diff --git a/examples/grid_convergence.ipynb b/examples/grid_convergence.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8a52864e",
+   "metadata": {},
+   "source": [
+    "# Grid Convergence for the TRTS \n",
+    "This is an example of a grid conergence study using a Delft3d case on the Tanana River Test Site (TRTS) located in Nenana, Alaska. In the figure shown bellow is the arial veiw of the test site with the river entering from the bottom of the image and exiting at the top. THe TRTS is used as a test deploment location for Current Energy Converters (CECs). The figure shows an example of a set of transects taken to survey the test site. This particular set of trasects was taken without a CEC in the water, however, a similar patern of trasects would be used if a CEC was operating. This experimental data is used as a comparison for the Delft3D model. Befor comparing to experimental data a grid convergence study is done on the DElft3D modle. The IEC 62600-301 specifies that the grid should be refined so the Grid convergence Idex (GCI) is less than 6% for water level and depth-averaged current speed. The TRTS Delft3D grid used a retagular grid squar grid cells. This example will plot the depth-averaged current speed and water level for the TRTS model with a 8m, 4m, 2m and 1m grid cell edge length.\n",
+    "\n",
+    "<img src=\"data/river/ADCP_transect/transects_w_labels.png\" />  \n",
+    "\n",
+    "First the Delft3d moduel for MHKiT is imported along with sevreal other python packages. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "1bdeb6cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipy.interpolate as interp\n",
+    "import matplotlib.pyplot as plt\n",
+    "from mhkit.river.io import d3d\n",
+    "import geopandas as gpd\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import pygeoops\n",
+    "import netCDF4\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ab347c4",
+   "metadata": {},
+   "source": [
+    "## Load data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a153432",
+   "metadata": {},
+   "source": [
+    "### Load Delft3d Data\n",
+    "\n",
+    "Load the Delft3D model output for all the grid lenghs: 8m, 4m, 2m, and 1m. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "be06f1b2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the NetCDF file\n",
+    "dataset_8m_raw = netCDF4.Dataset('FlowFM_map_8m-test.nc') #FlowFM_8m_map.nc # test was run on th HPC cluster\n",
+    "## Test xarray input #dataset_8m_xr = xr.open_dataset('FlowFM_8m_map.nc')\n",
+    "#dataset_4m_raw = netCDF4.Dataset('')\n",
+    "#dataset_2m_raw = netCDF4.Dataset('')\n",
+    "#dataset_1m_raw = netCDF4.Dataset('')\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f327230",
+   "metadata": {},
+   "source": [
+    "### Load River Shape \n",
+    "\n",
+    "Load the shape of the river to calculate the centerline points. The shape of the river was found by doing a bathimetric survey. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "af932c27",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Load the shapefile\n",
+    "combined_geometry = gpd.read_file('combined_geometry.shp')\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "688329f3",
+   "metadata": {},
+   "source": [
+    "## Centerline Points \n",
+    "Use pygeoops to find the centeline shape. Then the shape is used to find 100 points on the cneterline."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f1c54ad4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_39752\\3746482274.py:1: DeprecationWarning: The 'unary_union' attribute is deprecated, use the 'union_all()' method instead.\n",
+      "  river  =combined_geometry.geometry.unary_union\n",
+      "C:\\Users\\ashly\\AppData\\Local\\Temp\\ipykernel_39752\\3746482274.py:18: UserWarning: Legend does not support handles for PatchCollection instances.\n",
+      "See: https://matplotlib.org/stable/tutorials/intermediate/legend_guide.html#implementing-a-custom-legend-handler\n",
+      "  ax.legend()\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Calculate the centerline using pygeoops\n",
+    "river  =combined_geometry.geometry.unary_union\n",
+    "centerline_py= pygeoops.centerline(river)\n",
+    "\n",
+    "\n",
+    "# Plot the combined geometry and centerline\n",
+    "fig, ax = plt.subplots(figsize=(10, 6))\n",
+    "\n",
+    "# Plot the shape\n",
+    "combined_geometry.plot(ax=ax, color='lightblue', edgecolor='black', alpha=0.5, label='River Shape')\n",
+    "\n",
+    "# Plot the centerline\n",
+    "x, y = centerline_py.xy\n",
+    "ax.plot(x, y, color='red', linewidth=2, label='Centerline')\n",
+    "\n",
+    "# Add labels and legend\n",
+    "ax.set_xlabel('UTM x')\n",
+    "ax.set_ylabel('UTM y')\n",
+    "ax.legend()\n",
+    "plt.title('Centerline with River Shape')\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ff984de5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
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+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get the total length of the centerline\n",
