving:  Stress period:     8    Time step:     1\n",
-      "    Solving:  Stress period:     8    Time step:     2\n",
-      "    Solving:  Stress period:     8    Time step:     3\n",
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-      "    Solving:  Stress period:     8    Time step:     6\n",
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-      "    Solving:  Stress period:     8    Time step:     8\n",
-      "    Solving:  Stress period:     8    Time step:     9\n",
-      "    Solving:  Stress period:     8    Time step:    10\n",
-      "    Solving:  Stress period:     8    Time step:    11\n",
-      "    Solving:  Stress period:     8    Time step:    12\n",
-      "    Solving:  Stress period:     8    Time step:    13\n",
-      "    Solving:  Stress period:     8    Time step:    14\n",
-      "    Solving:  Stress period:     8    Time step:    15\n",
-      "    Solving:  Stress period:     9    Time step:     1\n",
-      "    Solving:  Stress period:     9    Time step:     2\n",
-      "    Solving:  Stress period:     9    Time step:     3\n",
-      "    Solving:  Stress period:     9    Time step:     4\n",
-      "    Solving:  Stress period:     9    Time step:     5\n",
-      "    Solving:  Stress period:     9    Time step:     6\n",
-      "    Solving:  Stress period:     9    Time step:     7\n",
-      "    Solving:  Stress period:     9    Time step:     8\n",
-      "    Solving:  Stress period:     9    Time step:     9\n",
-      "    Solving:  Stress period:     9    Time step:    10\n",
-      "    Solving:  Stress period:     9    Time step:    11\n",
-      "    Solving:  Stress period:     9    Time step:    12\n",
-      "    Solving:  Stress period:     9    Time step:    13\n",
-      "    Solving:  Stress period:     9    Time step:    14\n",
-      "    Solving:  Stress period:     9    Time step:    15\n",
-      "    Solving:  Stress period:    10    Time step:     1\n",
-      "    Solving:  Stress period:    10    Time step:     2\n",
-      "    Solving:  Stress period:    10    Time step:     3\n",
-      "    Solving:  Stress period:    10    Time step:     4\n",
-      "    Solving:  Stress period:    10    Time step:     5\n",
-      "    Solving:  Stress period:    10    Time step:     6\n",
-      "    Solving:  Stress period:    10    Time step:     7\n",
-      "    Solving:  Stress period:    10    Time step:     8\n",
-      "    Solving:  Stress period:    10    Time step:     9\n",
-      "    Solving:  Stress period:    10    Time step:    10\n",
-      "    Solving:  Stress period:    10    Time step:    11\n",
-      "    Solving:  Stress period:    10    Time step:    12\n",
-      "    Solving:  Stress period:    10    Time step:    13\n",
-      "    Solving:  Stress period:    10    Time step:    14\n",
-      "    Solving:  Stress period:    10    Time step:    15\n",
-      "    Solving:  Stress period:    11    Time step:     1\n",
-      "    Solving:  Stress period:    11    Time step:     2\n",
-      "    Solving:  Stress period:    11    Time step:     3\n",
-      "    Solving:  Stress period:    11    Time step:     4\n",
-      "    Solving:  Stress period:    11    Time step:     5\n",
-      "    Solving:  Stress period:    11    Time step:     6\n",
-      "    Solving:  Stress period:    11    Time step:     7\n",
-      "    Solving:  Stress period:    11    Time step:     8\n",
-      "    Solving:  Stress period:    11    Time step:     9\n",
-      "    Solving:  Stress period:    11    Time step:    10\n",
-      "    Solving:  Stress period:    11    Time step:    11\n",
-      "    Solving:  Stress period:    11    Time step:    12\n",
-      "    Solving:  Stress period:    11    Time step:    13\n",
-      "    Solving:  Stress period:    11    Time step:    14\n",
-      "    Solving:  Stress period:    11    Time step:    15\n",
-      "    Solving:  Stress period:    12    Time step:     1\n",
-      "    Solving:  Stress period:    12    Time step:     2\n",
-      "    Solving:  Stress period:    12    Time step:     3\n",
-      "    Solving:  Stress period:    12    Time step:     4\n",
-      "    Solving:  Stress period:    12    Time step:     5\n",
-      "    Solving:  Stress period:    12    Time step:     6\n",
-      "    Solving:  Stress period:    12    Time step:     7\n",
-      "    Solving:  Stress period:    12    Time step:     8\n",
-      "    Solving:  Stress period:    12    Time step:     9\n",
-      "    Solving:  Stress period:    12    Time step:    10\n",
-      "    Solving:  Stress period:    12    Time step:    11\n",
-      "    Solving:  Stress period:    12    Time step:    12\n",
-      "    Solving:  Stress period:    12    Time step:    13\n",
-      "    Solving:  Stress period:    12    Time step:    14\n",
-      "    Solving:  Stress period:    12    Time step:    15\n",
-      "    Solving:  Stress period:    13    Time step:     1\n",
-      "    Solving:  Stress period:    13    Time step:     2\n",
-      "    Solving:  Stress period:    13    Time step:     3\n",
-      "    Solving:  Stress period:    13    Time step:     4\n",
-      "    Solving:  Stress period:    13    Time step:     5\n",
-      "    Solving:  Stress period:    13    Time step:     6\n",
-      "    Solving:  Stress period:    13    Time step:     7\n",
-      "    Solving:  Stress period:    13    Time step:     8\n",
-      "    Solving:  Stress period:    13    Time step:     9\n",
-      "    Solving:  Stress period:    13    Time step:    10\n",
-      "    Solving:  Stress period:    13    Time step:    11\n",
-      "    Solving:  Stress period:    13    Time step:    12\n",
-      "    Solving:  Stress period:    13    Time step:    13\n",
-      "    Solving:  Stress period:    13    Time step:    14\n",
-      "    Solving:  Stress period:    13    Time step:    15\n",
-      "    Solving:  Stress period:    14    Time step:     1\n",
-      "    Solving:  Stress period:    14    Time step:     2\n",
-      "    Solving:  Stress period:    14    Time step:     3\n",
-      "    Solving:  Stress period:    14    Time step:     4\n",
-      "    Solving:  Stress period:    14    Time step:     5\n",
-      "    Solving:  Stress period:    14    Time step:     6\n",
-      "    Solving:  Stress period:    14    Time step:     7\n",
-      "    Solving:  Stress period:    14    Time step:     8\n",
-      "    Solving:  Stress period:    14    Time step:     9\n",
-      "    Solving:  Stress period:    14    Time step:    10\n",
-      "    Solving:  Stress period:    14    Time step:    11\n",
-      "    Solving:  Stress period:    14    Time step:    12\n",
-      "    Solving:  Stress period:    14    Time step:    13\n",
-      "    Solving:  Stress period:    14    Time step:    14\n",
-      "    Solving:  Stress period:    14    Time step:    15\n",
-      "    Solving:  Stress period:    15    Time step:     1\n",
-      "    Solving:  Stress period:    15    Time step:     2\n",
-      "    Solving:  Stress period:    15    Time step:     3\n",
-      "    Solving:  Stress period:    15    Time step:     4\n",
-      "    Solving:  Stress period:    15    Time step:     5\n",
-      "    Solving:  Stress period:    15    Time step:     6\n",
-      "    Solving:  Stress period:    15    Time step:     7\n",
-      "    Solving:  Stress period:    15    Time step:     8\n",
-      "    Solving:  Stress period:    15    Time step:     9\n",
-      "    Solving:  Stress period:    15    Time step:    10\n",
-      "    Solving:  Stress period:    15    Time step:    11\n",
-      "    Solving:  Stress period:    15    Time step:    12\n",
-      "    Solving:  Stress period:    15    Time step:    13\n",
-      "    Solving:  Stress period:    15    Time step:    14\n",
-      "    Solving:  Stress period:    15    Time step:    15\n",
-      "    Solving:  Stress period:    16    Time step:     1\n",
-      "    Solving:  Stress period:    16    Time step:     2\n",
-      "    Solving:  Stress period:    16    Time step:     3\n",
-      "    Solving:  Stress period:    16    Time step:     4\n",
-      "    Solving:  Stress period:    16    Time step:     5\n",
-      "    Solving:  Stress period:    16    Time step:     6\n",
-      "    Solving:  Stress period:    16    Time step:     7\n",
-      "    Solving:  Stress period:    16    Time step:     8\n",
-      "    Solving:  Stress period:    16    Time step:     9\n",
-      "    Solving:  Stress period:    16    Time step:    10\n",
-      "    Solving:  Stress period:    16    Time step:    11\n",
-      "    Solving:  Stress period:    16    Time step:    12\n",
-      "    Solving:  Stress period:    16    Time step:    13\n",
-      "    Solving:  Stress period:    16    Time step:    14\n",
-      "    Solving:  Stress period:    16    Time step:    15\n",
-      "    Solving:  Stress period:    17    Time step:     1\n",
-      "    Solving:  Stress period:    17    Time step:     2\n",
-      "    Solving:  Stress period:    17    Time step:     3\n",
-      "    Solving:  Stress period:    17    Time step:     4\n",
-      "    Solving:  Stress period:    17    Time step:     5\n",
-      "    Solving:  Stress period:    17    Time step:     6\n",
-      "    Solving:  Stress period:    17    Time step:     7\n",
-      "    Solving:  Stress period:    17    Time step:     8\n",
-      "    Solving:  Stress period:    17    Time step:     9\n",
-      "    Solving:  Stress period:    17    Time step:    10\n",
-      "    Solving:  Stress period:    17    Time step:    11\n",
-      "    Solving:  Stress period:    17    Time step:    12\n",
-      "    Solving:  Stress period:    17    Time step:    13\n",
-      "    Solving:  Stress period:    17    Time step:    14\n",
-      "    Solving:  Stress period:    17    Time step:    15\n",
-      "    Solving:  Stress period:    18    Time step:     1\n",
-      "    Solving:  Stress period:    18    Time step:     2\n",
-      "    Solving:  Stress period:    18    Time step:     3\n",
-      "    Solving:  Stress period:    18    Time step:     4\n",
-      "    Solving:  Stress period:    18    Time step:     5\n",
-      "    Solving:  Stress period:    18    Time step:     6\n",
-      "    Solving:  Stress period:    18    Time step:     7\n",
-      "    Solving:  Stress period:    18    Time step:     8\n",
-      "    Solving:  Stress period:    18    Time step:     9\n",
-      "    Solving:  Stress period:    18    Time step:    10\n",
-      "    Solving:  Stress period:    18    Time step:    11\n",
-      "    Solving:  Stress period:    18    Time step:    12\n",
-      "    Solving:  Stress period:    18    Time step:    13\n",
-      "    Solving:  Stress period:    18    Time step:    14\n",
-      "    Solving:  Stress period:    18    Time step:    15\n",
-      "    Solving:  Stress period:    19    Time step:     1\n",
-      "    Solving:  Stress period:    19    Time step:     2\n",
-      "    Solving:  Stress period:    19    Time step:     3\n",
-      "    Solving:  Stress period:    19    Time step:     4\n",
-      "    Solving:  Stress period:    19    Time step:     5\n",
-      "    Solving:  Stress period:    19    Time step:     6\n",
-      "    Solving:  Stress period:    19    Time step:     7\n",
-      "    Solving:  Stress period:    19    Time step:     8\n",
-      "    Solving:  Stress period:    19    Time step:     9\n",
-      "    Solving:  Stress period:    19    Time step:    10\n",
-      "    Solving:  Stress period:    19    Time step:    11\n",
-      "    Solving:  Stress period:    19    Time step:    12\n",
-      "    Solving:  Stress period:    19    Time step:    13\n",
-      "    Solving:  Stress period:    19    Time step:    14\n",
-      "    Solving:  Stress period:    19    Time step:    15\n",
-      "    Solving:  Stress period:    20    Time step:     1\n",
-      "    Solving:  Stress period:    20    Time step:     2\n",
-      "    Solving:  Stress period:    20    Time step:     3\n",
-      "    Solving:  Stress period:    20    Time step:     4\n",
-      "    Solving:  Stress period:    20    Time step:     5\n",
-      "    Solving:  Stress period:    20    Time step:     6\n",
-      "    Solving:  Stress period:    20    Time step:     7\n",
-      "    Solving:  Stress period:    20    Time step:     8\n",
-      "    Solving:  Stress period:    20    Time step:     9\n",
-      "    Solving:  Stress period:    20    Time step:    10\n",
-      "    Solving:  Stress period:    20    Time step:    11\n",
-      "    Solving:  Stress period:    20    Time step:    12\n",
-      "    Solving:  Stress period:    20    Time step:    13\n",
-      "    Solving:  Stress period:    20    Time step:    14\n",
-      "    Solving:  Stress period:    20    Time step:    15\n",
-      "    Solving:  Stress period:    21    Time step:     1\n",
-      "    Solving:  Stress period:    21    Time step:     2\n",
-      "    Solving:  Stress period:    21    Time step:     3\n",
-      "    Solving:  Stress period:    21    Time step:     4\n",
-      "    Solving:  Stress period:    21    Time step:     5\n",
-      "    Solving:  Stress period:    21    Time step:     6\n",
-      "    Solving:  Stress period:    21    Time step:     7\n",
-      "    Solving:  Stress period:    21    Time step:     8\n",
-      "    Solving:  Stress period:    21    Time step:     9\n",
-      "    Solving:  Stress period:    21    Time step:    10\n",
-      "    Solving:  Stress period:    21    Time step:    11\n",
-      "    Solving:  Stress period:    21    Time step:    12\n",
-      "    Solving:  Stress period:    21    Time step:    13\n",
-      "    Solving:  Stress period:    21    Time step:    14\n",
-      "    Solving:  Stress period:    21    Time step:    15\n",
-      "    Solving:  Stress period:    22    Time step:     1\n",
-      "    Solving:  Stress period:    22    Time step:     2\n",
-      "    Solving:  Stress period:    22    Time step:     3\n",
-      "    Solving:  Stress period:    22    Time step:     4\n",
-      "    Solving:  Stress period:    22    Time step:     5\n",
-      "    Solving:  Stress period:    22    Time step:     6\n",
-      "    Solving:  Stress period:    22    Time step:     7\n",
-      "    Solving:  Stress period:    22    Time step:     8\n",
-      "    Solving:  Stress period:    22    Time step:     9\n",
-      "    Solving:  Stress period:    22    Time step:    10\n",
-      "    Solving:  Stress period:    22    Time step:    11\n",
-      "    Solving:  Stress period:    22    Time step:    12\n",
-      "    Solving:  Stress period:    22    Time step:    13\n",
-      "    Solving:  Stress period:    22    Time step:    14\n",
-      "    Solving:  Stress period:    22    Time step:    15\n",
-      "    Solving:  Stress period:    23    Time step:     1\n",
-      "    Solving:  Stress period:    23    Time step:     2\n",
-      "    Solving:  Stress period:    23    Time step:     3\n",
-      "    Solving:  Stress period:    23    Time step:     4\n",
-      "    Solving:  Stress period:    23    Time step:     5\n",
-      "    Solving:  Stress period:    23    Time step:     6\n",
-      "    Solving:  Stress period:    23    Time step:     7\n",
-      "    Solving:  Stress period:    23    Time step:     8\n",
-      "    Solving:  Stress period:    23    Time step:     9\n",
-      "    Solving:  Stress period:    23    Time step:    10\n",
-      "    Solving:  Stress period:    23    Time step:    11\n",
-      "    Solving:  Stress period:    23    Time step:    12\n",
-      "    Solving:  Stress period:    23    Time step:    13\n",
-      "    Solving:  Stress period:    23    Time step:    14\n",
-      "    Solving:  Stress period:    23    Time step:    15\n",
-      "    Solving:  Stress period:    24    Time step:     1\n",
-      "    Solving:  Stress period:    24    Time step:     2\n",
-      "    Solving:  Stress period:    24    Time step:     3\n",
-      "    Solving:  Stress period:    24    Time step:     4\n",
-      "    Solving:  Stress period:    24    Time step:     5\n",
-      "    Solving:  Stress period:    24    Time step:     6\n",
-      "    Solving:  Stress period:    24    Time step:     7\n",
-      "    Solving:  Stress period:    24    Time step:     8\n",
-      "    Solving:  Stress period:    24    Time step:     9\n",
-      "    Solving:  Stress period:    24    Time step:    10\n",
-      "    Solving:  Stress period:    24    Time step:    11\n",
-      "    Solving:  Stress period:    24    Time step:    12\n",
-      "    Solving:  Stress period:    24    Time step:    13\n",
-      "    Solving:  Stress period:    24    Time step:    14\n",
-      "    Solving:  Stress period:    24    Time step:    15\n",
-      "    Solving:  Stress period:    25    Time step:     1\n",
-      "    Solving:  Stress period:    25    Time step:     2\n",
-      "    Solving:  Stress period:    25    Time step:     3\n",
-      "    Solving:  Stress period:    25    Time step:     4\n",
-      "    Solving:  Stress period:    25    Time step:     5\n",
-      "    Solving:  Stress period:    25    Time step:     6\n",
-      "    Solving:  Stress period:    25    Time step:     7\n",
-      "    Solving:  Stress period:    25    Time step:     8\n",
-      "    Solving:  Stress period:    25    Time step:     9\n",
-      "    Solving:  Stress period:    25    Time step:    10\n",
-      "    Solving:  Stress period:    25    Time step:    11\n",
-      "    Solving:  Stress period:    25    Time step:    12\n",
-      "    Solving:  Stress period:    25    Time step:    13\n",
-      "    Solving:  Stress period:    25    Time step:    14\n",
-      "    Solving:  Stress period:    25    Time step:    15\n",
-      "    Solving:  Stress period:    26    Time step:     1\n",
-      "    Solving:  Stress period:    26    Time step:     2\n",
-      "    Solving:  Stress period:    26    Time step:     3\n",
-      "    Solving:  Stress period:    26    Time step:     4\n",
-      "    Solving:  Stress period:    26    Time step:     5\n",
-      "    Solving:  Stress period:    26    Time step:     6\n",
-      "    Solving:  Stress period:    26    Time step:     7\n",
-      "    Solving:  Stress period:    26    Time step:     8\n",
-      "    Solving:  Stress period:    26    Time step:     9\n",
-      "    Solving:  Stress period:    26    Time step:    10\n",
-      "    Solving:  Stress period:    26    Time step:    11\n",
-      "    Solving:  Stress period:    26    Time step:    12\n",
-      "    Solving:  Stress period:    26    Time step:    13\n",
-      "    Solving:  Stress period:    26    Time step:    14\n",
-      "    Solving:  Stress period:    26    Time step:    15\n",
-      "    Solving:  Stress period:    27    Time step:     1\n",
-      "    Solving:  Stress period:    27    Time step:     2\n",
-      "    Solving:  Stress period:    27    Time step:     3\n",
-      "    Solving:  Stress period:    27    Time step:     4\n",
-      "    Solving:  Stress period:    27    Time step:     5\n",
-      "    Solving:  Stress period:    27    Time step:     6\n",
-      "    Solving:  Stress period:    27    Time step:     7\n",
-      "    Solving:  Stress period:    27    Time step:     8\n",
-      "    Solving:  Stress period:    27    Time step:     9\n",
-      "    Solving:  Stress period:    27    Time step:    10\n",
-      "    Solving:  Stress period:    27    Time step:    11\n",
-      "    Solving:  Stress period:    27    Time step:    12\n",
-      "    Solving:  Stress period:    27    Time step:    13\n",
-      "    Solving:  Stress period:    27    Time step:    14\n",
-      "    Solving:  Stress period:    27    Time step:    15\n",
-      "    Solving:  Stress period:    28    Time step:     1\n",
-      "    Solving:  Stress period:    28    Time step:     2\n",
-      "    Solving:  Stress period:    28    Time step:     3\n",
-      "    Solving:  Stress period:    28    Time step:     4\n",
-      "    Solving:  Stress period:    28    Time step:     5\n",
-      "    Solving:  Stress period:    28    Time step:     6\n",
-      "    Solving:  Stress period:    28    Time step:     7\n",
-      "    Solving:  Stress period:    28    Time step:     8\n",
-      "    Solving:  Stress period:    28    Time step:     9\n",
-      "    Solving:  Stress period:    28    Time step:    10\n",
-      "    Solving:  Stress period:    28    Time step:    11\n",
-      "    Solving:  Stress period:    28    Time step:    12\n",
-      "    Solving:  Stress period:    28    Time step:    13\n",
-      "    Solving:  Stress period:    28    Time step:    14\n",
-      "    Solving:  Stress period:    28    Time step:    15\n",
-      "    Solving:  Stress period:    29    Time step:     1\n",
-      "    Solving:  Stress period:    29    Time step:     2\n",
-      "    Solving:  Stress period:    29    Time step:     3\n",
-      "    Solving:  Stress period:    29    Time step:     4\n",
-      "    Solving:  Stress period:    29    Time step:     5\n",
-      "    Solving:  Stress period:    29    Time step:     6\n",
-      "    Solving:  Stress period:    29    Time step:     7\n",
-      "    Solving:  Stress period:    29    Time step:     8\n",
-      "    Solving:  Stress period:    29    Time step:     9\n",
-      "    Solving:  Stress period:    29    Time step:    10\n",
-      "    Solving:  Stress period:    29    Time step:    11\n",
-      "    Solving:  Stress period:    29    Time step:    12\n",
-      "    Solving:  Stress period:    29    Time step:    13\n",
-      "    Solving:  Stress period:    29    Time step:    14\n",
-      "    Solving:  Stress period:    29    Time step:    15\n",
-      "    Solving:  Stress period:    30    Time step:     1\n",
-      "    Solving:  Stress period:    30    Time step:     2\n",
-      "    Solving:  Stress period:    30    Time step:     3\n",
-      "    Solving:  Stress period:    30    Time step:     4\n",
-      "    Solving:  Stress period:    30    Time step:     5\n",
-      "    Solving:  Stress period:    30    Time step:     6\n",
-      "    Solving:  Stress period:    30    Time step:     7\n",
-      "    Solving:  Stress period:    30    Time step:     8\n",
-      "    Solving:  Stress period:    30    Time step:     9\n",
-      "    Solving:  Stress period:    30    Time step:    10\n",
-      "    Solving:  Stress period:    30    Time step:    11\n",
-      "    Solving:  Stress period:    30    Time step:    12\n",
-      "    Solving:  Stress period:    30    Time step:    13\n",
-      "    Solving:  Stress period:    30    Time step:    14\n",
-      "    Solving:  Stress period:    30    Time step:    15\n",
-      "    Solving:  Stress period:    31    Time step:     1\n",
-      "    Solving:  Stress period:    31    Time step:     2\n",
-      "    Solving:  Stress period:    31    Time step:     3\n",
-      "    Solving:  Stress period:    31    Time step:     4\n",
-      "    Solving:  Stress period:    31    Time step:     5\n",
-      "    Solving:  Stress period:    31    Time step:     6\n",
-      "    Solving:  Stress period:    31    Time step:     7\n",
-      "    Solving:  Stress period:    31    Time step:     8\n",
-      "    Solving:  Stress period:    31    Time step:     9\n",
-      "    Solving:  Stress period:    31    Time step:    10\n",
-      "    Solving:  Stress period:    31    Time step:    11\n",
-      "    Solving:  Stress period:    31    Time step:    12\n",
-      "    Solving:  Stress period:    31    Time step:    13\n",
-      "    Solving:  Stress period:    31    Time step:    14\n",
-      "    Solving:  Stress period:    31    Time step:    15\n",
-      "    Solving:  Stress period:    32    Time step:     1\n",
-      "    Solving:  Stress period:    32    Time step:     2\n",
-      "    Solving:  Stress period:    32    Time step:     3\n",
-      "    Solving:  Stress period:    32    Time step:     4\n",
-      "    Solving:  Stress period:    32    Time step:     5\n",
-      "    Solving:  Stress period:    32    Time step:     6\n",
-      "    Solving:  Stress period:    32    Time step:     7\n",
-      "    Solving:  Stress period:    32    Time step:     8\n",
-      "    Solving:  Stress period:    32    Time step:     9\n",
-      "    Solving:  Stress period:    32    Time step:    10\n",
-      "    Solving:  Stress period:    32    Time step:    11\n",
-      "    Solving:  Stress period:    32    Time step:    12\n",
-      "    Solving:  Stress period:    32    Time step:    13\n",
-      "    Solving:  Stress period:    32    Time step:    14\n",
-      "    Solving:  Stress period:    32    Time step:    15\n",
-      "    Solving:  Stress period:    33    Time step:     1\n",
-      "    Solving:  Stress period:    33    Time step:     2\n",
-      "    Solving:  Stress period:    33    Time step:     3\n",
-      "    Solving:  Stress period:    33    Time step:     4\n",
-      "    Solving:  Stress period:    33    Time step:     5\n",
-      "    Solving:  Stress period:    33    Time step:     6\n",
-      "    Solving:  Stress period:    33    Time step:     7\n",
-      "    Solving:  Stress period:    33    Time step:     8\n",
-      "    Solving:  Stress period:    33    Time step:     9\n",
-      "    Solving:  Stress period:    33    Time step:    10\n",
-      "    Solving:  Stress period:    33    Time step:    11\n",
-      "    Solving:  Stress period:    33    Time step:    12\n",
-      "    Solving:  Stress period:    33    Time step:    13\n",
-      "    Solving:  Stress period:    33    Time step:    14\n",
-      "    Solving:  Stress period:    33    Time step:    15\n",
-      "\n",
-      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2026/03/04 15:11:58\n",
-      " Elapsed run time: 40.281 Seconds\n",
-      "\n",
-      " Normal termination of simulation.\n",
-      "\n",
-      "stderr:\n",
-      "\n",
-      "returncode: 0\n"
+      "/tmp/ipykernel_78070/3549307819.py:46: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
+      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha=\"right\")\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if os.getenv(\"MF6_EXTENDED\"):\n",
+    "    sim.run()\n",
+    "\n",
+    "    # Load head results\n",
+    "    head = flopy4.mf6.utils.open_hds(\n",
+    "        workspace / \"ff.hds\",\n",
+    "        workspace / \"ff.dis.grb\",\n",
+    "    )\n",
+    "\n",
+    "    # Load budget results\n",
+    "    cbc = flopy4.mf6.utils.open_cbc(\n",
+    "        workspace / \"ff.cbc\",\n",
+    "        workspace / \"ff.dis.grb\",\n",
+    "    )\n",
+    "\n",
+    "    # Plot head results\n",
+    "    plot_head(head, workspace)\n",
+    "    plot_head_ugrid(head, cbc, grid, workspace)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8ee3f813",
+   "metadata": {},
+   "source": [
+    "### Array-based NetCDF WEL packages + layered mesh NetCDF output"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64005723",
+   "metadata": {},
+   "source": [
+    "Switch the three WEL packages from list-based to array-based (`Welg`),\n",
+    "combine with a layered-mesh NetCDF input file, and also request mesh2d\n",
+    "NetCDF output (`gwf.netcdf_mesh2d_file`).  `FILL_DNODATA` marks inactive\n",
+    "cells so MODFLOW ignores them for those stress periods."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "83518416",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:21:47.379975Z",
+     "iopub.status.busy": "2026-03-06T20:21:47.379836Z",
+     "iopub.status.idle": "2026-03-06T20:21:47.383108Z",
+     "shell.execute_reply": "2026-03-06T20:21:47.381937Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# update simulation with array based inputs\n",
+    "GRID_NODATA = np.full((nlay, nrow, ncol), flopy4.mf6.constants.FILL_DNODATA, dtype=float)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "9ad711c3",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:21:47.385646Z",
+     "iopub.status.busy": "2026-03-06T20:21:47.385521Z",
+     "iopub.status.idle": "2026-03-06T20:21:47.398758Z",
+     "shell.execute_reply": "2026-03-06T20:21:47.397958Z"
+    },
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "# Constant-rate pumping — array form of wel_crt.\n",
+    "q_crt = np.repeat(np.expand_dims(GRID_NODATA, axis=0), repeats=nper, axis=0)\n",
+    "q_crt[0, 1, 43, 43] = -30992.50\n",
+    "q_crt[1, 1, 43, 43] = -00000.0\n",
+    "q_crt[2, 1, 43, 43] = -30992.50\n",
+    "q_crt[3, 1, 43, 43] = -00000.0\n",
+    "q_crt[4, 1, 43, 43] = -30992.50\n",
+    "q_crt[5, 1, 43, 43] = -00000.0\n",
+    "welg_crt = flopy4.mf6.gwf.Welg(\n",
+    "    filename=\"ff.crt.welg\",\n",
+    "    q=q_crt,\n",
+    "    print_input=True,\n",
+    "    print_flows=True,\n",
+    "    save_flows=True,\n",
+    "    dims=dims,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "0a65da84",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:21:47.400865Z",
+     "iopub.status.busy": "2026-03-06T20:21:47.400731Z",
+     "iopub.status.idle": "2026-03-06T20:21:47.419064Z",
+     "shell.execute_reply": "2026-03-06T20:21:47.418419Z"
+    },
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "# Leakage — array form of wel_leak.\n",
+    "q_leak = np.repeat(np.expand_dims(GRID_NODATA, axis=0), repeats=nper, axis=0)\n",
+    "q_leak[0, 1, 43, 43] = 1.0000000e-05\n",
+    "q_leak[7, 1, 43, 43] = 1.5000000e03\n",
+    "q_leak[8, 1, 43, 43] = 2.6500000e03\n",
+    "q_leak[9, 1, 43, 43] = 3.1500000e03\n",
+    "q_leak[10, 1, 43, 43] = 4.1000000e03\n",
+    "q_leak[11, 1, 43, 43] = 4.6500000e03\n",
+    "q_leak[12, 1, 43, 43] = 4.9500000e03\n",
+    "q_leak[13, 1, 43, 43] = 5.3000000e03\n",
+    "q_leak[14, 1, 43, 43] = 5.8000000e03\n",
+    "q_leak[16, 1, 43, 43] = 5.9000000e03\n",
+    "q_leak[17, 1, 43, 43] = 5.8000000e03\n",
+    "q_leak[19, 1, 43, 43] = 5.6000000e03\n",
+    "q_leak[20, 1, 43, 43] = 4.7000000e03\n",
+    "q_leak[22, 1, 43, 43] = 3.4000000e03\n",
+    "q_leak[23, 1, 43, 43] = 1.0000000e-05\n",
+    "welg_leak = flopy4.mf6.gwf.Welg(\n",
+    "    filename=\"ff.leak.welg\",\n",
+    "    q=q_leak,\n",
+    "    print_input=True,\n",
+    "    print_flows=True,\n",
+    "    save_flows=True,\n",
+    "    dims=dims,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "acfdd562",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:21:47.421190Z",
+     "iopub.status.busy": "2026-03-06T20:21:47.421064Z",
+     "iopub.status.idle": "2026-03-06T20:21:47.444721Z",
+     "shell.execute_reply": "2026-03-06T20:21:47.443670Z"
+    },
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "# Sampling — array form of wel_sampleQ.\n",
+    "q_sampleQ = np.repeat(np.expand_dims(GRID_NODATA, axis=0), repeats=nper, axis=0)\n",
+    "q_sampleQ[0, 1, 43, 43] = -00000.0\n",
+    "q_sampleQ[22, 1, 43, 43] = -04981.90\n",
+    "q_sampleQ[23, 1, 43, 43] = -00000.0\n",
+    "q_sampleQ[24, 1, 43, 43] = -04059.83\n",
+    "q_sampleQ[25, 1, 43, 43] = -00000.0\n",
+    "q_sampleQ[26, 1, 43, 43] = -05678.75\n",
+    "q_sampleQ[27, 1, 43, 43] = -00000.0\n",
+    "q_sampleQ[28, 1, 43, 43] = -05755.75\n",
+    "q_sampleQ[29, 1, 43, 43] = -00000.0\n",
+    "q_sampleQ[30, 1, 43, 43] = -04117.58\n",
+    "q_sampleQ[31, 1, 43, 43] = -00000.0\n",
+    "welg_sampleQ = flopy4.mf6.gwf.Welg(\n",
+    "    filename=\"ff.sampleQ.welg\",\n",
+    "    q=q_sampleQ,\n",
+    "    print_input=True,\n",
+    "    print_flows=True,\n",
+    "    save_flows=True,\n",
+    "    dims=dims,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "dc5b49f5",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:21:47.446577Z",
+     "iopub.status.busy": "2026-03-06T20:21:47.446446Z",
+     "iopub.status.idle": "2026-03-06T20:21:47.449420Z",
+     "shell.execute_reply": "2026-03-06T20:21:47.448629Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Swap list-based WEL packages for their array-based equivalents.\n",
+    "del gwf.wel[0]\n",
+    "del gwf.wel[1]\n",
+    "del gwf.wel[2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "ccf7a35e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:21:47.451291Z",
+     "iopub.status.busy": "2026-03-06T20:21:47.451175Z",
+     "iopub.status.idle": "2026-03-06T20:21:47.464701Z",
+     "shell.execute_reply": "2026-03-06T20:21:47.463988Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Attach the array-based WEL packages.\n",
+    "gwf.wel = [welg_crt, welg_leak, welg_sampleQ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "4bb06dbf",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:21:47.466473Z",
+     "iopub.status.busy": "2026-03-06T20:21:47.466360Z",
+     "iopub.status.idle": "2026-03-06T20:21:47.469063Z",
+     "shell.execute_reply": "2026-03-06T20:21:47.468417Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# create new workspace\n",
+    "workspace = FF_ROOT / \"frenchman-flat\" / \"netcdf_mesh\"\n",
+    "workspace.mkdir(parents=True, exist_ok=True)\n",
+    "sim.workspace = workspace"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "bece80da",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:21:47.470720Z",
+     "iopub.status.busy": "2026-03-06T20:21:47.470602Z",
+     "iopub.status.idle": "2026-03-06T20:21:47.473001Z",
+     "shell.execute_reply": "2026-03-06T20:21:47.472343Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "gwf.netcdf_mesh2d_file = Path(\"frenchman-flat.nc\")\n",
+    "gwf.netcdf_file = Path(\"frenchman-flat.input.nc\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "cb9866a5",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:21:47.474529Z",
+     "iopub.status.busy": "2026-03-06T20:21:47.474417Z",
+     "iopub.status.idle": "2026-03-06T20:21:47.636611Z",
+     "shell.execute_reply": "2026-03-06T20:21:47.635767Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Again, with grid and time info\n",
+    "nc_model = flopy4.mf6.netcdf.NetCDFModel.from_model(gwf, mesh=\"layered\", grid=grid, time=time)\n",
+    "nc_model.to_netcdf(workspace / \"frenchman-flat.input.nc\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "7f90bba7",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:21:47.639428Z",
+     "iopub.status.busy": "2026-03-06T20:21:47.639241Z",
+     "iopub.status.idle": "2026-03-06T20:22:29.493759Z",
+     "shell.execute_reply": "2026-03-06T20:22:29.493120Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mjreno/.clone/dev/pyphoenix-project/flopy4/mf6/utils/cbc_reader.py:147: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  return xr.merge([cbc])\n",
+      "/home/mjreno/.clone/dev/pyphoenix-project/flopy4/mf6/utils/cbc_reader.py:147: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  return xr.merge([cbc])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_78070/3549307819.py:46: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
+      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha=\"right\")\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with flopy4.mf6.write_context.WriteContext(use_netcdf=True):\n",
+    "    sim.write()\n",
+    "if os.getenv(\"MF6_EXTENDED\"):\n",
+    "    sim.run()\n",
+    "\n",
+    "    # Load head results\n",
+    "    # head = flopy4.mf6.utils.open_hds(\n",
+    "    #    workspace / \"ff.hds\",\n",
+    "    #    workspace / \"ff.dis.grb\",\n",
+    "    # )\n",
+    "    # Load head results — `UgridDataArray` backed by the NetCDF mesh2d output file.\n",
+    "    head = flopy4.mf6.utils.open_hds(\n",
+    "        workspace / gwf.netcdf_mesh2d_file,\n",
+    "        workspace / \"ff.dis.grb\",\n",
+    "    )\n",
+    "\n",
+    "    # Load budget results\n",
+    "    cbc = flopy4.mf6.utils.open_cbc(\n",
+    "        workspace / \"ff.cbc\",\n",
+    "        workspace / \"ff.dis.grb\",\n",
