notebooks/gridmet.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_marker": "\"\"\""
   },
   "source": [
    "# Climate Data from GridMET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-12T23:05:08.325945Z",
     "iopub.status.busy": "2021-11-12T23:05:08.325171Z",
     "iopub.status.idle": "2021-11-12T23:05:09.703892Z",
     "shell.execute_reply": "2021-11-12T23:05:09.704334Z"
    }
   },
   "outputs": [],
   "source": [
    "from __future__ import annotations\n",
    "\n",
    "from pathlib import Path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import pygridmet as gridmet\n",
    "from pygridmet import GridMET\n",
    "from pynhd import NLDI"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Daymet database provides climatology data at 1-km resolution. First, we use [PyNHD](/~https://github.com/hyriver/pynhd) to get the contributing watershed geometry of a NWIS station with the ID of `USGS-01318500`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-12T23:05:09.708922Z",
     "iopub.status.busy": "2021-11-12T23:05:09.708051Z",
     "iopub.status.idle": "2021-11-12T23:05:10.262066Z",
     "shell.execute_reply": "2021-11-12T23:05:10.262509Z"
    }
   },
   "outputs": [],
   "source": [
    "geometry = NLDI().get_basins(\"01318500\").geometry.iloc[0]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[PyGridMET](/~https://github.com/hyriver/pygridmet) allows us to get the data for a single pixel or for a region as gridded data. The function to get single pixel is called `pygridmet.get_bycoords` and for gridded data is called `pygridmet.get_bygeom`. The arguments of these functions are identical except the first argument where the latter\n",
    "should be polygon and the former should be a coordinate (a tuple of length two as in `(x, y)`).\n",
    "\n",
    "The input geometry or coordinate can be in any valid CRS (defaults to EPSG:4326). The `date`\n",
    "argument can be either a tuple of length two like `(start_str, end_str)` or a list of years\n",
    "like `[2000, 2005]`.\n",
    "\n",
    "We can get a dataframe of available variables and their info by calling `GridMET().gridmet_table`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>variable</th>\n",
       "      <th>abbr</th>\n",
       "      <th>long_name</th>\n",
       "      <th>units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Precipitation</td>\n",
       "      <td>pr</td>\n",
       "      <td>precipitation_amount</td>\n",
       "      <td>mm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Maximum Relative Humidity</td>\n",
       "      <td>rmax</td>\n",
       "      <td>daily_maximum_relative_humidity</td>\n",
       "      <td>%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Minimum Relative Humidity</td>\n",
       "      <td>rmin</td>\n",
       "      <td>daily_minimum_relative_humidity</td>\n",
       "      <td>%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Specific Humidity</td>\n",
       "      <td>sph</td>\n",
       "      <td>daily_mean_specific_humidity</td>\n",
       "      <td>kg/kg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Surface Radiation</td>\n",
       "      <td>srad</td>\n",
       "      <td>daily_mean_shortwave_radiation_at_surface</td>\n",
       "      <td>W/m2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Wind Direction</td>\n",
       "      <td>th</td>\n",
       "      <td>daily_mean_wind_direction</td>\n",
       "      <td>Degrees clockwise from north</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Minimum Air Temperature</td>\n",
       "      <td>tmmn</td>\n",
       "      <td>daily_minimum_temperature</td>\n",
       "      <td>K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Maximum Air Temperature</td>\n",
       "      <td>tmmx</td>\n",
       "      <td>daily_maximum_temperature</td>\n",
       "      <td>K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Wind Speed</td>\n",
       "      <td>vs</td>\n",
       "      <td>daily_mean_wind_speed</td>\n",
       "      <td>m/s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Burning Index</td>\n",
       "      <td>bi</td>\n",
       "      <td>daily_mean_burning_index_g</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Fuel Moisture (100-hr)</td>\n",
       "      <td>fm100</td>\n",
       "      <td>dead_fuel_moisture_100hr</td>\n",
       "      <td>%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Fuel Moisture (1000-hr)</td>\n",
       "      <td>fm1000</td>\n",
       "      <td>dead_fuel_moisture_1000hr</td>\n",
       "      <td>%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Energy Release Component</td>\n",
       "      <td>erc</td>\n",
       "      <td>daily_mean_energy_release_component-g</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Reference Evapotranspiration (Alfalfa)</td>\n",
       "      <td>etr</td>\n",
       "      <td>daily_mean_reference_evapotranspiration_alfalfa</td>\n",
       "      <td>mm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Reference Evapotranspiration (Grass)</td>\n",
       "      <td>pet</td>\n",
       "      <td>daily_mean_reference_evapotranspiration_grass</td>\n",
       "      <td>mm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Vapor Pressure Deficit</td>\n",
       "      <td>vpd</td>\n",
       "      <td>daily_mean_vapor_pressure_deficit</td>\n",
       "      <td>kPa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  variable    abbr  \\\n",
       "0                            Precipitation      pr   \n",
       "1                Maximum Relative Humidity    rmax   \n",
       "2                Minimum Relative Humidity    rmin   \n",