+    "centerline_length = centerline_py.length\n",
+    "\n",
+    "# Generate 100 evenly spaced points along the centerline\n",
+    "points_on_centerline = [centerline_py.interpolate(distance) for distance in np.linspace(0, centerline_length, 100)]\n",
+    "\n",
+    "# Convert the points to a list of coordinates\n",
+    "points_coordinates = [(point.x, point.y) for point in points_on_centerline]\n",
+    "\n",
+    "# Display the first 100 points\n",
+    "points_coordinates"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "343f0251",
+   "metadata": {},
+   "source": [
+    "## Depth Averaged Current Speed\n",
+    "\n",
+    "The MHKiT Delft3D (D3D) function `get_all_data_points` is used to import the variable `mesh2d_ucmaga` which is the Flow element center depth-averaged velocity magnitude. The `get_all_data_points` function outputs the coresponding x and y cordinates along with the water depth, water level and time step. The time step defalts to -1 or the last time step in the simulation. The datafram for the 8m grid length resolution is shown."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ff69cb2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>x</th>\n",
+       "      <th>y</th>\n",
+       "      <th>waterdepth</th>\n",
+       "      <th>waterlevel</th>\n",
+       "      <th>mesh2d_ucmaga</th>\n",
+       "      <th>time</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>index</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>400993.370690</td>\n",
+       "      <td>7.160784e+06</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>124.629313</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>86400.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>401000.888231</td>\n",
+       "      <td>7.160787e+06</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>124.601929</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>86400.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>400990.634529</td>\n",
+       "      <td>7.160792e+06</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>124.629313</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>86400.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>401008.405772</td>\n",
+       "      <td>7.160790e+06</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>124.557983</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>86400.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>400998.152070</td>\n",
+       "      <td>7.160795e+06</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>124.655420</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>86400.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10277</th>\n",
+       "      <td>401116.829970</td>\n",
+       "      <td>7.161638e+06</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>121.468827</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>86400.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10278</th>\n",
+       "      <td>401106.576268</td>\n",
+       "      <td>7.161643e+06</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>120.626144</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>86400.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10279</th>\n",
+       "      <td>401124.347511</td>\n",
+       "      <td>7.161641e+06</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>122.397965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>86400.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10280</th>\n",
+       "      <td>401114.093809</td>\n",
+       "      <td>7.161646e+06</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>121.468827</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>86400.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10281</th>\n",
+       "      <td>401121.611350</td>\n",
+       "      <td>7.161648e+06</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>122.530091</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>86400.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10282 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   x             y  waterdepth  waterlevel  mesh2d_ucmaga  \\\n",
+       "index                                                                       \n",
+       "0      400993.370690  7.160784e+06         0.0  124.629313            0.0   \n",
+       "1      401000.888231  7.160787e+06         0.0  124.601929            0.0   \n",
+       "2      400990.634529  7.160792e+06         0.0  124.629313            0.0   \n",
+       "3      401008.405772  7.160790e+06         0.0  124.557983            0.0   \n",
+       "4      400998.152070  7.160795e+06         0.0  124.655420            0.0   \n",
+       "...              ...           ...         ...         ...            ...   \n",
+       "10277  401116.829970  7.161638e+06         0.0  121.468827            0.0   \n",
+       "10278  401106.576268  7.161643e+06         0.0  120.626144            0.0   \n",
+       "10279  401124.347511  7.161641e+06         0.0  122.397965            0.0   \n",
+       "10280  401114.093809  7.161646e+06         0.0  121.468827            0.0   \n",