+    "    )\n",
+    "\n",
+    "    # Plot head results\n",
+    "    plot_head(head, workspace)\n",
+    "    plot_head_ugrid(head, cbc, grid, workspace)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "866b5667",
+   "metadata": {},
+   "source": [
+    "# NetCDF input — structured (no mesh)\n",
+    "\n",
+    "![QGIS: Frenchman Flat K layer 7 input — layered mesh](images/ff.qgis.npf-k-layer7.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fd9d5418",
+   "metadata": {},
+   "source": [
+    "### NetCDF input — structured (no mesh)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "749fc15b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:22:29.496632Z",
+     "iopub.status.busy": "2026-03-06T20:22:29.496510Z",
+     "iopub.status.idle": "2026-03-06T20:22:29.499421Z",
+     "shell.execute_reply": "2026-03-06T20:22:29.498612Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# `NetCDFModel.from_model(gwf, grid=grid, time=time)` (no `mesh` argument)\n",
+    "# writes a CF-convention structured NetCDF.  This is a simpler format than\n",
+    "# the layered-mesh variant and does not require a UGRID-capable MODFLOW build.\n",
+    "workspace = FF_ROOT / \"frenchman-flat\" / \"netcdf_structured\"\n",
+    "workspace.mkdir(parents=True, exist_ok=True)\n",
+    "sim.workspace = workspace"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "4adf29c8",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:22:29.501262Z",
+     "iopub.status.busy": "2026-03-06T20:22:29.501149Z",
+     "iopub.status.idle": "2026-03-06T20:22:29.503588Z",
+     "shell.execute_reply": "2026-03-06T20:22:29.502861Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "nc_fpth = workspace / \"frenchnam-flat.input.nc\"\n",
+    "gwf.netcdf_file = nc_fpth"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "9f7e8a2b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:22:29.504914Z",
+     "iopub.status.busy": "2026-03-06T20:22:29.504795Z",
+     "iopub.status.idle": "2026-03-06T20:22:29.585628Z",
+     "shell.execute_reply": "2026-03-06T20:22:29.584674Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Again, with grid and time info\n",
+    "nc_model = flopy4.mf6.netcdf.NetCDFModel.from_model(gwf, grid=grid, time=time)\n",
+    "nc_model.to_netcdf(nc_fpth)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "9a844c90",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-06T20:22:29.587630Z",
+     "iopub.status.busy": "2026-03-06T20:22:29.587503Z",
+     "iopub.status.idle": "2026-03-06T20:23:11.497604Z",
+     "shell.execute_reply": "2026-03-06T20:23:11.496573Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mjreno/.clone/dev/pyphoenix-project/flopy4/mf6/utils/cbc_reader.py:147: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  return xr.merge([cbc])\n",
+      "/home/mjreno/.clone/dev/pyphoenix-project/flopy4/mf6/utils/cbc_reader.py:147: FutureWarning: In a future version of xarray the default value for compat will change from compat='no_conflicts' to compat='override'. This is likely to lead to different results when combining overlapping variables with the same name. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set compat explicitly.\n",
+      "  return xr.merge([cbc])\n"
      ]
     },
     {
      "data": {
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",
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hVVE6t9HGokWLNHz4cGVkZKhly5Z69dVX7YERAOAiD+5Md8mRZLzzzjv25+e3rfX393d4NGjQwLjtttsMwzCMkSNHGpKM7Oxs+3kZGRmGJGPv3r0X+xaAWnF+i+xfbhn8S+5ukW0YhvHzzz8bU6ZMMTp06GD4+PgYzZs3N+Li4oy5c+capaWl9n7/+c9/jLvuussIDAw0mjRpYtx1113Gzp07a7RF9nlHjx41JkyYYFx++eWGr6+v4efnZ3Tv3t2YNWuWcfLkSYe+ixYtMqKiooyGDRsaoaGhxgMPPGD897//dejTu3dv46qrrqpwnV9vW20YhvHuu+8aV155pdGgQYMKc169erVx9dVXG1ar1QgODjaSkpKMH3/8scK4r732mtGuXTvDx8fH6Nq1q/Hvf/+70i2yn3nmmRrVxDDO/XsZNGhQtf2cbdOdl5dn3HPPPUbz5s0NHx8fIyYmxum/j61btxrdu3c3fHx8qt0u+/wW2WvWrKl2Ps7qXxOSjNGjRzs9Vtnr/cCBA8bw4cONsLAwo2HDhkbr1q2NP/7xj8abb75p73PmzBlj4sSJRsuWLY1GjRoZv/vd74z09HSntfvhhx+Mm2++2fDz8zOaN29ujBs3ztiwYQNbZAOAG7wMo5a/jWtiXl5eDrvDrV69WklJSfrmm28qfBk6ICBAYWFhmj59up588kmVlZXZj50+fVp+fn768MMP9Yc//OFi3gIAAABwyePjcHXo6quvVnl5ufLz8yv8dsV5v/vd73T27FkdOHBA7du3lyT7dxB++SVnAAAAALWDlaALVFRUpP3790s6F3qeffZZ9e3bV8HBwYqIiNCdd96pzz//XPPmzdPVV1+t48ePa+PGjercubMGDRokm82ma665RgEBAVqwYIFsNptGjx6twMBAffjhhx6+OwAAAODSQwi6QJs3b1bfvn0rtN9999166aWXVFZWpieeeEKvvPKKjhw5oubNm+vaa6/VzJkzFRMTI0k6evSoHnzwQX344Yfy9/fXgAEDNG/ePAUHB1/s2wEAAAAueYQgAAAAwOQWL16sZ555Rrm5uerSpYsWLlyonj17Vtp/zZo1mjZtmg4dOqSOHTvqqaee0sCBAy/ijC8MvxMEAAAAmNjq1auVkpKi6dOna8eOHerSpYv69++v/Px8p/23bt2qYcOGKTk5WTt37lRiYqISExOVlZV1kWfuPlaCAAAAABPr1auXrrnmGi1atEiSZLPZFB4ergcffFCTJ0+u0P/222/XqVOn9P7779vbrr32WnXt2lVLly69aPO+EOwO5yabzaajR4+qcePG8vLy8vR0AAAA8CuGYejnn39Wq1atZLHUvw9AnTlzRqWlpXUytmEYFd6jWq1WWa1Wh7bS0lJlZGRoypQp9jaLxaL4+Hilp6c7HTs9PV0pKSkObf3799fatWtrZ/IXASHITUePHlV4eLinpwEAAIBqHD58WJdddpmnp+HgzJkzCo/w10/HbXUyfkBAgIqKihzapk+frhkzZji0/fTTTyovL1doaKhDe2hoqPbu3et07NzcXKf9c3NzL3ziFwkhyE2NGzeWJKV9GSr/AMe/WbCV+yona5Yioh+WxfuMJ6b3m0PN3EPdXEfN3EPdXEfN3EPd3EPdnDtVZNMfeuXZ37fVJ6WlpfrpuE0ffhmmgIDa/VRRUZGhG3vl6vDhwwoMDLS3/3oVyMwIQW46v7zoH2BRQONfhyCL/Pz8FNDYIot3/Vt6rY+omXuom+uomXuom+uomXuom3uoW9Xq81cXAgK8KryXvHDnVpcCAwMdQpAzzZs3l7e3t/Ly8hza8/LyFBYW5vScsLAwl/rXR/xXAgAAAJiUj4+Punfvro0bN9rbbDabNm7cqNjYWKfnxMbGOvSXpLS0tEr710esBAEAAAAmlpKSorvvvls9evRQz549tWDBAp06dUr33HOPJGn48OFq3bq1Zs+eLUkaN26cevfurXnz5mnQoEFKTU3V9u3btXz5ck/ehksIQQAAAICJ3X777Tp+/LgeffRR5ebmqmvXrtqwYYN984OcnByH3fXi4uK0atUqPfLII5o6dao6duyotWvXKjo62lO34DJCEAAAAGByY8aM0ZgxY5we27x5c4W2oUOHaujQoXU8q7rDd4IAAAAAmAohCAAAAICpEIIAAAAAmAohCAAAAICp1JsQNGfOHHl5eWn8+PGV9vnmm280ZMgQtWnTRl5eXlqwYIHTfosXL1abNm3k6+urXr166auvvnI4fubMGY0ePVrNmjVTQECAhgwZUuEHnwAAAABcmupFCNq2bZuWLVumzp07V9mvuLhY7dq105w5cyr9RdrVq1crJSVF06dP144dO9SlSxf1799f+fn59j4TJkzQe++9pzVr1uiTTz7R0aNHNXjw4Fq9JwAAAAD1k8dDUFFRkZKSkrRixQo1bdq0yr7XXHONnnnmGf35z3+W1Wp12ufZZ5/VyJEjdc899+jKK6/U0qVL5efnpxdffFGSdPLkSb3wwgt69tln9fvf/17du3fXypUrtXXrVn3xxRe1fn8AAAAA6heP/07Q6NGjNWjQIMXHx+uJJ564oLFKS0uVkZGhKVOm2NssFovi4+OVnp4uScrIyFBZWZni4+PtfaKiohQREaH09HRde+21TscuKSlRSUmJ/XlhYaEkyVbuK1u5Y5a0lVsd/onqUTP3UDfXUTP3UDfXUTP3UDf3UDfnbOU2T08B9ZRHQ1Bqaqp27Nihbdu21cp4P/30k8rLy+2/bnteaGio9u7dK0nKzc2Vj4+PgoKCKvTJzc2tdOzZs2dr5syZFdpzsmbJz8/P6Tk5WU+6eAegZu6hbq6jZu6hbq6jZu6hbu6hbo6Ki4sl3eHpaaAe8lgIOnz4sMaNG6e0tDT5+vp6aho1NmXKFKWkpNifFxYWKjw8XBHRDyugccWVoJysJxURPVUW75JfDwUnqJl7qJvrqJl7qJvrqJl7qJt7qJtzRT+zEgTnPBaCMjIylJ+fr27dutnbysvLtWXLFi1atEglJSXy9vZ2aczmzZvL29u7wk5veXl59o0UwsLCVFpaqoKCAofVoF/2ccZqtTr9HpLF+4ws3s6/WmXxLpHF+4xL92B21Mw91M111Mw91M111Mw91M091M2RxZsQBOc8tjFCv379tHv3bmVmZtofPXr0UFJSkjIzM10OQJLk4+Oj7t27a+PGjfY2m82mjRs3KjY2VpLUvXt3NWzY0KFPdna2cnJy7H0AAAAAXLo8thLUuHFjRUdHO7T5+/urWbNm9vbhw4erdevWmj17tqRzGx98++239j8fOXJEmZmZCggIUIcOHSRJKSkpuvvuu9WjRw/17NlTCxYs0KlTp3TPPfdIkpo0aaLk5GSlpKQoODhYgYGBevDBBxUbG1vppggAAAAALh0e3x2uKjk5ObJY/rdYdfToUV199dX253PnztXcuXPVu3dvbd68WZJ0++236/jx43r00UeVm5urrl27asOGDQ6bJcyfP18Wi0VDhgxRSUmJ+vfvr+eff/6i3RcAAAAAz6lXIeh8kKnseZs2bWQYRrXjjBkzRmPGjKn0uK+vrxYvXqzFixe7M00AAAAAv2Ee/7FUAAAAALiYCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAKBGTpw4oaSkJAUGBiooKEjJyckqKiqq0bmGYWjAgAHy8vLS2rVr63ai1SAEAQAAAKiRpKQkffPNN0pLS9P777+vLVu2aNSoUTU6d8GCBfLy8qrjGdZMA09PAAAAAEDtKywsdHhutVpltVrdHm/Pnj3asGGDtm3bph49ekiSFi5cqIEDB2ru3Llq1apVpedmZmZq3rx52r59u1q2bOn2HGoLIQgAAADwkNT/9pS1rGGtjllSVCbpXYWHhzu0T58+XTNmzHB73PT0dAUFBdkDkCTFx8fLYrHoyy+/1K233ur0vOLiYt1xxx1avHixwsLC3L5+bSIEAQAAAJegw4cPKzAw0P78QlaBJCk3N1ctWrRwaGvQoIGCg4OVm5tb6XkTJkxQXFycbrnllgu6fm3iO0EAAADAJSgwMNDhUVkImjx5sry8vKp87N271605rFu3Tps2bdKCBQsu4E5qHytBAAAAgIlNnDhRI0aMqLJPu3btFBYWpvz8fIf2s2fP6sSJE5V+zG3Tpk06cOCAgoKCHNqHDBmi66+/Xps3b76AmbuPEAQAAACYWEhIiEJCQqrtFxsbq4KCAmVkZKh79+6SzoUcm82mXr16OT1n8uTJ+stf/uLQFhMTo/nz5+umm2668Mm7iRAEAAAAoFqdOnVSQkKCRo4cqaVLl6qsrExjxozRn//8Z/vOcEeOHFG/fv30yiuvqGfPngoLC3O6ShQREaG2bdte7Fuw4ztBAAAAAGrk9ddfV1RUlPr166eBAwfquuuu0/Lly+3Hy8rKlJ2dreLiYg/OsnqsBAEAAACokeDgYK1atarS423atJFhGFWOUd3xi4GVIAAAAACmQggCAAAAYCqEIAAAAACmQggCAAAAYCr1JgTNmTNHXl5eGj9+fJX91qxZo6ioKPn6+iomJkbr1693OJ6Xl6cRI0aoVatW8vPzU0JCgvbt2+fQp0+fPhV+Bff++++v7VsCAAAAUA/VixC0bds2LVu2TJ07d66y39atWzVs2DAlJydr586dSkxMVGJiorKysiSd22kiMTFR33//vd59913t3LlTkZGRio+P16lTpxzGGjlypI4dO2Z/PP3003V2fwAAAADqD4+HoKKiIiUlJWnFihVq2rRplX2fe+45JSQkaNKkSerUqZMef/xxdevWTYsWLZIk7du3T1988YWWLFmia665RldccYWWLFmi06dP65///KfDWH5+fvYfbwoLC1NgYGCd3SMAAACA+sPjvxM0evRoDRo0SPHx8XriiSeq7Juenq6UlBSHtv79+2vt2rWSpJKSEkmSr6+v/bjFYpHVatVnn32mv/zlL/b2119/Xa+99prCwsJ00003adq0afLz86v02iUlJfbxJamwsFCSZCv3la3cMUvayq0O/0T1qJl7qJvrqJl7qJvrqJl7qJt7qJtztnKbp6eAesqjISg1NVU7duzQtm3batQ/NzdXoaGhDm2hoaHKzc2VJEVFRSkiIkJTpkzRsmXL5O/vr/nz5+vHH3/UsWPH7OfccccdioyMVKtWrbRr1y499NBDys7O1ttvv13ptWfPnq2ZM2dWaM/JmlVpeMrJerJG94X/oWbuoW6uo2buoW6uo2buoW7uoW6OiouLJd3h6WmgHvJYCDp8+LDGjRuntLQ0h5WbC9GwYUO9/fbbSk5OVnBwsLy9vRUfH68BAwY4/DLtqFGj7H+OiYlRy5Yt1a9fPx04cEDt27d3OvaUKVMcVqEKCwsVHh6uiOiHFdC44kpQTtaTioieKot3ya+HghPUzD3UzXXUzD3UzXXUzD3UzT3Uzbmin1kJgnMeC0EZGRnKz89Xt27d7G3l5eXasmWLFi1apJKSEnl7ezucExYWpry8PIe2vLw8hYWF2Z93795dmZmZOnnypEpLSxUSEqJevXqpR48elc6lV69ekqT9+/dXGoKsVqus1opLzBbvM7J4O/9qlcW7RBbvM5VeFxVRM/dQN9dRM/dQN9dRM/dQN/dQN0cWb0IQnPPYxgj9+vXT7t27lZmZaX/06NFDSUlJyszMrBCAJCk2NlYbN250aEtLS1NsbGyFvk2aNFFISIj27dun7du365Zbbql0LpmZmZKkli1bXthNAQAAAKj3PLYS1LhxY0VHRzu0+fv7q1mzZvb24cOHq3Xr1po9e7Ykady4cerdu7fmzZunQYMGKTU1Vdu3b9fy5cvtY6xZs0YhISGKiIjQ7t27NW7cOCUmJurGG2+UJB04cECrVq3SwIED1axZM+3atUsTJkzQDTfcUO0W3QAAAAB++zy+O1xVcnJyZLH8b7EqLi5Oq1at0iOPPKKpU6eqY8eOWrt2rUOYOnbsmFJSUpSXl6eWLVtq+PDhmjZtmv24j4+PPvroIy1YsECnTp1SeHi4hgwZokceeeSi3hsAAAAAz6hXIWjz5s1VPpekoUOHaujQoZWOMXbsWI0dO7bS4+Hh4frkk0/cnSIAAACA3ziP/1gqAAAAAFxMhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAANXLixAklJSUpMDBQQUFBSk5OVlFRUZXn5Obm6q677lJYWJj8/f3VrVs3vfXWWxdpxs4RggAAAADUSFJSkr755hulpaXp/fff15YtWzRq1Kgqzxk+fLiys7O1bt067d69W4MHD9Ztt92mnTt3XqRZV0QIAgAAAFCtPXv2aMOGDfrHP/6hXr166brrrtPChQuVmpqqo0ePVnre1q1b9eCDD6pnz55q166dHnnkEQUFBSkjI+Mizt4RIQgAAAC4BBUWFjo8SkpKLmi89PR0BQUFqUePHva2+Ph4WSwWffnll5WeFxcXp9WrV+vEiROy2WxKTU3VmTNn1KdPnwuaz4Vo4LErAwAAACa36ejl8vaz1uqY5cXnwk54eLhD+/Tp0zVjxgy3x83NzVWLFi0c2ho0aKDg4GDl5uZWet4bb7yh22+/Xc2aNVODBg3k5+end955Rx06dHB7LheKEAQAAABcgg4fPqzAwED7c6vVediaPHmynnrqqSrH2rNnj9vzmDZtmgoKCvTRRx+pefPmWrt2rW677TZ9+umniomJcXvcC0EIAgAAAC5BgYGBDiGoMhMnTtSIESOq7NOuXTuFhYUpPz/fof3s2bM6ceKEwsLCnJ534MABLVq0SFlZWbrqqqskSV26dNGnn36qxYsXa+nSpTW7mVpGCAIAAABMLCQkRCEhIdX2i42NVUFBgTIyMtS9e3dJ0qZNm2Sz2dSrVy+n5xQXF0uSLBbHrQi8vb1ls9kucObuY2MEAAAAANXq1KmTEhISNHLkSH311Vf6/PPPNWbMGP35z39Wq1atJElHjhxRVFSUvvrqK0lSVFSUOnTooPvuu09fffWVDhw4oHnz5iktLU2JiYkeuxdCEAAAAIAaef311xUVFaV+/fpp4MCBuu6667R8+XL78bKyMmVnZ9tXgBo2bKj169crJCREN910kzp37qxXXnlFL7/8sgYOHOip2+DjcAAAAABqJjg4WKtWrar0eJs2bWQYhkNbx44d9dZbb9X11FzCShAAAAAAUyEEAQAAADAVQhAAAAAAUyEEAQAAADCVehOC5syZIy8vL40fP77KfmvWrFFUVJR8fX0VExOj9evXOxzPy8vTiBEj1KpVK/n5+SkhIUH79u1z6HPmzBmNHj1azZo1U0BAgIYMGaK8vLzaviUAAAAA9VC9CEHbtm3TsmXL1Llz5yr7bd26VcOGDVNycrJ27typxMREJSYmKisrS5JkGIYSExP1/fff691339XOnTsVGRmp+Ph4nTp1yj7OhAkT9N5772nNmjX65JNPdPToUQ0ePLhO7xEAAABA/eDxEFRUVKSkpCStWLFCTZs2rbLvc889p4SEBE2aNEmdOnXS448/rm7dumnRokWSpH379umLL77QkiVLdM011+iKK67QkiVLdPr0af3zn/+UJJ08eVIvvPCCnn32Wf3+979X9+7dtXLlSm3dulVffPFFnd8vAAAAAM/y+O8EjR49WoMGDVJ8fLyeeOKJKvump6crJSXFoa1///5au3atJKmkpESS5Ovraz9usVhktVr12Wef6S9/+YsyMjJUVlam+Ph4e5+oqChFREQoPT1d1157rdNrl5SU2MeXpMLCQkmSrdxXtnLHLGkrtzr8E9WjZu6hbq6jZu6hbq6jZu6hbu6hbs7Zym2engLqKY+GoNTUVO3YsUPbtm2rUf/c3FyFhoY6tIWGhio3N1fS/8LMlClTtGzZMvn7+2v+/Pn68ccfdezYMfsYPj4+CgoKqnQcZ2bPnq2ZM2dWaM/JmiU/Pz+n5+RkPVmj+8L/UDP3UDfXUTP3UDfXUTP3UDf3UDdHxcXFku7w9DRQD3ksBB0+fFjjxo1TWlqaw8rNhWjYsKHefvttJScnKzg4WN7e3oqPj9eAAQMq/HKtq6ZMmeKwClVYWKjw8HBFRD+sgMYVV4Jysp5URPRUWbxLfj0UnKBm7qFurqNm7qFurqNm7qFu7qFuzhX9zEoQnPNYCMrIyFB+fr66detmbysvL9eWLVu0aNEilZSUyNvb2+GcsLCwCru45eXlKSwszP68e/fuyszM1MmTJ1VaWqqQkBD16tVLPXr0sI9RWlqqgoICh9WgX4/za1arVVZrxSVmi/cZWbydf7XK4l0ii/eZyouACqiZe6ib66iZe6ib66iZe6ibe6ibI4s3IQjOeWxjhH79+mn37t3KzMy0P3r06KGkpCRlZmZWCECSFBsbq40bNzq0paWlKTY2tkLfJk2aKCQkRPv27dP27dt1yy23SDoXkho2bOgwTnZ2tnJycpyOAwAAAODS4rGVoMaNGys6Otqhzd/fX82aNbO3Dx8+XK1bt9bs2bMlSePGjVPv3r01b948DRo0SKmpqdq+fbuWL19uH2PNmjUKCQlRRESEdu/erXHjxikxMVE33nijpHPhKDk5WSkpKQoODlZgYKAefPBBxcbGVropAgAAAIBLh8d3h6tKTk6OLJb/LVbFxcVp1apVeuSRRzR16lR17NhRa9eudQhTx44dU0pKivLy8tSyZUsNHz5c06ZNcxh3/vz5slgsGjJkiEpKStS/f389//zzF+2+AAAAAHhOvQpBmzdvrvK5JA0dOlRDhw6tdIyxY8dq7NixVV7H19dXixcv1uLFi92ZJgAAAIDfMI//WCoAAAAAXEyEIAAAAACmQggCAAAAYCqEIAAAAACmQggCAAAAYCqEIAAAAACmQggCAAAAYCqEIAAAAACmQggCAAAAYCqEIAAAAACmQggCAAAAYCqEIAAAAACmQggCAAAAYCqEIAAAAACmQggCAAAAYCqEIAAAAACmQggCAAAAYCqEIAAAAACmQggCAAAAYCqEIAAAAACmQggCAAAAYCqEIAAAAACmQggCAAAAYCqEIAAAAACmQggCAAAAYCqEIAAAAACmQggCAAAAYCqEIAAAAACmQggCAAAAYCqEIAAAAACmQggCAAAAYCqEIAAAAACmQggCAAAAUCOzZs1SXFyc/Pz8FBQUVG3/srIyPfTQQ4qJiZG/v79atWql4cOH6+jRo3U/2SoQggAAAADUSGlpqYYOHaoHHnigRv2Li4u1Y8cOTZs2TTt27NDbb7+t7Oxs3XzzzXU806o18OjVAQAAAPxmzJw5U5L00ksv1ah/kyZNlJaW5tC2aNEi9ezZUzk5OYqIiKjtKdYIIQgAAAC4BBUWFjo8t1qtslqtHprN/5w8eVJeXl41+jhdXSEEAQAAAB5SeLCJLI18a3VM2+kzkqTw8HCH9unTp2vGjBm1ei1XnTlzRg899JCGDRumwMBAj82D7wQBAAAAl6DDhw/r5MmT9seUKVOc9ps8ebK8vLyqfOzdu/eC51NWVqbbbrtNhmFoyZIlFzzehWAlCAAAALgEBQYG1mi1ZeLEiRoxYkSVfdq1a3dBczkfgH744Qdt2rTJo6tAEiEIAAAAMLWQkBCFhITU2fjnA9C+ffv08ccfq1mzZnV2rZri43AAAAAAaiQnJ0eZmZnKyclReXm5MjMzlZmZqaKiInufqKgovfPOO5LOBaA//elP2r59u15//XWVl5crNzdXubm5Ki0t9dRtsBIEAAAAoGYeffRRvfzyy/bnV199tSTp448/Vp8+fSRJ2dnZOnnypCTpyJEjWrdunSSpa9euDmP98pyLjRAEAAAAoEZeeumlan8jyDAM+5/btGnj8Ly+4ONwAAAAAEyFEAQAAADAVAhBAAAAAEyFEAQAAADAVOpNCJozZ468vLw0fvz4KvutWbNGUVFR8vX1VUxMjNavX+9wvKioSGPGjNFll12mRo0a6corr9TSpUsd+vTp06fCr+Def//9tX1LAAAAAOqhehGCtm3bpmXLlqlz585V9tu6dauGDRum5ORk7dy5U4mJiUpMTFRWVpa9T0pKijZs2KDXXntNe/bs0fjx4zVmzBj71nznjRw5UseOHbM/nn766Tq5NwAAAAD1i8dDUFFRkZKSkrRixQo1bdq0yr7PPfecEhISNGnSJHXq1EmPP/64unXrpkWLFtn7bN26VXfffbf69OmjNm3aaNSoUerSpYu++uorh7H8/PwUFhZmfwQGBtbJ/QEAAACoXzz+O0GjR4/WoEGDFB8fryeeeKLKvunp6UpJSXFo69+/v9auXWt/HhcXp3Xr1unee+9Vq1attHnzZn333XeaP3++w3mvv/66XnvtNYWFhemmm27StGnT5OfnV+m1S0pKVFJSYn9eWFgoSbKV+8pW7pglbeVWh3+ietTMPdTNddTMPdTNddTMPdTNPdTNOVu5zdNTQD3l0RCUmpqqHTt2aNu2bTXqn5ubq9DQUIe20NBQ5ebm2p8vXLhQo0aN0mWXXaYGDRrIYrFoxYoVuuGGG+x97rjjDkVGRqpVq1batWuXHnroIWVnZ+vtt9+u9NqzZ8/WzJkzK7TnZM2qNDzlZD1Zo/vC/1Az91A311Ez91A311Ez91A391A3R8XFxZLu8PQ0UA95LAQdPnxY48aNU1pamnx9fWtt3IULF+qLL77QunXrFBkZqS1btmj06NFq1aqV4uPjJUmjRo2y94+JiVHLli3Vr18/HThwQO3bt3c67pQpUxxWoQoLCxUeHq6I6IcV0LjiSlBO1pOKiJ4qi3fJr4eCE9TMPdTNddTMPdTNddTMPdTNPdTNuaKfWQmCcx4LQRkZGcrPz1e3bt3sbeXl5dqyZYsWLVqkkpISeXt7O5wTFhamvLw8h7a8vDyFhYVJkk6fPq2pU6fqnXfe0aBBgyRJnTt3VmZmpubOnWsPQb/Wq1cvSdL+/fsrDUFWq1VWa8UlZov3GVm8nX+1yuJdIov3GafH4Bw1cw91cx01cw91cx01cw91cw91c2TxJgTBOY9tjNCvXz/t3r1bmZmZ9kePHj2UlJSkzMzMCgFIkmJjY7Vx40aHtrS0NMXGxkqSysrKVFZWJovF8ba8vb1ls1X+H0FmZqYkqWXLlhd4VwAAAADqO4+tBDVu3FjR0dEObf7+/mrWrJm9ffjw4WrdurVmz54tSRo3bpx69+6tefPmadCgQUpNTdX27du1fPlySVJgYKB69+6tSZMmqVGjRoqMjNQnn3yiV155Rc8++6wk6cCBA1q1apUGDhyoZs2aadeuXZowYYJuuOGGarfoBgAAAPDb5/Hd4aqSk5PjsKoTFxenVatW6ZFHHtHUqVPVsWNHrV271iFMpaamasqUKUpKStKJEycUGRmpWbNm2X8M1cfHRx999JEWLFigU6dOKTw8XEOGDNEjjzxy0e8PAAAAwMVXr0LQ5s2bq3wuSUOHDtXQoUMrHSMsLEwrV66s9Hh4eLg++eQTd6cIAAAA4DfO4z+WCgAAAAAXEyEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKk08PQEAACe8+qJ2CqP3xWcfpFmAgDAxUMIAgATqS70VNbf29ZQ19bFhAAAqMTf//73GvcdO3asS2MTggDABFwNP5VJ/W9PlVvKWCECANS5+fPnOzw/fvy4iouLFRQUJEkqKCiQn5+fWrRo4XII4jtBAHAJe/VEbK0FoF+PCwBAXTp48KD9MWvWLHXt2lV79uzRiRMndOLECe3Zs0fdunXT448/7vLYrAQBwCXKlaDy4Y9RDs9vvGxvjcdnVQgAUNemTZumN998U1dccYW97YorrtD8+fP1pz/9SUlJSS6NRwgCgEtMTcLPr0NPdccHtDpQ5fUIQgCAunTs2DGdPXu2Qnt5ebny8vJcHo+PwwHAJaS6APThj1HVBiBnNh3peEHXBQDgQvTr10/33XefduzYYW/LyMjQAw88oPj4eJfHIwQBgEk4Cz8nvw+q9OFMVWGIIAQAqCsvvviiwsLC1KNHD1mtVlmtVvXs2VOhoaH6xz/+4fJ4fBwOAC4RlYWQysJPdc73adKuoMJ4lX1niI/GAQDqQkhIiNavX6/vvvtOe/ee+/+gqKgoXX755W6NRwgCgEuAK6swvw5AjQ86/1DAz21t9v5WLy8p/H/HqgpCAADUlcsvv9zt4PNLhCAAuIT9ehXolwHo1+Gn6b6z+m/HBg7HzwehysZ2FoRYDQKAS9esWbP0wQcfKDMzUz4+PiooKKjReXv27NFDDz2kTz75RGfPntWVV16pt956SxERETW+9o8//qh169YpJydHpaWlDseeffZZV27D9RB09913Kzk5WTfccIOrpwIAPKiyANR031mnf/5vxwb2fqXtjLqfIACg3istLdXQoUMVGxurF154oUbnHDhwQNddd52Sk5M1c+ZMBQYG6ptvvpGvr2+Nr7tx40bdfPPNateunfbu3avo6GgdOnRIhmGoW7duLt+HyyHo5MmTio+PV2RkpO655x7dfffdat26tcsXBgDUDle+CyQ5D0B+WcfsbcXRLe3HfrkyJEknDzWRb9v/XtB8AQC/XTNnzpQkvfTSSzU+5+GHH9bAgQP19NNP29vat2/v0nWnTJmiv/71r5o5c6YaN26st956Sy1atFBSUpISEhJcGktyY3e4tWvX6siRI3rggQe0evVqtWnTRgMGDNCbb76psrIylycAAPCMXweg8+Hnl4GoOu5stw0AuDgKCwsdHiUlJRd9DjabTR988IEuv/xy9e/fXy1atFCvXr20du1al8bZs2ePhg8fLklq0KCBTp8+rYCAAD322GN66qmnXJ6XW1tkh4SEKCUlRV9//bW+/PJLdejQQXfddZdatWqlCRMmaN++fe4MCwDwAHfCz3lsjgAAFybgB4saH6zdR8AP597ih4eHq0mTJvbH7NmzL/r95efnq6ioSHPmzFFCQoI+/PBD3XrrrRo8eLA++eSTGo/j7+9v/x5Qy5YtdeDA/37E+6effnJ5Xhf0O0HHjh1TWlqa0tLS5O3trYEDB2r37t268sorNX/+/AsZGgBQQ65uQnB+s4NfftStOLqlw+PXxwEAvz2HDx/WyZMn7Y8pU6Y47Td58mR5eXlV+Ti/LbWrbLZz/59zyy23aMKECeratasmT56sP/7xj1q6dGmNx7n22mv12WefSZIGDhyoiRMnatasWbr33nt17bXXujwvl/8frqysTOvWrdPKlSv14YcfqnPnzho/frzuuOMOBQYGSpLeeecd3XvvvZowYYLLEwIA1I4bL9vr8HG1Ju0K7Jsj/NzWpsYHLfag88sNESTHAPRzW5us8jo3RpuTuvgfpgAAuCMwMND+/rwqEydO1IgRI6rs065dO7fm0Lx5czVo0EBXXnmlQ3unTp3soaYmnn32WRUVFUk6972koqIirV69Wh07dnR5ZzjJjRDUsmVL2Ww2DRs2TF999ZW6du1aoU/fvn0VFBTk8mQAABfP+SAkVb7qU9UW2ZV9FI7tsQHgtyUkJEQhISF1MraPj4+uueYaZWdnO7R/9913ioyMrPE4vwxh/v7+Lq0iOeNyCJo/f76GDh1a5ZZ2QUFBOnjw4AVNDABQc3cFpzvdJc7ZapAkhxWh884HImfBp0mbk1K547gAAPPJycnRiRMnlJOTo/LycmVmZkqSOnTooICAAElSVFSUZs+erVtvvVWSNGnSJN1+++264YYb1LdvX23YsEHvvfeeNm/e7NK1CwoK9Oabb+rAgQOaNGmSgoODtWPHDoWGhrq8W7XLIeiuu+5y9RQAgAf9OghJjh+NO89p+Pn/oUnG/75CWlUAYhUIAC5tjz76qF5++WX786uvvlqS9PHHH6tPnz6SpOzsbJ08edLe59Zbb9XSpUs1e/ZsjR07VldccYXeeustXXfddTW+7q5duxQfH68mTZro0KFDGjlypIKDg/X2228rJydHr7zyikv3wbdeAeASUdlqkFR5EPqlk98HVWj7td+33vfLBaEK1wcAXNpeeumlan8jyDAq/sD2vffeq3vvvdft66akpGjEiBF6+umn1bhxY3v7wIEDdccdd7g83gXtDgcAqF+qCiI3Xra3ylWcqgLQ71vz0wcAAM/Ztm2b7rvvvgrtrVu3Vm5ursvjsRIEAJeYqlaEJMePs1X3Y6f2vraGVV4PAIC6ZLVaVVhYWKH9u+++c2tTB0IQAFyCqgtC513oBgcEIADAxXDzzTfrscce0xtvvCFJ8vLyUk5Ojh566CENGTLE5fH4OBwAXKLuCk6v05BCAAIAXCzz5s1TUVGRWrRoodOnT6t37972HelmzZrl8nj1JgTNmTNHXl5eGj9+fJX91qxZo6ioKPn6+iomJkbr1693OF5UVKQxY8bosssuU6NGjXTllVdW2Ef8zJkzGj16tJo1a6aAgAANGTJEeXl5tX1LAFAv1HYYqutwBQDArzVp0kRpaWl6//339fe//11jxozR+vXrtWXLFvn7+7s8Xr0IQdu2bdOyZcvUuXPnKvtt3bpVw4YNU3Jysnbu3KnExEQlJiYqKyvL3iclJUUbNmzQa6+9pj179mj8+PEaM2aM1q1bZ+8zYcIEvffee1qzZo0++eQTHT16VIMHD66z+wOA+uB8eHEnwPy56VcO/wQA4GLbuHGjPvjgA+3YsUN79+7VqlWr3N51zuPfCSoqKlJSUpJWrFihJ554osq+zz33nBISEjRp0iRJ0uOPP660tDQtWrTIvtqzdetW3X333fZ9ykeNGqVly5bpq6++0s0336yTJ0/qhRde0KpVq/T73/9ekrRy5Up16tRJX3zxha699tq6u1kAqCdcDUK28sp/IBsAgLo2c+ZMPfbYY+rRo4datmwpLy+vCxrP4yFo9OjRGjRokOLj46sNQenp6UpJSXFo69+/v9auXWt/HhcXp3Xr1unee+9Vq1attHnzZn333XeaP3++JCkjI0NlZWWKj4+3nxMVFaWIiAilp6dXGoJKSkpUUlJif35+dwpbua9s5Y4LarZyq8M/UT1q5h7q5jpq5h7q5jpq5h7q5h7q5pytvOKPQOO3aenSpXrppZd011131cp4Hg1Bqamp2rFjh7Zt21aj/rm5uQoNDXVoCw0NddgbfOHChRo1apQuu+wyNWjQQBaLRStWrNANN9xgH8PHx0dBQUFVjvNrs2fP1syZMyu052TNkp+fn9NzcrKerNF94X+omXuom+uomXuom+uomXuom3uom6Pi4mJJrv+QJuqf0tJSxcXF1dp4HgtBhw8f1rhx45SWliZf39r7mMXChQv1xRdfaN26dYqMjNSWLVs0evRotWrVymH1x1VTpkxxWIUqLCxUeHi4IqIfVkDjiitBOVlPKiJ6qizeJb8eCk5QM/dQN9dRM/dQN9dRM/dQN/dQN+eKfmYl6FLxl7/8RatWrdK0adNqZTyPhaCMjAzl5+erW7du9rby8nJt2bJFixYtUklJiby9vR3OCQsLq7CLW15ensLCwiRJp0+f1tSpU/XOO+9o0KBBkqTOnTsrMzNTc+fOVXx8vMLCwlRaWqqCggKH1aBfjuOM1WqV1VpxidnifUYWb+f7S1i8S2TxPlN1IeCAmrmHurmOmrmHurmOmrmHurmHujmyeBOCfst+uQBhs9m0fPlyffTRR+rcubMaNnT8Ee9nn33WpbE9FoL69eun3bt3O7Tdc889ioqK0kMPPVQhAElSbGysNm7c6LCNdlpammJjz/0gYFlZmcrKymSxOIYSb29v2Wzn/iPo3r27GjZsqI0bN9p/WCk7O1s5OTn2cQAAAAB41s6dOx2ed+3aVZIcdoaW5NYmCR4LQY0bN1Z0dLRDm7+/v5o1a2ZvHz58uFq3bq3Zs2dLksaNG6fevXtr3rx5GjRokFJTU7V9+3YtX75ckhQYGKjevXtr0qRJatSokSIjI/XJJ5/olVdesafDJk2aKDk5WSkpKQoODlZgYKAefPBBxcbGsjMcAAAAUE98/PHHdTa2x3eHq0pOTo7Dqk5cXJxWrVqlRx55RFOnTlXHjh21du1ahzCVmpqqKVOmKCkpSSdOnFBkZKRmzZql+++/395n/vz5slgsGjJkiEpKStS/f389//zzF/XeAAAAAHhGvQpBmzdvrvK5JA0dOlRDhw6tdIywsDCtXLmyyuv4+vpq8eLFWrx4sTvTBAAAAPAb5vwb/QAAAABwiSIEAQAAADAVQhAAAAAAUyEEAQAAADAVQhAAAAAAUyEEAQAAADAVQhAAAAAAUyEEAQAAADAVQhAAAAAAUyEEAQAAADAVQhAAAAAAUyEEAQAAADAVQhAAAAAAUyEEAQAAADAVQhAAAAAAUyEEAQAAADAVQhAAAAAAUyEEAQAAADAVQhAAAAAAUyEEAQAAADAVQhAAAAAAUyEEAQAAADAVQhAAAAAAUyEEAQAAADAVQhAAAAAAUyEEAQAAADAVQhAAAAAAUyEEAQAAADAVQhAAAAAAUyEEAQAAADAVQhAAAAAAUyEEAQAAADAVQhAAAACAah06dEjJyclq27atGjVqpPbt22v69OkqLS2t8rwzZ85o9OjRatasmQICAjRkyBDl5eVdpFk7RwgCAAAAUK29e/fKZrNp2bJl+uabbzR//nwtXbpUU6dOrfK8CRMm6L333tOaNWv0ySef6OjRoxo8ePBFmrVzDTx6dQAAAAB1orCw0OG51WqV1Wp1e7yEhAQlJCTYn7dr107Z2dlasmSJ5s6d6/SckydP6oUXXtCqVav0+9//XpK0cuVKderUSV988YWuvfZat+dzIQhBAAAAgIcEHTirBg3P1uqYZ8vOjRceHu7QPn36dM2YMaNWr3Xy5EkFBwdXejwjI0NlZWWKj4+3t0VFRSkiIkLp6emEIAAAAAC15/DhwwoMDLQ/v5BVIGf279+vhQsXVroKJEm5ubny8fFRUFCQQ3toaKhyc3NrdT6u4DtBAAAAwCUoMDDQ4VFZCJo8ebK8vLyqfOzdu9fhnCNHjighIUFDhw7VyJEjL8bt1CpWggAAAAATmzhxokaMGFFln3bt2tn/fPToUfXt21dxcXFavnx5leeFhYWptLRUBQUFDqtBeXl5CgsLu5BpXxBCEAAAAGBiISEhCgkJqVHfI0eOqG/fvurevbtWrlwpi6XqD5Z1795dDRs21MaNGzVkyBBJUnZ2tnJychQbG3vBc3cXH4cDAAAAUK0jR46oT58+ioiI0Ny5c3X8+HHl5uY6fLfnyJEjioqK0ldffSVJatKkiZKTk5WSkqKPP/5YGRkZuueeexQbG+uxTREkVoIAAAAA1EBaWpr279+v/fv367LLLnM4ZhiGJKmsrEzZ2dkqLi62H5s/f74sFouGDBmikpIS9e/fX88///xFnfuvEYIAAAAAVGvEiBHVfneoTZs29kB0nq+vrxYvXqzFixfX4excw8fhAAAAAJgKIQgAAACAqRCCAAAAAJgKIQgAAACAqdSbEDRnzhx5eXlp/PjxVfZbs2aNoqKi5Ovrq5iYGK1fv97heGW/cvvMM8/Y+7Rp06bC8Tlz5tTFbQEAAACoZ+pFCNq2bZuWLVumzp07V9lv69atGjZsmJKTk7Vz504lJiYqMTFRWVlZ9j7Hjh1zeLz44ovy8vKy/zjTeY899phDvwcffLBO7g0AAABA/eLxEFRUVKSkpCStWLFCTZs2rbLvc889p4SEBE2aNEmdOnXS448/rm7dumnRokX2PmFhYQ6Pd999V3379lW7du0cxmrcuLFDP39//zq5PwAAAAD1i8d/J2j06NEaNGiQ4uPj9cQTT1TZNz09XSkpKQ5t/fv319q1a532z8vL0wcffKCXX365wrE5c+bo8ccfV0REhO644w5NmDBBDRpUXo6SkhKVlJTYnxcWFkqSbOW+spU7ZklbudXhn6geNXMPdXMdNXMPdXMdNXMPdXMPdXPOVm7z9BRQT3k0BKWmpmrHjh3atm1bjfrn5uYqNDTUoS00NFS5ublO+7/88stq3LixBg8e7NA+duxYdevWTcHBwdq6daumTJmiY8eO6dlnn6302rNnz9bMmTMrtOdkzZKfn5/Tc3KynqzulvAr1Mw91M111Mw91M111Mw91M091M1RcXGxpDs8PQ3UQx4LQYcPH9a4ceOUlpYmX1/fOrnGiy++qKSkpArj/3I1qXPnzvLx8dF9992n2bNny2p1/jcoU6ZMcTivsLBQ4eHhioh+WAGNK64E5WQ9qYjoqbJ4l/x6KDhBzdxD3VxHzdxD3VxHzdxD3dxD3Zwr+pmVIDjnsRCUkZGh/Px8devWzd5WXl6uLVu2aNGiRSopKZG3t7fDOWFhYcrLy3Noy8vLU1hYWIXxP/30U2VnZ2v16tXVzqVXr146e/asDh06pCuuuMJpH6vV6jQgWbzPyOLt/KtVFu8SWbzPVHt9/A81cw91cx01cw91cx01cw91cw91c2TxJgTBOY9tjNCvXz/t3r1bmZmZ9kePHj2UlJSkzMzMCgFIkmJjY7Vx40aHtrS0NMXGxlbo+8ILL6h79+7q0qVLtXPJzMyUxWJRixYt3L8hAAAAAL8JHlsJaty4saKjox3a/P391axZM3v78OHD1bp1a82ePVuSNG7cOPXu3Vvz5s3ToEGDlJqaqu3bt2v58uUO4xQWFmrNmjWaN29eheump6fryy+/VN++fdW4cWOlp6drwoQJuvPOO6vdnQ4AAADAb5/Hd4erSk5OjiyW/y1WxcXFadWqVXrkkUc0depUdezYUWvXrq0QplJTU2UYhoYNG1ZhTKvVqtTUVM2YMUMlJSVq27atJkyYUGHXOQAAAACXpnoVgjZv3lzlc0kaOnSohg4dWuU4o0aN0qhRo5we69atm7744gt3pwgAAADgN87jP5YKAAAAABcTIQgAAACAqRCCAAAAAJgKIQgAAACAqRCCAAAAAJgKIQgAAACAqRCCAAAAAJgKIQgAAACAqRCCAAAAAJgKIQgAAACAqRCCAAAAAJgKIQgAAACAqRCCAAAAAJgKIQgAAACAqRCCAAAAAJgKIQgAAACAqRCCAAAAAJgKIQgAAACAqRCCAAAAAJgKIQgAAACAqRCCAAAAAJgKIQgAAACAqRCCAAAAAJgKIQgAAACAqRCCAAAAAJgKIQgAAACAqRCCAAAAAJgKIQgAAACAqRCCAAAAAJgKIQgAAACAqRCCAAAAAJgKIQgAAABAtQ4dOqTk5GS1bdtWjRo1Uvv27TV9+nSVlpbW6HzDMDRgwAB5eXlp7dq1dTvZajTw6NUBAAAA/Cbs3btXNptNy5YtU4cOHZSVlaWRI0fq1KlTmjt3brXnL1iwQF5eXhdhptUjBAEAAACoVkJCghISEuzP27Vrp+zsbC1ZsqTaEJSZmal58+Zp+/btatmyZV1PtVqEIAAAAOASVFhY6PDcarXKarXW6jVOnjyp4ODgKvsUFxfrjjvu0OLFixUWFlar13cXIQgAAADwEL89uWpgqd1gctZWIkkKDw93aJ8+fbpmzJhRa9fZv3+/Fi5cWO0q0IQJExQXF6dbbrml1q59oQhBAAAAwCXo8OHDCgwMtD+vbBVo8uTJeuqpp6oca8+ePYqKirI/P3LkiBISEjR06FCNHDmy0vPWrVunTZs2aefOnS7Ovm4RggAAAIBLUGBgoEMIqszEiRM1YsSIKvu0a9fO/uejR4+qb9++iouL0/Lly6s8b9OmTTpw4ICCgoIc2ocMGaLrr79emzdvrnZ+dYEQBAAAAJhYSEiIQkJCatT3yJEj6tu3r7p3766VK1fKYqn6F3cmT56sv/zlLw5tMTExmj9/vm666Sa353yhCEEAAAAAqnXkyBH16dNHkZGRmjt3ro4fP24/dn7DgyNHjqhfv3565ZVX1LNnT4WFhTndDCEiIkJt27a9aHP/NUIQAAAAgGqlpaVp//792r9/vy677DKHY4ZhSJLKysqUnZ2t4uJiT0yxxghBAAAAAKo1YsSIar871KZNG3sgqkx1xy+Gqj/EBwAAAACXGEIQAAAAAFMhBAEAAAAwFUIQAAAAAFMhBAEAAAAwlXoTgubMmSMvLy+NHz++yn5r1qxRVFSUfH19FRMTo/Xr1zsc9/Lycvp45pln7H1OnDihpKQkBQYGKigoSMnJySoqKqqL2wIAAABQz9SLELRt2zYtW7ZMnTt3rrLf1q1bNWzYMCUnJ2vnzp1KTExUYmKisrKy7H2OHTvm8HjxxRfl5eWlIUOG2PskJSXpm2++UVpamt5//31t2bJFo0aNqrP7AwAAAFB/eDwEFRUVKSkpSStWrFDTpk2r7Pvcc88pISFBkyZNUqdOnfT444+rW7duWrRokb3P+V+lPf9499131bdvX7Vr106StGfPHm3YsEH/+Mc/1KtXL1133XVauHChUlNTdfTo0Tq9VwAAAACe5/EfSx09erQGDRqk+Ph4PfHEE1X2TU9PV0pKikNb//79tXbtWqf98/Ly9MEHH+jll192GCMoKEg9evSwt8XHx8tisejLL7/Urbfe6nSskpISlZSU2J8XFhZKkmzlvrKVO2ZJW7nV4Z+oHjVzD3VzHTVzD3VzHTVzD3VzD3VzzlZu8/QUUE95NASlpqZqx44d2rZtW4365+bmKjQ01KEtNDRUubm5Tvu//PLLaty4sQYPHuwwRosWLRz6NWjQQMHBwZWOI0mzZ8/WzJkzK7TnZM2Sn5+f03Nysp6sdDw4R83cQ91cR83cQ91cR83cQ93cQ90cFRcXS7rD09NAPeSxEHT48GGNGzdOaWlp8vX1rZNrvPjii0pKSqqV8adMmeKwClVYWKjw8HBFRD+sgMYVV4Jysp5URPRUWbxLfj0UnKBm7qFurqNm7qFurqNm7qFu7qFuzhX9zEoQnPNYCMrIyFB+fr66detmbysvL9eWLVu0aNEilZSUyNvb2+GcsLAw5eXlObTl5eUpLCyswviffvqpsrOztXr16gpj5OfnO7SdPXtWJ06ccDrOeVarVVZrxSVmi/cZWbydf7XK4l0ii/eZSsdERdTMPdTNddTMPdTNddTMPdTNPdTNkcWbEATnPLYxQr9+/bR7925lZmbaHz169FBSUpIyMzMrBCBJio2N1caNGx3a0tLSFBsbW6HvCy+8oO7du6tLly4VxigoKFBGRoa9bdOmTbLZbOrVq1ct3R0AAACA+spjK0GNGzdWdHS0Q5u/v7+aNWtmbx8+fLhat26t2bNnS5LGjRun3r17a968eRo0aJBSU1O1fft2LV++3GGcwsJCrVmzRvPmzatw3U6dOikhIUEjR47U0qVLVVZWpjFjxujPf/6zWrVqVUd3CwAAAKC+8PgW2VXJycnRsWPH7M/j4uK0atUqLV++XF26dNGbb76ptWvXVghTqampMgxDw4YNczru66+/rqioKPXr108DBw7UddddVyFIAQAAALg0eXyL7F/avHlzlc8laejQoRo6dGiV44waNarKHz8NDg7WqlWr3JkiAAAAgN+4er0SBAAAAAC1jRAEAAAAwFQIQQAAAABMhRAEAAAAwFQIQQAAAABMhRAEAAAAwFQIQQAAAABMhRAEAAAAwFQIQQAAAABMhRAEAAAAwFQIQQAAAABMhRAEAAAAwFQIQQAAAABMhRAEAAAAwFQIQQAAAABMhRAEAAAAwFQIQQAAAABMhRAEAAAAwFQIQQAAAABMhRAEAAAAwFQIQQAAAABMhRAEAAAAwFQIQQAAAABMhRAEAAAAwFQIQQAAAABMhRAEAAAAwFQIQQAAAABMhRAEAAAAwFQIQQAAAABMhRAEAAAAwFQIQQAAAABMhRAEAAAAoFqHDh1ScnKy2rZtq0aNGql9+/aaPn26SktLqzwvNzdXd911l8LCwuTv769u3brprbfeukizdq6BR68OAAAA4Ddh7969stlsWrZsmTp06KCsrCyNHDlSp06d0ty5cys9b/jw4SooKNC6devUvHlzrVq1Srfddpu2b9+uq6+++iLewf8QggAAAABUKyEhQQkJCfbn7dq1U3Z2tpYsWVJlCNq6dauWLFminj17SpIeeeQRzZ8/XxkZGR4LQXwcDgAAALgEFRYWOjxKSkpq/RonT55UcHBwlX3i4uK0evVqnThxQjabTampqTpz5oz69OlT6/OpKVaCAAAAAA85m3NE8mpYu2MaZZKk8PBwh/bp06drxowZtXad/fv3a+HChVWuAknSG2+8odtvv13NmjVTgwYN5Ofnp3feeUcdOnSotbm4ipUgAAAA4BJ0+PBhnTx50v6YMmWK036TJ0+Wl5dXlY+9e/c6nHPkyBElJCRo6NChGjlyZJXzmDZtmgoKCvTRRx9p+/btSklJ0W233abdu3fX2r26ipUgAAAA4BIUGBiowMDAavtNnDhRI0aMqLJPu3bt7H8+evSo+vbtq7i4OC1fvrzK8w4cOKBFixYpKytLV111lSSpS5cu+vTTT7V48WItXbq0+hupA4QgAAAAwMRCQkIUEhJSo75HjhxR37591b17d61cuVIWS9UfLCsuLpakCv28vb1ls9ncm3At4ONwAAAAAKp15MgR9enTRxEREZo7d66OHz+u3Nxc5ebmOvSJiorSV199JUmKiopShw4ddN999+mrr77SgQMHNG/ePKWlpSkxMdFDd8JKEAAAAIAaSEtL0/79+7V//35ddtllDscMw5AklZWVKTs7274C1LBhQ61fv16TJ0/WTTfdpKKiInXo0EEvv/yyBg4ceNHv4TxCEAAAAIBqjRgxotrvDrVp08YeiM7r2LGj3nrrrTqcmev4OBwAAAAAUyEEAQAAADAVQhAAAAAAUyEEAQAAADCVehOC5syZIy8vL40fP77KfmvWrFFUVJR8fX0VExOj9evXV+izZ88e3XzzzWrSpIn8/f11zTXXKCcnx368T58+FX4F9/7776/tWwIAAABQD9WLELRt2zYtW7ZMnTt3rrLf1q1bNWzYMCUnJ2vnzp1KTExUYmKisrKy7H0OHDig6667TlFRUdq8ebN27dqladOmydfX12GskSNH6tixY/bH008/XSf3BgAAAKB+8fgW2UVFRUpKStKKFSv0xBNPVNn3ueeeU0JCgiZNmiRJevzxx5WWlqZFixZp6dKlkqSHH35YAwcOdAg17du3rzCWn5+fwsLCavFOAAAAAPwWeDwEjR49WoMGDVJ8fHy1ISg9PV0pKSkObf3799fatWslSTabTR988IH+9re/qX///tq5c6fatm2rKVOmVPhF2tdff12vvfaawsLCdNNNN2natGny8/Or9NolJSUqKSmxPy8sLDx3zXJf2codF9Rs5VaHf6J61Mw91M111Mw91M111Mw91M091M05W7nN01NAPeXREJSamqodO3Zo27ZtNeqfm5ur0NBQh7bQ0FDl5uZKkvLz81VUVKQ5c+boiSee0FNPPaUNGzZo8ODB+vjjj9W7d29J0h133KHIyEi1atVKu3bt0kMPPaTs7Gy9/fbblV579uzZmjlzZoX2nKxZlYannKwna3Rf+B9q5h7q5jpq5h7q5jpq5h7q5h7q5qi4uFjSHZ6eBuohj4Wgw4cPa9y4cUpLS6vwfR132Wzn0v4tt9yiCRMmSJK6du2qrVu3aunSpfYQNGrUKPs5MTExatmypfr166cDBw44/eicJE2ZMsVhFaqwsFDh4eGKiH5YAY0rrgTlZD2piOipsniX/HooOEHN3EPdXEfN3EPdXEfN3EPd3EPdnCv6mZUgOOexEJSRkaH8/Hx169bN3lZeXq4tW7Zo0aJFKikpkbe3t8M5YWFhysvLc2jLy8uzf7enefPmatCgga688kqHPp06ddJnn31W6Vx69eolSdq/f3+lIchqtcpqrbjEbPE+I4u38/0lLN4lsnifqfS6qIiauYe6uY6auYe6uY6auYe6uYe6ObJ4E4LgnMd2h+vXr592796tzMxM+6NHjx5KSkpSZmZmhQAkSbGxsdq4caNDW1pammJjYyVJPj4+uuaaa5Sdne3Q57vvvlNkZGSlc8nMzJQktWzZ8gLvCgAAAEB957GVoMaNGys6Otqhzd/fX82aNbO3Dx8+XK1bt9bs2bMlSePGjVPv3r01b948DRo0SKmpqdq+fbuWL19uH2PSpEm6/fbbdcMNN6hv377asGGD3nvvPW3evFnSuS20V61apYEDB6pZs2batWuXJkyYoBtuuKHaLboBAAAA/PbVi98JqkxOTo6OHTtmfx4XF6dVq1Zp+fLl6tKli958802tXbvWIUzdeuutWrp0qZ5++mnFxMToH//4h9566y1dd911ks6tFn300Ue68cYbFRUVpYkTJ2rIkCF67733Lvr9AQAAALj4PL5F9i+dX62p7LkkDR06VEOHDq1ynHvvvVf33nuv02Ph4eH65JNP3J0iAAAAgN+4er0SBAAAAAC1jRAEAAAAwFQIQQAAAABMhRAEAAAAwFQIQQAAAABMhRAEAAAAwFQIQQAAAABMhRAEAAAAwFQIQQAAAABMhRAEAAAAwFQIQQAAAABMhRAEAAAAwFQIQQAAAABMhRAEAAAAwFQIQQAAAABMhRAEAAAAwFQIQQAAAABMhRAEAAAAwFQIQQAAAABMhRAEAAAAwFQIQQAAAABMhRAEAAAAwFQIQQAAAABMhRAEAAAAwFQIQQAAAABMhRAEAAAAwFQIQQAAAABMhRAEAAAAwFQIQQAAAABMhRAEAAAAwFQIQQAAAABMhRAEAAAAwFQIQQAAAABq5Oabb1ZERIR8fX3VsmVL3XXXXTp69Gil/U+cOKEHH3xQV1xxhRo1aqSIiAiNHTtWJ0+evIizrogQBAAAAKBG+vbtqzfeeEPZ2dl66623dODAAf3pT3+qtP/Ro0d19OhRzZ07V1lZWXrppZe0YcMGJScnX8RZV9TAo1cHAAAA8JsxYcIE+58jIyM1efJkJSYmqqysTA0bNqzQPzo6Wm+99Zb9efv27TVr1izdeeedOnv2rBo08EwcIQS5yTAMSdKpIluFY7Zym4qLi1X0s00W74rHURE1cw91cx01cw91cx01cw91cw91c+78+7Tz79vqo7Mqk2p5emdVJkkqLCx0aLdarbJarbV2nRMnTuj1119XXFyc0wBUmZMnTyowMNBjAUiSZMAthw8fNnTuJcuDBw8ePHjw4MGjHj8OHz7s6beOFZw+fdoICwurs3sOCAio0DZ9+vRamfvf/vY3w8/Pz5BkXHvttcZPP/1U43OPHz9uREREGFOnTq2VubjLyzDqcTSux2w2m44eParGjRvLy8vL4VhhYaHCw8N1+PBhBQYGemiGvy3UzD3UzXXUzD3UzXXUzD3UzT3UzTnDMPTzzz+rVatWsljq31fhz5w5o9LS0joZ2zCMCu9RK1sJmjx5sp566qkqx9uzZ4+ioqIkST/99JNOnDihH374QTNnzlSTJk30/vvvV7jerxUWFuoPf/iDgoODtW7dOpdWj2obIagOFBYWqkmTJvalPlSPmrmHurmOmrmHurmOmrmHurmHuuFCHD9+XP/5z3+q7NOuXTv5+PhUaP/xxx8VHh6urVu3KjY2ttLzf/75Z/Xv319+fn56//335evre8HzvhB8JwgAAAAwsZCQEIWEhLh1rs127ntXJSUllfYpLCxU//79ZbVatW7dOo8HIIktsgEAAADUwJdffqlFixYpMzNTP/zwgzZt2qRhw4apffv29lWgI0eOKCoqSl999ZWkcwHoxhtv1KlTp/TCCy+osLBQubm5ys3NVXl5ucfuhZWgOmC1WjV9+vRa3X3jUkfN3EPdXEfN3EPdXEfN3EPd3EPdcDH4+fnp7bff1vTp03Xq1Cm1bNlSCQkJeuSRR+yvvbKyMmVnZ6u4uFiStGPHDn355ZeSpA4dOjiMd/DgQbVp0+ai3sN5fCcIAAAAgKnwcTgAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqpgtBM2bMkJeXl8Pj/K/fHjp0qMKx8481a9bYx3B2PDU11eE6mzdvVrdu3WS1WtWhQwe99NJLFeayePFitWnTRr6+vurVq5d9K8Hzzpw5o9GjR6tZs2YKCAjQkCFDlJeXV/tFqYGq6iZJubm5uuuuuxQWFiZ/f39169ZNb731lsMYJ06cUFJSkgIDAxUUFKTk5GQVFRU59Nm1a5euv/56+fr6Kjw8XE8//XSFuaxZs0ZRUVHy9fVVTEyM1q9f73DcMAw9+uijatmypRo1aqT4+Hjt27evFqtRM7VRszZt2lQYY86cOQ59LqWaSdXX7cCBA7r11lsVEhKiwMBA3XbbbRX+uzDba02qnbqZ8fV25MgR3XnnnWrWrJkaNWqkmJgYbd++3aW5mvH1Vht1M9vrrbqavf3227rxxhvVrFkzeXl5KTMzs8IYNXlfkJOTo0GDBsnPz08tWrTQpEmTdPbsWYc+tfEeBfjNM0xm+vTpxlVXXWUcO3bM/jh+/LhhGIZx9uxZh/Zjx44ZM2fONAICAoyff/7ZPoYkY+XKlQ79Tp8+bT/+/fffG35+fkZKSorx7bffGgsXLjS8vb2NDRs22PukpqYaPj4+xosvvmh88803xsiRI42goCAjLy/P3uf+++83wsPDjY0bNxrbt283rr32WiMuLu4iVKmiqupmGIbxhz/8wbjmmmuML7/80jhw4IDx+OOPGxaLxdixY4e9T0JCgtGlSxfjiy++MD799FOjQ4cOxrBhw+zHT548aYSGhhpJSUlGVlaW8c9//tNo1KiRsWzZMnufzz//3PD29jaefvpp49tvvzUeeeQRo2HDhsbu3bvtfebMmWM0adLEWLt2rfH1118bN998s9G2bVuHf0cXQ23ULDIy0njsscccxigqKrIfv9RqZhhV162oqMho166dceuttxq7du0ydu3aZdxyyy3GNddcY5SXl9vHMNtrzTBqp25me72dOHHCiIyMNEaMGGF8+eWXxvfff2/8+9//Nvbv3+/SXM32equtupnp9VaTmr3yyivGzJkzjRUrVhiSjJ07d1YYp7r3BWfPnjWio6ON+Ph4Y+fOncb69euN5s2bG1OmTLH3qa33KMBvnSlDUJcuXWrcv2vXrsa9997r0CbJeOeddyo9529/+5tx1VVXObTdfvvtRv/+/e3Pe/bsaYwePdr+vLy83GjVqpUxe/ZswzAMo6CgwGjYsKGxZs0ae589e/YYkoz09PQaz7+2VFc3f39/45VXXnFoCw4ONlasWGEYhmF8++23hiRj27Zt9uP/+te/DC8vL+PIkSOGYRjG888/bzRt2tQoKSmx93nooYeMK664wv78tttuMwYNGuRwnV69ehn33XefYRiGYbPZjLCwMOOZZ56xHy8oKDCsVqvxz3/+08W7vjAXWjPDOPcmYf78+ZWOcanVzDCqrtu///1vw2KxGCdPnrS3FRQUGF5eXkZaWpphGOZ8rRnGhdfNMMz3envooYeM6667rtLjNZmrGV9vtVE3wzDX6626mv3SwYMHnYagmrwvWL9+vWGxWIzc3Fx7nyVLlhiBgYH2OtbGexTgUmC6j8NJ0r59+9SqVSu1a9dOSUlJysnJcdovIyNDmZmZSk5OrnBs9OjRat68uXr27KkXX3xRxi9+bik9PV3x8fEO/fv376/09HRJUmlpqTIyMhz6WCwWxcfH2/tkZGSorKzMoU9UVJQiIiLsfS62quoWFxen1atX68SJE7LZbEpNTdWZM2fUp08fSedqEhQUpB49etjPiY+Pl8Visf+AVnp6um644Qb5+PjY+/Tv31/Z2dn673//a+9TVW0PHjyo3Nxchz5NmjRRr169PFK3C6nZeXPmzFGzZs109dVX65lnnnH4WMOlWDOp8rqVlJTIy8vL4ccAfX19ZbFY9Nlnn0ky72tNurC6nWem19u6devUo0cPDR06VC1atNDVV1+tFStW2I/XZK5mfL3VRt3OM8vrrbqa1URN3hekp6crJiZGoaGh9j79+/dXYWGhvvnmG3ufC32PAlwKTBeCevXqpZdeekkbNmzQkiVLdPDgQV1//fX6+eefK/R94YUX1KlTJ8XFxTm0P/bYY3rjjTeUlpamIUOG6P/+7/+0cOFC+/Hc3FyH/wGSpNDQUBUWFur06dP66aefVF5e7rRPbm6ufQwfHx8FBQVV2udiqq5ub7zxhsrKytSsWTNZrVbdd999euedd+y/DJybm6sWLVo4jNmgQQMFBwc73LOzmpw/VlWfXx7/5XnO+lwsF1ozSRo7dqxSU1P18ccf67777tOTTz6pv/3tb/bjl1rNpKrrdu2118rf318PPfSQiouLderUKf31r39VeXm5jh07Zr8fs73WpAuvm2S+19v333+vJUuWqGPHjvr3v/+tBx54QGPHjtXLL79c47ma8fVWG3WTzPV6q65mNVGT9wUXUjNX3qMAl4IGnp7AxTZgwAD7nzt37qxevXopMjJSb7zxhsOKz+nTp7Vq1SpNmzatwhi/bLv66qt16tQpPfPMMxo7dmzdTt6DqqvbtGnTVFBQoI8++kjNmzfX2rVrddttt+nTTz9VTEyMB2fuObVRs5SUFIcxfHx8dN9992n27NkOf6t/KamubmvWrNEDDzygv//977JYLBo2bJi6desmi8V0f6fjoDbqZrbXm81mU48ePfTkk09KOve/51lZWVq6dKnuvvtuD8+u/qqtupnp9cZrDah/zP2uQVJQUJAuv/xy7d+/36H9zTffVHFxsYYPH17tGL169dKPP/6okpISSVJYWFiF3Vry8vIUGBioRo0aqXnz5vL29nbaJywszD5GaWmpCgoKKu3jSb+s24EDB7Ro0SK9+OKL6tevn7p06aLp06erR48eWrx4saRz95Ofn+8wxtmzZ3XixAmHe3ZWk/PHqurzy+O/PM9ZH09xtWbO9OrVS2fPntWhQ4ckXfo1kyr+N3rjjTfqwIEDys/P108//aRXX31VR44cUbt27STxWjvP1bo5c6m/3lq2bKkrr7zSoa1Tp072jxHWZK5mfL3VRt2cuZRfb9XVrCZq8r7gQmrmynsU4FJg+hBUVFSkAwcOqGXLlg7tL7zwgm6++WaFhIRUO0ZmZqaaNm1q/5ur2NhYbdy40aFPWlqaYmNjJUk+Pj7q3r27Qx+bzaaNGzfa+3Tv3l0NGzZ06JOdna2cnBx7H0/6Zd2Ki4slqcLfxHt7e8tms0k6V5OCggJlZGTYj2/atEk2m029evWy99myZYvKysrsfdLS0nTFFVeoadOm9j5V1bZt27YKCwtz6FNYWKgvv/zS43VztWbOZGZmymKx2D9+c6nXTKr8v9HmzZsrKChImzZtUn5+vm6++WZJvNbOc7Vuzlzqr7ff/e53ys7Odmj77rvvFBkZWeO5mvH1Vht1c+ZSfr1VV7OaqMn7gtjYWO3evdshmKelpSkwMNAewmrjPQpwSfD0zgwX28SJE43NmzcbBw8eND7//HMjPj7eaN68uZGfn2/vs2/fPsPLy8v417/+VeH8devWGStWrDB2795t7Nu3z3j++ecNPz8/49FHH7X3Ob/95KRJk4w9e/YYixcvdrr9pNVqNV566SXj22+/NUaNGmUEBQU57Ohy//33GxEREcamTZuM7du3G7GxsUZsbGwdVaZqVdWttLTU6NChg3H99dcbX375pbF//35j7ty5hpeXl/HBBx/Yx0hISDCuvvpq48svvzQ+++wzo2PHjg7byBYUFBihoaHGXXfdZWRlZRmpqamGn59fhe1QGzRoYMydO9fYs2ePMX36dKfboQYFBRnvvvuufStgT2wje6E127p1qzF//nwjMzPTOHDggPHaa68ZISEhxvDhw+3XuNRqZhjV/zf64osvGunp6cb+/fuNV1991QgODjZSUlIcxjDba80wLrxuZny9ffXVV0aDBg2MWbNmGfv27TNef/11w8/Pz3jttddcmqvZXm+1UTezvd5qUrP//Oc/xs6dO40PPvjAkGSkpqYaO3fuNI4dO2bvU937gvNbZN94441GZmamsWHDBiMkJMTpFtkX+h4F+K0zXQi6/fbbjZYtWxo+Pj5G69atjdtvv91hn37DMIwpU6YY4eHhDr+fcd6//vUvo2vXrkZAQIDh7+9vdOnSxVi6dGmFvh9//LHRtWtXw8fHx2jXrp2xcuXKCmMtXLjQiIiIMHx8fIyePXsaX3zxhcPx06dPG//3f/9nNG3a1PDz8zNuvfVWh/8xvJiqq9t3331nDB482GjRooXh5+dndO7cucL2z//5z3+MYcOGGQEBAUZgYKBxzz33OPz+kmEYxtdff21cd911htVqNVq3bm3MmTOnwlzeeOMN4/LLLzd8fHyMq666yiFoGca5LVGnTZtmhIaGGlar1ejXr5+RnZ1di9WomQutWUZGhtGrVy+jSZMmhq+vr9GpUyfjySefNM6cOeNwnUupZoZRfd0eeughIzQ01GjYsKHRsWNHY968eYbNZnMYw2yvNcO48LqZ9fX23nvvGdHR0YbVajWioqKM5cuXuzxXM77eLrRuZny9VVezlStXGpIqPKZPn27vU5P3BYcOHTIGDBhgNGrUyGjevLkxceJEo6yszKFPbbxHAX7rvAzjF3s7AwAAAMAlzvTfCQIAAABgLoQgAAAAAKZCCAIAAABgKoQgAAAAAKZCCAIAAABgKoQgAAAAAKZCCAIAAABgKoQgAAAAAKZCCAIAAABgKoQgAAAAAKZCCAIAAABgKoQgADCx48ePKywsTE8++aS9bevWrfLx8dHGjRs9ODMAAOqOl2EYhqcnAQDwnPXr1ysxMVFbt27VFVdcoa5du+qWW27Rs88+6+mpAQBQJwhBAACNHj1aH330kXr06KHdu3dr27Ztslqtnp4WAAB1ghAEANDp06cVHR2tw4cPKyMjQzExMZ6eEgAAdYbvBAEAdODAAR09elQ2m02HDh3y9HQAAKhTrAQBgMmVlpaqZ8+e6tq1q6644gotWLBAu3fvVosWLTw9NQAA6gQhCABMbtKkSXrzzTf19ddfKyAgQL1791aTJk30/vvve3pqAADUCT4OBwAmtnnzZi1YsECvvvqqAgMDZbFY9Oqrr+rTTz/VkiVLPD09AADqBCtBAAAAAEyFlSAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApvL/AC4bdnA5D6ZWAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 1000x600 with 2 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_78070/3549307819.py:46: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
+      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha=\"right\")\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
     "with flopy4.mf6.write_context.WriteContext(use_netcdf=True):\n",
     "    sim.write()\n",
     "if os.getenv(\"MF6_EXTENDED\"):\n",
-    "    sim.run(verbose=True)\n",
+    "    sim.run()\n",
     "\n",
     "    # Load head results\n",
     "    head = flopy4.mf6.utils.open_hds(\n",
@@ -3894,8 +2482,15 @@
     "        workspace / \"ff.dis.grb\",\n",
     "    )\n",
     "\n",
+    "    # Load budget results\n",
+    "    cbc = flopy4.mf6.utils.open_cbc(\n",
+    "        workspace / \"ff.cbc\",\n",
+    "        workspace / \"ff.dis.grb\",\n",
+    "    )\n",
+    "\n",
     "    # Plot head results\n",
-    "    plot_head(head, workspace)"
+    "    plot_head(head, workspace)\n",
+    "    plot_head_ugrid(head, cbc, grid, workspace)"
    ]
   }
  ],
@@ -3915,7 +2510,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.14"
+   "version": "3.11.15"
   }
  },
  "nbformat": 4,
diff --git a/docs/examples/frenchman-flat.py b/docs/examples/frenchman-flat.py
index af94930d..c0a26dad 100644
--- a/docs/examples/frenchman-flat.py
+++ b/docs/examples/frenchman-flat.py
@@ -35,7 +35,7 @@
 except NameError:
     FF_ROOT = Path.cwd()
 
