ResamplETR2Grid.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interpolate SNODAS Infil to model grid\n",
    "Here I'll interpolate the SNODAS infiltration netcdf to the model grid.  I'll start with just the single mean daily infiltration.  Which SEEMS way too high at the moment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#imports\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as mcolors\n",
    "import numpy as np\n",
    "import flopy\n",
    "from gsflow.builder import GenerateFishnet, FlowAccumulation\n",
    "from gsflow.builder import ModflowBuilder\n",
    "import pdb\n",
    "import rasterio as rio\n",
    "from rasterio.plot import show as rshow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read in the average netcdf raster data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "##################################################\n",
    "# Input path management\n",
    "##################################################\n",
    "\n",
    "# set our cell size in meters.  #I'll start out with 100m for now, but will likely refine\n",
    "cellsize=100\n",
    "method='nearest'\n",
    "\n",
    "#model name\n",
    "model_name = \"Rattlesnakecreek_%3im\"%cellsize+method\n",
    "\n",
    "#project path\n",
    "project_path = \"/Users/payton.gardner/proj/active/RattlensnakeModel\"\n",
    "\n",
    "#toplevel output path\n",
    "model_path = os.path.join(project_path,\"models\",model_name)\n",
    "\n",
    "#derived gis output path\n",
    "gis_derived_path = os.path.join(project_path,model_path,\"gis_deriv\")\n",
    "\n",
    "#gis data path\n",
    "gis_data_path = os.path.join(project_path,'data','gis')\n",
    "\n",
    "# precip data path\n",
    "etr_file = \"daily_mean_etr.nc\"\n",
    "etr_data_path = os.path.join(project_path,'data','ETR',etr_file)\n",
    "\n",
    "#grid file path\n",
    "gridname = 'grid.bin'\n",
    "mg_file = os.path.join(model_path, gridname)\n",
    "\n",
    "#mf output path\n",
    "#mf_path = os.path.join(model_path,'modflow')\n",
    "mf_path = model_path\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#######################################################\n",
    "#Load Data\n",
    "#######################################################\n",
    "# load model grid\n",
    "modelgrid = GenerateFishnet.load_from_file(mg_file)\n",
    "\n",
    "# load raster data with flopy\n",
    "robj = flopy.utils.Raster.load(etr_data_path)\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# resample etr data to grid\n",
    "etr_data = robj.resample_to_grid(\n",
    "    modelgrid, \n",
    "    robj.bands[0], \n",
    "    method=method, \n",
    "    multithread=False, \n",
    "    thread_pool=12\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make a plot of the data\n",
    "# plot the resampled DEM with the modelgrid overlain\n",
    "fig2 = plt.figure(figsize=(10,9))\n",
    "ax2 = fig2.add_subplot(1, 1, 1, aspect=\"equal\")\n",
    "\n",
    "pmv = flopy.plot.PlotMapView(modelgrid=modelgrid, ax=ax2)\n",
    "ax2 = pmv.plot_array(\n",
    "    etr_data, masked_values=robj.nodatavals, vmin=etr_data.min(), vmax=etr_data.max()\n",
    ")\n",
    "lc = pmv.plot_grid(color=\"grey\", lw=0.3)\n",
    "plt.title(\"Resample etr data\")\n",
    "plt.colorbar(ax2, shrink=0.7)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 316, 405)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelgrid.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(316, 405)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "etr_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the resampled data\n",
    "etrresampled_file = 'etrresampled.txt'\n",
    "etrresampled_rfile = 'etrresampled.tif'\n",
    "etrresampled_file = os.path.join(gis_derived_path,etrresampled_file)\n",
    "etrresampled_rfile = os.path.join(gis_derived_path,etrresampled_rfile)\n",
    "\n",
    "#######################################################\n",
    "#Save Data\n",
    "#######################################################\n",
    "\n",
    "np.savetxt(etrresampled_file, etr_data, delimiter=\"  \")\n",
    "\n",
    "#output a tif file for post processing with SAGA.  \n",
    "flopy.export.utils.export_array(modelgrid,etrresampled_rfile,\\\n",
    "                                etr_data,fieldname='ETR')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "gsflow",
   "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}