+       "10281  401121.611350  7.161648e+06         0.0  122.530091            0.0   \n",
+       "\n",
+       "          time  \n",
+       "index           \n",
+       "0      86400.0  \n",
+       "1      86400.0  \n",
+       "2      86400.0  \n",
+       "3      86400.0  \n",
+       "4      86400.0  \n",
+       "...        ...  \n",
+       "10277  86400.0  \n",
+       "10278  86400.0  \n",
+       "10279  86400.0  \n",
+       "10280  86400.0  \n",
+       "10281  86400.0  \n",
+       "\n",
+       "[10282 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "variable=\"mesh2d_ucmaga\"\n",
+    "dataset_8m =d3d.get_all_data_points(dataset_8m_raw, variable)\n",
+    "#dataset_4m =d3d.get_all_data_points(dataset_4m_raw, variable)\n",
+    "#dataset_2m =d3d.get_all_data_points(dataset_2m_raw, variable)\n",
+    "#dataset_1m =d3d.get_all_data_points(dataset_1m_raw, variable)\n",
+    "dataset_8m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0d519280",
+   "metadata": {},
+   "source": [
+    "### Interpolate Velocity onto Centerline \n",
+    "\n",
+    "Scipy's interp griddata is used to interpolate the velocity data onto the centerline points. A for loop is used to iterate over the 4 grid resolutions. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "bd1d76bf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grid_resolutions = ['8m']# '4m', '2m', '1m']\n",
+    "center_line_velocity = {}\n",
+    "\n",
+    "for resolution in grid_resolutions:\n",
+    "    dataset = globals()[f'dataset_{resolution}']\n",
+    "    center_line_velocity[resolution] = interp.griddata(\n",
+    "        dataset[[\"x\", \"y\"]],\n",
+    "        dataset[variable],\n",
+    "        centerline_points[[\"x\", \"y\"]],\n",
+    "    )\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "c4ea6a28",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'4m'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[10], line 4\u001b[0m\n\u001b[0;32m      2\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m10\u001b[39m, \u001b[38;5;241m6\u001b[39m))\n\u001b[0;32m      3\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(centerline_points\u001b[38;5;241m.\u001b[39mx, center_line_velocity[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m8m\u001b[39m\u001b[38;5;124m'\u001b[39m], marker\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mo\u001b[39m\u001b[38;5;124m'\u001b[39m, linestyle\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m-\u001b[39m\u001b[38;5;124m'\u001b[39m, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m'\u001b[39m,label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m8m_grid\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m----> 4\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(centerline_points\u001b[38;5;241m.\u001b[39mx, \u001b[43mcenter_line_velocity\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m4m\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m, marker\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mo\u001b[39m\u001b[38;5;124m'\u001b[39m, linestyle\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m-\u001b[39m\u001b[38;5;124m'\u001b[39m, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m'\u001b[39m,label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m4m_grid\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      5\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(centerline_points\u001b[38;5;241m.\u001b[39mx, center_line_velocity[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m2m\u001b[39m\u001b[38;5;124m'\u001b[39m], marker\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mo\u001b[39m\u001b[38;5;124m'\u001b[39m, linestyle\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m-\u001b[39m\u001b[38;5;124m'\u001b[39m, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m'\u001b[39m,label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m2m_grid\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      6\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(centerline_points\u001b[38;5;241m.\u001b[39mx, center_line_velocity[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m1m\u001b[39m\u001b[38;5;124m'\u001b[39m], marker\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mo\u001b[39m\u001b[38;5;124m'\u001b[39m, linestyle\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m-\u001b[39m\u001b[38;5;124m'\u001b[39m, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m'\u001b[39m,label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m1m_grid\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "\u001b[1;31mKeyError\u001b[0m: '4m'"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot Depth Averaged Velocity over Centerline\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(centerline_points.x, center_line_velocity['8m'], marker='o', linestyle='-', color='b',label=\"8m_grid\")\n",
+    "plt.plot(centerline_points.x, center_line_velocity['4m'], marker='o', linestyle='-', color='b',label=\"4m_grid\")\n",
+    "plt.plot(centerline_points.x, center_line_velocity['2m'], marker='o', linestyle='-', color='b',label=\"2m_grid\")\n",