-# ### Define plot function
+# ### Define plot functions
 
 
 def plot_head(head, workspace):
@@ -49,12 +49,66 @@ def plot_head(head, workspace):
     plt.ylabel("y")
     plt.grid(True)
     plt.savefig(workspace / "head.png", dpi=300, bbox_inches="tight")
-    # plt.show()
+    if not os.environ.get("PYTEST_CURRENT_TEST"):
+        plt.show()
     plt.close()
 
 
-# ### Timing
+def plot_head_ugrid(head, cbc, grid, workspace):
+    """Plot head and flow vectors using xugrid on a DIS (structured) grid.
 
+    Even though the model uses DIS discretization, we can wrap the 2-D
+    (y, x) head slice in a ``xu.UgridDataArray`` by flattening it to a
+    face dimension whose topology is defined by ``grid.ugrid``.  This lets
+    us use xugrid's plotting API without converting the model to DISV.
+
+    Flow vectors come from the CBC budget terms:
+    * ``u = flow-right-face``  (positive = eastward / +x direction)
+    * ``v = -flow-front-face`` (negated because MODFLOW's "front" face is
+      the south face; positive "front" flow is southward, so we negate to
+      get the northward (+y) component for a conventional quiver plot)
+    """
+    import matplotlib.pyplot as plt
+    import xarray as xr
+    import xugrid as xu
+
+    ugrid = grid.ugrid
+    facedim = ugrid.face_dimension
+
+    # Select first timestep and first layer; flatten (y, x) -> face dimension
+    h = head.isel(time=0, layer=0).compute()
+    head_uda = xu.UgridDataArray(
+        xr.DataArray(h.values.ravel(), dims=[facedim], name="head"),
+        grid=ugrid,
+    )
+
+    # Flow vectors: u = flow-right-face (+x/east), v = -flow-front-face (+y/north)
+    u = cbc["flow-right-face"].isel(time=0, layer=0).compute()
+    v = -cbc["flow-front-face"].isel(time=0, layer=0).compute()
+    ds = xu.UgridDataset(grids=ugrid)
+    ds["u"] = xu.UgridDataArray(
+        xr.DataArray(u.values.ravel(), dims=[facedim], name="u"), grid=ugrid
+    )
+    ds["v"] = xu.UgridDataArray(
+        xr.DataArray(v.values.ravel(), dims=[facedim], name="v"), grid=ugrid
+    )
+    ds = ds.ugrid.assign_face_coords()
+
+    fig, ax = plt.subplots(figsize=(10, 8))
+    head_uda.ugrid.plot(ax=ax)
+    xu.plot.line(ugrid, ax=ax, color="white", linewidth=0.1)
+    ds.plot.quiver(x="mesh2d_face_x", y="mesh2d_face_y", u="u", v="v", color="black")
+    ax.set_aspect(1)
+    ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha="right")
+    ax.set_title("Head with flow vectors (layer 1, time 0)")
+    plt.savefig(workspace / "head_ugrid.png", dpi=300, bbox_inches="tight")
+    if not os.environ.get("PYTEST_CURRENT_TEST"):
+        plt.show()
+    plt.close()
+
+
+# # Timing
+#
 # 33 transient stress periods matching the original pumping schedule,
 # with 15 time steps per period and a 1.1× geometric time-step multiplier.
 time = flopy4.mf6.utils.time.Time(
@@ -679,19 +733,27 @@ def plot_head(head, workspace):
     workspace=workspace,
 )
 
+# run verbose only this time
 sim.write()
 sim.run(verbose=True)
 
 # ### Load head results
 
+# Load head results
 head = flopy4.mf6.utils.open_hds(
     workspace / "ff.hds",
     workspace / "ff.dis.grb",
 )
 
-# ### Plot head results
+# Load budget results
+cbc = flopy4.mf6.utils.open_cbc(
+    workspace / "ff.cbc",
+    workspace / "ff.dis.grb",
+)
 
+# Plot head results
 plot_head(head, workspace)
+plot_head_ugrid(head, cbc, grid, workspace)
 
 # ### NetCDF (mesh) base package input
 
@@ -707,6 +769,8 @@ def plot_head(head, workspace):
 nc_fpth = workspace / "frenchman-flat.input.nc"
 gwf.netcdf_file = nc_fpth
 
+# Here, grid and time info is passed to the `NetCDFModel' constructor
+# so that coordinate and mesh data is written to the NetCDF file.
 nc_model = flopy4.mf6.netcdf.NetCDFModel.from_model(gwf, mesh="layered", grid=grid, time=time)
 nc_model.to_netcdf(nc_fpth)
 
@@ -714,7 +778,7 @@ def plot_head(head, workspace):
     sim.write()
 
 if os.getenv("MF6_EXTENDED"):
-    sim.run(verbose=True)
+    sim.run()
 
     # Load head results
     head = flopy4.mf6.utils.open_hds(
@@ -722,8 +786,15 @@ def plot_head(head, workspace):
         workspace / "ff.dis.grb",
     )
 
+    # Load budget results
+    cbc = flopy4.mf6.utils.open_cbc(
+        workspace / "ff.cbc",
+        workspace / "ff.dis.grb",
+    )
+
     # Plot head results
     plot_head(head, workspace)
+    plot_head_ugrid(head, cbc, grid, workspace)
 
 # ### Array-based NetCDF WEL packages + layered mesh NetCDF output
 
@@ -733,7 +804,6 @@ def plot_head(head, workspace):
 # cells so MODFLOW ignores them for those stress periods.
 