       "3                        Specific Humidity     sph   \n",
       "4                        Surface Radiation    srad   \n",
       "5                           Wind Direction      th   \n",
       "6                  Minimum Air Temperature    tmmn   \n",
       "7                  Maximum Air Temperature    tmmx   \n",
       "8                               Wind Speed      vs   \n",
       "9                            Burning Index      bi   \n",
       "10                  Fuel Moisture (100-hr)   fm100   \n",
       "11                 Fuel Moisture (1000-hr)  fm1000   \n",
       "12                Energy Release Component     erc   \n",
       "13  Reference Evapotranspiration (Alfalfa)     etr   \n",
       "14    Reference Evapotranspiration (Grass)     pet   \n",
       "15                  Vapor Pressure Deficit     vpd   \n",
       "\n",
       "                                          long_name  \\\n",
       "0                              precipitation_amount   \n",
       "1                   daily_maximum_relative_humidity   \n",
       "2                   daily_minimum_relative_humidity   \n",
       "3                      daily_mean_specific_humidity   \n",
       "4         daily_mean_shortwave_radiation_at_surface   \n",
       "5                         daily_mean_wind_direction   \n",
       "6                         daily_minimum_temperature   \n",
       "7                         daily_maximum_temperature   \n",
       "8                             daily_mean_wind_speed   \n",
       "9                        daily_mean_burning_index_g   \n",
       "10                         dead_fuel_moisture_100hr   \n",
       "11                        dead_fuel_moisture_1000hr   \n",
       "12            daily_mean_energy_release_component-g   \n",
       "13  daily_mean_reference_evapotranspiration_alfalfa   \n",
       "14    daily_mean_reference_evapotranspiration_grass   \n",
       "15                daily_mean_vapor_pressure_deficit   \n",
       "\n",
       "                           units  \n",
       "0                             mm  \n",
       "1                              %  \n",
       "2                              %  \n",
       "3                          kg/kg  \n",
       "4                           W/m2  \n",
       "5   Degrees clockwise from north  \n",
       "6                              K  \n",
       "7                              K  \n",
       "8                            m/s  \n",
       "9                              -  \n",
       "10                             %  \n",
       "11                             %  \n",
       "12                             -  \n",
       "13                            mm  \n",
       "14                            mm  \n",
       "15                           kPa  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GridMET().gridmet_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-12T23:05:10.271498Z",
     "iopub.status.busy": "2021-11-12T23:05:10.264889Z",
     "iopub.status.idle": "2021-11-12T23:05:12.068712Z",
     "shell.execute_reply": "2021-11-12T23:05:12.069139Z"
    }
   },
   "outputs": [],
   "source": [
    "dates = (\"2000-01-01\", \"2000-01-06\")\n",
    "daily = gridmet.get_bygeom(geometry, dates, variables=[\"pr\", \"pet\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-12T23:05:12.079769Z",
     "iopub.status.busy": "2021-11-12T23:05:12.079054Z",
     "iopub.status.idle": "2021-11-12T23:05:12.876835Z",
     "shell.execute_reply": "2021-11-12T23:05:12.877379Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = daily.where(daily.pet > 0).pet.plot(x=\"lon\", y=\"lat\", row=\"time\", col_wrap=3)\n",
    "ax.fig.savefig(Path(\"_static\", \"gridmet_grid.png\"), facecolor=\"w\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the default CRS is EPSG:4326. If the input geometry (or coordinate) is in a different CRS we can pass it to the function. The gridded data are automatically masked to the input geometry. Now, Let's get the data for a coordinate in EPSG:3542 CRS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-12T23:05:16.182780Z",
     "iopub.status.busy": "2021-11-12T23:05:16.182324Z",
     "iopub.status.idle": "2021-11-12T23:05:19.172582Z",
     "shell.execute_reply": "2021-11-12T23:05:19.172905Z"
    }
   },
   "outputs": [],
   "source": [
    "coords = (-1431147.7928, 318483.4618)\n",
    "crs = 3542\n",
    "dates = (\"2000-01-01\", \"2006-12-31\")\n",
    "clm = gridmet.get_bycoords(coords, dates, variables=[\"pr\", \"tmmn\"], crs=crs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-12T23:05:19.178170Z",
     "iopub.status.busy": "2021-11-12T23:05:19.177597Z",
     "iopub.status.idle": "2021-11-12T23:05:19.652747Z",
     "shell.execute_reply": "2021-11-12T23:05:19.653081Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6, 4), facecolor=\"w\")\n",
    "\n",
    "gs = fig.add_gridspec(1, 2)\n",
    "axes = gs[:].subgridspec(2, 1, hspace=0).subplots(sharex=True)\n",
    "clm[\"tmmn (K)\"].plot(ax=axes[0], color=\"r\")\n",
    "axes[0].set_ylabel(r\"$T_{min}$ ($^\\circ$C)\")\n",
    "axes[0].xaxis.set_ticks_position(\"none\")\n",
    "clm[\"pr (mm)\"].plot(ax=axes[1])\n",
    "axes[1].set_ylabel(\"$P$ (mm/day)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "fig.savefig(\"_static/gridmet_loc.png\", facecolor=\"w\", bbox_inches=\"tight\")"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "003c82ad5971cfdf345b67bc4c483230f4144cd11e9aff06d93cf3af71d62f25"
  },
  "kernelspec": {
   "display_name": "Python 3.9.7 64-bit ('hyriver-dev': conda)",
   "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": 4
}