+    "plt.plot(centerline_points.x, center_line_velocity['1m'], marker='o', linestyle='-', color='b',label=\"1m_grid\")\n",
+    "plt.xlabel('UTM x')\n",
+    "plt.ylabel('Depth Averaged Velocity (m/s)')\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb58926f",
+   "metadata": {},
+   "source": [
+    "## Waterlevel\n",
+    "\n",
+    "The waterlevel data was already obtrained from the depth averaged velocity dataframe. The same for loop process is used to interpolate all 4 grid sizes onto the centerline poins.The results are then ploted. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ad2a1d78",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grid_resolutions = ['8m']# '4m', '2m', '1m']\n",
+    "center_line_velocity = {}\n",
+    "\n",
+    "for resolution in grid_resolutions:\n",
+    "    dataset = globals()[f'dataset_{resolution}']\n",
+    "    center_line_waterlevel[resolution] = interp.griddata(\n",
+    "        dataset[[\"x\", \"y\"]],\n",
+    "        dataset[\"waterlevel\"],\n",
+    "        centerline_points[[\"x\", \"y\"]],\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6b0e39ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(centerline_points.x, center_line_waterlevel['8m'], marker='o', linestyle='-', color='b', label=\"8m_grid\")\n",
+    "plt.plot(centerline_points.x, center_line_waterlevel['4m'], marker='o', linestyle='-', color='b', label=\"4m_grid\")\n",
+    "plt.plot(centerline_points.x, center_line_waterlevel['2m'], marker='o', linestyle='-', color='b', label=\"2m_grid\")\n",
+    "plt.plot(centerline_points.x, center_line_waterlevel['1m'], marker='o', linestyle='-', color='b', label=\"1m_grid\")\n",
+    "plt.xlabel('UTM x')\n",
+    "plt.ylabel('Water Level')\n",
+    "plt.grid(True)\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "30bba60f",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'4m'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[26], line 28\u001b[0m\n\u001b[0;32m     26\u001b[0m gci_results \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m     27\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(grid_resolutions) \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m):\n\u001b[1;32m---> 28\u001b[0m     fine \u001b[38;5;241m=\u001b[39m \u001b[43mcenter_line_velocity\u001b[49m\u001b[43m[\u001b[49m\u001b[43mgrid_resolutions\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\n\u001b[0;32m     29\u001b[0m     coarse \u001b[38;5;241m=\u001b[39m center_line_velocity[grid_resolutions[i]]\n\u001b[0;32m     30\u001b[0m     refinement_ratio \u001b[38;5;241m=\u001b[39m refinement_ratios[i]\n",
+      "\u001b[1;31mKeyError\u001b[0m: '4m'"
+     ]
+    }
+   ],
+   "source": [
+    "def calculate_grid_convergence_index(fine_grid, coarse_grid, refinement_ratio,factor_of_safety=1.25, order=2):\n",
+    "    \"\"\"\n",
+    "    Calculate the Grid Convergence Index (GCI) between two grid sizes. https://www.grc.nasa.gov/WWW/wind/valid/tutorial/spatconv.html\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    fine_grid: numpy.ndarray\n",
+    "        Results from the finer grid.\n",
+    "    coarse_grid: numpy.ndarray\n",
+    "        Results from the coarser grid.\n",
+    "    refinement_ratio: float \n",
+    "        Refinement ratio between the grids.\n",
+    "    order: int\n",
+    "        Order of accuracy (default is 2).\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    gci: float\n",
+    "        Grid Convergence Index (GCI).\n",
+    "    \"\"\"\n",
+    "    # Calculate the approximate relative error\n",
+    "    error = np.abs((fine_grid - coarse_grid) / fine_grid)\n",
+    "\n",
+    "    # Calculate the GCI\n",
+    "    gci = (factor_of_safety * error) / (refinement_ratio**order - 1)\n",
+    "    return gci\n",
+    "\n",
+    "# Example usage\n",
+    "# Assuming `center_line_velocity` contains velocity data for different grid resolutions\n",
+    "grid_resolutions = ['8m', '4m', '2m', '1m']\n",
+    "refinement_ratios = [2, 2, 2]  # Refinement ratio between consecutive grids\n",
+    "\n",
+    "gci_results = {}\n",
+    "for i in range(len(grid_resolutions) - 1):\n",
+    "    fine = center_line_velocity[grid_resolutions[i + 1]]\n",
+    "    coarse = center_line_velocity[grid_resolutions[i]]\n",
+    "    refinement_ratio = refinement_ratios[i]\n",
+    "    gci_results[f\"{grid_resolutions[i]}-{grid_resolutions[i + 1]}\"] = calculate_grid_convergence_index(\n",
+    "        fine, coarse, refinement_ratio\n",
+    "    )\n",
+    "\n",
+    "# Print GCI results\n",
+    "for key, value in gci_results.items():\n",
+    "    print(f\"GCI between {key}: {value}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5ba67eac",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python_310",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/mhkit/river/io/d3d.py b/mhkit/river/io/d3d.py
index 7295d7e1..847795a8 100644
--- a/mhkit/river/io/d3d.py
+++ b/mhkit/river/io/d3d.py
@@ -35,8 +35,8 @@ def get_all_time(data: netCDF4.Dataset) -> NDArray:
         simulation conditions at that time.