 # update simulation with array based inputs
-LAYER_NODATA = np.full((nrow, ncol), flopy4.mf6.constants.FILL_DNODATA, dtype=float)
 GRID_NODATA = np.full((nlay, nrow, ncol), flopy4.mf6.constants.FILL_DNODATA, dtype=float)
 
 # Constant-rate pumping — array form of wel_crt.
@@ -820,28 +890,37 @@ def plot_head(head, workspace):
 gwf.netcdf_mesh2d_file = Path("frenchman-flat.nc")
 gwf.netcdf_file = Path("frenchman-flat.input.nc")
 
+# Again, with grid and time info
 nc_model = flopy4.mf6.netcdf.NetCDFModel.from_model(gwf, mesh="layered", grid=grid, time=time)
 nc_model.to_netcdf(workspace / "frenchman-flat.input.nc")
 
 with flopy4.mf6.write_context.WriteContext(use_netcdf=True):
     sim.write()
 if os.getenv("MF6_EXTENDED"):
-    sim.run(verbose=True)
+    sim.run()
 
     # Load head results
+    # head = flopy4.mf6.utils.open_hds(
+    #    workspace / "ff.hds",
+    #    workspace / "ff.dis.grb",
+    # )
+    # Load head results — `UgridDataArray` backed by the NetCDF mesh2d output file.
     head = flopy4.mf6.utils.open_hds(
-        workspace / "ff.hds",
+        workspace / gwf.netcdf_mesh2d_file,
+        workspace / "ff.dis.grb",
+    )
+
+    # Load budget results
+    cbc = flopy4.mf6.utils.open_cbc(
+        workspace / "ff.cbc",
         workspace / "ff.dis.grb",
     )
 
     # Plot head results
     plot_head(head, workspace)
+    plot_head_ugrid(head, cbc, grid, workspace)
 
-# The mesh2d NetCDF written to `netcdf_mesh/frenchman-flat.input.nc` can be
-# loaded into QGIS as a mesh layer via **Layer -> Add Layer -> Add Mesh Layer**.
-# The screenshot below shows the field NPF K layer 7 overlaid on the variable-
-# resolution Frenchman Flat grid.  The mesh is properly geolocated because a
-# CRS user input string was provided on grid construction.
+# # NetCDF input — structured (no mesh)
 #
 # ![QGIS: Frenchman Flat K layer 7 input — layered mesh](images/ff.qgis.npf-k-layer7.png)
 
@@ -857,13 +936,14 @@ def plot_head(head, workspace):
 nc_fpth = workspace / "frenchnam-flat.input.nc"
 gwf.netcdf_file = nc_fpth
 
+# Again, with grid and time info
 nc_model = flopy4.mf6.netcdf.NetCDFModel.from_model(gwf, grid=grid, time=time)
 nc_model.to_netcdf(nc_fpth)
 
 with flopy4.mf6.write_context.WriteContext(use_netcdf=True):
     sim.write()
 if os.getenv("MF6_EXTENDED"):
-    sim.run(verbose=True)
+    sim.run()
 
     # Load head results
     head = flopy4.mf6.utils.open_hds(
@@ -871,5 +951,12 @@ def plot_head(head, workspace):
         workspace / "ff.dis.grb",
     )
 
+    # Load budget results
+    cbc = flopy4.mf6.utils.open_cbc(
+        workspace / "ff.cbc",
+        workspace / "ff.dis.grb",
+    )
+
     # Plot head results
     plot_head(head, workspace)
+    plot_head_ugrid(head, cbc, grid, workspace)
diff --git a/docs/examples/twri.ipynb b/docs/examples/twri.ipynb
index e37dae22..7d2f7c46 100644
--- a/docs/examples/twri.ipynb
+++ b/docs/examples/twri.ipynb
@@ -36,10 +36,10 @@
    "id": "2e404a1f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:51.324049Z",
-     "iopub.status.busy": "2026-03-04T20:08:51.323786Z",
-     "iopub.status.idle": "2026-03-04T20:08:51.329077Z",
-     "shell.execute_reply": "2026-03-04T20:08:51.327406Z"
+     "iopub.execute_input": "2026-03-06T20:19:08.815937Z",
+     "iopub.status.busy": "2026-03-06T20:19:08.815753Z",
+     "iopub.status.idle": "2026-03-06T20:19:08.823317Z",
+     "shell.execute_reply": "2026-03-06T20:19:08.822201Z"
     }
    },
    "outputs": [],
@@ -54,10 +54,10 @@
    "id": "aab2b9b8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:51.332334Z",
-     "iopub.status.busy": "2026-03-04T20:08:51.332140Z",
-     "iopub.status.idle": "2026-03-04T20:08:51.485603Z",
-     "shell.execute_reply": "2026-03-04T20:08:51.484018Z"
+     "iopub.execute_input": "2026-03-06T20:19:08.825096Z",
+     "iopub.status.busy": "2026-03-06T20:19:08.824972Z",
+     "iopub.status.idle": "2026-03-06T20:19:09.045508Z",
+     "shell.execute_reply": "2026-03-06T20:19:09.044612Z"
     }
    },
    "outputs": [],
@@ -71,13 +71,23 @@
    "id": "53d42e49",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:51.487803Z",
-     "iopub.status.busy": "2026-03-04T20:08:51.487642Z",
-     "iopub.status.idle": "2026-03-04T20:08:53.518114Z",
-     "shell.execute_reply": "2026-03-04T20:08:53.517261Z"
+     "iopub.execute_input": "2026-03-06T20:19:09.047280Z",
+     "iopub.status.busy": "2026-03-06T20:19:09.047109Z",
+     "iopub.status.idle": "2026-03-06T20:19:11.074360Z",
+     "shell.execute_reply": "2026-03-06T20:19:11.073427Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mjreno/.clone/dev/pyphoenix-project/flopy4/mf6/spec.py:14: FutureWarning: The modflow_devtools.dfns API is experimental and may change or be removed in future versions without following normal deprecation procedures. Use at your own risk. To suppress this warning, use:\n",
+      "  warnings.filterwarnings('ignore', message='.*modflow_devtools.dfns.*experimental.*')\n",
+      "  from modflow_devtools.dfns.schema.block import block_sort_key\n"
+     ]
+    }
+   ],
    "source": [
     "import flopy4"
    ]
@@ -96,10 +106,10 @@
    "id": "665f4ba7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:53.520028Z",
-     "iopub.status.busy": "2026-03-04T20:08:53.519689Z",
-     "iopub.status.idle": "2026-03-04T20:08:53.522837Z",
-     "shell.execute_reply": "2026-03-04T20:08:53.521978Z"
+     "iopub.execute_input": "2026-03-06T20:19:11.076399Z",
+     "iopub.status.busy": "2026-03-06T20:19:11.076065Z",
+     "iopub.status.idle": "2026-03-06T20:19:11.079094Z",
+     "shell.execute_reply": "2026-03-06T20:19:11.078133Z"
     }
    },
    "outputs": [],
@@ -126,10 +136,10 @@
    "id": "1e06dfe3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:53.524285Z",
-     "iopub.status.busy": "2026-03-04T20:08:53.524162Z",
-     "iopub.status.idle": "2026-03-04T20:08:53.527182Z",
-     "shell.execute_reply": "2026-03-04T20:08:53.526478Z"
+     "iopub.execute_input": "2026-03-06T20:19:11.080908Z",
+     "iopub.status.busy": "2026-03-06T20:19:11.080784Z",
+     "iopub.status.idle": "2026-03-06T20:19:11.084055Z",
+     "shell.execute_reply": "2026-03-06T20:19:11.083215Z"
     }
    },
    "outputs": [],
@@ -145,7 +155,8 @@
     "    plt.ylabel(\"y\")\n",
     "    plt.grid(True)\n",
     "    plt.savefig(workspace / \"head.png\", dpi=300, bbox_inches=\"tight\")\n",
-    "    plt.show()\n",
+    "    if not os.environ.get(\"PYTEST_CURRENT_TEST\"):\n",
+    "        plt.show()\n",
     "    plt.close()"
    ]
   },
@@ -163,10 +174,10 @@
    "id": "39f99df8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:53.528629Z",
-     "iopub.status.busy": "2026-03-04T20:08:53.528508Z",
-     "iopub.status.idle": "2026-03-04T20:08:53.533402Z",
-     "shell.execute_reply": "2026-03-04T20:08:53.532775Z"
+     "iopub.execute_input": "2026-03-06T20:19:11.085677Z",
+     "iopub.status.busy": "2026-03-06T20:19:11.085558Z",
+     "iopub.status.idle": "2026-03-06T20:19:11.090851Z",
+     "shell.execute_reply": "2026-03-06T20:19:11.090169Z"
     }
    },
    "outputs": [],
@@ -192,10 +203,10 @@
    "id": "e92c9e64",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:53.534832Z",
-     "iopub.status.busy": "2026-03-04T20:08:53.534716Z",
-     "iopub.status.idle": "2026-03-04T20:08:53.544440Z",
-     "shell.execute_reply": "2026-03-04T20:08:53.543697Z"
+     "iopub.execute_input": "2026-03-06T20:19:11.092301Z",
+     "iopub.status.busy": "2026-03-06T20:19:11.092185Z",
+     "iopub.status.idle": "2026-03-06T20:19:11.103047Z",
+     "shell.execute_reply": "2026-03-06T20:19:11.102511Z"
     }
    },
    "outputs": [],
@@ -231,10 +242,10 @@
    "id": "8fbc61b5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:53.545847Z",
-     "iopub.status.busy": "2026-03-04T20:08:53.545728Z",
-     "iopub.status.idle": "2026-03-04T20:08:53.550596Z",
-     "shell.execute_reply": "2026-03-04T20:08:53.549892Z"
+     "iopub.execute_input": "2026-03-06T20:19:11.104705Z",
+     "iopub.status.busy": "2026-03-06T20:19:11.104591Z",
+     "iopub.status.idle": "2026-03-06T20:19:11.109668Z",
+     "shell.execute_reply": "2026-03-06T20:19:11.109037Z"
     }
    },
    "outputs": [],
@@ -251,10 +262,10 @@
    "id": "93afe573",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:53.552172Z",
-     "iopub.status.busy": "2026-03-04T20:08:53.552063Z",
-     "iopub.status.idle": "2026-03-04T20:08:53.555106Z",
-     "shell.execute_reply": "2026-03-04T20:08:53.554334Z"
+     "iopub.execute_input": "2026-03-06T20:19:11.110960Z",
+     "iopub.status.busy": "2026-03-06T20:19:11.110849Z",
+     "iopub.status.idle": "2026-03-06T20:19:11.114129Z",
+     "shell.execute_reply": "2026-03-06T20:19:11.113487Z"
     }
    },
    "outputs": [],
@@ -276,10 +287,10 @@
    "id": "8c8a45dd",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:53.556572Z",
-     "iopub.status.busy": "2026-03-04T20:08:53.556451Z",
-     "iopub.status.idle": "2026-03-04T20:08:53.560032Z",
-     "shell.execute_reply": "2026-03-04T20:08:53.559278Z"
+     "iopub.execute_input": "2026-03-06T20:19:11.115494Z",
+     "iopub.status.busy": "2026-03-06T20:19:11.115382Z",
+     "iopub.status.idle": "2026-03-06T20:19:11.119030Z",
+     "shell.execute_reply": "2026-03-06T20:19:11.118210Z"
     }
    },
    "outputs": [],
@@ -304,10 +315,10 @@
    "id": "fb00a977",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:53.561365Z",
-     "iopub.status.busy": "2026-03-04T20:08:53.561250Z",
-     "iopub.status.idle": "2026-03-04T20:08:53.564047Z",
-     "shell.execute_reply": "2026-03-04T20:08:53.563320Z"
+     "iopub.execute_input": "2026-03-06T20:19:11.120394Z",
+     "iopub.status.busy": "2026-03-06T20:19:11.120278Z",
+     "iopub.status.idle": "2026-03-06T20:19:11.122775Z",
+     "shell.execute_reply": "2026-03-06T20:19:11.122148Z"
     }
    },
    "outputs": [],
@@ -322,10 +333,10 @@
    "id": "32f66521",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:53.565452Z",
-     "iopub.status.busy": "2026-03-04T20:08:53.565330Z",
-     "iopub.status.idle": "2026-03-04T20:08:53.569008Z",
-     "shell.execute_reply": "2026-03-04T20:08:53.568279Z"
+     "iopub.execute_input": "2026-03-06T20:19:11.124080Z",
+     "iopub.status.busy": "2026-03-06T20:19:11.123970Z",
+     "iopub.status.idle": "2026-03-06T20:19:11.127392Z",
+     "shell.execute_reply": "2026-03-06T20:19:11.126622Z"
     }
    },
    "outputs": [],
@@ -352,10 +363,10 @@
    "id": "c9aa1404",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:53.570401Z",
-     "iopub.status.busy": "2026-03-04T20:08:53.570289Z",
-     "iopub.status.idle": "2026-03-04T20:08:53.573342Z",
-     "shell.execute_reply": "2026-03-04T20:08:53.572673Z"
+     "iopub.execute_input": "2026-03-06T20:19:11.128777Z",
+     "iopub.status.busy": "2026-03-06T20:19:11.128663Z",
+     "iopub.status.idle": "2026-03-06T20:19:11.131737Z",
+     "shell.execute_reply": "2026-03-06T20:19:11.130814Z"
     }
    },
    "outputs": [],
@@ -377,10 +388,10 @@
    "id": "9357839c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:53.574750Z",
-     "iopub.status.busy": "2026-03-04T20:08:53.574637Z",
-     "iopub.status.idle": "2026-03-04T20:08:53.577915Z",
-     "shell.execute_reply": "2026-03-04T20:08:53.577189Z"
+     "iopub.execute_input": "2026-03-06T20:19:11.133096Z",
+     "iopub.status.busy": "2026-03-06T20:19:11.132987Z",
+     "iopub.status.idle": "2026-03-06T20:19:11.135995Z",
+     "shell.execute_reply": "2026-03-06T20:19:11.135278Z"
     }
    },
    "outputs": [],
@@ -399,10 +410,10 @@
    "id": "94977184",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:53.579285Z",
-     "iopub.status.busy": "2026-03-04T20:08:53.579166Z",
-     "iopub.status.idle": "2026-03-04T20:08:53.582107Z",
-     "shell.execute_reply": "2026-03-04T20:08:53.581317Z"
+     "iopub.execute_input": "2026-03-06T20:19:11.137463Z",
+     "iopub.status.busy": "2026-03-06T20:19:11.137355Z",
+     "iopub.status.idle": "2026-03-06T20:19:11.140242Z",
+     "shell.execute_reply": "2026-03-06T20:19:11.139629Z"
     }
    },
    "outputs": [],
@@ -423,10 +434,10 @@
    "id": "4e57e177",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:53.583521Z",
-     "iopub.status.busy": "2026-03-04T20:08:53.583410Z",
-     "iopub.status.idle": "2026-03-04T20:08:53.586977Z",
-     "shell.execute_reply": "2026-03-04T20:08:53.586196Z"
+     "iopub.execute_input": "2026-03-06T20:19:11.141705Z",
+     "iopub.status.busy": "2026-03-06T20:19:11.141594Z",
+     "iopub.status.idle": "2026-03-06T20:19:11.145142Z",
+     "shell.execute_reply": "2026-03-06T20:19:11.144488Z"
     }
    },
    "outputs": [],
@@ -470,10 +481,10 @@
    "id": "e617ee74",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:53.588362Z",
-     "iopub.status.busy": "2026-03-04T20:08:53.588242Z",
-     "iopub.status.idle": "2026-03-04T20:08:53.596308Z",
-     "shell.execute_reply": "2026-03-04T20:08:53.595450Z"
+     "iopub.execute_input": "2026-03-06T20:19:11.146587Z",
+     "iopub.status.busy": "2026-03-06T20:19:11.146465Z",
+     "iopub.status.idle": "2026-03-06T20:19:11.154731Z",
+     "shell.execute_reply": "2026-03-06T20:19:11.153977Z"
     }
    },
    "outputs": [],
@@ -507,10 +518,10 @@
    "id": "3e3b6784",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:53.597760Z",
-     "iopub.status.busy": "2026-03-04T20:08:53.597646Z",
-     "iopub.status.idle": "2026-03-04T20:08:53.600863Z",
-     "shell.execute_reply": "2026-03-04T20:08:53.600124Z"
+     "iopub.execute_input": "2026-03-06T20:19:11.156074Z",
+     "iopub.status.busy": "2026-03-06T20:19:11.155965Z",
+     "iopub.status.idle": "2026-03-06T20:19:11.158626Z",
+     "shell.execute_reply": "2026-03-06T20:19:11.158048Z"
     }
    },
    "outputs": [],
@@ -546,10 +557,10 @@
    "id": "09c7b852",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:53.602365Z",
-     "iopub.status.busy": "2026-03-04T20:08:53.602243Z",
-     "iopub.status.idle": "2026-03-04T20:08:53.604956Z",
-     "shell.execute_reply": "2026-03-04T20:08:53.604330Z"
+     "iopub.execute_input": "2026-03-06T20:19:11.159918Z",
+     "iopub.status.busy": "2026-03-06T20:19:11.159802Z",
+     "iopub.status.idle": "2026-03-06T20:19:11.162524Z",
+     "shell.execute_reply": "2026-03-06T20:19:11.161754Z"
     }
    },
    "outputs": [],
@@ -571,10 +582,10 @@
    "id": "2fc2b393",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:53.606269Z",
-     "iopub.status.busy": "2026-03-04T20:08:53.606150Z",
-     "iopub.status.idle": "2026-03-04T20:08:53.608490Z",
-     "shell.execute_reply": "2026-03-04T20:08:53.607908Z"
+     "iopub.execute_input": "2026-03-06T20:19:11.163979Z",
+     "iopub.status.busy": "2026-03-06T20:19:11.163862Z",
+     "iopub.status.idle": "2026-03-06T20:19:11.166345Z",
+     "shell.execute_reply": "2026-03-06T20:19:11.165745Z"
     }
    },
    "outputs": [],
@@ -590,10 +601,10 @@
    "id": "8b07b387",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:53.609778Z",
-     "iopub.status.busy": "2026-03-04T20:08:53.609669Z",
-     "iopub.status.idle": "2026-03-04T20:08:53.616924Z",
-     "shell.execute_reply": "2026-03-04T20:08:53.616313Z"
+     "iopub.execute_input": "2026-03-06T20:19:11.167665Z",
+     "iopub.status.busy": "2026-03-06T20:19:11.167551Z",
+     "iopub.status.idle": "2026-03-06T20:19:11.175236Z",
+     "shell.execute_reply": "2026-03-06T20:19:11.174417Z"
     }
    },
    "outputs": [],
@@ -614,10 +625,10 @@
    "id": "91808901",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:53.618344Z",
-     "iopub.status.busy": "2026-03-04T20:08:53.618208Z",
-     "iopub.status.idle": "2026-03-04T20:08:53.801478Z",
-     "shell.execute_reply": "2026-03-04T20:08:53.800669Z"
+     "iopub.execute_input": "2026-03-06T20:19:11.176528Z",
+     "iopub.status.busy": "2026-03-06T20:19:11.176419Z",
+     "iopub.status.idle": "2026-03-06T20:19:11.345857Z",
+     "shell.execute_reply": "2026-03-06T20:19:11.345222Z"
     }
    },
    "outputs": [
@@ -648,7 +659,7 @@
       "\n",
       " MODFLOW runs in SEQUENTIAL mode\n",
       "\n",
-      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2026/03/04 15:08:53\n",
+      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2026/03/06 15:19:11\n",
       "\n",
       " Writing simulation list file: mfsim.lst\n",
       " Using Simulation name file: mfsim.nam\n",
@@ -657,8 +668,8 @@
       "    Solving:  Stress period:     2    Time step:     1\n",
       "    Solving:  Stress period:     3    Time step:     1\n",
       "\n",
-      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2026/03/04 15:08:53\n",
-      " Elapsed run time:  0.023 Seconds\n",
+      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2026/03/06 15:19:11\n",
+      " Elapsed run time:  0.022 Seconds\n",
       "\n",
       " Normal termination of simulation.\n",
       "\n",
@@ -688,14 +699,15 @@
    "id": "eacdd5de",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:53.803065Z",
-     "iopub.status.busy": "2026-03-04T20:08:53.802935Z",
-     "iopub.status.idle": "2026-03-04T20:08:53.808620Z",
-     "shell.execute_reply": "2026-03-04T20:08:53.807926Z"
+     "iopub.execute_input": "2026-03-06T20:19:11.347364Z",
+     "iopub.status.busy": "2026-03-06T20:19:11.347246Z",
+     "iopub.status.idle": "2026-03-06T20:19:11.352443Z",
+     "shell.execute_reply": "2026-03-06T20:19:11.351784Z"
     }
    },
    "outputs": [],
    "source": [
+    "# Load head results\n",
     "head = flopy4.mf6.utils.open_hds(\n",
     "    workspace / f\"{gwf.name}.hds\",\n",
     "    workspace / f\"{gwf.name}.dis.grb\",\n",
@@ -716,16 +728,16 @@
    "id": "c136eac9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:53.810418Z",
-     "iopub.status.busy": "2026-03-04T20:08:53.810300Z",
-     "iopub.status.idle": "2026-03-04T20:08:54.243044Z",
-     "shell.execute_reply": "2026-03-04T20:08:54.242049Z"
+     "iopub.execute_input": "2026-03-06T20:19:11.354307Z",
+     "iopub.status.busy": "2026-03-06T20:19:11.354174Z",
+     "iopub.status.idle": "2026-03-06T20:19:11.844730Z",
+     "shell.execute_reply": "2026-03-06T20:19:11.843826Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 2 Axes>"
       ]
@@ -762,10 +774,10 @@
    "id": "8676f8bf",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:54.244895Z",
-     "iopub.status.busy": "2026-03-04T20:08:54.244766Z",
-     "iopub.status.idle": "2026-03-04T20:08:54.247672Z",
-     "shell.execute_reply": "2026-03-04T20:08:54.246746Z"
+     "iopub.execute_input": "2026-03-06T20:19:11.846576Z",
+     "iopub.status.busy": "2026-03-06T20:19:11.846455Z",
+     "iopub.status.idle": "2026-03-06T20:19:11.849598Z",
+     "shell.execute_reply": "2026-03-06T20:19:11.848805Z"
     }
    },
    "outputs": [],
@@ -781,10 +793,10 @@
    "id": "f251053c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:54.249230Z",
-     "iopub.status.busy": "2026-03-04T20:08:54.249108Z",
-     "iopub.status.idle": "2026-03-04T20:08:54.252496Z",
-     "shell.execute_reply": "2026-03-04T20:08:54.251846Z"
+     "iopub.execute_input": "2026-03-06T20:19:11.851378Z",
+     "iopub.status.busy": "2026-03-06T20:19:11.851258Z",
+     "iopub.status.idle": "2026-03-06T20:19:11.854802Z",
+     "shell.execute_reply": "2026-03-06T20:19:11.854177Z"
     }
    },
    "outputs": [],
@@ -808,10 +820,10 @@
    "id": "7eec3940",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:54.253982Z",
-     "iopub.status.busy": "2026-03-04T20:08:54.253860Z",
-     "iopub.status.idle": "2026-03-04T20:08:54.257482Z",
-     "shell.execute_reply": "2026-03-04T20:08:54.256854Z"
+     "iopub.execute_input": "2026-03-06T20:19:11.856196Z",
+     "iopub.status.busy": "2026-03-06T20:19:11.856082Z",
+     "iopub.status.idle": "2026-03-06T20:19:11.859607Z",
+     "shell.execute_reply": "2026-03-06T20:19:11.858882Z"
     }
    },
    "outputs": [],
@@ -838,10 +850,10 @@
    "id": "015d9d2e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:54.258975Z",
-     "iopub.status.busy": "2026-03-04T20:08:54.258853Z",
-     "iopub.status.idle": "2026-03-04T20:08:54.262007Z",
-     "shell.execute_reply": "2026-03-04T20:08:54.261245Z"
+     "iopub.execute_input": "2026-03-06T20:19:11.861234Z",
+     "iopub.status.busy": "2026-03-06T20:19:11.861119Z",
+     "iopub.status.idle": "2026-03-06T20:19:11.864370Z",
+     "shell.execute_reply": "2026-03-06T20:19:11.863493Z"
     }
    },
    "outputs": [],
@@ -862,10 +874,10 @@
    "id": "b578f97e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:54.263557Z",
-     "iopub.status.busy": "2026-03-04T20:08:54.263445Z",
-     "iopub.status.idle": "2026-03-04T20:08:54.266253Z",
-     "shell.execute_reply": "2026-03-04T20:08:54.265537Z"
+     "iopub.execute_input": "2026-03-06T20:19:11.865819Z",
+     "iopub.status.busy": "2026-03-06T20:19:11.865705Z",
+     "iopub.status.idle": "2026-03-06T20:19:11.868583Z",
+     "shell.execute_reply": "2026-03-06T20:19:11.867965Z"
     }
    },
    "outputs": [],
@@ -882,10 +894,10 @@
    "id": "b8703b19",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:54.267604Z",
-     "iopub.status.busy": "2026-03-04T20:08:54.267492Z",
-     "iopub.status.idle": "2026-03-04T20:08:54.270095Z",
-     "shell.execute_reply": "2026-03-04T20:08:54.269366Z"
+     "iopub.execute_input": "2026-03-06T20:19:11.869978Z",
+     "iopub.status.busy": "2026-03-06T20:19:11.869868Z",
+     "iopub.status.idle": "2026-03-06T20:19:11.872569Z",
+     "shell.execute_reply": "2026-03-06T20:19:11.871988Z"
     }
    },
    "outputs": [],
@@ -905,10 +917,10 @@
    "id": "c6f5ed4a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:54.271288Z",
-     "iopub.status.busy": "2026-03-04T20:08:54.271171Z",
-     "iopub.status.idle": "2026-03-04T20:08:54.319876Z",
-     "shell.execute_reply": "2026-03-04T20:08:54.319292Z"
+     "iopub.execute_input": "2026-03-06T20:19:11.874187Z",
+     "iopub.status.busy": "2026-03-06T20:19:11.874071Z",
+     "iopub.status.idle": "2026-03-06T20:19:11.924171Z",
+     "shell.execute_reply": "2026-03-06T20:19:11.923354Z"
     }
    },
    "outputs": [],
@@ -934,10 +946,10 @@
    "id": "7db8e040",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:54.321860Z",
-     "iopub.status.busy": "2026-03-04T20:08:54.321680Z",
-     "iopub.status.idle": "2026-03-04T20:08:54.324596Z",
-     "shell.execute_reply": "2026-03-04T20:08:54.323867Z"
+     "iopub.execute_input": "2026-03-06T20:19:11.925912Z",
+     "iopub.status.busy": "2026-03-06T20:19:11.925800Z",
+     "iopub.status.idle": "2026-03-06T20:19:11.928294Z",
+     "shell.execute_reply": "2026-03-06T20:19:11.927710Z"
     }
    },
    "outputs": [],
@@ -954,10 +966,10 @@
    "id": "bbb112b9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:54.326332Z",
-     "iopub.status.busy": "2026-03-04T20:08:54.326194Z",
-     "iopub.status.idle": "2026-03-04T20:08:54.379125Z",
-     "shell.execute_reply": "2026-03-04T20:08:54.378111Z"
+     "iopub.execute_input": "2026-03-06T20:19:11.929769Z",
+     "iopub.status.busy": "2026-03-06T20:19:11.929655Z",
+     "iopub.status.idle": "2026-03-06T20:19:11.981028Z",
+     "shell.execute_reply": "2026-03-06T20:19:11.980208Z"
     }
    },
    "outputs": [
@@ -988,7 +1000,7 @@
       "\n",
       " MODFLOW runs in SEQUENTIAL mode\n",
       "\n",
-      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2026/03/04 15:08:54\n",
+      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2026/03/06 15:19:11\n",
       "\n",
       " Writing simulation list file: mfsim.lst\n",
       " Using Simulation name file: mfsim.nam\n",
@@ -997,8 +1009,8 @@
       "    Solving:  Stress period:     2    Time step:     1\n",
       "    Solving:  Stress period:     3    Time step:     1\n",
       "\n",
-      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2026/03/04 15:08:54\n",
-      " Elapsed run time:  0.012 Seconds\n",
+      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2026/03/06 15:19:11\n",
+      " Elapsed run time:  0.011 Seconds\n",
       "\n",
       " Normal termination of simulation.\n",
       "\n",
@@ -1027,14 +1039,15 @@
    "id": "7ecc1e98",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:54.380912Z",
-     "iopub.status.busy": "2026-03-04T20:08:54.380785Z",
-     "iopub.status.idle": "2026-03-04T20:08:54.385677Z",
-     "shell.execute_reply": "2026-03-04T20:08:54.385025Z"
+     "iopub.execute_input": "2026-03-06T20:19:11.982522Z",
+     "iopub.status.busy": "2026-03-06T20:19:11.982403Z",
+     "iopub.status.idle": "2026-03-06T20:19:11.986910Z",
+     "shell.execute_reply": "2026-03-06T20:19:11.986309Z"
     }
    },
    "outputs": [],
    "source": [
+    "# Load head results\n",
     "head = flopy4.mf6.utils.open_hds(\n",
     "    workspace / f\"{gwf.name}.hds\",\n",
     "    workspace / f\"{gwf.name}.dis.grb\",\n",
@@ -1055,16 +1068,16 @@
    "id": "645556b4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:54.387109Z",
-     "iopub.status.busy": "2026-03-04T20:08:54.386997Z",
-     "iopub.status.idle": "2026-03-04T20:08:54.647956Z",
-     "shell.execute_reply": "2026-03-04T20:08:54.646931Z"
+     "iopub.execute_input": "2026-03-06T20:19:11.988239Z",
+     "iopub.status.busy": "2026-03-06T20:19:11.988123Z",
+     "iopub.status.idle": "2026-03-06T20:19:12.252449Z",
+     "shell.execute_reply": "2026-03-06T20:19:12.251688Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 2 Axes>"
       ]
@@ -1101,10 +1114,10 @@
    "id": "e11e87fc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:54.650074Z",
-     "iopub.status.busy": "2026-03-04T20:08:54.649904Z",
-     "iopub.status.idle": "2026-03-04T20:08:54.652691Z",
-     "shell.execute_reply": "2026-03-04T20:08:54.651896Z"
+     "iopub.execute_input": "2026-03-06T20:19:12.254295Z",
+     "iopub.status.busy": "2026-03-06T20:19:12.254126Z",
+     "iopub.status.idle": "2026-03-06T20:19:12.257052Z",
+     "shell.execute_reply": "2026-03-06T20:19:12.256321Z"
     }
    },
    "outputs": [],
@@ -1121,10 +1134,10 @@
    "id": "7b97e131",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:54.654492Z",
-     "iopub.status.busy": "2026-03-04T20:08:54.654371Z",
-     "iopub.status.idle": "2026-03-04T20:08:54.656925Z",
-     "shell.execute_reply": "2026-03-04T20:08:54.656326Z"
+     "iopub.execute_input": "2026-03-06T20:19:12.258645Z",
+     "iopub.status.busy": "2026-03-06T20:19:12.258524Z",
+     "iopub.status.idle": "2026-03-06T20:19:12.261189Z",
+     "shell.execute_reply": "2026-03-06T20:19:12.260494Z"
     }
    },
    "outputs": [],
@@ -1139,14 +1152,18 @@
    "id": "1a9300ca",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:54.658398Z",
-     "iopub.status.busy": "2026-03-04T20:08:54.658265Z",
-     "iopub.status.idle": "2026-03-04T20:08:54.717808Z",
-     "shell.execute_reply": "2026-03-04T20:08:54.716955Z"
+     "iopub.execute_input": "2026-03-06T20:19:12.262655Z",
+     "iopub.status.busy": "2026-03-06T20:19:12.262540Z",
+     "iopub.status.idle": "2026-03-06T20:19:12.326304Z",
+     "shell.execute_reply": "2026-03-06T20:19:12.325519Z"
     }
    },
    "outputs": [],
    "source": [
+    "# Here the `NetCDFModel` object is created without passing optional grid\n",
+    "# and time arguments.  This generates a data only file (no coordinate or\n",
+    "# mesh variables), which is sufficient as an `mf6` input but not for\n",
+    "# visualization in QGIS.\n",
     "nc_model = flopy4.mf6.netcdf.NetCDFModel.from_model(gwf)\n",
     "nc_model.to_netcdf(nc_fpth)"
    ]
@@ -1157,10 +1174,10 @@
    "id": "b4902139",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:54.719667Z",
-     "iopub.status.busy": "2026-03-04T20:08:54.719539Z",
-     "iopub.status.idle": "2026-03-04T20:08:54.739273Z",
-     "shell.execute_reply": "2026-03-04T20:08:54.738495Z"
+     "iopub.execute_input": "2026-03-06T20:19:12.328405Z",
+     "iopub.status.busy": "2026-03-06T20:19:12.327724Z",
+     "iopub.status.idle": "2026-03-06T20:19:12.353166Z",
+     "shell.execute_reply": "2026-03-06T20:19:12.352467Z"
     }
    },
    "outputs": [],
@@ -1175,10 +1192,10 @@
    "id": "d315e02c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:54.741146Z",
-     "iopub.status.busy": "2026-03-04T20:08:54.741026Z",
-     "iopub.status.idle": "2026-03-04T20:08:55.086928Z",
-     "shell.execute_reply": "2026-03-04T20:08:55.085610Z"
+     "iopub.execute_input": "2026-03-06T20:19:12.354792Z",
+     "iopub.status.busy": "2026-03-06T20:19:12.354672Z",
+     "iopub.status.idle": "2026-03-06T20:19:12.645487Z",
+     "shell.execute_reply": "2026-03-06T20:19:12.644484Z"
     }
    },
    "outputs": [
@@ -1209,7 +1226,7 @@
       "\n",
       " MODFLOW runs in SEQUENTIAL mode\n",
       "\n",
-      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2026/03/04 15:08:54\n",
+      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2026/03/06 15:19:12\n",
       "\n",
       " Writing simulation list file: mfsim.lst\n",
       " Using Simulation name file: mfsim.nam\n",
@@ -1218,7 +1235,7 @@
       "    Solving:  Stress period:     2    Time step:     1\n",
       "    Solving:  Stress period:     3    Time step:     1\n",
       "\n",
-      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2026/03/04 15:08:54\n",
+      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2026/03/06 15:19:12\n",
       " Elapsed run time:  0.025 Seconds\n",
       "\n",
       " Normal termination of simulation.\n",
@@ -1230,7 +1247,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 2 Axes>"
       ]
@@ -1258,14 +1275,8 @@
    "id": "583427fb",
    "metadata": {},
    "source": [
-    "### NetCDF input — layered mesh"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "de83f8aa",
-   "metadata": {},
-   "source": [
+    "# NetCDF input — layered mesh\n",
+    "\n",
     "`mesh=\"layered\"` writes a layered UGRID mesh NetCDF, which MODFLOW 6\n",
     "reads with its NetCDF-mesh2d input mode."
    ]
@@ -1276,10 +1287,10 @@
    "id": "b857d04b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:55.088974Z",
-     "iopub.status.busy": "2026-03-04T20:08:55.088850Z",
-     "iopub.status.idle": "2026-03-04T20:08:55.091705Z",
-     "shell.execute_reply": "2026-03-04T20:08:55.090863Z"
+     "iopub.execute_input": "2026-03-06T20:19:12.647183Z",
+     "iopub.status.busy": "2026-03-06T20:19:12.647066Z",
+     "iopub.status.idle": "2026-03-06T20:19:12.649952Z",
+     "shell.execute_reply": "2026-03-06T20:19:12.649160Z"
     }
    },
    "outputs": [],
@@ -1296,10 +1307,10 @@
    "id": "46d97b49",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:55.093373Z",
-     "iopub.status.busy": "2026-03-04T20:08:55.093258Z",
-     "iopub.status.idle": "2026-03-04T20:08:55.095862Z",
-     "shell.execute_reply": "2026-03-04T20:08:55.095045Z"
+     "iopub.execute_input": "2026-03-06T20:19:12.651323Z",
+     "iopub.status.busy": "2026-03-06T20:19:12.651203Z",
+     "iopub.status.idle": "2026-03-06T20:19:12.653831Z",
+     "shell.execute_reply": "2026-03-06T20:19:12.652856Z"
     }
    },
    "outputs": [],
@@ -1314,14 +1325,15 @@
    "id": "d901b3fe",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:55.097256Z",
-     "iopub.status.busy": "2026-03-04T20:08:55.097144Z",
-     "iopub.status.idle": "2026-03-04T20:08:55.128451Z",
-     "shell.execute_reply": "2026-03-04T20:08:55.127684Z"
+     "iopub.execute_input": "2026-03-06T20:19:12.655303Z",
+     "iopub.status.busy": "2026-03-06T20:19:12.655185Z",
+     "iopub.status.idle": "2026-03-06T20:19:12.687980Z",
+     "shell.execute_reply": "2026-03-06T20:19:12.687084Z"
     }
    },
    "outputs": [],
    "source": [
+    "# Again, no grid or time arguments defined\n",
     "nc_model = flopy4.mf6.netcdf.NetCDFModel.from_model(gwf, mesh=\"layered\")\n",
     "nc_model.to_netcdf(nc_fpth)"
    ]
@@ -1332,10 +1344,10 @@
    "id": "a6265a8b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:55.130168Z",
-     "iopub.status.busy": "2026-03-04T20:08:55.130049Z",
-     "iopub.status.idle": "2026-03-04T20:08:55.148918Z",
-     "shell.execute_reply": "2026-03-04T20:08:55.148154Z"
+     "iopub.execute_input": "2026-03-06T20:19:12.690047Z",
+     "iopub.status.busy": "2026-03-06T20:19:12.689917Z",
+     "iopub.status.idle": "2026-03-06T20:19:12.711412Z",
+     "shell.execute_reply": "2026-03-06T20:19:12.710602Z"
     }
    },
    "outputs": [],
@@ -1350,10 +1362,10 @@
    "id": "190225a3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-03-04T20:08:55.151031Z",
-     "iopub.status.busy": "2026-03-04T20:08:55.150906Z",
-     "iopub.status.idle": "2026-03-04T20:08:55.432494Z",
-     "shell.execute_reply": "2026-03-04T20:08:55.431533Z"
+     "iopub.execute_input": "2026-03-06T20:19:12.712985Z",
+     "iopub.status.busy": "2026-03-06T20:19:12.712857Z",
+     "iopub.status.idle": "2026-03-06T20:19:12.997939Z",
+     "shell.execute_reply": "2026-03-06T20:19:12.997296Z"
     }
    },
    "outputs": [
@@ -1384,7 +1396,7 @@
       "\n",
       " MODFLOW runs in SEQUENTIAL mode\n",
       "\n",
-      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2026/03/04 15:08:55\n",
+      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2026/03/06 15:19:12\n",
       "\n",
       " Writing simulation list file: mfsim.lst\n",
       " Using Simulation name file: mfsim.nam\n",
@@ -1393,8 +1405,8 @@
       "    Solving:  Stress period:     2    Time step:     1\n",
       "    Solving:  Stress period:     3    Time step:     1\n",
       "\n",
-      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2026/03/04 15:08:55\n",
-      " Elapsed run time:  0.016 Seconds\n",
+      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2026/03/06 15:19:12\n",
+      " Elapsed run time:  0.017 Seconds\n",
       "\n",
       " Normal termination of simulation.\n",
       "\n",
@@ -1405,7 +1417,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 2 Axes>"
       ]
@@ -1445,7 +1457,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.14"
+   "version": "3.11.15"
   }
  },
  "nbformat": 4,
diff --git a/docs/examples/twri.py b/docs/examples/twri.py
index 8e891c17..7784426b 100644
--- a/docs/examples/twri.py
+++ b/docs/examples/twri.py
@@ -45,7 +45,8 @@ def plot_head(head, workspace):
     plt.ylabel("y")
     plt.grid(True)
     plt.savefig(workspace / "head.png", dpi=300, bbox_inches="tight")
-    # plt.show()
+    if not os.environ.get("PYTEST_CURRENT_TEST"):
+        plt.show()
     plt.close()
 
 
@@ -232,6 +233,7 @@ def plot_head(head, workspace):
 
 # ### Load head results
 
+# Load head results
 head = flopy4.mf6.utils.open_hds(
     workspace / f"{gwf.name}.hds",
     workspace / f"{gwf.name}.dis.grb",
@@ -318,6 +320,7 @@ def plot_head(head, workspace):
 
 # ### Load head results
 
+# Load head results
 head = flopy4.mf6.utils.open_hds(
     workspace / f"{gwf.name}.hds",
     workspace / f"{gwf.name}.dis.grb",
@@ -341,6 +344,10 @@ def plot_head(head, workspace):
 nc_fpth = workspace / "twri.input.nc"
 gwf.netcdf_file = nc_fpth
 
+# Here the `NetCDFModel` object is created without passing optional grid
+# and time arguments.  This generates a data only file (no coordinate or
+# mesh variables), which is sufficient as an `mf6` input but not for
+# visualization in QGIS.
 nc_model = flopy4.mf6.netcdf.NetCDFModel.from_model(gwf)
 nc_model.to_netcdf(nc_fpth)
 
@@ -359,8 +366,8 @@ def plot_head(head, workspace):
     # Plot head results
     plot_head(head, workspace)
 
-# ### NetCDF input — layered mesh
-
+# # NetCDF input — layered mesh
+#
 # `mesh="layered"` writes a layered UGRID mesh NetCDF, which MODFLOW 6
 # reads with its NetCDF-mesh2d input mode.
 
@@ -372,6 +379,7 @@ def plot_head(head, workspace):
 nc_fpth = workspace / "twri.input.nc"
 gwf.netcdf_file = nc_fpth
 
+# Again, no grid or time arguments defined
 nc_model = flopy4.mf6.netcdf.NetCDFModel.from_model(gwf, mesh="layered")
 nc_model.to_netcdf(nc_fpth)
 
diff --git a/docs/index.md b/docs/index.md
index 84776307..62bbb9ec 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -20,6 +20,7 @@ The library exposes MODFLOW 6 input and output through:
 - {doc}`examples/quickstart` — 10×10 steady-state DIS model with contour + quiver plot
 - {doc}`examples/twri` — 3-layer transient benchmark; list, array, and NetCDF inputs
 - {doc}`examples/frenchman-flat` — real-world 87×87 DIS model from ScienceBase
+- {doc}`examples/circle` — DISV vertex grid with xugrid
 
 ## Source
 
diff --git a/flopy4/adapters.py b/flopy4/adapters.py
index 8a3cec3e..b0568e73 100644
--- a/flopy4/adapters.py
+++ b/flopy4/adapters.py
@@ -1,14 +1,27 @@
 import os
+from typing import Any
 
-from flopy.discretization.grid import Grid
+import numpy as np
+import scipy.sparse
+import xugrid as xu
 from flopy.discretization.structuredgrid import StructuredGrid
-from flopy.discretization.unstructuredgrid import UnstructuredGrid
-from flopy.discretization.vertexgrid import VertexGrid
+
+__all__ = [
+    "StructuredGridWrapper",
+    "read_binary_grid_file",
+    "get_nn",
+]
 
 
 class StructuredGridWrapper(StructuredGrid):
     """
     Wrapper for StructuredGrid to add ia and ja properties.
+
+    ``ia`` and ``ja`` follow the same 0-based CSR convention returned by
+    :class:`flopy.mf6.utils.binarygrid_util.MfGrdFile` (which converts the
+    Fortran 1-based arrays to 0-based on read).  All DISV connectivity arrays
+    in this module use the same 0-based convention.
+
     TODO: add this to flopy3 and this can be removed.
     """
 
@@ -63,6 +76,42 @@ def ja(self):
     def ia(self):
         return self._ia
 
+    @classmethod
+    def _from_grb(cls, grb_obj):
+        """
+        Construct from an already-opened :class:`~flopy.mf6.utils.binarygrid_util.MfGrdFile`.
+
+        Parameters
+        ----------
+        grb_obj : MfGrdFile
+            An already-opened binary grid file object for a DIS grid.
+
+        Returns
+        -------
+        StructuredGridWrapper
+        """
+        crs = grb_obj._datadict["CRS"] if grb_obj._version == "2" else None
+        nlay, nrow, ncol = (grb_obj.nlay, grb_obj.nrow, grb_obj.ncol)
+        delr, delc = grb_obj.delr, grb_obj.delc
+        top, botm = grb_obj.top, grb_obj.bot
+        top.shape = (nrow, ncol)
+        botm.shape = (nlay, nrow, ncol)
+        # ia and ja are already 0-based (MfGrdFile subtracts 1 from the
+        # Fortran 1-based arrays stored in the binary file).
+        return cls(
+            delc,
+            delr,
+            top,
+            botm,
+            idomain=grb_obj.idomain,
+            crs=crs,
+            xoff=grb_obj.xorigin,
+            yoff=grb_obj.yorigin,
+            angrot=grb_obj.angrot,
+            ia=grb_obj.ia,
+            ja=grb_obj.ja,
+        )
+
     @classmethod
     def from_binary_grid_file(cls, file_path, verbose=False):
         """
@@ -88,63 +137,142 @@ def from_binary_grid_file(cls, file_path, verbose=False):
             raise ValueError(
                 f"Binary grid file ({os.path.basename(file_path)}) is not a structured (DIS) grid."
             )
+        return cls._from_grb(grb_obj)
 
-        idomain = grb_obj.idomain
-        xorigin = grb_obj.xorigin
-        yorigin = grb_obj.yorigin
-        angrot = grb_obj.angrot
 
-        nlay, nrow, ncol = (grb_obj.nlay, grb_obj.nrow, grb_obj.ncol)
-        delr, delc = grb_obj.delr, grb_obj.delc
-        top, botm = grb_obj.top, grb_obj.bot
-        top.shape = (nrow, ncol)
-        botm.shape = (nlay, nrow, ncol)
-        return cls(
-            delc,
-            delr,
-            top,
-            botm,
-            idomain=idomain,
-            xoff=xorigin,
-            yoff=yorigin,
-            angrot=angrot,
-            ia=grb_obj.ia,
-            ja=grb_obj.ja,
+def _ugrid_iavert_javert(iavert: np.ndarray, javert: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
+    """
+    Convert MODFLOW 6 iavert/javert (0-based) to UGRID conventions.
+    Removes the closing vertex from each cell's vertex list.
+
+    Parameters
+    ----------
+    iavert : 0-based indptr array (from MfGrdFile), shape (ncpl+1,)
+    javert : 0-based vertex indices (from MfGrdFile), shape (njavert,)
+
+    Returns
+    -------
+    ia : 0-based indptr for UGRID (no closing vertex)
+    ja : 0-based vertex indices for UGRID
+    """
+    # Each cell's vertex list has one extra closing vertex (first == last).
+    # Remove it for UGRID conventions.
+    n = np.diff(iavert) - 1  # number of unique vertices per cell
+    ia = np.concatenate(([0], np.cumsum(n)))
+    keep = np.ones_like(javert, dtype=bool)
+    # The closing vertex of each cell is at position iavert[i+1] - 1
+    closing_indices = iavert[1:] - 1
+    keep[closing_indices] = False
+    return ia, javert[keep]
+
+
+def read_binary_grid_file(file_path: str | os.PathLike, verbose: bool = False) -> dict[str, Any]:
+    """
+    Read a MODFLOW 6 binary grid (GRB) file and return grid info.
+
+    Parameters
+    ----------
+    file_path : str or Path
+        Path to the MODFLOW 6 binary grid file.
+    verbose : bool, optional
+        Print info to stdout. Default False.
+
+    Returns
+    -------
+    dict
+        Grid info dictionary.
+    """
+    from flopy.mf6.utils.binarygrid_util import MfGrdFile
+
+    grb = MfGrdFile(file_path, verbose=verbose)
+
+    if grb.grid_type == "DIS":
+        # Use the already-opened grb object — avoids reading the file twice.
+        grid = StructuredGridWrapper._from_grb(grb)
+        return {"grid_type": "DIS", "grid": grid}
+
+    elif grb.grid_type == "DISV":
+        return _read_disv_grb(grb)
+
+    else:
+        raise ValueError(
+            f"Unsupported grid type '{grb.grid_type}' in {os.path.basename(str(file_path))}. "
+            "Only DIS and DISV are supported."
         )
 
 
-def get_kij(nn: int, nlay: int, nrow: int, ncol: int) -> tuple[int, int, int]:
-    nodes = nlay * nrow * ncol
-    if nn < 0 or nn >= nodes:
-        raise ValueError(f"Node number {nn} is out of bounds (1 to {nodes})")
-    k = (nn - 1) / (ncol * nrow) + 1
-    ij = nn - (k - 1) * ncol * nrow
-    i = (ij - 1) / ncol + 1
-    j = ij - (i - 1) * ncol
-    return int(k), int(i), int(j)
-
-
-def get_jk(nn: int, ncpl: int) -> tuple[int, int]:
-    if nn < 0 or nn >= ncpl:
-        raise ValueError(f"Node number {nn} is out of bounds (1 to {ncpl})")
-    k = (nn - 1) / ncpl + 1
-    j = nn - (k - 1) * ncpl
-    return int(j), int(k)
-
-
-def get_cellid(nn: int, grid: Grid) -> tuple[int, ...]:
-    match grid:
-        case StructuredGrid():
-            return get_kij(nn, *grid.shape)
-        case VertexGrid():
-            return get_jk(nn, grid.ncpl)
-        case UnstructuredGrid():
-            return (nn,)
-        case _:
-            raise TypeError(f"Unsupported grid type: {type(grid)}")
+def _read_disv_grb(grb) -> dict[str, Any]:
+    nlay = grb.nlay
+    ncpl = grb.ncpl
+    ncells = nlay * ncpl
+    # ia and ja are already 0-based (MfGrdFile subtracts 1 from the Fortran
+    # 1-based arrays on read).  All connectivity helpers in cbc_reader expect
+    # 0-based CSR arrays.
+    ia = grb.ia
+    ja = grb.ja
+
+    # Get vertex data (0-based from MfGrdFile)
+    iavert_0 = grb.iavert
+    javert_0 = grb.javert
+
+    # Convert iavert/javert to UGRID conventions (remove closing vertex)
+    ugrid_ia, ugrid_ja = _ugrid_iavert_javert(iavert_0, javert_0)
+
+    # Build Ugrid2d from vertex info
+    verts = grb.verts
+    xorigin = grb.xorigin
+    yorigin = grb.yorigin
+    node_x = verts[:, 0] + xorigin
+    node_y = verts[:, 1] + yorigin
 
+    n_nodes = len(verts)
+    ncpl_faces = len(ugrid_ia) - 1
+    face_nodes = scipy.sparse.csr_matrix(
+        (np.ones(len(ugrid_ja), dtype=np.intp), ugrid_ja, ugrid_ia),
+        shape=(ncpl_faces, n_nodes),
+    )
+    grid = xu.Ugrid2d(node_x, node_y, -1, face_nodes)
+    facedim = grid.face_dimension
 
-def get_nn(cellid, **kwargs):
+    idomain = grb.idomain.reshape((nlay, ncpl))
+
+    coords = {"layer": np.arange(1, nlay + 1)}
+    crs = grb._datadict["CRS"] if grb._version == "2" else None
+
+    return {
+        "grid_type": "DISV",
+        "grid": grid,
+        "nlayer": nlay,
+        "ncells_per_layer": ncpl,
+        "ncells": ncells,
+        "nja": grb._datadict.get("NJA", ia[-1] - 1),
+        "ia": ia,
+        "ja": ja,
+        "idomain": idomain,
+        "coords": coords,
+        "face_dimension": facedim,
+        "crs": crs,
+    }
+
+
+def get_nn(cellid, **kwargs) -> int:
+    """
+    Convert a cell ID tuple to a flat node number (0-based).
+
+    Parameters
+    ----------
+    cellid : tuple
+        - 1-element: (node,) — unstructured or DISV node number
+        - 2-element: (layer, cell) — DISV (k, j)
+        - 3-element: (layer, row, col) — DIS (k, i, j)
+    **kwargs
+        Dimension sizes: ``ncpl`` for DISV, ``nrow``/``ncol`` for DIS.
+
+    Returns
+    -------
+    int
+        0-based flat node index.
+    """
     ndim = len(cellid)
     match ndim:
         case 1:
diff --git a/flopy4/mf6/gwf/__init__.py b/flopy4/mf6/gwf/__init__.py
index 47ea427c..947f187e 100644
--- a/flopy4/mf6/gwf/__init__.py
+++ b/flopy4/mf6/gwf/__init__.py
@@ -3,6 +3,7 @@
 
 import attrs
 import xarray as xr
+import xugrid as xu
 from attrs import define
 from flopy.discretization.grid import Grid
 from flopy.discretization.structuredgrid import StructuredGrid
@@ -78,19 +79,51 @@ class Output:
         parent: "Gwf" = attrs.field(repr=False)
 
         @property
-        def head(self) -> xr.DataArray:
-            # TODO support other extensions than .hds (e.g. .hed)
+        def head(self) -> xr.DataArray | xu.UgridDataArray:
+            path = self.parent.workspace
+            dis_ext = "disv" if isinstance(self.parent.dis, Disv) else "dis"
+
+            hds_fpth = None
+            head_file = self.parent.oc.head_file if self.parent.oc is not None else None
+            if head_file is not None:
+                fpth = path / head_file.name
+                if fpth.exists():
+                    hds_fpth = fpth
+
+            if hds_fpth is None:
+                # Check for output NC file configured on the model
+                nc_fname = self.parent.netcdf_mesh2d_file or self.parent.netcdf_structured_file
+                if nc_fname is not None:
+                    fpth = path / nc_fname.name
+                    if fpth.exists():
+                        hds_fpth = fpth
+
+            if hds_fpth is None:
+                raise FileNotFoundError(f"No head file (*.hds, *.hed, *.nc) found in {path}")
+
             return open_hds(
-                self.parent.parent.workspace / f"{self.parent.name}.hds",  # type: ignore
-                self.parent.parent.workspace / f"{self.parent.name}.dis.grb",  # type: ignore
+                hds_fpth,
+                self.parent.workspace / f"{self.parent.name}.{dis_ext}.grb",  # type: ignore
             )
 
         @property
-        def budget(self):
-            # TODO support other extensions than .bud (e.g. .cbc)
+        def budget(self) -> xr.Dataset | xu.UgridDataset:
+            path = self.parent.workspace
+            dis_ext = "disv" if isinstance(self.parent.dis, Disv) else "dis"
+
+            cbc_fpth = None
+            cbc_file = self.parent.oc.budget_file if self.parent.oc is not None else None
+            if cbc_file is not None:
+                fpth = path / cbc_file.name
+                if fpth.exists():
+                    cbc_fpth = fpth
+
+            if cbc_fpth is None:
+                raise FileNotFoundError(f"No budget file (*.bud, *.cbc) found in {path}")
+
             return open_cbc(
-                self.parent.parent.workspace / f"{self.parent.name}.bud",
-                self.parent.parent.workspace / f"{self.parent.name}.dis.grb",
+                cbc_fpth,
+                self.parent.workspace / f"{self.parent.name}.{dis_ext}.grb",  # type: ignore
             )
 
     _list: Optional[str] = field(block="options", default=None)
diff --git a/flopy4/mf6/gwf/dis.py b/flopy4/mf6/gwf/dis.py
index 1b3fca03..b2e74362 100644
--- a/flopy4/mf6/gwf/dis.py
+++ b/flopy4/mf6/gwf/dis.py
@@ -148,6 +148,9 @@ def to_grid(self) -> StructuredGrid:
             A `StructuredGrid` with the same dimensions and data as the `Dis`.
         """
         return StructuredGrid(
+            length_units=self.length_units,
+            xoff=self.xorigin,
+            yoff=self.yorigin,
             nlay=self.nlay,
             nrow=self.nrow,
             ncol=self.ncol,
diff --git a/flopy4/mf6/gwf/disv.py b/flopy4/mf6/gwf/disv.py
index 5d422b84..c0bb106f 100644
--- a/flopy4/mf6/gwf/disv.py
+++ b/flopy4/mf6/gwf/disv.py
@@ -148,6 +148,10 @@ def to_grid(self) -> VertexGrid:
             vert.append(self.yv.values[i])  # type: ignore
             vertices.append(vert)
         return VertexGrid(
+            length_units=self.length_units,
+            xoff=self.xorigin,
+            yoff=self.yorigin,
+            crs=self.crs,
             nlay=self.nlay,
             top=self.top,
             botm=self.botm,
@@ -172,15 +176,19 @@ def from_grid(cls, grid: VertexGrid) -> "Disv":
             A discretization with the same dimensions and data as the grid.
         """
         return Disv(
+            xorigin=grid.xoffset,
+            yorigin=grid.yoffset,
             nlay=grid.nlay,
             ncpl=grid.ncpl,
             nvert=grid.nvert,
             top=grid.top,
             botm=grid.botm,
-            idomain=grid.idomain.reshape(grid.nlay, grid.ncpl) if grid.idomain else None,
+            idomain=np.asarray(grid.idomain).reshape(grid.nlay, grid.ncpl)
+            if grid.idomain is not None
+            else None,
             iv=np.array([v[0] for v in grid._vertices], dtype=int),
             xv=grid.verts[:, 0].ravel(),
-            yv=grid.verts[:, -1].ravel(),
+            yv=grid.verts[:, 1].ravel(),
             cell2ddata=Disv.grid_to_disv_cell2d(grid.cell2d),
         )
 
@@ -214,7 +222,7 @@ def grid_to_disv_cell2d(cell2d):
                 cell[0],
                 cell[1],
                 cell[2],
-                len(cell) - 3,
+                len(verts),  # ncvert includes the closing (repeated) vertex
                 tuple(verts),
             )
             cell2ddata.append(rec)
diff --git a/flopy4/mf6/netcdf.py b/flopy4/mf6/netcdf.py
index 5ea27049..5771776c 100644
--- a/flopy4/mf6/netcdf.py
+++ b/flopy4/mf6/netcdf.py
@@ -7,7 +7,6 @@
     BaseModel,
     ConfigDict,
     Field,
-    ValidationError,
     ValidationInfo,
     field_validator,
 )
@@ -113,15 +112,12 @@ def model_post_init(self, __context) -> None:
 
     @classmethod
     def from_dict(cls, meta, context=None):
-        try:
-            if context:
-                context = lower(context)
-            _meta = NetCDFModel._backfill_meta(meta, context)
-            inst = cls.model_validate(_meta, context=context)
-            inst._context |= context if context is not None else inst._context
-            return inst
-        except ValidationError:
-            raise
+        if context:
+            context = lower(context)
+        _meta = NetCDFModel._backfill_meta(meta, context)
+        inst = cls.model_validate(_meta, context=context)
+        inst._context |= context if context is not None else inst._context
+        return inst
 
     @classmethod
     def from_model(
@@ -131,8 +127,10 @@ def from_model(
         grid: StructuredGrid | VertexGrid | None = None,
         time: Time | None = None,
     ):
-        assert hasattr(model, "name")
-        assert hasattr(model, "data")
+        if not hasattr(model, "name"):
+            raise ValueError("model must have a 'name' attribute")
+        if not hasattr(model, "data"):
+            raise ValueError("model must have a 'data' attribute")
 
         modeltype = model.__class__.__name__.lower()
         attrs = {"title": f"{model.name.upper()} model input"}
@@ -175,17 +173,22 @@ def from_model(
                 packages.append(p)
 
         dims = [
-            model.data.dims["nper"],
-            model.data.dims["nlay"],
+            model.data.dims["nper"],  # type: ignore
+            model.data.dims["nlay"],  # type: ignore
         ]
 
         if distype == "dis":
-            dims.append(model.data.dims["nrow"])
-            dims.append(model.data.dims["ncol"])
+            dims.append(model.data.dims["nrow"])  # type: ignore
+            dims.append(model.data.dims["ncol"])  # type: ignore
             gridtype = "structured"
         elif distype == "disv":
-            dims.append(model.data.dims["ncpl"])
+            dims.append(model.data.dims["ncpl"])  # type: ignore
             gridtype = "vertex"
+        else:
+            raise ValueError(
+                f"model has no supported discretization package (dis/disv); "
+                f"found distype={distype!r}"
+            )
 
         nc_model = NetCDFModel.from_dict(
             meta={
@@ -252,9 +255,7 @@ def grid(self):
     def grid(self, value):
         from flopy.discretization import StructuredGrid, VertexGrid
 
-        if (
-            not isinstance(value, StructuredGrid) and not isinstance(value, VertexGrid)
-        ) or not value:
+        if not isinstance(value, StructuredGrid) and not isinstance(value, VertexGrid):
             raise ValueError(f"invalid grid type: {type(value)}")
         self._grid = value
 
@@ -265,7 +266,7 @@ def time(self):
 
     @time.setter
     def time(self, value):
-        if not isinstance(value, Time) or not value:
+        if not isinstance(value, Time):
             raise ValueError("invalid Time type")
         self._time = value
 
@@ -318,9 +319,9 @@ def validate_mesh(cls, v: str | None, info: ValidationInfo) -> str | None:
         validate model mesh attribute
         """
         if v is not None:
-            v = v.lower()
-            if v != "layered":
+            if v.lower() != "layered":
                 raise ValueError("only LAYERED mesh supported")
+            v = "LAYERED"  # normalize to uppercase to match MODFLOW 6 NC output convention
             info.context["mesh"] = v  # type: ignore
         return v
 
@@ -334,15 +335,15 @@ def validate_modflow_grid(cls, v: str, info: ValidationInfo) -> str:
         dims = info.context.get("dims")  # type: ignore
         if v == "structured":
             if len(dims) != 4:
-                raise AssertionError(
+                raise ValueError(
                     "expected 4 input dimensions [time, nlay, nrow, ncol]"
-                    " for structured discretization: {dims}"
+                    f" for structured discretization: {dims}"
                 )
         elif v == "vertex":
             if len(dims) != 3:
-                raise AssertionError(
+                raise ValueError(
                     "expected 3 input dimensions [time, nlay, ncpl]"
-                    " for vertex discretization: {dims}"
+                    f" for vertex discretization: {dims}"
                 )
         info.context["gridtype"] = v  # type: ignore
         return v
@@ -381,24 +382,21 @@ def model_post_init(self, __context) -> None:
 
     @classmethod
     def from_dict(cls, meta, context):
-        try:
-            if context:
-                context = lower(context)
-            _meta = NetCDFPackage._backfill_meta(meta, context)
-            inst = cls.model_validate(_meta, context=context)
-            inst._context |= context if context is not None else inst._context
-            return inst
-        except ValidationError:
-            raise
+        if context:
+            context = lower(context)
+        _meta = NetCDFPackage._backfill_meta(meta, context)
+        inst = cls.model_validate(_meta, context=context)
+        inst._context |= context if context is not None else inst._context
+        return inst
 
     def to_xarray(self) -> xr.Dataset:
-        ds = []
+        dss = []
         for p in self.params:
             if "grid" in self._context:
                 p._context["grid"] = self._context["grid"]
-            ds.append(p.to_xarray())
+            dss.append(p.to_xarray())
 
-        return xr.merge(ds)
+        return xr.merge(dss)
 
     def to_netcdf(self, path: str | PathLike) -> None:
         self.to_xarray().to_netcdf(path)
@@ -446,9 +444,7 @@ def _backfill_meta(meta: dict, context: dict, verbose: bool = True) -> dict:
         _meta = dict(meta)
 
         if "package_name" not in _meta or "package_type" not in _meta:
-            raise AssertionError(
-                "package missing required package_name or package_type attribute(s)."
-            )
+            raise ValueError("package missing required package_name or package_type attribute(s).")
 
         auxiliary = _meta.get("auxiliary", None)
 
@@ -476,7 +472,7 @@ def _add_layered_param(p):
                 p["attrs"] = {}
 
             if p["name"].lower() == "aux" and (auxiliary is None or len(auxiliary) == 0):
-                raise AssertionError("AUX parameter requires auxiliary list input.")
+                raise ValueError("AUX parameter requires auxiliary list input.")
 
             shape = spec.arrays[p["name"]].dims
             assert shape is not None
@@ -521,28 +517,24 @@ def model_post_init(self, __context) -> None:
 
     @classmethod
     def from_dict(cls, meta, context):
-        try:
-            if context:
-                context = lower(context)
-                # TODO: verify required context
-                if (
-                    "modelname" not in context
-                    or "package_name" not in context
-                    or "package_type" not in context
-                    or "gridtype" not in context
-                    or "dims" not in context
-                ):
-                    raise AssertionError(
-                        "NetCDFParam incomplete context: modelanme, package_name, "
-                        "package_type, gridtype and dims are required."
-                    )
-                context["dimmap"] = dimmap(context["gridtype"], context["dims"])
-            _meta = NetCDFParam._backfill_meta(meta, context)
-            inst = cls.model_validate(_meta, context=context)
-            inst._context |= context if context is not None else inst._context
-            return inst
-        except ValidationError:
-            raise
+        if context:
+            context = lower(context)
+            if (
+                "modelname" not in context
+                or "package_name" not in context
+                or "package_type" not in context
+                or "gridtype" not in context
+                or "dims" not in context
+            ):
+                raise ValueError(
+                    "NetCDFParam incomplete context: modelname, package_name, "
+                    "package_type, gridtype and dims are required."
+                )
+            context["dimmap"] = dimmap(context["gridtype"], context["dims"])
+        _meta = NetCDFParam._backfill_meta(meta, context)
+        inst = cls.model_validate(_meta, context=context)
+        inst._context |= context if context is not None else inst._context
+        return inst
 
     def to_xarray(self) -> xr.Dataset:
         meta = self.model_dump(by_alias=True)
@@ -651,7 +643,7 @@ def validate_shape(cls, v: list[str]) -> list[str]:
         v = [dim.lower() if isinstance(dim, str) else dim for dim in v]
         valid = ["time", "nmesh_face", "z", "y", "x"]
         if not all(dim in valid for dim in v):
-            raise AssertionError(f"invalid param shape={v}. Valid dims={valid}.")
+            raise ValueError(f"invalid param shape={v}. Valid dims={valid}.")
         return v
 
     @field_validator("attrs", mode="before")
@@ -787,7 +779,7 @@ def _mesh_shape(dfn_shape):
                 layer = _meta["attrs"]["layer"] - 1
                 if data.size == nval * context["dimmap"]["z"]:
                     # provided data is for full grid
-                    s = dims
+                    s = list(dims)  # copy to avoid mutating dims in-place
                     if "nodes" in spec.arrays[param].dims:  # type: ignore
                         if _meta["shape"][0] == "time":
                             s.insert(1, context["dimmap"]["z"])
diff --git a/flopy4/mf6/utils/cbc_reader.py b/flopy4/mf6/utils/cbc_reader.py
index aac33acb..e1e52ee1 100644
--- a/flopy4/mf6/utils/cbc_reader.py
+++ b/flopy4/mf6/utils/cbc_reader.py
@@ -7,13 +7,14 @@
 import dask
 import dask.array
 import numpy as np
-import pandas as pd
 import xarray as xr
+import xugrid as xu
 from attrs import define
 from flopy.discretization import StructuredGrid
 
-from flopy4.adapters import StructuredGridWrapper
+from flopy4.adapters import read_binary_grid_file
 from flopy4.mf6.utils.grid import get_coords
+from flopy4.mf6.utils.time import assign_datetime_coords
 
 
 @define
@@ -53,13 +54,17 @@ class Imeth6Header:
     nlist: int
 
 
+XUGRID_FILL_VALUE = -1
+IDOMAIN_ACTIVE = 1
+
+
 def open_cbc(
     cbc_path: Path,
     grb_path: Path,
     flowja: bool = False,
     simulation_start_time: np.datetime64 | None = None,
     time_unit: str | None = "d",
-) -> xr.Dataset:
+) -> xr.Dataset | xu.UgridDataset:
     """
     Open modflow6 cell-by-cell (.cbc) file.
 
@@ -75,6 +80,7 @@ def open_cbc(
 
         * DIS: in right, front, and lower face flow. All flows are placed in
           the cell.
+        * DISV: in horizontal, horizontal-x, horizontal-y, and lower face flow.
 
     When ``flowja=True``, the flow-ja-face array is returned as it is found in
     the CBC file, with a flow for every cell to cell connection. Additionally,
@@ -113,7 +119,8 @@ def open_cbc(
     -------
     cbc_content: xr.Dataset | Dict[str, xr.DataArray]
         DataArray contains float64 data of the budgets,
-        with dimensions ("time", "layer", "y", "x").
+        with dimensions ("time", "layer", "y", "x") for DIS
+        or ("time", "layer", face_dimension) for DISV.
 
     Examples
     --------
@@ -129,13 +136,33 @@ def open_cbc(
 
     Get the drainage budget, compute a time mean for the first layer:
 
-    >>> drn_budget = cbc_content["drn]
+    >>> drn_budget = cbc_content["drn"]
     >>> mean = drn_budget.sel(layer=1).mean("time")
 
     """
-    grid = StructuredGridWrapper.from_binary_grid_file(grb_path)
-    cbc = _open_cbc_dis(cbc_path, grid, flowja, simulation_start_time, time_unit)
-    return xr.merge([cbc])
+    grb_info = read_binary_grid_file(grb_path)
+
+    if grb_info["grid_type"] == "DIS":
+        cbc = _open_cbc_dis(cbc_path, grb_info["grid"], flowja, simulation_start_time, time_unit)
+        return xr.merge([cbc])
+    elif grb_info["grid_type"] == "DISV":
+        cbc = _open_cbc_disv(cbc_path, grb_info, flowja, simulation_start_time, time_unit)
+        # Build xr.Dataset from dict, extracting underlying xr.DataArrays
+        # and assigning proper names
+        ds_vars = {}
+        for key, val in cbc.items():
+            if isinstance(val, xu.UgridDataArray):
+                da = val.obj
+                da.name = key
+                ds_vars[key] = da
+            else:
+                if val.name is None:
+                    val.name = key
+                ds_vars[key] = val
+        ds = xr.Dataset(ds_vars)
+        return xu.UgridDataset(ds, grids=grb_info["grid"])
+    else:
+        raise ValueError(f"Unsupported grid type: {grb_info['grid_type']}")
 
 
 def _open_cbc_dis(
@@ -167,7 +194,8 @@ def _open_cbc_dis(
         # TODO: validate homogeneity of header_list, ndat consistent,
         # nlist consistent etc.
         if key == "flow-ja-face" and isinstance(header_list[0], Imeth1Header):
-            assert all(isinstance(x, Imeth1Header) for x in header_list)
+            if not all(isinstance(x, Imeth1Header) for x in header_list):
+                raise TypeError(f"Mixed header types for key {key!r}")
             if flowja:
                 flowjaface, nm = open_face_budgets_as_flowja(
                     cbc_path, cast(list[Imeth1Header], header_list), grid
@@ -185,12 +213,14 @@ def _open_cbc_dis(
                 cbc_content["flow-lower-face"] = lower
         else:
             if isinstance(header_list[0], Imeth1Header):
-                assert all(isinstance(x, Imeth1Header) for x in header_list)
+                if not all(isinstance(x, Imeth1Header) for x in header_list):
+                    raise TypeError(f"Mixed header types for key {key!r}")
                 cbc_content[key] = open_imeth1_budgets(
                     cbc_path, grid, cast(list[Imeth1Header], header_list)
                 )
             elif isinstance(header_list[0], Imeth6Header):
-                assert all(isinstance(x, Imeth6Header) for x in header_list)
+                if not all(isinstance(x, Imeth6Header) for x in header_list):
+                    raise TypeError(f"Mixed header types for key {key!r}")
 
                 # for non cell flow budget terms,
                 # use auxiliary variables as return value
@@ -220,6 +250,89 @@ def _open_cbc_dis(
     return cbc_content
 
 
+def _open_cbc_disv(
+    cbc_path: Path,
+    grb_info: dict[str, Any],
+    flowja: bool = False,
+    simulation_start_time: np.datetime64 | None = None,
+    time_unit: str | None = "d",
+) -> dict[str, xu.UgridDataArray | xr.DataArray]:
+    headers = read_cbc_headers(cbc_path)
+    indices = None
+    header_advanced_package = get_first_header_advanced_package(headers)
+    if header_advanced_package is not None:
+        dtype = np.dtype(
+            [("id1", np.int32), ("id2", np.int32), ("budget", np.float64)]
+            + [(name, np.float64) for name in header_advanced_package.auxtxt]
+        )
+        table = read_imeth6_budgets(
+            cbc_path,
+            header_advanced_package.nlist,
+            dtype,
+            header_advanced_package.pos,
+        )
+        indices = table["id2"] - 1
+
+    cbc_content: dict[str, xu.UgridDataArray | xr.DataArray] = {}
+    for key, header_list in headers.items():
+        if key == "flow-ja-face" and isinstance(header_list[0], Imeth1Header):
+            if not all(isinstance(x, Imeth1Header) for x in header_list):
+                raise TypeError(f"Mixed header types for key {key!r}")
+            if flowja:
+                flowjaface, nm = open_face_budgets_as_flowja(
+                    cbc_path, cast(list[Imeth1Header], header_list), grb_info
+                )
+                cbc_content["flow-ja-face"] = flowjaface
+                cbc_content["connectivity"] = nm
+            else:
+                horizontal, flow_x, flow_y, lower = disv_open_face_budgets(
+                    cbc_path,
+                    grb_info,
+                    cast(list[Imeth1Header], header_list),
+                )
+                cbc_content["flow-horizontal-face"] = horizontal
+                cbc_content["flow-horizontal-face-x"] = flow_x
+                cbc_content["flow-horizontal-face-y"] = flow_y
+                cbc_content["flow-lower-face"] = lower
+        elif isinstance(header_list[0], Imeth1Header):
+            if not all(isinstance(x, Imeth1Header) for x in header_list):
+                raise TypeError(f"Mixed header types for key {key!r}")
+            cbc_content[key] = disv_open_imeth1_budgets(
+                cbc_path, grb_info, cast(list[Imeth1Header], header_list)
+            )
+        elif isinstance(header_list[0], Imeth6Header):
+            if not all(isinstance(x, Imeth6Header) for x in header_list):
+                raise TypeError(f"Mixed header types for key {key!r}")
+            if header_list[0].text.startswith("data-"):
+                for return_variable in header_list[0].auxtxt:
+                    key_aux = f"{header_list[0].txt2id1}-{return_variable}"
+                    cbc_content[key_aux] = disv_open_imeth6_budgets(
+                        cbc_path,
+                        grb_info,
+                        cast(list[Imeth6Header], header_list),
+                        return_variable,
+                        indices=indices,
+                    )
+            else:
+                cbc_content[key] = disv_open_imeth6_budgets(
+                    cbc_path,
+                    grb_info,
+                    cast(list[Imeth6Header], header_list),
+                    indices=indices,
+                )
+
+    if simulation_start_time is not None:
+        for cbc_name, cbc_array in cbc_content.items():
+            cbc_content[cbc_name] = assign_datetime_coords(
+                cbc_array, simulation_start_time, time_unit
+            )
+
+    return cbc_content
+
+
+# grid-independent functions
+
+
 def get_first_header_advanced_package(
     headers: dict[str, list[Any]],
 ) -> Any:
@@ -326,16 +439,90 @@ def read_imeth6_header(f: BinaryIO) -> dict[str, Any]:
     return content
 
 
-def assign_datetime_coords(
-    da: xr.DataArray,
-    simulation_start_time: np.datetime64,
-    time_unit: str | None = "d",
-) -> xr.DataArray:
-    if "time" not in da.coords:
-        raise ValueError("cannot convert time column, because a time column could not be found")
+# imeth=6 budget reading (grid-independent core)
+
+
+def read_imeth6_budgets(cbc_path: Path, count: int, dtype: np.dtype, pos: int) -> Any:
+    """
+    Read the data for an imeth==6 budget section for a single timestep.
+
+    Returns a numpy structured array containing:
+    * id1: the model cell number
+    * id2: the boundary condition index
+    * budget: the budget terms
+    * and assorted auxiliary columns, if present
+    """
+    with open(cbc_path, "rb") as f:
+        f.seek(pos)
+        table = np.fromfile(f, dtype, count)
+    return table
+
+
+def read_imeth6_budgets_dense(
+    cbc_path: Path,
+    count: int,
+    dtype: np.dtype,
+    pos: int,
+    size: int,
+    shape: tuple,
+    return_variable: str,
+    indices: np.ndarray | None,
+) -> np.ndarray:
+    """
+    Read the data for an imeth==6 budget section.
+
+    Allocates a dense array for the entire domain and fills in values
+    from the sparse budget data.
+    """
+    out = np.full(size, np.nan, dtype=np.float64)
+    table = read_imeth6_budgets(cbc_path, count, dtype, pos)
+    if indices is None:
+        indices = table["id1"] - 1  # Convert to 0 based index
+    out[indices] = 0
+    np.add.at(out, indices, table[return_variable])
+    return out.reshape(shape)
+
 
-    time = pd.Timestamp(simulation_start_time) + pd.to_timedelta(da["time"], unit=time_unit)
-    return da.assign_coords(time=time)
+# imeth=1 budget reading (grid-independent core)
+
+
+def read_imeth1_budgets(cbc_path: Path, count: int, pos: int) -> np.ndarray:
+    """
+    Read the data for an imeth=1 budget section.
+    """
+    with open(cbc_path, "rb") as f:
+        f.seek(pos)
+        timestep_budgets = np.fromfile(f, np.float64, count)
+    return timestep_budgets
+
+
+def cbc_open_imeth1_budgets(cbc_path: Path, header_list: list[Imeth1Header]) -> xr.DataArray:
+    """
+    Open the data for an imeth==1 budget section. Data is read lazily per
+    timestep. The cell data is not spatially labelled.
+
+    Returns
+    -------
+    xr.DataArray with dims ("time", "linear_index")
+    """
+    dask_list = []
+    time = np.empty(len(header_list), dtype=np.float64)
+    for i, header in enumerate(header_list):
+        time[i] = header.totim
+        count = header.ndim1 * header.ndim2 * header.ndim3 * -1
+        a = dask.delayed(read_imeth1_budgets)(cbc_path, count, header.pos)
+        x = dask.array.from_delayed(a, shape=(count,), dtype=np.float64)
+        dask_list.append(x)
+
+    return xr.DataArray(
+        data=dask.array.stack(dask_list, axis=0),
+        coords={"time": time},
+        dims=("time", "linear_index"),
+        name=header_list[0].text,
+    )
+
+
+# DIS-specific budget functions
 
 
 def open_imeth6_budgets(
@@ -346,26 +533,12 @@ def open_imeth6_budgets(
     indices: np.ndarray | None = None,
 ) -> xr.DataArray:
     """
-    Open the data for an imeth==6 budget section.
-
-    Uses the information of the DIS GRB file to create the properly sized dense
-    xr.DataArrays (which store the entire domain).
-    Doing so ignores the boundary condition internal index (id2) and any
-    present auxiliary columns.
-
-    Parameters
-    ----------
-    cbc_path: str, pathlib.Path
-    grid: StructuredGrid
-    header_list: List[Imeth1Header]
-    return_variable: str
-    return_id: np.ndarray | None
+    Open the data for an imeth==6 budget section (DIS).
 