     """
 
-    if not isinstance(data, netCDF4.Dataset):
-        raise TypeError("data must be a NetCDF4 object")
+    if not isinstance(data, (netCDF4.Dataset, xr.Dataset)):
+        raise TypeError("data must be a NetCDF4 object or xarray Dataset")
 
     seconds_run = np.ma.getdata(data.variables["time"][:], False)
 
@@ -244,6 +244,10 @@ def get_layer_data(
                 "name": "mesh2d_nLayers",
                 "coords": data.variables["mesh2d_layer_sigma"][:],
             },
+            "mesh2d_face_x mesh2d_face_y mesh2d_layer_sigma": {
+                "name": "mesh2d_nLayers",
+                "coords": data.variables["mesh2d_layer_sigma"][:],
+            },
             "mesh2d_edge_x mesh2d_edge_y": {
                 "name": "mesh2d_nInterfaces",
                 "coords": data.variables["mesh2d_interface_sigma"][:],
@@ -253,7 +257,7 @@ def get_layer_data(
             data.variables["mesh2d_waterdepth"][time_index, :], False
         )
         waterlevel = np.ma.getdata(data.variables["mesh2d_s1"][time_index, :], False)
-        coords = str(data.variables["waterdepth"].coordinates).split()
+        coords = str(data.variables["mesh2d_waterdepth"].coordinates).split()
 
     elif str(data.variables[variable].coordinates) == "FlowElem_xcc FlowElem_ycc":
         cords_to_layers = {
@@ -637,6 +641,10 @@ def get_all_data_points(
                 "name": "mesh2d_nLayers",
                 "coords": data.variables["mesh2d_layer_sigma"][:],
             },
+            "mesh2d_face_x mesh2d_face_y mesh2d_layer_sigma": {
+                "name": "mesh2d_nLayers",
+                "coords": data.variables["mesh2d_layer_sigma"][:],
+            },
             "mesh2d_edge_x mesh2d_edge_y": {
                 "name": "mesh2d_nInterfaces",
                 "coords": data.variables["mesh2d_interface_sigma"][:],
@@ -886,3 +894,33 @@ def list_variables(data: Union[netCDF4.Dataset, xr.Dataset, xr.DataArray]) -> Li
         "data must be a NetCDF4 Dataset, xarray Dataset, or "
         f"xarray DataArray. Got: {type(data)}"
     )
+
+
+def calculate_grid_convergence_index(
+    fine_grid, coarse_grid, refinement_ratio, factor_of_safety=1.25, order=2
+):
+    """
+    Calculate the Grid Convergence Index (GCI) between two grid sizes. https://www.grc.nasa.gov/WWW/wind/valid/tutorial/spatconv.html
+
+    Parameters
+    ----------
+    fine_grid: numpy.ndarray
+        Results from the finer grid.
+    coarse_grid: numpy.ndarray
+        Results from the coarser grid.
+    refinement_ratio: float
+        Refinement ratio between the grids.
+    order: int
+        Order of accuracy (default is 2).
+
+    Returns
+    -------
+    gci: float
+        Grid Convergence Index (GCI).
+    """
+    # Calculate the approximate relative error
+    error = np.abs((fine_grid - coarse_grid) / fine_grid)
+
+    # Calculate the GCI
+    gci = (factor_of_safety * error) / (refinement_ratio**order - 1)
+    return gci