     Returns
     -------
     xr.DataArray with dims ("time", "layer", "y", "x")
     """
-    # Allocates dense arrays for the entire model domain
     dtype = np.dtype(
         [("id1", np.int32), ("id2", np.int32), ("budget", np.float64)]
         + [(name, np.float64) for name in header_list[0].auxtxt]
@@ -396,118 +569,15 @@ def open_imeth6_budgets(
     return xr.DataArray(daskarr, coords, ("time", "layer", "y", "x"), name=name)
 
 
-def read_imeth6_budgets_dense(
-    cbc_path: Path,
-    count: int,
-    dtype: np.dtype,
-    pos: int,
-    size: int,
-    shape: tuple,
-    return_variable: str,
-    indices: np.ndarray | None,
-) -> np.ndarray:
-    """
-    Read the data for an imeth==6 budget section.
-
-    Utilizes the shape information from the DIS GRB file to create a
-    dense numpy array. Always allocates for the entire domain
-    (all layers, rows, columns).
-
-    Parameters
-    ----------
-    cbc_path: str, pathlib.Path
-    count: int
-        number of values to read
-    dtype: numpy dtype
-        Data type of the structured array. Contains at least "id1", "id2",
-        and "budget".
-        Optionally contains auxiliary columns.
-    pos: int
-        position in the file where the data for a timestep starts
-    size: int
-        size of the entire model domain
-    shape: tuple[int, int, int]
-        Shape (nlayer, nrow, ncolumn) of entire model domain.
-    return_variable: str
-        variable name to return from budget table
-    indices: np.ndarray | None
-        optional array that contains the indices to map return_variable
-        to model topology
-
-    Returns
-    -------
-    Three-dimensional array of floats
-    """
-    # Allocates a dense array for the entire domain
-    out = np.full(size, np.nan, dtype=np.float64)
-    table = read_imeth6_budgets(cbc_path, count, dtype, pos)
-    if indices is None:
-        indices = table["id1"] - 1  # Convert to 0 based index
-    # Zero the relevant values, overwrite the NaN value.
-    out[indices] = 0
-    # Sum all the budget terms.
-    np.add.at(out, indices, table[return_variable])
-    return out.reshape(shape)
-
-
-def read_imeth6_budgets(cbc_path: Path, count: int, dtype: np.dtype, pos: int) -> Any:
-    """
-    Read the data for an imeth==6 budget section for a single timestep.
-
-    Returns a numpy structured array containing:
-    * id1: the model cell number
-    * id2: the boundary condition index
-    * budget: the budget terms
-    * and assorted auxiliary columns, if present
-
-    Parameters
-    ----------
-    cbc_path: str, pathlib.Path
-    count: int
-        number of values to read
-    dtype: numpy dtype
-        Data type of the structured array. Contains at least "id1", "id2",
-        and "budget".
-        Optionally contains auxiliary columns.
-    pos:
-        position in the file where the data for a timestep starts
-
-    Returns
-    -------
-    Numpy structured array of type dtype
-    """
-    with open(cbc_path, "rb") as f:
-        f.seek(pos)
-        table = np.fromfile(f, dtype, count)
-    return table
-
-
 def open_imeth1_budgets(
     cbc_path: Path,
     grid: StructuredGrid,
     header_list: list[Imeth1Header],
 ) -> xr.DataArray:
     """
-    Open the data for an imeth==1 budget section. Data is read lazily per
+    Open the data for an imeth==1 budget section (DIS). Data is read lazily per
     timestep.
 
-    Can be used for:
-
-        * STO-SS
-        * STO-SY
-        * CSUB-CGELASTIC
-        * CSUB-WATERCOMP
-
-    Utilizes the shape information from the DIS GRB file to create a dense
-    array; (lazily) allocates for the entire domain (all layers, rows, columns)
-    per timestep.
-
-    Parameters
-    ----------
-    cbc_path: str, pathlib.Path
-    grid: StructuredGrid
-    header_list: List[Imeth1Header]
-
     Returns
     -------
     xr.DataArray with dims ("time", "layer", "y", "x")
@@ -516,7 +586,6 @@ def open_imeth1_budgets(
     nrow = grid.nrow
     ncol = grid.ncol
     budgets = cbc_open_imeth1_budgets(cbc_path, header_list)
-    # Merge dictionaries
     coords = get_coords(grid) | {"time": budgets["time"]}
 
     return xr.DataArray(
@@ -527,60 +596,6 @@ def open_imeth1_budgets(
     )
 
 
-def cbc_open_imeth1_budgets(cbc_path: Path, header_list: list[Imeth1Header]) -> xr.DataArray:
-    """
-    Open the data for an imeth==1 budget section. Data is read lazily per
-    timestep. The cell data is not spatially labelled.
-
-    Parameters
-    ----------
-    cbc_path: str, pathlib.Path
-    header_list: List[Imeth1Header]
-
-    Returns
-    -------
-    xr.DataArray with dims ("time", "linear_index")
-    """
-    # Gather times from the headers
-    dask_list = []
-    time = np.empty(len(header_list), dtype=np.float64)
-    for i, header in enumerate(header_list):
-        time[i] = header.totim
-        count = header.ndim1 * header.ndim2 * header.ndim3 * -1
-        a = dask.delayed(read_imeth1_budgets)(cbc_path, count, header.pos)
-        x = dask.array.from_delayed(a, shape=(count,), dtype=np.float64)
-        dask_list.append(x)
-
-    return xr.DataArray(
-        data=dask.array.stack(dask_list, axis=0),
-        coords={"time": time},
-        dims=("time", "linear_index"),
-        name=header_list[0].text,
-    )
-
-
-def read_imeth1_budgets(cbc_path: Path, count: int, pos: int) -> np.ndarray:
-    """
-    Read the data for an imeth=1 budget section.
-
-    Parameters
-    ----------
-    cbc_path: str, pathlib.Path
-    count: int
-        number of values to read
-    pos:
-        position in the file where the data for a timestep starts
-
-    Returns
-    -------
-    1-D array of floats
-    """
-    with open(cbc_path, "rb") as f:
-        f.seek(pos)
-        timestep_budgets = np.fromfile(f, np.float64, count)
-    return timestep_budgets
-
-
 def dis_open_face_budgets(
     cbc_path: Path,
     grid: StructuredGrid,
@@ -589,12 +604,6 @@ def dis_open_face_budgets(
     """
     Open the flow-ja-face, and extract right, front, and lower face flows.
 
-    Parameters
-    ----------
-    cbc_path: str, pathlib.Path
-    grid: StructuredGrid
-    header_list: List[Imeth1Header]
-
     Returns
     -------
     right: xr.DataArray of floats with dims ("time", "layer", "y", "x")
@@ -612,27 +621,9 @@ def dis_open_face_budgets(
 def dis_extract_face_budgets(budgets: xr.DataArray, index: xr.DataArray) -> xr.DataArray:
     """
     Grab right, front, or lower face flows from the flow-ja-face array.
-
-    This could be done by a single .isel() indexing operation, but those
-    are extremely slow in this case, which seems to be an xarray issue.
-
-    Parameters
-    ----------
-    budgets: xr.DataArray of floats
-        flow-ja-face array, dims ("time", "linear_index")
-        The linear index enumerates cell-to-cell connections in this case, not
-        the individual cells.
-    index: xr.DataArray of ints
-        right, front, or lower index array with dims("layer", "y", "x")
-
-    Returns
-    -------
-    xr.DataArray of floats with dims ("time", "layer", "y", "x")
     """
     coords = dict(index.coords)
     coords["time"] = budgets["time"]
-    # isel with a 3D array is extremely slow
-    # this followed by the dask reshape is much faster for some reason.
     data = budgets.isel(linear_index=index.values.ravel()).data
     da = xr.DataArray(
         data=data.reshape((budgets["time"].size, *index.shape)),
@@ -649,16 +640,6 @@ def dis_to_right_front_lower_indices(
     """
     Infer the indices to extract right, front, and lower face flows from the
     flow-ja-face array.
-
-    Parameters
-    ----------
-    grid: StructuredGrid
-
-    Returns
-    -------
-    right: xr.DataArray of ints with dims ("layer", "y", "x")
-    front: xr.DataArray of ints with dims ("layer", "y", "x")
-    lower: xr.DataArray of ints with dims ("layer", "y", "x")
     """
     right, front, lower = dis_indices(grid)
     coords = get_coords(grid)
@@ -677,22 +658,11 @@ def dis_indices(
     that can be used for extracting right, front, and lower face flow from the
     flow-ja-face array.
 
-    In a structured grid, using a linear index:
-    * the right neighbor is +(1)
-    * the front neighbor is +(number of cells in a column)
-    * the lower neighbor is +(number of cells in a layer)
-    * lower "vertical passthrough" cells (idomain <0) are multitude of (number
-      of cells in a layer)
-
-    Parameters
-    ----------
-    grid: StructuredGrid
-
-    Returns
-    -------
-    right: 3D array of ints
-    front: 3D array of ints
-    lower: 3D array of ints
+    ``grid.ia`` and ``grid.ja`` must be 0-based CSR arrays (as returned by
+    :class:`~flopy4.adapters.StructuredGridWrapper`, which reads them from the
+    GRB file via ``MfGrdFile`` — which converts Fortran 1-based indices to
+    0-based on read).  ``nzi`` is a direct index into ``ja``; no offset
+    adjustment is needed.
     """
     shape = (grid.nlay, grid.nrow, grid.ncol)
     ncells_per_layer = grid.nrow * grid.ncol
@@ -702,10 +672,11 @@ def dis_indices(
 
     for i in range(grid.nnodes):
         for nzi in range(grid.ia[i], grid.ia[i + 1]):
-            nzi -= 1  # python is 0-based, modflow6 is 1-based
-            j = grid.ja[nzi] - 1  # python is 0-based, modflow6 is 1-based
+            # ia/ja are 0-based: nzi is both the position in ja AND the
+            # position in the flat flow-ja-face budget array.
+            j = grid.ja[nzi]  # 0-based connected-cell index
             d = j - i
-            if d <= 0:  # left, back, upper
+            if d <= 0:  # self, left, back, or upper
                 continue
             elif d == 1 and grid.ncol > 1:  # right neighbor
                 right[i] = nzi
@@ -713,7 +684,7 @@ def dis_indices(
                 front[i] = nzi
             elif d == ncells_per_layer:  # lower neighbor
                 lower[i] = nzi
-            else:  # skips one: must be pass through
+            else:  # skips one or more layers: pass-through
                 npassed = int(d / ncells_per_layer)
                 for ipass in range(0, npassed):
                     lower[i + ipass * ncells_per_layer] = nzi
@@ -724,12 +695,24 @@ def dis_indices(
 def open_face_budgets_as_flowja(
     cbc_path: Path,
     header_list: list[Imeth1Header],
-    grid: StructuredGrid,
+    grid_or_info,
 ) -> tuple[xr.DataArray, xr.DataArray]:
+    """Return flow-ja-face as-is with connectivity. Works for both DIS and DISV."""
     flowja = cbc_open_imeth1_budgets(cbc_path, header_list)
     flowja = flowja.rename({"linear_index": "connection"})
-    n = expand_indptr(grid.ia)
-    m = grid.ja - 1
+
+    if isinstance(grid_or_info, dict):
+        # DISV grb_info dict
+        ia = grid_or_info["ia"]
+        ja = grid_or_info["ja"]
+    else:
+        # DIS StructuredGrid with ia/ja properties
+        ia = grid_or_info.ia
+        ja = grid_or_info.ja
+
+    # ia and ja are 0-based in both DIS and DISV (MfGrdFile converts on read).
+    n = expand_indptr(ia)
+    m = ja  # 0-based connected-cell indices
     nm = xr.DataArray(
         np.column_stack([n, m]),
         coords={"cell": ["n", "m"]},
@@ -741,3 +724,252 @@ def open_face_budgets_as_flowja(
 def expand_indptr(ia) -> np.ndarray:
     n = np.diff(ia)
     return np.repeat(np.arange(ia.size - 1), n)
+
+
+# DISV-specific budget functions
+
+
+def disv_open_imeth1_budgets(
+    cbc_path: Path,
+    grb_info: dict[str, Any],
+    header_list: list[Imeth1Header],
+) -> xu.UgridDataArray:
+    """
+    Open the data for an imeth==1 budget section (DISV).
+
+    Returns
+    -------
+    xu.UgridDataArray with dims ("time", "layer", face_dimension)
+    """
+    grid = grb_info["grid"]
+    facedim = grb_info["face_dimension"]
+    nlayer = grb_info["nlayer"]
+    ncells_per_layer = grb_info["ncells_per_layer"]
+    budgets = cbc_open_imeth1_budgets(cbc_path, header_list)
+    coords = grb_info["coords"].copy() | {"time": budgets["time"]}
+
+    da = xr.DataArray(
+        data=budgets.data.reshape((budgets["time"].size, nlayer, ncells_per_layer)),
+        coords=coords,
+        dims=("time", "layer", facedim),
+        name=budgets.name,
+    )
+    return xu.UgridDataArray(da, grid)
+
+
+def disv_open_imeth6_budgets(
+    cbc_path: Path,
+    grb_info: dict[str, Any],
+    header_list: list[Imeth6Header],
+    return_variable: str = "budget",
+    indices: np.ndarray | None = None,
+) -> xu.UgridDataArray:
+    """
+    Open the data for an imeth==6 budget section (DISV).
+
+    Returns
+    -------
+    xu.UgridDataArray with dims ("time", "layer", face_dimension)
+    """
+    dtype = np.dtype(
+        [("id1", np.int32), ("id2", np.int32), ("budget", np.float64)]
+        + [(name, np.float64) for name in header_list[0].auxtxt]
+    )
+    shape = (grb_info["nlayer"], grb_info["ncells_per_layer"])
+    size = np.prod(shape)
+    dask_list = []
+    time = np.empty(len(header_list), dtype=np.float64)
+    for i, header in enumerate(header_list):
+        time[i] = header.totim
+        a = dask.delayed(read_imeth6_budgets_dense)(
+            cbc_path,
+            header.nlist,
+            dtype,
+            header.pos,
+            size,
+            shape,
+            return_variable,
+            indices,
+        )
+        x = dask.array.from_delayed(a, shape=shape, dtype=np.float64)
+        dask_list.append(x)
+
+    daskarr = dask.array.stack(dask_list, axis=0)
+    coords = grb_info["coords"].copy()
+    coords["time"] = time
+    name = header_list[0].text
+    grid = grb_info["grid"]
+    da = xr.DataArray(daskarr, coords, ("time", "layer", grb_info["face_dimension"]), name=name)
+    return xu.UgridDataArray(da, grid)
+
+
+# DISV face-budget helpers
+
+
+def compute_flow_orientation(
+    edge_face_connectivity: np.ndarray, face_coordinates: np.ndarray
+) -> tuple[np.ndarray, np.ndarray]:
+    """Compute unit flow direction components (u, v) for each edge."""
+    nedge = len(edge_face_connectivity)
+    is_connection = edge_face_connectivity[:, 1] != XUGRID_FILL_VALUE
+    edge_faces = edge_face_connectivity[is_connection]
+    edge_faces.sort(axis=1)
+    u = np.full(nedge, np.nan)
+    v = np.full(nedge, np.nan)
+    xy = face_coordinates[edge_faces]
+    dx = xy[:, 1, 0] - xy[:, 0, 0]
+    dy = xy[:, 1, 1] - xy[:, 0, 1]
+    t = np.sqrt(dx**2 + dy**2)
+    u[is_connection] = dx / t
+    v[is_connection] = dy / t
+    return u, v
+
+
+def mf6_csr_to_coo(ia: np.ndarray, ja: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
+    """
+    Convert 0-based CSR arrays (ia, ja) into COO row/col arrays.
+
+    Both ``ia`` and ``ja`` are 0-based (as returned by
+    :class:`~flopy.mf6.utils.binarygrid_util.MfGrdFile`).
+    The returned ``i`` and ``j`` are also 0-based cell indices.
+    """
+    n = np.diff(ia)
+    i = np.repeat(np.arange(ia.size - 1), n)
+    j = ja  # already 0-based
+    return i, j
+
+
+def alt_cumsum(a):
+    """Alternative cumsum, start 0 and omit the last value."""
+    out = np.empty(a.size, a.dtype)
+    out[0] = 0
+    np.cumsum(a[:-1], out=out[1:])
+    return out
+
+
+def ragged_arange(n: np.ndarray) -> np.ndarray:
+    """Equal to: np.concatenate([np.arange(e) for e in n])"""
+    return alt_cumsum(np.ones(int(n.sum()), dtype=int)) - np.repeat(alt_cumsum(n), n)
+
+
+def disv_indices(
+    ia: np.ndarray,
+    ja: np.ndarray,
+    idomain: np.ndarray,
+    edge_face_connectivity: np.ndarray,
+) -> tuple[np.ndarray, np.ndarray]:
+    """
+    Infer lower and horizontal flow indices for DISV grids.
+
+    Returns
+    -------
+    lower: ndarray of shape (nlayer, ncells_per_layer)
+    horizontal: ndarray of shape (nlayer, nedge)
+    """
+    nlayer, ncells_per_layer = idomain.shape
+    nedge = len(edge_face_connectivity)
+    horizontal = np.full((nlayer, nedge), -1)
+    lower = np.full((nlayer, ncells_per_layer), -1)
+
+    i, j = mf6_csr_to_coo(ia, ja)
+    diff = j - i
+    is_vertical = diff >= ncells_per_layer
+    is_horizontal = (diff > 0) & (~is_vertical)
+    index = np.arange(j.size)
+
+    # Vertical flows
+    n_pass = diff[is_vertical] // ncells_per_layer
+    ii = np.repeat(i[is_vertical], n_pass) + ragged_arange(n_pass) * ncells_per_layer
+    lower.ravel()[ii] = np.repeat(index[is_vertical], n_pass)
+
+    # Horizontal flows
+    layered_edge_faces = np.add.outer(
+        np.arange(nlayer) * ncells_per_layer,
+        edge_face_connectivity,
+    )
+    is_active = idomain.ravel()[layered_edge_faces] == IDOMAIN_ACTIVE
+    is_inner_edge = edge_face_connectivity[:, 1] != XUGRID_FILL_VALUE
+    is_connection = (is_active.all(axis=2) & is_inner_edge[np.newaxis, :]).ravel()
+
+    i_to_j = layered_edge_faces.reshape((-1, 2))[is_connection]
+    order = np.argsort(np.lexsort(i_to_j.T[::-1]))
+
+    horizontal.ravel()[is_connection] = index[is_horizontal][order]
+    return lower, horizontal
+
+
+def disv_to_horizontal_lower_indices(
+    grb_info: dict[str, Any],
+) -> tuple[xr.DataArray, xr.DataArray, xr.DataArray, xr.DataArray]:
+    grid = grb_info["grid"]
+    lower, horizontal = disv_indices(
+        ia=grb_info["ia"],
+        ja=grb_info["ja"],
+        idomain=grb_info["idomain"],
+        edge_face_connectivity=grid.edge_face_connectivity,
+    )
+    u, v = compute_flow_orientation(grid.edge_face_connectivity, grid.face_coordinates)
+    return (
+        xr.DataArray(horizontal, grb_info["coords"], dims=["layer", grid.edge_dimension]),
+        xr.DataArray(u, dims=[grid.edge_dimension]),
+        xr.DataArray(v, dims=[grid.edge_dimension]),
+        xr.DataArray(lower, grb_info["coords"], dims=["layer", grid.face_dimension]),
+    )
+
+
+def disv_extract_lower_budget(budgets: xr.DataArray, index: xr.DataArray) -> xr.DataArray:
+    face_dimension = index.dims[-1]
+    coords = dict(index.coords)
+    coords["time"] = budgets["time"]
+    data = budgets.isel(linear_index=index.values.ravel()).data
+    da = xr.DataArray(
+        data=data.reshape((budgets["time"].size, *index.shape)),
+        coords=coords,
+        dims=("time", "layer", face_dimension),
+        name="flow-ja-face",
+    )
+    return da.where(index >= 0, other=0.0)
+
+
+def disv_extract_horizontal_budget(budgets: xr.DataArray, index: xr.DataArray) -> xr.DataArray:
+    """
+    Horizontal flows from the flow-ja-face array.
+    """
+    edge_dimension = index.dims[-1]
+    coords = dict(index.coords)
+    coords["time"] = budgets["time"]
+    data = budgets.isel(linear_index=index.values.ravel()).data
+    da = xr.DataArray(
+        data=data.reshape((budgets["time"].size, *index.shape)),
+        coords=coords,
+        dims=("time", "layer", edge_dimension),
+        name="flow-ja-face",
+    )
+    return da.where(index >= 0, other=0.0)
+
+
+def disv_open_face_budgets(
+    cbc_path: Path,
+    grb_info: dict[str, Any],
+    header_list: list[Imeth1Header],
+) -> tuple[xu.UgridDataArray, xu.UgridDataArray, xu.UgridDataArray, xu.UgridDataArray]:
+    """
+    Open the flow-ja-face and extract horizontal + lower face flows for DISV.
+
+    Returns
+    -------
+    horizontal, flow_x, flow_y, lower: xu.UgridDataArray
+    """
+    horizontal_index, u, v, lower_index = disv_to_horizontal_lower_indices(grb_info)
+    budgets = cbc_open_imeth1_budgets(cbc_path, header_list)
+    horizontal = disv_extract_horizontal_budget(budgets, horizontal_index)
+    lower = disv_extract_lower_budget(budgets, lower_index)
+    flow_x = -horizontal * u
+    flow_y = -horizontal * v
+    grid = grb_info["grid"]
+    return (
+        xu.UgridDataArray(horizontal, grid),
+        xu.UgridDataArray(flow_x, grid),
+        xu.UgridDataArray(flow_y, grid),
+        xu.UgridDataArray(lower, grid),
+    )
diff --git a/flopy4/mf6/utils/grid.py b/flopy4/mf6/utils/grid.py
index 3703e74e..8517c55d 100644
--- a/flopy4/mf6/utils/grid.py
+++ b/flopy4/mf6/utils/grid.py
@@ -4,6 +4,7 @@
 import numpy as np
 import sparse
 import xarray as xr
+import xugrid as xu
 from attrs import fields
 from flopy.discretization import StructuredGrid as LegacyStructuredGrid
 from flopy.discretization import VertexGrid as LegacyVertexGrid
@@ -118,6 +119,7 @@ def from_dis(cls, dis):
             length_units=dis.length_units,
             xoff=dis.xorigin,
             yoff=dis.yorigin,
+            crs=dis.crs,
             nlay=dis.nlay,
             nrow=dis.nrow,
             ncol=dis.ncol,
@@ -134,6 +136,14 @@ def __init__(self, *args, **kwargs):
         self._legacy = False
         if (units := kwargs.pop("length_units", None)) is not None:
             kwargs["lenuni"] = units.lower()
+        if (xoff := kwargs.pop("xoff", None)) is not None:
+            kwargs["xoff"] = xoff
+        else:
+            kwargs["xoff"] = 0.0
+        if (yoff := kwargs.pop("yoff", None)) is not None:
+            kwargs["yoff"] = yoff
+        else:
+            kwargs["yoff"] = 0.0
         if (top := kwargs.get("top", None)) is not None and isinstance(top, Scalar):
             nrow = kwargs.get("nrow", 1)
             ncol = kwargs.get("ncol", 1)
@@ -146,13 +156,19 @@ def __init__(self, *args, **kwargs):
                 ncol = kwargs.get("ncol", 1)
                 kwargs["botm"] = np.array([np.full((nrow, ncol), float(b)) for b in botm])
 
-        if (delr := kwargs.get("delr", None)) is not None and isinstance(delr, Scalar):
-            ncol = kwargs.get("ncol", 1)
-            kwargs["delr"] = np.full(ncol, float(delr))
+        if (delr := kwargs.get("delr", None)) is not None:
+            if isinstance(delr, Scalar):
+                ncol = kwargs.get("ncol", 1)
+                kwargs["delr"] = np.full(ncol, float(delr))
+            else:
+                kwargs["delr"] = np.asarray(delr)
 
-        if (delc := kwargs.get("delc", None)) is not None and isinstance(delc, Scalar):
-            nrow = kwargs.get("nrow", 1)
-            kwargs["delc"] = np.full(nrow, float(delc))
+        if (delc := kwargs.get("delc", None)) is not None:
+            if isinstance(delc, Scalar):
+                nrow = kwargs.get("nrow", 1)
+                kwargs["delc"] = np.full(nrow, float(delc))
+            else:
+                kwargs["delc"] = np.asarray(delc)
 
         super().__init__(*args, **kwargs)
         self._dims_coords = {
@@ -471,26 +487,33 @@ def to_xarray(self, modeltime=None, mesh_type=None, configuration=None):
                valid mesh types are "layered" or None (i.e. "structured")
         configuration : configuration dictionary
         """
-        self.legacy = True
-
         if modeltime is None:
             raise ValueError("modeltime required for dataset timeseries")
 
-        ds = xr.Dataset()
-        ds.attrs["modflow_grid"] = "STRUCTURED"
+        self.legacy = True
+        try:
+            ds = xr.Dataset()
+            ds.attrs["modflow_grid"] = "STRUCTURED"
 
-        if mesh_type and mesh_type.upper() == "LAYERED":
-            ds = self._layered_mesh_dataset(ds, modeltime, configuration)
-        elif mesh_type is None:
-            ds = self._structured_dataset(ds, modeltime, configuration)
+            if mesh_type and mesh_type.upper() == "LAYERED":
+                ds = self._layered_mesh_dataset(ds, modeltime, configuration)
+            elif mesh_type is None:
+                ds = self._structured_dataset(ds, modeltime, configuration)
+            else:
+                raise ValueError(f"Unknown mesh_type {mesh_type!r}. Expected 'LAYERED' or None.")
 
-        self.legacy = False
-        return ds
+            return ds
+        finally:
+            self.legacy = False
 
     def _layered_mesh_dataset(self, ds, modeltime=None, configuration=None):
-        lenunits = {0: "u", 1: "ft", 2: "m", 3: "cm"}
+        lenunits = {0: "unknown", 1: "ft", 2: "m", 3: "cm"}
+
+        # All topology arrays computed once; self.legacy is already True here.
+        topo = self._topology()
 
         # create dataset coordinate vars
+        # Use cumulative per-period time (one value per stress period)
         var_d = {
             "time": (["time"], np.cumsum(modeltime.perlen)),
         }
@@ -513,72 +536,37 @@ def _layered_mesh_dataset(self, ds, modeltime=None, configuration=None):
 
         # mesh node x and y
         var_d = {
-            "mesh_node_x": (["nmesh_node"], self.verts[:, 0]),
-            "mesh_node_y": (["nmesh_node"], self.verts[:, 1]),
+            "mesh_node_x": (["nmesh_node"], topo["node_x"]),
+            "mesh_node_y": (["nmesh_node"], topo["node_y"]),
         }
         ds = ds.assign(var_d)
-        ds["mesh_node_x"].attrs["units"] = lenunits[self.lenuni]
+        ds["mesh_node_x"].attrs["units"] = lenunits.get(self.lenuni, "unknown")
         ds["mesh_node_x"].attrs["standard_name"] = "projection_x_coordinate"
         ds["mesh_node_x"].attrs["long_name"] = "Easting"
-        ds["mesh_node_y"].attrs["units"] = lenunits[self.lenuni]
+        ds["mesh_node_y"].attrs["units"] = lenunits.get(self.lenuni, "unknown")
         ds["mesh_node_y"].attrs["standard_name"] = "projection_y_coordinate"
         ds["mesh_node_y"].attrs["long_name"] = "Northing"
 
-        # mesh face x and y
-        x_bnds = []
-        x_verts = self.verts[:, 0].reshape(self.nrow + 1, self.ncol + 1)
-        for i in range(self.nrow):
-            if i + 1 > self.nrow:
-                break
-            for j in range(self.ncol):
-                if j + 1 <= self.ncol:
-                    bnd = []
-                    bnd.append(x_verts[i + 1][j])
-                    bnd.append(x_verts[i + 1][j + 1])
-                    bnd.append(x_verts[i][j + 1])
-                    bnd.append(x_verts[i][j])
-                    x_bnds.append(bnd)
-
-        y_bnds = []
-        y_verts = self.verts[:, 1].reshape(self.nrow + 1, self.ncol + 1)
-        for i in range(self.nrow):
-            if i + 1 > self.nrow:
-                break
-            for j in range(self.ncol):
-                if j + 1 <= self.ncol:
-                    bnd = []
-                    bnd.append(y_verts[i + 1][j])
-                    bnd.append(y_verts[i + 1][j + 1])
-                    bnd.append(y_verts[i][j + 1])
-                    bnd.append(y_verts[i][j])
-                    y_bnds.append(bnd)
-
+        # mesh face x, y and bounds
         var_d = {
-            "mesh_face_x": (["nmesh_face"], self.xcellcenters.flatten()),
-            "mesh_face_xbnds": (["nmesh_face", "max_nmesh_face_nodes"], x_bnds),
-            "mesh_face_y": (["nmesh_face"], self.ycellcenters.flatten()),
-            "mesh_face_ybnds": (["nmesh_face", "max_nmesh_face_nodes"], y_bnds),
+            "mesh_face_x": (["nmesh_face"], topo["face_x"]),
+            "mesh_face_xbnds": (["nmesh_face", "max_nmesh_face_nodes"], topo["x_bnds"]),
+            "mesh_face_y": (["nmesh_face"], topo["face_y"]),
+            "mesh_face_ybnds": (["nmesh_face", "max_nmesh_face_nodes"], topo["y_bnds"]),
         }
         ds = ds.assign(var_d)
-        ds["mesh_face_x"].attrs["units"] = lenunits[self.lenuni]
+        ds["mesh_face_x"].attrs["units"] = lenunits.get(self.lenuni, "unknown")
         ds["mesh_face_x"].attrs["standard_name"] = "projection_x_coordinate"
         ds["mesh_face_x"].attrs["long_name"] = "Easting"
         ds["mesh_face_x"].attrs["bounds"] = "mesh_face_xbnds"
-        ds["mesh_face_y"].attrs["units"] = lenunits[self.lenuni]
+        ds["mesh_face_y"].attrs["units"] = lenunits.get(self.lenuni, "unknown")
         ds["mesh_face_y"].attrs["standard_name"] = "projection_y_coordinate"
         ds["mesh_face_y"].attrs["long_name"] = "Northing"
         ds["mesh_face_y"].attrs["bounds"] = "mesh_face_ybnds"
 
         # mesh face nodes
-        max_face_nodes = 4
-        face_nodes = []
-        for r in self.iverts:
-            nodes = [np.int64(x + 1) for x in r]
-            nodes.reverse()
-            face_nodes.append(nodes)
-
         var_d = {
-            "mesh_face_nodes": (["nmesh_face", "max_nmesh_face_nodes"], face_nodes),
+            "mesh_face_nodes": (["nmesh_face", "max_nmesh_face_nodes"], topo["face_nodes"]),
         }
         ds = ds.assign(var_d)
         ds["mesh_face_nodes"].attrs["cf_role"] = "face_node_connectivity"
@@ -587,15 +575,16 @@ def _layered_mesh_dataset(self, ds, modeltime=None, configuration=None):
         ds["mesh_face_nodes"].attrs["start_index"] = np.int64(1)
 
         # create grid index auxiliary coordinate variables
+        # k is on a new "z" dim (orphaned from nmesh_face) for layer indexing,
+        # consistent with VertexGrid. i/j are omitted here because the mesh
+        # uses nmesh_face as its spatial dim — not y/x — so row/col indices
+        # would create disconnected dimensions. Use icell2d on nmesh_face instead.
         ds = ds.assign_coords(k=("z", np.arange(self.nlay, dtype=int)))
-        ds = ds.assign_coords(i=("y", np.arange(self.nrow, dtype=int)))
-        ds = ds.assign_coords(j=("x", np.arange(self.ncol, dtype=int)))
+        ds = ds.assign_coords(icell2d=("nmesh_face", np.arange(self.nrow * self.ncol, dtype=int)))
         ds = ds.set_xindex("k", PandasIndex)
-        ds = ds.set_xindex("i", PandasIndex)
-        ds = ds.set_xindex("j", PandasIndex)
+        ds = ds.set_xindex("icell2d", PandasIndex)
         ds["k"].attrs["long_name"] = "layer index auxiliary coordinate"
-        ds["i"].attrs["long_name"] = "row index auxiliary coordinate"
-        ds["j"].attrs["long_name"] = "column index auxiliary coordinate"
+        ds["icell2d"].attrs["long_name"] = "cell index auxiliary coordinate"
 
         wkt_configured = (
             configuration is not None
@@ -620,7 +609,9 @@ def _layered_mesh_dataset(self, ds, modeltime=None, configuration=None):
         return ds
 
     def _structured_dataset(self, ds, modeltime=None, configuration=None):
-        lenunits = {0: "u", 1: "ft", 2: "m", 3: "cm"}
+        # Produces a conventional CF structured dataset (x/y dimension coords,
+        # no UGRID mesh variable).
+        lenunits = {0: "unknown", 1: "ft", 2: "m", 3: "cm"}
 
         xc = self.xoffset + self.xycenters[0]
         yc = self.yoffset + self.xycenters[1]
@@ -646,6 +637,7 @@ def _structured_dataset(self, ds, modeltime=None, configuration=None):
                 y_bnds.append(bnd)
 
         # create dataset coordinate vars
+        # Use cumulative per-period time (one value per stress period)
         var_d = {
             "time": (["time"], np.cumsum(modeltime.perlen)),
             "y": (["y"], yc),
@@ -673,14 +665,14 @@ def _structured_dataset(self, ds, modeltime=None, configuration=None):
         ds["time"].attrs["axis"] = "T"
         ds["time"].attrs["standard_name"] = "time"
         ds["time"].attrs["long_name"] = "time"
-        ds["z"].attrs["units"] = "layer"
-        ds["z"].attrs["long_name"] = "layer number"
-        ds["y"].attrs["units"] = lenunits[self.lenuni]
+        ds["k"].attrs["units"] = "layer"
+        ds["k"].attrs["long_name"] = "layer number"
+        ds["y"].attrs["units"] = lenunits.get(self.lenuni, "unknown")
         ds["y"].attrs["axis"] = "Y"
         ds["y"].attrs["standard_name"] = "projection_y_coordinate"
         ds["y"].attrs["long_name"] = "Northing"
         ds["y"].attrs["bounds"] = "y_bnds"
-        ds["x"].attrs["units"] = lenunits[self.lenuni]
+        ds["x"].attrs["units"] = lenunits.get(self.lenuni, "unknown")
         ds["x"].attrs["axis"] = "X"
         ds["x"].attrs["standard_name"] = "projection_x_coordinate"
         ds["x"].attrs["long_name"] = "Easting"
@@ -730,6 +722,67 @@ def _structured_dataset(self, ds, modeltime=None, configuration=None):
 
         return ds
 
+    def _topology(self) -> dict:
+        """
+        Compute UGRID mesh topology arrays.
+
+        Returns node coordinates, face centroids, 1-based CCW face-node
+        connectivity, and face-vertex bound arrays.  Must be called while
+        ``self.legacy`` is ``True`` so that ``iverts`` and ``verts`` return
+        raw arrays rather than xarray-wrapped values.
+        """
+        node_x = self.verts[:, 0]
+        node_y = self.verts[:, 1]
+
+        # 1-based CCW face-node connectivity (structured cells always have 4 nodes)
+        face_nodes_list = []
+        for r in self.iverts:
+            nodes = [np.int64(x + 1) for x in r]
+            nodes.reverse()
+            face_nodes_list.append(nodes)
+        face_nodes = np.array(face_nodes_list, dtype=np.int64)  # (nfaces, 4)
+
+        # Face-vertex bounds: x/y coordinates of each face's nodes in CCW order.
+        # Vectorized indexing replaces the nested vertex loops.
+        x_bnds = node_x[face_nodes - 1]  # (nfaces, 4)
+        y_bnds = node_y[face_nodes - 1]  # (nfaces, 4)
+
+        return {
+            "node_x": node_x,
+            "node_y": node_y,
+            "face_x": self.xcellcenters.flatten(),
+            "face_y": self.ycellcenters.flatten(),
+            "face_nodes": face_nodes,
+            "x_bnds": x_bnds,
+            "y_bnds": y_bnds,
+        }
+
+    @property
+    def ugrid(self) -> xu.Ugrid2d:
+        """
+        Build a :class:`xugrid.Ugrid2d` mesh from this structured grid.
+
+        Returns
+        -------
+        xu.Ugrid2d
+            A 2-D unstructured grid object whose faces correspond to the
+            structured grid cells in row-major order.
+        """
+        self.legacy = True
+        try:
+            topo = self._topology()
+            return xu.Ugrid2d(
+                topo["node_x"],
+                topo["node_y"],
+                FILL_INT64,
+                topo["face_nodes"],
+                projected=True,
+                crs=self.crs,
+                start_index=1,
+            )
+        finally:
+            self.legacy = False
+
 
 class VertexGrid(LegacyVertexGrid):
     """
@@ -770,15 +823,18 @@ def from_dis(cls, dis, **kwargs):
         Examples
         --------
         >>> from flopy4.mf6.gwf.disv import Disv
-        >>> dis = Disv(nlay=3, ncpl=1, nvert=4, top=30.0, botm=[20.0, 10.0, 0.0]
+        >>> dis = Disv(nlay=3, ncpl=1, nvert=4, top=30.0, botm=[20.0, 10.0, 0.0],
         ...            iv=[0, 1, 2, 3], xv=[0.0, 0.0, 1.0, 1.0], yv=[0.0, 1.0, 1.0, 0.0],
         ...            cell2ddata=[Disv.Cell2dRecord(
         ...                 0, 0.50000000, 0.50000000, 5, (0, 1, 2, 3, 0)
-        ...            )]
+        ...            )])
         >>> grid = VertexGrid.from_dis(dis)
         """
         return cls(
             length_units=dis.length_units,
+            xoff=dis.xorigin,
+            yoff=dis.yorigin,
+            crs=dis.crs,
             nlay=dis.nlay,
             ncpl=dis.ncpl,
             top=dis.top,
@@ -796,27 +852,29 @@ def __init__(self, *args, **kwargs):
         self._legacy = False
         if (units := kwargs.pop("length_units", None)) is not None:
             kwargs["lenuni"] = units.lower()
+        if (xoff := kwargs.pop("xoff", None)) is not None:
+            kwargs["xoff"] = xoff
+        else:
+            kwargs["xoff"] = 0.0
+        if (yoff := kwargs.pop("yoff", None)) is not None:
+            kwargs["yoff"] = yoff
+        else:
+            kwargs["yoff"] = 0.0
         if (top := kwargs.get("top", None)) is not None and isinstance(top, Scalar):
             ncpl = kwargs.get("ncpl", None)
-            kwargs["top"] = np.full((ncpl), float(top))
+            kwargs["top"] = np.full((ncpl,), float(top))
 
         if (botm := kwargs.get("botm", None)) is not None:
             if isinstance(botm, (list, tuple)) and all(isinstance(b, Scalar) for b in botm):
                 nlay = kwargs.get("nlay", len(botm))
                 ncpl = kwargs.get("ncpl", None)
-                kwargs["botm"] = np.array([np.full((ncpl), float(b)) for b in botm])
+                kwargs["botm"] = np.array([np.full((ncpl,), float(b)) for b in botm])
 
         if "iv" in kwargs and "xv" in kwargs and "yv" in kwargs:
-            kwargs["vertices"] = []
-            for i in range(len(kwargs["iv"].values)):
-                vert = []
-                vert.append(kwargs["iv"].values[i])
-                vert.append(kwargs["xv"].values[i])
-                vert.append(kwargs["yv"].values[i])
-                kwargs["vertices"].append(vert)
-            kwargs.pop("iv")
-            kwargs.pop("xv")
-            kwargs.pop("yv")
+            iv = np.asarray(kwargs.pop("iv"))
+            xv = np.asarray(kwargs.pop("xv"))
+            yv = np.asarray(kwargs.pop("yv"))
+            kwargs["vertices"] = [[iv[i], xv[i], yv[i]] for i in range(len(iv))]
 
         super().__init__(*args, **kwargs)
         self._dims_coords = {
@@ -861,13 +919,16 @@ def _get_world_coords(self) -> dict:
         dict
             Dictionary with 'x', 'y', 'z' coordinate DataArrays
         """
-        # Access the parent class's properties directly
+        # Access the parent class's properties directly, guarding with
+        # try/finally so _legacy is always reset even if an exception occurs.
         self.legacy = True
-        xcellcenters = self.xcellcenters
-        ycellcenters = self.ycellcenters
-        legacy_top = self.top
-        legacy_botm = self.botm
-        self.legacy = False
+        try:
+            xcellcenters = self.xcellcenters
+            ycellcenters = self.ycellcenters
+            legacy_top = self.top
+            legacy_botm = self.botm
+        finally:
+            self.legacy = False
 
         # If spatial data is not provided, use default coordinates (indices)
         if xcellcenters is None:
@@ -890,8 +951,8 @@ def _get_world_coords(self) -> dict:
             # Ensure top and botm are proper arrays
             top_1d = np.atleast_1d(legacy_top)
             if top_1d.size == 1:
-                top_1d = np.full((self.ncpl), top_1d[0])
-            elif top_1d.shape != (self.ncpl):
+                top_1d = np.full((self.ncpl,), top_1d[0])
+            elif top_1d.shape != (self.ncpl,):
                 top_1d = top_1d.reshape(self.ncpl)
 
             botm_2d = np.atleast_2d(legacy_botm).reshape(self.nlay, self.ncpl)
@@ -962,19 +1023,18 @@ def top(self):
         if legacy_top is None:
             return None
 
-        dims = "ncpl"
+        dims = ("ncpl",)
         # Check if data shape matches expected grid dimensions
         # If not, return the raw legacy data without coordinates
-        if legacy_top.shape != (self.ncpl):
+        if legacy_top.shape != (self.ncpl,):
             return legacy_top
 
-        coord_names = self._dims_coords[dims[0]]
-        coords = {coord_name: self._coords[coord_name] for coord_name in coord_names}
+        coord_name = self._dims_coords["ncpl"]  # "icpl"
+        coords = {coord_name: self._coords[coord_name]}
         coords.update({"x": self._coords["x"], "y": self._coords["y"]})
         return (
             xr.DataArray(legacy_top, coords=coords, dims=dims)
-            .set_xindex(coord_names[0], PandasIndex)
-            .set_xindex(coord_names[1], PandasIndex)
+            .set_xindex(coord_name, PandasIndex)
             .set_xindex("x", PandasIndex)
             .set_xindex("y", PandasIndex)
         )
@@ -1014,7 +1074,6 @@ def idomain(self):
             return super().idomain
 
         legacy_idomain = super().idomain
-        # return legacy_idomain
         if legacy_idomain is None:
             return None
 
@@ -1046,146 +1105,193 @@ def to_xarray(self, modeltime=None, mesh_type=None, configuration=None):
                VertexGrid objects only support layered mesh
         configuration : configuration dictionary
         """
-        self.legacy = True
-
-        lenunits = {0: "u", 1: "ft", 2: "m", 3: "cm"}
-
         if mesh_type is None or mesh_type.upper() != "LAYERED":
-            raise ValueError("Vextex grid only supports layered mesh datasets")
+            raise ValueError("Vertex grid only supports layered mesh datasets")
 
         if modeltime is None:
             raise ValueError("modeltime required for dataset timeseries")
 
-        ds = xr.Dataset()
-        ds.attrs["modflow_grid"] = "VERTEX"
+        lenunits = {0: "unknown", 1: "ft", 2: "m", 3: "cm"}
 
-        # create dataset coordinate vars
-        var_d = {
-            "time": (["time"], modeltime.totim),
-        }
-        ds = ds.assign(var_d)
-        ds["time"].attrs["calendar"] = "standard"
-        ds["time"].attrs["units"] = f"{modeltime.time_units} since {modeltime.start_datetime}"
-        ds["time"].attrs["axis"] = "T"
-        ds["time"].attrs["standard_name"] = "time"
-        ds["time"].attrs["long_name"] = "time"
+        self.legacy = True
+        try:
+            # All topology arrays computed once from cell2d/verts.
+            topo = self._topology()
 
-        # mesh container variable
-        ds = ds.assign({"mesh": ([], np.int64(1))})
-        ds["mesh"].attrs["cf_role"] = "mesh_topology"
-        ds["mesh"].attrs["long_name"] = "2D mesh topology"
-        ds["mesh"].attrs["topology_dimension"] = np.int64(2)
-        ds["mesh"].attrs["face_dimension"] = "nmesh_face"
-        ds["mesh"].attrs["node_coordinates"] = "mesh_node_x mesh_node_y"
-        ds["mesh"].attrs["face_coordinates"] = "mesh_face_x mesh_face_y"
-        ds["mesh"].attrs["face_node_connectivity"] = "mesh_face_nodes"
+            ds = xr.Dataset()
+            ds.attrs["modflow_grid"] = "VERTEX"
 
-        # mesh node x and y
-        var_d = {
-            "mesh_node_x": (["nmesh_node"], self.verts[:, 0]),
-            "mesh_node_y": (["nmesh_node"], self.verts[:, 1]),
-        }
-        ds = ds.assign(var_d)
-        ds["mesh_node_x"].attrs["units"] = lenunits[self.lenuni]
-        ds["mesh_node_x"].attrs["standard_name"] = "projection_x_coordinate"
-        ds["mesh_node_x"].attrs["long_name"] = "Easting"
-        ds["mesh_node_y"].attrs["units"] = lenunits[self.lenuni]
-        ds["mesh_node_y"].attrs["standard_name"] = "projection_y_coordinate"
-        ds["mesh_node_y"].attrs["long_name"] = "Northing"
+            # create dataset coordinate vars
+            # Use cumulative per-period time (one value per stress period)
+            var_d = {
+                "time": (["time"], np.cumsum(modeltime.perlen)),
+            }
+            ds = ds.assign(var_d)
+            ds["time"].attrs["calendar"] = "standard"
+            ds["time"].attrs["units"] = f"{modeltime.time_units} since {modeltime.start_datetime}"
+            ds["time"].attrs["axis"] = "T"
+            ds["time"].attrs["standard_name"] = "time"
+            ds["time"].attrs["long_name"] = "time"
+
+            # mesh container variable
+            ds = ds.assign({"mesh": ([], np.int64(1))})
+            ds["mesh"].attrs["cf_role"] = "mesh_topology"
+            ds["mesh"].attrs["long_name"] = "2D mesh topology"
+            ds["mesh"].attrs["topology_dimension"] = np.int64(2)
+            ds["mesh"].attrs["face_dimension"] = "nmesh_face"
+            ds["mesh"].attrs["node_coordinates"] = "mesh_node_x mesh_node_y"
+            ds["mesh"].attrs["face_coordinates"] = "mesh_face_x mesh_face_y"
+            ds["mesh"].attrs["face_node_connectivity"] = "mesh_face_nodes"
+
+            # mesh node x and y
+            var_d = {
+                "mesh_node_x": (["nmesh_node"], topo["node_x"]),
+                "mesh_node_y": (["nmesh_node"], topo["node_y"]),
+            }
+            ds = ds.assign(var_d)
+            ds["mesh_node_x"].attrs["units"] = lenunits.get(self.lenuni, "unknown")
+            ds["mesh_node_x"].attrs["standard_name"] = "projection_x_coordinate"
+            ds["mesh_node_x"].attrs["long_name"] = "Easting"
+            ds["mesh_node_y"].attrs["units"] = lenunits.get(self.lenuni, "unknown")
+            ds["mesh_node_y"].attrs["standard_name"] = "projection_y_coordinate"
+            ds["mesh_node_y"].attrs["long_name"] = "Northing"
+
+            # mesh face x, y and bounds
+            var_d = {
+                "mesh_face_x": (["nmesh_face"], topo["face_x"]),
+                "mesh_face_xbnds": (["nmesh_face", "max_nmesh_face_nodes"], topo["x_bnds"]),
+                "mesh_face_y": (["nmesh_face"], topo["face_y"]),
+                "mesh_face_ybnds": (["nmesh_face", "max_nmesh_face_nodes"], topo["y_bnds"]),
+            }
+            ds = ds.assign(var_d)
+            ds["mesh_face_x"].attrs["units"] = lenunits.get(self.lenuni, "unknown")
+            ds["mesh_face_x"].attrs["standard_name"] = "projection_x_coordinate"
+            ds["mesh_face_x"].attrs["long_name"] = "Easting"
+            ds["mesh_face_x"].attrs["bounds"] = "mesh_face_xbnds"
+            ds["mesh_face_y"].attrs["units"] = lenunits.get(self.lenuni, "unknown")
+            ds["mesh_face_y"].attrs["standard_name"] = "projection_y_coordinate"
+            ds["mesh_face_y"].attrs["long_name"] = "Northing"
+            ds["mesh_face_y"].attrs["bounds"] = "mesh_face_ybnds"
+
+            # mesh face nodes
+            var_d = {
+                "mesh_face_nodes": (["nmesh_face", "max_nmesh_face_nodes"], topo["face_nodes"]),
+            }
+            ds = ds.assign(var_d)
+            ds["mesh_face_nodes"].attrs["cf_role"] = "face_node_connectivity"
+            ds["mesh_face_nodes"].attrs["long_name"] = "Vertices bounding cell (counterclockwise)"
+            ds["mesh_face_nodes"].attrs["_FillValue"] = FILL_INT64
+            ds["mesh_face_nodes"].attrs["start_index"] = np.int64(1)
+
+            # create grid index auxiliary coordinate variables
+            ds = ds.assign_coords(k=("z", np.arange(self.nlay, dtype=int)))
+            ds = ds.assign_coords(icpl=("nmesh_face", np.arange(self.ncpl, dtype=int)))
+            ds = ds.set_xindex("k", PandasIndex)
+            ds = ds.set_xindex("icpl", PandasIndex)
+            ds["k"].attrs["long_name"] = "layer index auxiliary coordinate"
+            ds["icpl"].attrs["long_name"] = "cell index auxiliary coordinate"
+
+            wkt_configured = (
+                configuration is not None
+                and "wkt" in configuration
+                and configuration["wkt"] is not None
+            )
 
-        # determine max number of cell vertices
-        # cell_nverts = [cell2d[3] for cell2d in self.cell2d]
-        cell_nverts = [len(cell2d) - 3 for cell2d in self.cell2d]
-        max_face_nodes = max(cell_nverts)
+            if wkt_configured or self.crs is not None:
+                ds["mesh_node_x"].attrs["grid_mapping"] = "projection"
+                ds["mesh_node_y"].attrs["grid_mapping"] = "projection"
+                ds["mesh_face_x"].attrs["grid_mapping"] = "projection"
+                ds["mesh_face_y"].attrs["grid_mapping"] = "projection"
+                ds = ds.assign({"projection": ([], np.int64(1))})
+                if wkt_configured:
+                    # wkt override to existing crs
+                    ds["projection"].attrs["wkt"] = configuration["wkt"]
+                else:
+                    from pyproj.enums import WktVersion
 
-        # mesh face x and y
-        x_bnds = []
-        for x in self.xvertices:
-            x = x[::-1]
-            if len(x) < max_face_nodes:
-                # TODO: set fill value?
-                x.extend([FILL_INT64] * (max_face_nodes - len(x)))
-            x_bnds.append(x)
+                    ds["projection"].attrs["wkt"] = self.crs.to_wkt(WktVersion.WKT1_GDAL)
 
-        y_bnds = []
-        for y in self.yvertices:
-            y = y[::-1]
-            if len(y) < max_face_nodes:
-                # TODO: set fill value?
-                y.extend([FILL_INT64] * (max_face_nodes - len(y)))
-            y_bnds.append(y)
+            return ds
+        finally:
+            self.legacy = False
 
-        var_d = {
-            "mesh_face_x": (["nmesh_face"], self.xcellcenters),
-            "mesh_face_xbnds": (["nmesh_face", "max_nmesh_face_nodes"], x_bnds),
-            "mesh_face_y": (["nmesh_face"], self.ycellcenters),
-            "mesh_face_ybnds": (["nmesh_face", "max_nmesh_face_nodes"], y_bnds),
-        }
-        ds = ds.assign(var_d)
-        ds["mesh_face_x"].attrs["units"] = lenunits[self.lenuni]
-        ds["mesh_face_x"].attrs["standard_name"] = "projection_x_coordinate"
-        ds["mesh_face_x"].attrs["long_name"] = "Easting"
-        ds["mesh_face_x"].attrs["bounds"] = "mesh_face_xbnds"
-        ds["mesh_face_y"].attrs["units"] = lenunits[self.lenuni]
-        ds["mesh_face_y"].attrs["standard_name"] = "projection_y_coordinate"
-        ds["mesh_face_y"].attrs["long_name"] = "Northing"
-        ds["mesh_face_y"].attrs["bounds"] = "mesh_face_ybnds"
+    def _topology(self) -> dict:
+        """
+        Compute UGRID mesh topology arrays.
 
-        # mesh face nodes
-        face_nodes = []
-        for idx, r in enumerate(self.cell2d):
-            nodes = self.cell2d[idx][3:]
-            nodes = [np.int64(x + 1) for x in nodes]
+        Returns node coordinates, face centroids, 1-based CCW face-node
+        connectivity (padded with ``FILL_INT64``), and face-vertex bound
+        arrays.  Must be called while ``self.legacy`` is ``True`` so that
+        ``cell2d`` and ``verts`` return raw arrays rather than xarray-wrapped
+        values.
+        """
+        if not self.cell2d:
+            raise ValueError("VertexGrid has no cell2d data; cannot build mesh topology.")
+
+        node_x = self.verts[:, 0]
+        node_y = self.verts[:, 1]
+
+        # 1-based CCW face-node connectivity, padded to max_face_nodes
+        cell_nverts = [len(cell) - 3 for cell in self.cell2d]
+        max_face_nodes = max(cell_nverts)
+        face_nodes_list = []
+        for cell in self.cell2d:
+            nodes = [np.int64(x + 1) for x in cell[3:]]
             nodes.reverse()
             if nodes[0] == nodes[-1]:
                 nodes.pop()
             if len(nodes) < max_face_nodes:
-                # TODO set fill value?
                 nodes.extend([FILL_INT64] * (max_face_nodes - len(nodes)))
-            face_nodes.append(nodes)
+            face_nodes_list.append(nodes)
+        face_nodes = np.array(face_nodes_list, dtype=np.int64)  # (ncpl, max_face_nodes)
+
+        # Face-vertex bounds: x/y coordinates of each face's nodes in CCW order.
+        # Padding slots (FILL_INT64) must not be used as array indices, so mask them.
+        mask = face_nodes == FILL_INT64
+        idx = np.where(mask, 0, face_nodes - 1)
+        x_bnds = node_x[idx].copy()
+        y_bnds = node_y[idx].copy()
+        x_bnds[mask] = FILL_INT64
+        y_bnds[mask] = FILL_INT64
 
-        var_d = {
-            "mesh_face_nodes": (["nmesh_face", "max_nmesh_face_nodes"], face_nodes),
+        return {
+            "node_x": node_x,
+            "node_y": node_y,
+            "face_x": self.xcellcenters,
+            "face_y": self.ycellcenters,
+            "face_nodes": face_nodes,
+            "x_bnds": x_bnds,
+            "y_bnds": y_bnds,
         }
-        ds = ds.assign(var_d)
-        ds["mesh_face_nodes"].attrs["cf_role"] = "face_node_connectivity"
-        ds["mesh_face_nodes"].attrs["long_name"] = "Vertices bounding cell (counterclockwise)"
-        ds["mesh_face_nodes"].attrs["_FillValue"] = FILL_INT64
-        ds["mesh_face_nodes"].attrs["start_index"] = np.int64(1)
-
-        # create grid index auxiliary coordinate variables
-        ds = ds.assign_coords(k=("z", np.arange(self.nlay, dtype=int)))
-        ds = ds.assign_coords(icpl=("nmesh_face", np.arange(self.ncpl, dtype=int)))
-        ds = ds.set_xindex("k", PandasIndex)
-        ds = ds.set_xindex("icpl", PandasIndex)
-        ds["k"].attrs["long_name"] = "layer index auxiliary coordinate"
-        ds["icpl"].attrs["long_name"] = "cell index auxiliary coordinate"
-
-        wkt_configured = (
-            configuration is not None
-            and "wkt" in configuration
-            and configuration["wkt"] is not None
-        )
-
-        if wkt_configured or self.crs is not None:
-            ds["mesh_node_x"].attrs["grid_mapping"] = "projection"
-            ds["mesh_node_y"].attrs["grid_mapping"] = "projection"
-            ds["mesh_face_x"].attrs["grid_mapping"] = "projection"
-            ds["mesh_face_y"].attrs["grid_mapping"] = "projection"
-            ds = ds.assign({"projection": ([], np.int64(1))})
-            if wkt_configured:
-                # wkt override to existing crs
-                ds["projection"].attrs["wkt"] = configuration["wkt"]
-            else:
-                from pyproj.enums import WktVersion
 
-                ds["projection"].attrs["wkt"] = self.crs.to_wkt(WktVersion.WKT1_GDAL)
+    @property
+    def ugrid(self) -> xu.Ugrid2d:
+        """
+        Build a :class:`xugrid.Ugrid2d` mesh from this vertex grid.
 
-        self.legacy = False
-        return ds
+        Returns
+        -------
+        xu.Ugrid2d
+            A 2-D unstructured grid object whose faces correspond to the
+            vertex grid cells.
+        """
+        self.legacy = True
+        try:
+            topo = self._topology()
+            return xu.Ugrid2d(
+                topo["node_x"],
+                topo["node_y"],
+                FILL_INT64,
+                topo["face_nodes"],
+                projected=True,
+                crs=self.crs,
+                start_index=1,
+            )
+        finally:
+            self.legacy = False
 
 
-def get_coords(grid: StructuredGrid) -> dict[str, Any]:
+def get_coords(grid: LegacyStructuredGrid) -> dict[str, Any]:
     # unpack tuples
     xmin, xmax, ymin, ymax = grid.extent
     dx, dy = (grid.delr, -grid.delc)  # type: ignore
diff --git a/flopy4/mf6/utils/heads_reader.py b/flopy4/mf6/utils/heads_reader.py
index 83712996..9c658fb2 100644
--- a/flopy4/mf6/utils/heads_reader.py
+++ b/flopy4/mf6/utils/heads_reader.py
@@ -4,11 +4,14 @@
 
 import dask
 import numpy as np
-import pandas as pd
 import xarray as xr
+import xugrid as xu
 from flopy.discretization import StructuredGrid
 
+from flopy4.adapters import read_binary_grid_file
+
 from .grid import get_coords
+from .time import assign_datetime_coords
 
 
 def open_hds(
@@ -17,20 +20,20 @@ def open_hds(
     dry_nan: bool = False,
     simulation_start_time: np.datetime64 | None = None,
     time_unit: str | None = "d",
-) -> xr.DataArray:
+) -> xr.DataArray | xu.UgridDataArray:
     """
     Open modflow6 heads (.hds) file.
 
     The data is lazily read per timestep and automatically converted into
-    (dense) xr.DataArrays for DIS.
+    (dense) xr.DataArrays for DIS or xu.UgridDataArrays for DISV.
     The conversion is done via the information stored in the Binary Grid file
     (GRB).
 
 
     Parameters
     ----------
-    hds_path: pathlib.Path
-    grb_path: pathlib.Path
+    hds_path: pathlib.Path, binary head or netcdf file path
+    grb_path: pathlib.Path, binary grid file
     dry_nan: bool, default value: False.
         Whether to convert dry values to NaN.
     simulation_start_time : Optional datetime
@@ -54,10 +57,19 @@ def open_hds(
 
     Returns
     -------
-    head: xr.DataArray
+    head: xr.DataArray or xu.UgridDataArray
     """
-    grid = StructuredGrid.from_binary_grid_file(grb_path)
-    return _open_hds_dis(hds_path, grid, dry_nan, simulation_start_time, time_unit)
+    grb_info = read_binary_grid_file(grb_path)
+
+    if hds_path.suffix == ".nc":
+        return _open_hds_netcdf(hds_path, grb_info, dry_nan)
+
+    if grb_info["grid_type"] == "DIS":
+        return _open_hds_dis(hds_path, grb_info["grid"], dry_nan, simulation_start_time, time_unit)
+    elif grb_info["grid_type"] == "DISV":
+        return _open_hds_disv(hds_path, grb_info, dry_nan, simulation_start_time, time_unit)
+    else:
+        raise ValueError(f"Unsupported grid type: {grb_info['grid_type']}")
 
 
 def _open_hds_dis(
@@ -72,28 +84,178 @@ def _open_hds_dis(
         grid.nrow,
         grid.ncol,
     )
+    ncells_per_layer = nrow * ncol
     filesize = os.path.getsize(path)
-    ntime = filesize // (nlayer * (52 + (nrow * ncol * 8)))
+    ntime = filesize // (nlayer * (52 + (ncells_per_layer * 8)))
     coords = get_coords(grid)
-    coords["time"] = read_times(path, ntime, nlayer, nrow, ncol)
+    coords["time"] = read_times(path, ntime, nlayer, ncells_per_layer)
 
     dask_list = []
-    # loop over times and add delayed arrays
     for i in range(ntime):
-        # TODO verify dimension order
-        pos = i * (nlayer * (52 + nrow * ncol * 8))
-        a = dask.delayed(read_hds_timestep)(path, nlayer, nrow, ncol, dry_nan, pos)
-        x = dask.array.from_delayed(a, shape=(nlayer, nrow, ncol), dtype=np.float64)
+        pos = i * (nlayer * (52 + ncells_per_layer * 8))
+        a = dask.delayed(read_hds_timestep)(path, nlayer, ncells_per_layer, dry_nan, pos)
+        x = dask.array.from_delayed(a, shape=(nlayer, ncells_per_layer), dtype=np.float64)
         dask_list.append(x)
 
     daskarr = dask.array.stack(dask_list, axis=0)
+    # reshape to (ntime, nlayer, nrow, ncol)
+    daskarr = daskarr.reshape((ntime, nlayer, nrow, ncol))
     data_array = xr.DataArray(daskarr, coords, ("time", "layer", "y", "x"), name="head")
     if simulation_start_time is not None:
         data_array = assign_datetime_coords(data_array, simulation_start_time, time_unit)
     return data_array
 
 
-def read_times(path: Path, ntime: int, nlayer: int, nrow: int, ncol: int) -> np.ndarray:
+def _open_hds_disv(
+    path: Path,
+    grb_info: dict,
+    dry_nan: bool,
+    simulation_start_time: np.datetime64 | None = None,
+    time_unit: str | None = "d",
+) -> xu.UgridDataArray:
+    grid = grb_info["grid"]
+    nlayer = grb_info["nlayer"]
+    ncells_per_layer = grb_info["ncells_per_layer"]
+    facedim = grb_info["face_dimension"]
+
+    filesize = os.path.getsize(path)
+    ntime = filesize // (nlayer * (52 + (ncells_per_layer * 8)))
+    times = read_times(path, ntime, nlayer, ncells_per_layer)
+
+    coords = grb_info["coords"].copy()
+    coords["time"] = times
+
+    dask_list = []
+    for i in range(ntime):
+        pos = i * (nlayer * (52 + ncells_per_layer * 8))
+        a = dask.delayed(read_hds_timestep)(path, nlayer, ncells_per_layer, dry_nan, pos)
+        x = dask.array.from_delayed(a, shape=(nlayer, ncells_per_layer), dtype=np.float64)
+        dask_list.append(x)
+
+    daskarr = dask.array.stack(dask_list, axis=0)
+    da = xr.DataArray(daskarr, coords, ("time", "layer", facedim), name="head")
+
+    if simulation_start_time is not None:
+        da = assign_datetime_coords(da, simulation_start_time, time_unit)
+    return xu.UgridDataArray(da, grid)
+
+
+def _open_hds_netcdf(
+    path: Path,
+    grb_info: dict,
+    dry_nan: bool,
+) -> xr.DataArray | xu.UgridDataArray:
+    """
+    Open a MODFLOW 6 heads NetCDF file.
+
+    Two NetCDF formats are supported:
+
+    1. **CF-UGRID layered** (``mesh`` global attribute present): one variable
+       per layer (``head_l1``, ``head_l2``, …) on ``(time, nmesh_face)``.
+       Written by MODFLOW 6 for both DIS and DISV grids.
+
+    2. **Conventional CF structured** (no ``mesh`` global attribute): a single
+       ``head`` variable on ``(time, z, y, x)`` with x/y as dimension
+       coordinates.  Written by MODFLOW 6 for DIS grids only.
+
+    The GRB file grid is used as the authoritative grid topology for the
+    returned array, ensuring consistency with binary-format readers.
+
+    Parameters
+    ----------
+    path : Path
+        Path to the NetCDF heads file.
+    grb_info : dict
+        Grid info dict returned by ``read_binary_grid_file``.
+    dry_nan : bool
+        Whether to convert dry cell values (-1e30) to NaN.
+
+    Returns
+    -------
+    xr.DataArray
+        For DIS grids: dims ``(time, layer, y, x)``.
+    xu.UgridDataArray
+        For DISV grids: dims ``(time, layer, <face_dim>)``.
+    """
+    # Open with chunks={"time": 1} so each timestep is a separate dask chunk,
+    # matching the per-timestep lazy loading of the binary reader.
+    ds = xr.open_dataset(path, chunks={"time": 1})
+    grid_type = ds.attrs.get("modflow_grid", "").upper()
+
+    # Validate that the NetCDF grid type is consistent with the GRB file.
+    _NC_TO_GRB = {"STRUCTURED": "DIS", "VERTEX": "DISV"}
+    grb_grid_type = grb_info["grid_type"]
+    if grid_type and _NC_TO_GRB.get(grid_type) != grb_grid_type:
+        raise ValueError(
+            f"Grid type mismatch: GRB reports {grb_grid_type!r} but NetCDF "
+            f"'modflow_grid' attribute is {grid_type!r}"
+        )
+
+    # Time is already CF-encoded datetime64; load eagerly (small coordinate).
+    time_values = ds["time"].values
+
+    # --- Conventional CF structured: no "mesh" global attribute ---
+    # Single head(time, z, y, x) variable; x/y are dimension coordinates.
+    if "mesh" not in ds.attrs:
+        if grid_type != "STRUCTURED":
+            raise ValueError(
+                f"Conventional CF format (no 'mesh' attribute) is only supported "
+                f"for STRUCTURED grids, got {grid_type!r} in {path.name}."
+            )
+        grid = grb_info["grid"]
+        data = ds["head"].data  # (ntime, nlayer, nrow, ncol) dask array
+        data = _dask_to_nan(data, dry_nan)
+        coords = get_coords(grid)
+        coords["time"] = time_values
+        return xr.DataArray(data, coords, ("time", "layer", "y", "x"), name="head")
+
+    # --- CF-UGRID layered: head_l1, head_l2, ... on (time, nmesh_face) ---
+    head_vars = sorted(
+        [v for v in ds.data_vars if v.startswith("head_l")],  # type: ignore
+        key=lambda v: int(v[len("head_l") :]),  # type: ignore
+    )
+    if not head_vars:
+        raise ValueError(f"No head layer variables (head_l1, head_l2, ...) found in {path.name}")
+
+    nlayer = len(head_vars)
+
+    if grid_type == "VERTEX":
+        # DISV: stack per-layer dask arrays -> (ntime, nlayer, ncpl)
+        # Use grb grid and face_dimension for consistency with binary reader.
+        grid = grb_info["grid"]
+        facedim = grb_info["face_dimension"]
+
+        arrays = [ds[v].data for v in head_vars]  # each (ntime, ncpl) dask array
+        data = dask.array.stack(arrays, axis=1)  # (ntime, nlayer, ncpl)
+        data = _dask_to_nan(data, dry_nan)
+
+        coords = {"time": time_values, "layer": np.arange(1, nlayer + 1)}
+        da = xr.DataArray(data, coords, ("time", "layer", facedim), name="head")
+        return xu.UgridDataArray(da, grid)
+
+    elif grid_type == "STRUCTURED":
+        # DIS: stack per-layer dask arrays and reshape nmesh_face -> (nrow, ncol)
+        grid = grb_info["grid"]
+        nrow, ncol = grid.nrow, grid.ncol
+        ntime = ds.sizes["time"]
+
+        arrays = [ds[v].data for v in head_vars]  # each (ntime, nmesh_face) dask array
+        data = dask.array.stack(arrays, axis=1)  # (ntime, nlayer, nmesh_face)
+        data = data.reshape(ntime, nlayer, nrow, ncol)
+        data = _dask_to_nan(data, dry_nan)
+
+        coords = get_coords(grid)
+        coords["time"] = time_values
+        return xr.DataArray(data, coords, ("time", "layer", "y", "x"), name="head")
+
+    else:
+        raise ValueError(
+            f"Unsupported modflow_grid type {grid_type!r} in {path.name}. "
+            "Expected 'VERTEX' or 'STRUCTURED'."
+        )
+
+
+def read_times(path: Path, ntime: int, nlayer: int, ncells_per_layer: int) -> np.ndarray:
     """
     Reads all total simulation times.
     """
@@ -102,7 +264,7 @@ def read_times(path: Path, ntime: int, nlayer: int, nrow: int, ncol: int) -> np.
     # Compute how much to skip to the next timestamp
     start_of_header = 16
     rest_of_header = 28
-    data_single_layer = nrow * ncol * 8
+    data_single_layer = ncells_per_layer * 8
     header = 52
     nskip = (
         rest_of_header
@@ -120,39 +282,37 @@ def read_times(path: Path, ntime: int, nlayer: int, nrow: int, ncol: int) -> np.
 
 
 def read_hds_timestep(
-    path: Path, nlayer: int, nrow: int, ncol: int, dry_nan: bool, pos: int
+    path: Path, nlayer: int, ncells_per_layer: int, dry_nan: bool, pos: int
 ) -> np.ndarray:
     """
-    Reads all values of one timestep.
+    Reads all values of one timestep. Returns shape (nlayer, ncells_per_layer).
     """
-    ncell_per_layer = nrow * ncol
     with open(path, "rb") as f:
         f.seek(pos)
-        a1d = np.empty(nlayer * nrow * ncol, dtype=np.float64)
+        a1d = np.empty(nlayer * ncells_per_layer, dtype=np.float64)
         for k in range(nlayer):
             f.seek(52, 1)  # skip kstp, kper, pertime
-            a1d[k * ncell_per_layer : (k + 1) * ncell_per_layer] = np.fromfile(
-                f, np.float64, nrow * ncol
+            a1d[k * ncells_per_layer : (k + 1) * ncells_per_layer] = np.fromfile(
+                f, np.float64, ncells_per_layer
             )
 
-    a3d = a1d.reshape((nlayer, nrow, ncol))
-    return _to_nan(a3d, dry_nan)
-
+    a2d = a1d.reshape((nlayer, ncells_per_layer))
+    return _to_nan(a2d, dry_nan)
 
-def assign_datetime_coords(
-    da: xr.DataArray,
-    simulation_start_time: np.datetime64,
-    time_unit: str | None = "d",
-) -> xr.DataArray:
-    if "time" not in da.coords:
-        raise ValueError("cannot convert time column, because a time column could not be found")
 
-    time = pd.Timestamp(simulation_start_time) + pd.to_timedelta(da["time"], unit=time_unit)
-    return da.assign_coords(time=time)
+def _dask_to_nan(a: dask.array.Array, dry_nan: bool) -> dask.array.Array:
+    a = dask.array.where(a == 1e30, np.nan, a)
+    if dry_nan:
+        a = dask.array.where(a == -1e30, np.nan, a)
+    return a
 
 
 def _to_nan(a: np.ndarray, dry_nan: bool) -> np.ndarray:
-    # TODO: this could really use a docstring?
+    """
+    Replace MODFLOW 6 no-data sentinels (1e30) with NaN, in-place.
+
+    If *dry_nan* is True, also replace dry-cell values (-1e30) with NaN.
+    """
     a[a == 1e30] = np.nan
     if dry_nan:
         a[a == -1e30] = np.nan
diff --git a/flopy4/mf6/utils/time.py b/flopy4/mf6/utils/time.py
index 10588237..f0ffe1ff 100644
--- a/flopy4/mf6/utils/time.py
+++ b/flopy4/mf6/utils/time.py
@@ -1,9 +1,44 @@
 import numpy as np
 import pandas as pd
+import xarray as xr
 from flopy.discretization.modeltime import ModelTime
 from numpy.typing import ArrayLike
 
 
+def assign_datetime_coords(
+    da: xr.DataArray,
+    simulation_start_time: np.datetime64,
+    time_unit: str | None = "d",
+) -> xr.DataArray:
+    """
+    Replace the numeric ``time`` coordinate on *da* with calendar datetimes.
+
+    Parameters
+    ----------
+    da : xr.DataArray
+        Array with a numeric ``time`` coordinate (simulation time in
+        *time_unit* since the start of the simulation).
+    simulation_start_time : np.datetime64
+        Calendar date/time corresponding to the start of the simulation
+        (i.e. model time = 0).
+    time_unit : str, optional
+        Unit of the numeric time values.  Passed directly to
+        :func:`pandas.to_timedelta`.  Common values: ``"d"`` (days,
+        default), ``"h"`` (hours), ``"s"`` (seconds).  Month and year
+        are not supported because they are ambiguous.
+
+    Returns
+    -------
+    xr.DataArray
+        A copy of *da* with the ``time`` coordinate replaced by
+        :class:`pandas.Timestamp` values.
+    """
+    if "time" not in da.coords:
+        raise ValueError("cannot convert time: no 'time' coordinate found")
+    time = pd.Timestamp(simulation_start_time) + pd.to_timedelta(da["time"], unit=time_unit)
+    return da.assign_coords(time=time)
+
+
 class Time(ModelTime):
     """Extend flopy3's ModelTime"""
 
diff --git a/pixi.lock b/pixi.lock
index 38d8227c..84cdac4d 100644
--- a/pixi.lock
+++ b/pixi.lock
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+      - pypi: https://files.pythonhosted.org/packages/2c/05/596993a30a1a7f802eede7f8a75c65a5b0f08409a36c667d78fe40612a88/numba_celltree-0.4.2-py3-none-any.whl
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-      - pypi: https://files.pythonhosted.org/packages/48/31/05e764397056194206169869b50cf2fee4dbbbc71b344705b9c0d878d4d8/platformdirs-4.9.2-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/63/d7/97f7e3a6abb67d8080dd406fd4df842c2be0efaf712d1c899c32a075027c/platformdirs-4.9.4-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/2a/2d/d4bf65e47cea8ff2c794a600c4fd1273a7902f268757c531e0ee9f18aa58/pooch-1.9.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/b5/70/5d8df3b09e25bce090399cf48e452d25c935ab72dad19406c77f4e828045/psutil-7.2.2-cp36-abi3-manylinux2010_x86_64.manylinux_2_12_x86_64.manylinux_2_28_x86_64.whl
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       - pypi: git+https://github.com/wpbonelli/xattree.git#82942ffb8237d56c97451765ac3e2b16d6cb0d2c
+      - pypi: https://files.pythonhosted.org/packages/52/07/1922a0c20ed4c95769937619d2c0217ee3f665a8b967438277d3f40860e4/xugrid-0.14.3-py3-none-any.whl
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+      - pypi: https://files.pythonhosted.org/packages/63/d7/97f7e3a6abb67d8080dd406fd4df842c2be0efaf712d1c899c32a075027c/platformdirs-4.9.4-py3-none-any.whl
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       - pypi: git+https://github.com/wpbonelli/xattree.git#82942ffb8237d56c97451765ac3e2b16d6cb0d2c
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   - libffi >=3.5.2,<3.6.0a0
   - liblzma >=5.8.2,<6.0a0
   - libsqlite >=3.51.2,<4.0a0
   - libzlib >=1.3.1,<2.0a0
   - ncurses >=6.5,<7.0a0
-  - openssl >=3.5.4,<4.0a0
+  - openssl >=3.5.5,<4.0a0
   - readline >=8.3,<9.0a0
   - tk >=8.6.13,<8.7.0a0
   - tzdata
@@ -13005,22 +13064,21 @@ packages:
   - python_abi 3.11.* *_cp311
   license: Python-2.0
   purls: []
-  size: 15666344
-  timestamp: 1769473006716
-- conda: https://conda.anaconda.org/conda-forge/osx-64/python-3.12.12-h74c2667_2_cpython.conda
-  build_number: 2
-  sha256: a0dc682959d43789313346549370579604020617718f9ff09f8dc99fe4fb1faa
-  md5: 64f6c57fd1d23500084194c740da395e
+  size: 15664115
+  timestamp: 1772730794934
+- conda: https://conda.anaconda.org/conda-forge/osx-64/python-3.12.13-ha9537fe_0_cpython.conda
+  sha256: fb592ceb1bc247d19247d5535083da4a79721553e29e1290f5d81c07d4f086b5
+  md5: ec05996c0d914a4e98ee3c7d789083f8
   depends:
-  - __osx >=10.13
+  - __osx >=11.0
   - bzip2 >=1.0.8,<2.0a0
-  - libexpat >=2.7.3,<3.0a0
+  - libexpat >=2.7.4,<3.0a0
   - libffi >=3.5.2,<3.6.0a0
   - liblzma >=5.8.2,<6.0a0
   - libsqlite >=3.51.2,<4.0a0
   - libzlib >=1.3.1,<2.0a0
   - ncurses >=6.5,<7.0a0
-  - openssl >=3.5.4,<4.0a0
+  - openssl >=3.5.5,<4.0a0
   - readline >=8.3,<9.0a0
   - tk >=8.6.13,<8.7.0a0
   - tzdata
@@ -13028,8 +13086,8 @@ packages:
   - python_abi 3.12.* *_cp312
   license: Python-2.0
   purls: []
-  size: 13739394
-  timestamp: 1769473128970
+  size: 13672169
+  timestamp: 1772730464626
 - conda: https://conda.anaconda.org/conda-forge/osx-64/python-3.13.12-h894a449_100_cp313.conda
   build_number: 100
   sha256: 9548dcf58cf6045aa4aa1f2f3fa6110115ca616a8d5fa142a24081d2b9d91291
@@ -13054,20 +13112,19 @@ packages:
   size: 17570178
   timestamp: 1770272361922
   python_site_packages_path: lib/python3.13/site-packages
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/python-3.11.14-h18782d2_3_cpython.conda
-  build_number: 3
-  sha256: f862d544a455bedfa5d77305f0a42c8e3392956e364f8f92d6ca1c844fb3fbd1
-  md5: b8f6a28ffd3c97367d4e58e8793ff47c
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/python-3.11.15-h8561d8f_0_cpython.conda
+  sha256: 9a846065863925b2562126a5c6fecd7a972e84aaa4de9e686ad3715ca506acfa
+  md5: 49c7d96c58b969585cf09fb01d74e08e
   depends:
   - __osx >=11.0
   - bzip2 >=1.0.8,<2.0a0
-  - libexpat >=2.7.3,<3.0a0
+  - libexpat >=2.7.4,<3.0a0
   - libffi >=3.5.2,<3.6.0a0
   - liblzma >=5.8.2,<6.0a0
   - libsqlite >=3.51.2,<4.0a0
   - libzlib >=1.3.1,<2.0a0
   - ncurses >=6.5,<7.0a0
-  - openssl >=3.5.4,<4.0a0
+  - openssl >=3.5.5,<4.0a0
   - readline >=8.3,<9.0a0
   - tk >=8.6.13,<8.7.0a0
   - tzdata
@@ -13075,22 +13132,21 @@ packages:
   - python_abi 3.11.* *_cp311
   license: Python-2.0
   purls: []
-  size: 13707418
-  timestamp: 1769472290774
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/python-3.12.12-h18782d2_2_cpython.conda
-  build_number: 2
-  sha256: 765e5d0f92dabc8c468d078a4409490e08181a6f9be6f5d5802a4e3131b9a69c
-  md5: e198b8f74b12292d138eb4eceb004fa3
+  size: 14753109
+  timestamp: 1772730203101
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/python-3.12.13-h8561d8f_0_cpython.conda
+  sha256: e658e647a4a15981573d6018928dec2c448b10c77c557c29872043ff23c0eb6a
+  md5: 8e7608172fa4d1b90de9a745c2fd2b81
   depends:
   - __osx >=11.0
   - bzip2 >=1.0.8,<2.0a0
-  - libexpat >=2.7.3,<3.0a0
+  - libexpat >=2.7.4,<3.0a0
   - libffi >=3.5.2,<3.6.0a0
   - liblzma >=5.8.2,<6.0a0
   - libsqlite >=3.51.2,<4.0a0
   - libzlib >=1.3.1,<2.0a0
   - ncurses >=6.5,<7.0a0
-  - openssl >=3.5.4,<4.0a0
+  - openssl >=3.5.5,<4.0a0
   - readline >=8.3,<9.0a0
   - tk >=8.6.13,<8.7.0a0
   - tzdata
@@ -13098,8 +13154,8 @@ packages:
   - python_abi 3.12.* *_cp312
   license: Python-2.0
   purls: []
-  size: 12953358
-  timestamp: 1769472376612
+  size: 12127424
+  timestamp: 1772730755512
 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/python-3.13.12-h20e6be0_100_cp313.conda
   build_number: 100
   sha256: 9a4f16a64def0853f0a7b6a7beb40d498fd6b09bee10b90c3d6069b664156817
@@ -13124,18 +13180,17 @@ packages:
   size: 12770674
   timestamp: 1770272314517
   python_site_packages_path: lib/python3.13/site-packages
-- conda: https://conda.anaconda.org/conda-forge/win-64/python-3.11.14-h0159041_3_cpython.conda
-  build_number: 3
-  sha256: 5676dadd9d4fba1bce51bd7e5cf8fcf76f85b88b7baa15bd10ca00557e67f10e
-  md5: 05ded1dca7befb66ec95a9ec6d34a71a
+- conda: https://conda.anaconda.org/conda-forge/win-64/python-3.11.15-h0159041_0_cpython.conda
+  sha256: a1f1031088ce69bc99c82b95980c1f54e16cbd5c21f042e9c1ea25745a8fc813
+  md5: d09dbf470b41bca48cbe6a78ba1e009b
   depends:
   - bzip2 >=1.0.8,<2.0a0
-  - libexpat >=2.7.3,<3.0a0
+  - libexpat >=2.7.4,<3.0a0
   - libffi >=3.5.2,<3.6.0a0
   - liblzma >=5.8.2,<6.0a0
   - libsqlite >=3.51.2,<4.0a0
   - libzlib >=1.3.1,<2.0a0
-  - openssl >=3.5.4,<4.0a0
+  - openssl >=3.5.5,<4.0a0
   - tk >=8.6.13,<8.7.0a0
   - tzdata
   - ucrt >=10.0.20348.0
@@ -13145,20 +13200,19 @@ packages:
   - python_abi 3.11.* *_cp311
   license: Python-2.0
   purls: []
-  size: 18353938
-  timestamp: 1769471078924
-- conda: https://conda.anaconda.org/conda-forge/win-64/python-3.12.12-h0159041_2_cpython.conda
-  build_number: 2
-  sha256: 5937ab50dfeb979f7405132f73e836a29690f21162308b95b240b8037aa99975
-  md5: 068897f82240d69580c2d93f93b56ff5
+  size: 18416208
+  timestamp: 1772728847666
+- conda: https://conda.anaconda.org/conda-forge/win-64/python-3.12.13-h0159041_0_cpython.conda
+  sha256: a02b446d8b7b167b61733a3de3be5de1342250403e72a63b18dac89e99e6180e
+  md5: 2956dff38eb9f8332ad4caeba941cfe7
   depends:
   - bzip2 >=1.0.8,<2.0a0
-  - libexpat >=2.7.3,<3.0a0
+  - libexpat >=2.7.4,<3.0a0
   - libffi >=3.5.2,<3.6.0a0
   - liblzma >=5.8.2,<6.0a0
   - libsqlite >=3.51.2,<4.0a0
   - libzlib >=1.3.1,<2.0a0
-  - openssl >=3.5.4,<4.0a0
+  - openssl >=3.5.5,<4.0a0
   - tk >=8.6.13,<8.7.0a0
   - tzdata
   - ucrt >=10.0.20348.0
@@ -13168,8 +13222,8 @@ packages:
   - python_abi 3.12.* *_cp312
   license: Python-2.0
   purls: []
-  size: 15829087
-  timestamp: 1769470991307
+  size: 15840187
+  timestamp: 1772728877265
 - conda: https://conda.anaconda.org/conda-forge/win-64/python-3.13.12-h09917c8_100_cp313.conda
   build_number: 100
   sha256: da70aec20ff5a5ae18bbba9fdd1e18190b419605cafaafb3bdad8becf11ce94d
@@ -13582,25 +13636,25 @@ packages:
   version: 0.30.0
   sha256: e7536cd91353c5273434b4e003cbda89034d67e7710eab8761fd918ec6c69cf8
   requires_python: '>=3.10'
-- pypi: https://files.pythonhosted.org/packages/b1/d8/0fab9f8842b83b1a9c2bf81b85063f65e93fb512e60effa95b0be49bfc54/ruff-0.15.4-py3-none-macosx_11_0_arm64.whl
+- pypi: https://files.pythonhosted.org/packages/44/ed/e81dd668547da281e5dce710cf0bc60193f8d3d43833e8241d006720e42b/ruff-0.15.5-py3-none-macosx_10_12_x86_64.whl
   name: ruff
-  version: 0.15.4
-  sha256: a4386ba2cd6c0f4ff75252845906acc7c7c8e1ac567b7bc3d373686ac8c222ba
+  version: 0.15.5
+  sha256: 6edd3792d408ebcf61adabc01822da687579a1a023f297618ac27a5b51ef0080
   requires_python: '>=3.7'
-- pypi: https://files.pythonhosted.org/packages/ce/5d/6a1f271f6e31dffb31855996493641edc3eef8077b883eaf007a2f1c2976/ruff-0.15.4-py3-none-macosx_10_12_x86_64.whl
+- pypi: https://files.pythonhosted.org/packages/b8/00/bf077a505b4e649bdd3c47ff8ec967735ce2544c8e4a43aba42ee9bf935d/ruff-0.15.5-py3-none-win_amd64.whl
   name: ruff
-  version: 0.15.4
-  sha256: 5a1632c66672b8b4d3e1d1782859e98d6e0b4e70829530666644286600a33992
+  version: 0.15.5
+  sha256: 821d41c5fa9e19117616c35eaa3f4b75046ec76c65e7ae20a333e9a8696bc7fe
   requires_python: '>=3.7'
-- pypi: https://files.pythonhosted.org/packages/d0/a8/c688ef7e29983976820d18710f955751d9f4d4eb69df658af3d006e2ba3e/ruff-0.15.4-py3-none-win_amd64.whl
+- pypi: https://files.pythonhosted.org/packages/c4/8f/533075f00aaf19b07c5cd6aa6e5d89424b06b3b3f4583bfa9c640a079059/ruff-0.15.5-py3-none-macosx_11_0_arm64.whl
   name: ruff
-  version: 0.15.4
-  sha256: 04196ad44f0df220c2ece5b0e959c2f37c777375ec744397d21d15b50a75264f
+  version: 0.15.5
+  sha256: 89f463f7c8205a9f8dea9d658d59eff49db05f88f89cc3047fb1a02d9f344010
   requires_python: '>=3.7'
-- pypi: https://files.pythonhosted.org/packages/ff/90/bf134f4c1e5243e62690e09d63c55df948a74084c8ac3e48a88468314da6/ruff-0.15.4-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
+- pypi: https://files.pythonhosted.org/packages/d3/01/a10fe54b653061585e655f5286c2662ebddb68831ed3eaebfb0eb08c0a16/ruff-0.15.5-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
   name: ruff
-  version: 0.15.4
-  sha256: 451a2e224151729b3b6c9ffb36aed9091b2996fe4bdbd11f47e27d8f2e8888ec
+  version: 0.15.5
+  sha256: c1cb7169f53c1ddb06e71a9aebd7e98fc0fea936b39afb36d8e86d36ecc2636a
   requires_python: '>=3.7'
 - pypi: https://files.pythonhosted.org/packages/01/8e/1e35281b8ab6d5d72ebe9911edcdffa3f36b04ed9d51dec6dd140396e220/scipy-1.17.1-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
   name: scipy
@@ -15281,6 +15335,29 @@ packages:
   - pytest-xdist ; extra == 'test'
   - ruff ; extra == 'test'
   requires_python: '>=3.11,<3.14'
+- pypi: https://files.pythonhosted.org/packages/52/07/1922a0c20ed4c95769937619d2c0217ee3f665a8b967438277d3f40860e4/xugrid-0.14.3-py3-none-any.whl
+  name: xugrid
+  version: 0.14.3
+  sha256: 844baba108b62d5a947063067135ec9f525765b8a8e56a5e8923865770f4a9a5
+  requires_dist:
+  - dask>=2025.1.0
+  - numba
+  - numba-celltree>=0.4.1
+  - numpy
+  - pandas
+  - pooch
+  - scipy
+  - xarray
+  - geopandas ; extra == 'all'
+  - mapbox-earcut ; extra == 'all'
+  - matplotlib ; extra == 'all'
+  - meshkernel>=3.0.0 ; extra == 'all'
+  - netcdf4 ; extra == 'all'
+  - pooch ; extra == 'all'
+  - pyproj ; extra == 'all'
+  - shapely>=2.0 ; extra == 'all'
+  - zarr ; extra == 'all'
+  requires_python: '>=3.10'
 - pypi: https://files.pythonhosted.org/packages/ef/5c/2c189d18d495dd0fa3f27ccc60762bbc787eed95b9b0147266e72bb76585/xyzservices-2025.11.0-py3-none-any.whl
   name: xyzservices
   version: 2025.11.0
diff --git a/pyproject.toml b/pyproject.toml
index e6dd0274..22a2d767 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -57,6 +57,7 @@ dependencies = [
     "pydantic",
     "tomli",
     "tomli_w",
+    "xugrid",
 ]
 dynamic = ["version"]
 
diff --git a/test/test_mf6_component.py b/test/test_mf6_component.py
index 029be479..1729cedc 100644
--- a/test/test_mf6_component.py
+++ b/test/test_mf6_component.py
@@ -986,6 +986,8 @@ def test_grid_from_disv_factory():
         )
 
     dis = Disv(
+        xorigin=200.0,
+        yorigin=100.0,
         nlay=nlay,
         ncpl=ncpl,
         nvert=nvert,
@@ -1011,8 +1013,6 @@ def test_grid_from_disv_factory():
 
     # Use the classmethod factory
     kwargs = {}
-    kwargs["xoff"] = 200.0
-    kwargs["yoff"] = 100.0
     grid = VertexGrid.from_dis(dis, **kwargs)
 
     # Check that dimensions match
@@ -1051,6 +1051,173 @@ def test_grid_from_disv_factory():
     assert botm_near_x.shape == (3,)
 
 
+def test_ugrid_from_dis_factory():
+    """Test the from_dis() factory method."""
+    nlay, nrow, ncol = 2, 5, 5
+    dis = Dis(
+        nlay=nlay,
+        nrow=nrow,
+        ncol=ncol,
+        delr=np.full(ncol, 10.0),
+        delc=np.full(nrow, 10.0),
+        top=np.full((nrow, ncol), 10.0),
+        botm=np.array([np.full((nrow, ncol), 0.0), np.full((nrow, ncol), -10.0)]),
+    )
+
+    # Use the classmethod factory
+    grid = StructuredGrid.from_dis(dis)
+
+    # Check that dimensions match
+    assert grid.nlay == nlay
+    assert grid.nrow == nrow
+    assert grid.ncol == ncol
+
+    # Check that spatial data matches
+    assert "x" in grid.dataset.coords
+    assert "y" in grid.dataset.coords
+    assert "z" in grid.dataset.coords
+
+    # Check z coordinates are cell centers
+    # Layer 0: (10 + 0) / 2 = 5
+    # Layer 1: (0 + (-10)) / 2 = -5
+    np.testing.assert_allclose(grid.dataset.coords["z"].values[0], np.full((5, 5), 5.0))
+    np.testing.assert_allclose(grid.dataset.coords["z"].values[1], np.full((5, 5), -5.0))
+
+    # Check that coordinate-based selection works
+    botm_near_x15 = grid.botm.sel(x=15.0, method="nearest")
+    assert botm_near_x15.shape == (2, 5)
+
+    ugrid = grid.ugrid
+    assert ugrid.n_face == nrow * ncol
+    assert ugrid.n_node == (nrow + 1) * (ncol + 1)
+
+
+def test_ugrid_from_disv_factory():
+    """Test the from_dis() factory method."""
+    import xugrid
+
+    nlay, ncpl, nvert = 3, 9, 16
+    top = np.ones((ncpl), dtype=float) * 0.0
+    botm = np.stack([np.full((ncpl), val) for val in [-10.0, -20.0, -30.0]])
+
+    cells = [
+        [0, 1, 5, 4],
+        [1, 2, 6, 5],
+        [2, 3, 7, 6],
+        [4, 5, 9, 8],
+        [5, 6, 10, 9],
+        [6, 7, 11, 10],
+        [8, 9, 13, 12],
+        [9, 10, 14, 13],
+        [10, 11, 15, 14],
+    ]
+
+    # Cell centers for the 3x3 quad grid with vertices at
+    # x=[1e8, 1e8+10, 1e8+20, 1e8+30], y=[1e8+30, 1e8+20, 1e8+10, 1e8]
+    _base = 1.00000000e08
+    _xc = [5.0, 15.0, 25.0, 5.0, 15.0, 25.0, 5.0, 15.0, 25.0]
+    _yc = [25.0, 25.0, 25.0, 15.0, 15.0, 15.0, 5.0, 5.0, 5.0]
+    cell2ddata = []
+    for n in range(ncpl):
+        cell2ddata.append(
+            Disv.Cell2dRecord(
+                n,
+                _base + _xc[n],
+                _base + _yc[n],
+                4,
+                tuple(cells[n]),
+            )
+        )
+
+    dis = Disv(
+        xorigin=200.0,
+        yorigin=100.0,
+        nlay=nlay,
+        ncpl=ncpl,
+        nvert=nvert,
+        top=top,
+        botm=botm,
+        idomain=1,
+        iv=np.arange(0, nvert, dtype=int),
+        xv=np.concatenate(
+            [
+                np.array([1.00000000e08, 1.00000010e08, 1.00000020e08, 1.00000030e08])
+                for i in range(4)
+            ]
+        ),
+        yv=np.concatenate(
+            [
+                np.array([1.00000030e08, 1.00000030e08, 1.00000030e08, 1.00000030e08])
+                - float(10 * (i % 4))
+                for i in range(4)
+            ]
+        ),
+        cell2ddata=cell2ddata,
+    )
+
+    # Use the classmethod factory
+    kwargs = {}
+    grid = VertexGrid.from_dis(dis, **kwargs)
+
+    # Check that dimensions match
+    assert grid.nlay == nlay
+    assert grid.ncpl == ncpl
+    assert grid.nvert == nvert
+    np.testing.assert_allclose(np.array(grid._vertices, dtype=int)[:, 0], dis.iv)
+    np.testing.assert_allclose(np.array(grid._vertices)[:, 1], dis.xv)
+    np.testing.assert_allclose(np.array(grid._vertices)[:, 2], dis.yv)
+    cell2d = []
+    for i in range(len(dis.cell2ddata.values)):
+        rec = [
+            dis.cell2ddata.values[i].icell2d,
+            dis.cell2ddata.values[i].xc,
+            dis.cell2ddata.values[i].yc,
+        ]
+        for v in dis.cell2ddata.values[i].icvert:
+            rec.append(v)
+        cell2d.append(rec)
+    assert grid.cell2d == cell2d
+
+    # Check that spatial data matches
+    assert "x" in grid.dataset.coords
+    assert "y" in grid.dataset.coords
+    assert "z" in grid.dataset.coords
+    assert "k" in grid.dataset.coords
+    assert "icpl" in grid.dataset.coords
+
+    # Check z coordinates are cell centers
+    np.testing.assert_allclose(grid.dataset.coords["z"].values[0], np.full((ncpl), -5.0))
+    np.testing.assert_allclose(grid.dataset.coords["z"].values[1], np.full((ncpl), -15.0))
+    np.testing.assert_allclose(grid.dataset.coords["z"].values[2], np.full((ncpl), -25.0))
+
+    ugrid = grid.ugrid
+    assert ugrid.n_face == ncpl
+    assert ugrid.n_node == nvert
+
+    udata = xugrid.UgridDataArray(dis.top, grid=ugrid)
+
+    udataset = xugrid.UgridDataset(dis.data.dataset, grids=ugrid)
+
+    # udata.to_netcdf("./udata.nc")
+    # drop global attributes, or filter?
+    uds = udataset.drop_attrs()
+    # drop objects that need serialization
+    uds = uds.drop_vars(["cell2ddata"])
+    # uds.to_netcdf("./udataset.nc")
+
+
+def test_ugrid_from_dis_uniform():
+    """StructuredGrid.uniform().ugrid should have the correct face/node counts."""
+    import xugrid
+
+    nrow, ncol = 4, 6
+    grid = StructuredGrid.uniform(nlay=2, nrow=nrow, ncol=ncol, delr=50.0, delc=50.0)
+    ugrid = grid.ugrid
+    assert isinstance(ugrid, xugrid.Ugrid2d)
+    assert ugrid.n_face == nrow * ncol
+    assert ugrid.n_node == (nrow + 1) * (ncol + 1)
+
+
 def test_grid_with_idomain():
     """Test grid with idomain array."""
     nlay, nrow, ncol = 2, 5, 5
diff --git a/test/test_mf6_netcdf.py b/test/test_mf6_netcdf.py
index 51fdfbb2..fbd34623 100644
--- a/test/test_mf6_netcdf.py
+++ b/test/test_mf6_netcdf.py
@@ -220,7 +220,7 @@ def test_model_mesh():
 
     assert ds.attrs["modflow_grid"] == "structured"
     assert ds.attrs["modflow_model"] == "gwf6: gwfmodel"
-    assert ds.attrs["mesh"] == "layered"
+    assert ds.attrs["mesh"] == "LAYERED"
     assert "dis_delr" in ds
     assert "dis_delc" in ds
     assert "rcha_0_recharge" in ds
diff --git a/test/test_mf6_reader.py b/test/test_mf6_reader.py
index b4190e21..3e0997ec 100644
--- a/test/test_mf6_reader.py
+++ b/test/test_mf6_reader.py
@@ -8,7 +8,7 @@
 import xarray as xr
 from lark import Lark
 from modflow_devtools.dfns import Dfn, MapV1To2, load_flat
-from modflow_devtools.models import get_models
+from modflow_devtools.download import download_and_unzip
 from packaging.version import Version
 
 from flopy4.mf6.codec.reader.parser import get_typed_parser
@@ -309,21 +309,33 @@ def test_transform_full_component():
 # Real model tests using modflow-devtools models API
 
 
-@pytest.fixture(scope="module")
-def example_models():
-    """Get MF6 example models from devtools."""
-    models = get_models()
-    # Filter to mf6 examples only
-    return {name: info for name, info in models.items() if name.startswith("mf6/example/")}
+MF6_EXAMPLES_URL = (
+    "https://github.com/MODFLOW-ORG/modflow6-examples/releases/download/current/mf6examples.zip"
+)
+
+
+@pytest.fixture(scope="session")
+def mf6_examples_path(tmp_path_factory):
+    """Download and cache MF6 example models for the test session."""
+    tmp_dir = tmp_path_factory.mktemp("mf6_examples")
+    download_and_unzip(MF6_EXAMPLES_URL, tmp_dir, verbose=False)
+    return tmp_dir
 
 
 @pytest.fixture
-def model_workspace(tmp_path, request):
-    """Copy a model to a temporary workspace."""
-    from modflow_devtools.models import copy_to
+def model_workspace(mf6_examples_path, request):
+    """Get a model directory from downloaded examples.
 
+    The request.param should be in the form 'mf6/example/ex-gwf-csub-p01',
+    and the model directory name is the last component.
+    """
     model_name = request.param
-    workspace = copy_to(tmp_path, model_name, verbose=False)
+    # Extract dir name from model path
+    # e.g. "mf6/example/ex-gwf-csub-p01" -> "ex-gwf-csub-p01"
+    dir_name = model_name.split("/")[-1]
+    workspace = mf6_examples_path / dir_name
+    if not workspace.exists():
+        pytest.skip(f"Model directory '{dir_name}' not found in downloaded examples")
     return workspace
 
 
diff --git a/test/test_output_readers.py b/test/test_output_readers.py
new file mode 100644
index 00000000..7f93986f
--- /dev/null
+++ b/test/test_output_readers.py
@@ -0,0 +1,406 @@
+import numpy as np
+import pytest
+import xarray as xr
+import xugrid as xu
+
+from flopy4.mf6.gwf import Chd, Dis, Disv, Gwf, Ic, Npf, Oc
+from flopy4.mf6.ims import Ims
+from flopy4.mf6.simulation import Simulation
+from flopy4.mf6.utils.cbc_reader import open_cbc
+from flopy4.mf6.utils.grid import VertexGrid
+from flopy4.mf6.utils.heads_reader import open_hds
+from flopy4.mf6.utils.time import Time
+
+
+@pytest.fixture
+def dis_model_output(function_tmpdir):
+    sim_name = "dis_test"
+    gwf_name = "gwf_dis"
+
+    time = Time(perlen=[1.0], nstp=[1], tsmult=[1.0], time_units="days")
+    ims = Ims(filename=f"{sim_name}.ims", models=[gwf_name], print_option="summary")
+    sim = Simulation(tdis=time, workspace=function_tmpdir, name=sim_name, solutions={"ims": ims})
+
+    nlay, nrow, ncol = 2, 3, 4
+    botm = np.stack([np.full((nrow, ncol), 0.0), np.full((nrow, ncol), -10.0)])
+    dis = Dis(
+        nlay=nlay,
+        nrow=nrow,
+        ncol=ncol,
+        delr=10.0,
+        delc=10.0,
+        top=10.0,
+        botm=botm,
+        idomain=1,
+    )
+
+    gwf = Gwf(parent=sim, save_flows=True, dis=dis, name=gwf_name)
+    Ic(parent=gwf, strt=5.0)
+    Npf(parent=gwf, k=1.0, icelltype=0)
+    Chd(parent=gwf, print_flows=True, head={0: {(0, 0): 10.0, (0, ncol - 1): 0.0}})
+    Oc(
+        parent=gwf,
+        budget_file=f"{gwf_name}.cbc",
+        head_file=f"{gwf_name}.hds",
+        save_head=["last"],
+        save_budget=["last"],
+    )
+
+    sim.write()
+    sim.run()
+
+    base_path = function_tmpdir
+    hds_path = function_tmpdir / f"{gwf_name}.hds"
+    cbc_path = function_tmpdir / f"{gwf_name}.cbc"
+    grb_path = function_tmpdir / f"{gwf_name}.dis.grb"
+
+    assert hds_path.is_file(), f"HDS file not found: {hds_path}"
+    assert cbc_path.is_file(), f"CBC file not found: {cbc_path}"
+    assert grb_path.is_file(), f"GRB file not found: {grb_path}"
+
+    return {
+        "base": base_path,
+        "hds": hds_path,
+        "cbc": cbc_path,
+        "grb": grb_path,
+        "nlay": nlay,
+        "nrow": nrow,
+        "ncol": ncol,
+    }
+
+
+@pytest.fixture
+def disv_model_output(function_tmpdir):
+    sim_name = "disv_test"
+    gwf_name = "gwf_disv"
+
+    time = Time(perlen=[1.0], nstp=[1], tsmult=[1.0], time_units="days")
+    ims = Ims(filename=f"{sim_name}.ims", models=[gwf_name], print_option="summary")
+    sim = Simulation(tdis=time, workspace=function_tmpdir, name=sim_name, solutions={"ims": ims})
+
+    nlay = 2
+    ncpl = 9
+    nvert = 16
+
+    top = np.ones(ncpl, dtype=float) * 10.0
+    botm = np.stack([np.full(ncpl, 0.0), np.full(ncpl, -10.0)])
+
+    # 3x3 grid of quads
+    cells = [
+        [0, 1, 5, 4],
+        [1, 2, 6, 5],
+        [2, 3, 7, 6],
+        [4, 5, 9, 8],
+        [5, 6, 10, 9],
+        [6, 7, 11, 10],
+        [8, 9, 13, 12],
+        [9, 10, 14, 13],
+        [10, 11, 15, 14],
+    ]
+
+    cell2ddata = []
+    for n in range(ncpl):
+        cell2ddata.append(
+            Disv.Cell2dRecord(
+                n,
+                float((n % 3) * 10 + 5),
+                float(25 - (n // 3) * 10),
+                4,
+                tuple(cells[n]),
+            )
+        )
+
+    # 4x4 grid of vertices (16 total)
+    xv = np.array([float(i * 10) for i in range(4)] * 4)
+    yv = np.array([float(30 - j * 10) for j in range(4) for _ in range(4)])
+
+    disv = Disv(
+        nlay=nlay,
+        ncpl=ncpl,
+        nvert=nvert,
+        top=top,
+        botm=botm,
+        idomain=1,
+        iv=np.arange(nvert, dtype=int),
+        xv=xv,
+        yv=yv,
+        cell2ddata=cell2ddata,
+    )
+
+    gwf = Gwf(parent=sim, save_flows=True, dis=disv, name=gwf_name)
+    Ic(parent=gwf, strt=5.0)
+    Npf(parent=gwf, k=1.0, icelltype=0)
+    Chd(parent=gwf, print_flows=True, head={0: {(0, 0): 10.0, (0, 8): 0.0}})
+    Oc(
+        parent=gwf,
+        budget_file=f"{gwf_name}.cbc",
+        head_file=f"{gwf_name}.hds",
+        save_head=["last"],
+        save_budget=["last"],
+    )
+
+    sim.write()
+    sim.run()
+
+    base_path = function_tmpdir
+    hds_path = function_tmpdir / f"{gwf_name}.hds"
+    cbc_path = function_tmpdir / f"{gwf_name}.cbc"
+    grb_path = function_tmpdir / f"{gwf_name}.disv.grb"
+
+    assert hds_path.is_file(), f"HDS file not found: {hds_path}"
+    assert cbc_path.is_file(), f"CBC file not found: {cbc_path}"
+    assert grb_path.is_file(), f"GRB file not found: {grb_path}"
+
+    return {
+        "base": base_path,
+        "hds": hds_path,
+        "cbc": cbc_path,
+        "grb": grb_path,
+        "nlay": nlay,
+        "ncpl": ncpl,
+    }
+
+
+def test_open_hds_dis(dis_model_output):
+    paths = dis_model_output
+    head = open_hds(paths["hds"], paths["grb"])
+
+    assert isinstance(head, xr.DataArray)
+    assert set(head.dims) == {"time", "layer", "y", "x"}
+    assert head.shape == (1, paths["nlay"], paths["nrow"], paths["ncol"])
+    assert not np.all(np.isnan(head.values))
+
+
+def test_open_cbc_dis(dis_model_output):
+    paths = dis_model_output
+    cbc = open_cbc(paths["cbc"], paths["grb"])
+
+    assert isinstance(cbc, xr.Dataset)
+    # Should have at least flow-ja-face decomposed keys
+    keys = set(cbc.data_vars)
+    assert len(keys) > 0
+
+
+def test_open_cbc_dis_flowja(dis_model_output):
+    paths = dis_model_output
+    cbc = open_cbc(paths["cbc"], paths["grb"], flowja=True)
+
+    assert isinstance(cbc, xr.Dataset)
+    assert "flow-ja-face" in cbc.data_vars
+    assert "connectivity" in cbc.data_vars
+
+
+def test_open_hds_disv(disv_model_output):
+    paths = disv_model_output
+    head = open_hds(paths["hds"], paths["grb"])
+
+    assert isinstance(head, xu.UgridDataArray)
+    assert "time" in head.dims
+    assert "layer" in head.dims
+    ntime, nlayer = 1, paths["nlay"]
+    assert head.shape[0] == ntime
+    assert head.shape[1] == nlayer
+    assert head.shape[2] == paths["ncpl"]
+    assert not np.all(np.isnan(head.values))
+
+
+def test_open_cbc_disv(disv_model_output):
+    paths = disv_model_output
+    cbc = open_cbc(paths["cbc"], paths["grb"])
+
+    assert isinstance(cbc, (xr.Dataset, xu.UgridDataset))
+    keys = set(cbc.data_vars)
+    assert len(keys) > 0
+
+
+def test_open_cbc_disv_flowja(disv_model_output):
+    paths = disv_model_output
+    cbc = open_cbc(paths["cbc"], paths["grb"], flowja=True)
+
+    assert isinstance(cbc, (xr.Dataset, xu.UgridDataset))
+    assert "flow-ja-face" in cbc.data_vars
+    assert "connectivity" in cbc.data_vars
+
+
+def test_disv_from_grid_round_trip():
+    """Disv -> VertexGrid -> Disv should preserve all geometry."""
+    nlay, ncpl, nvert = 2, 4, 6
+    cells = [[0, 1, 4, 3], [1, 2, 5, 4]]
+    # Two quads; extend to ncpl=4 by duplicating cells
+    cells_full = cells + cells
+    cell2ddata = [
+        Disv.Cell2dRecord(n, float(n % 2) + 0.5, 0.5, 4, tuple(cells_full[n])) for n in range(ncpl)
+    ]
+    # 2×3 grid of vertices
+    xv = np.array([0.0, 1.0, 2.0, 0.0, 1.0, 2.0])
+    yv = np.array([1.0, 1.0, 1.0, 0.0, 0.0, 0.0])
+    botm = np.stack([np.zeros(ncpl), np.full(ncpl, -10.0)])
+    disv_orig = Disv(
+        nlay=nlay,
+        ncpl=ncpl,
+        nvert=nvert,
+        top=np.ones(ncpl),
+        botm=botm,
+        idomain=np.ones((nlay, ncpl), dtype=int),
+        iv=np.arange(nvert, dtype=int),
+        xv=xv,
+        yv=yv,
+        cell2ddata=cell2ddata,
+    )
+
+    # Convert to VertexGrid, then back to Disv
+    grid = disv_orig.to_grid()
+    assert isinstance(grid, VertexGrid)
+    disv_rt = Disv.from_grid(grid)
+
+    # Dimensions must be preserved
+    assert disv_rt.nlay == nlay
+    assert disv_rt.ncpl == ncpl
+    assert disv_rt.nvert == nvert
+
+    # idomain must be preserved (tests the `is not None` fix)
+    assert disv_rt.idomain is not None
+    np.testing.assert_array_equal(disv_rt.idomain, disv_orig.idomain)
+
+    # Vertex coordinates must be preserved
+    np.testing.assert_allclose(disv_rt.xv, disv_orig.xv)
+    np.testing.assert_allclose(disv_rt.yv, disv_orig.yv)
+
+
+# NetCDF head-reader tests (_open_hds_netcdf paths)
+
+
+def test_open_hds_dis_netcdf_conventional(function_tmpdir, dis_model_output):
+    """
+    _open_hds_netcdf — conventional CF structured format.
+
+    Writes a synthetic NetCDF file with a ``head(time, z, y, x)`` variable
+    and ``modflow_grid = "STRUCTURED"`` attribute (no ``mesh`` global
+    attribute), matching what MODFLOW 6 writes for DIS grids in structured
+    mode.
+    """
+    from pathlib import Path
+
+    paths = dis_model_output
+    nlay, nrow, ncol = paths["nlay"], paths["nrow"], paths["ncol"]
+    ntime = 2
+
+    rng = np.random.default_rng(0)
+    data = rng.random((ntime, nlay, nrow, ncol))
+    ds = xr.Dataset(
+        {"head": xr.DataArray(data, dims=("time", "z", "y", "x"))},
+        coords={"time": np.array([1.0, 2.0])},
+        attrs={"modflow_grid": "STRUCTURED"},
+    )
+    nc_path = Path(function_tmpdir) / "head_structured.nc"
+    ds.to_netcdf(nc_path)
+
+    head = open_hds(nc_path, paths["grb"])
+
+    assert isinstance(head, xr.DataArray)
+    assert head.dims == ("time", "layer", "y", "x")
+    assert head.shape == (ntime, nlay, nrow, ncol)
+    np.testing.assert_allclose(head.values, data)
+
+
+def test_open_hds_disv_netcdf_ugrid_layered(function_tmpdir, disv_model_output):
+    """
+    _open_hds_netcdf — CF-UGRID layered format for DISV.
+
+    Writes a synthetic NetCDF file with ``head_l1``, ``head_l2`` variables
+    on ``(time, nmesh_face)`` dimensions and a ``mesh`` global attribute,
+    matching what MODFLOW 6 writes for DISV grids in layered mesh mode.
+    """
+    from pathlib import Path
+
+    paths = disv_model_output
+    nlay, ncpl = paths["nlay"], paths["ncpl"]
+    ntime = 2
+
+    rng = np.random.default_rng(1)
+    layer_data = [rng.random((ntime, ncpl)) for _ in range(nlay)]
+    data_vars = {
+        f"head_l{k + 1}": xr.DataArray(layer_data[k], dims=("time", "nmesh_face"))
+        for k in range(nlay)
+    }
+    ds = xr.Dataset(
+        data_vars,
+        coords={"time": np.array([1.0, 2.0])},
+        attrs={"mesh": 1, "modflow_grid": "VERTEX"},
+    )
+    nc_path = Path(function_tmpdir) / "head_ugrid.nc"
+    ds.to_netcdf(nc_path)
+
+    head = open_hds(nc_path, paths["grb"])
+
+    assert isinstance(head, xu.UgridDataArray)
+    assert "time" in head.dims
+    assert "layer" in head.dims
+    assert head.shape[0] == ntime
+    assert head.shape[1] == nlay
+    assert head.shape[2] == ncpl
+    # Data values should round-trip exactly
+    for k in range(nlay):
+        np.testing.assert_allclose(head.values[:, k, :], layer_data[k])
+
+
+def test_open_hds_netcdf_grid_type_mismatch(function_tmpdir, dis_model_output):
+    """
+    _open_hds_netcdf raises ValueError when the NetCDF modflow_grid attribute
+    disagrees with the GRB file's grid type.
+    """
+    from pathlib import Path
+
+    paths = dis_model_output
+    nlay, nrow, ncol = paths["nlay"], paths["nrow"], paths["ncol"]
+
+    # Write a file claiming VERTEX but use the DIS GRB (which is STRUCTURED)
+    ds = xr.Dataset(
+        {"head": xr.DataArray(np.zeros((1, nlay, nrow, ncol)), dims=("time", "z", "y", "x"))},
+        coords={"time": np.array([1.0])},
+        attrs={"modflow_grid": "VERTEX"},
+    )
+    nc_path = Path(function_tmpdir) / "head_mismatch.nc"
+    ds.to_netcdf(nc_path)
+
+    with pytest.raises(ValueError, match="Grid type mismatch"):
+        open_hds(nc_path, paths["grb"])
+
+
+def test_open_hds_file_not_found(function_tmpdir, dis_model_output):
+    """open_hds raises FileNotFoundError for a missing HDS file."""
+    from pathlib import Path
+
+    paths = dis_model_output
+    with pytest.raises(FileNotFoundError):
+        open_hds(Path(function_tmpdir) / "nonexistent.hds", paths["grb"])
+
+
+def test_open_cbc_file_not_found(function_tmpdir, dis_model_output):
+    """open_cbc raises FileNotFoundError / passes through for missing CBC."""
+    from pathlib import Path
+
+    paths = dis_model_output
+    with pytest.raises((FileNotFoundError, OSError)):
+        open_cbc(Path(function_tmpdir) / "nonexistent.cbc", paths["grb"])
+
+
+def test_open_hds_dis_face_budget_values(dis_model_output):
+    """
+    The right/front/lower face flows should be non-trivially populated.
+
+    This regression test guards against the bug where dis_indices computed
+    wrong indices due to incorrect 0/1-based adjustment of ia/ja.
+    """
+    paths = dis_model_output
+    cbc = open_cbc(paths["cbc"], paths["grb"])
+    assert isinstance(cbc, xr.Dataset)
+    # At least one of the directional flows must contain non-zero values;
+    # if dis_indices is broken all face flows are zero or misindexed.
+    assert "flow-right-face" in cbc.data_vars or "flow-ja-face" in cbc.data_vars
+    # Check that the face flows have the right shape
+    if "flow-right-face" in cbc.data_vars:
+        right = cbc["flow-right-face"]
+        assert right.dims == ("time", "layer", "y", "x")
+        # Should have at least some non-zero values (CHD drives flow)
+        assert float(np.abs(right.values).max()) > 0.0