notebooks/ShortRangeForecasts.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Short_range National Water Model Forecast\n",
    "### Workflow for creating an 18 hour stream flow forecast given a date, time, and NHDplus COMID (stream segment identifier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys; sys.path.append('..')\n",
    "from fcast import ShortRange, Assim, get_USGS_rc, plotShortRange\n",
    "import gcsfs\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set up the Google Cloud File System"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "fs = gcsfs.GCSFileSystem(project='national-water-model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set the parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "date = '20190802' # a date\n",
    "start_hr = '00' # start at 00:00 because its easy at the moment\n",
    "comid = 4512772 # some small segment on the Potomac near DC\n",
    "# comid = 229757 # the South Platte River in Denver\n",
    "# comid = 7471678 # somewhere on the Mississippi River"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get short range streamflow forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>streamflow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-08-02 00:00:00</th>\n",
       "      <td>112.699997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-02 01:00:00</th>\n",
       "      <td>119.389997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-02 02:00:00</th>\n",
       "      <td>124.859997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-02 03:00:00</th>\n",
       "      <td>129.759997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-02 04:00:00</th>\n",
       "      <td>133.689997</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     streamflow\n",
       "2019-08-02 00:00:00  112.699997\n",
       "2019-08-02 01:00:00  119.389997\n",
       "2019-08-02 02:00:00  124.859997\n",
       "2019-08-02 03:00:00  129.759997\n",
       "2019-08-02 04:00:00  133.689997"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim = Assim(fs, comid, date, start_hr)\n",
    "sr = ShortRange(fs, comid, date, start_hr)\n",
    "sr_df = sr.get_streamflow(sim.assim_time, sim.assim_flow)\n",
    "sr_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sr_df.all().values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotShortRange(sr_df, comid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convert Flow to Stage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Stage</th>\n",
       "      <th>Discharge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.3048</td>\n",
       "      <td>30.085741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.6096</td>\n",
       "      <td>96.720480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.9144</td>\n",
       "      <td>180.899337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.2192</td>\n",
       "      <td>323.340296</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Stage   Discharge\n",
       "0  0.0000    0.000000\n",
       "1  0.3048   30.085741\n",
       "2  0.6096   96.720480\n",
       "3  0.9144  180.899337\n",
       "4  1.2192  323.340296"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f, rc_df = sr.get_NWM_rc()\n",
    "rc_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply the NWM rating curve and convert from streamflow to stage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "stage_df = sr_df.apply(f).rename(columns={'streamflow': 'stage'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the forecasted stage depth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotShortRange(stage_df, comid, flow=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compare NWM rating curve with the USGS rating curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No rating curve available, please set `get_rc` = False for station 01646502\n",
      "<scipy.interpolate.interpolate.interp1d object at 0x000002AE45B878B8>\n"
     ]
    },
    {
     "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>INDEP</th>\n",
       "      <th>SHIFT</th>\n",
       "      <th>DEP</th>\n",
       "      <th>INDEP_SHIFT</th>\n",
       "      <th>INDEP_m</th>\n",
       "      <th>SHIFT_m</th>\n",
       "      <th>DEP_cms</th>\n",
       "      <th>INDEP_SHIFT_m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>223.00</td>\n",
       "      <td>2.01</td>\n",
       "      <td>0.612648</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.314657</td>\n",
       "      <td>0.612648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>226.14</td>\n",
       "      <td>2.02</td>\n",
       "      <td>0.615696</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.403572</td>\n",
       "      <td>0.615696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>228.00</td>\n",
       "      <td>2.03</td>\n",
       "      <td>0.618744</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.456241</td>\n",
       "      <td>0.618744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>230.80</td>\n",
       "      <td>2.04</td>\n",
       "      <td>0.621792</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.535528</td>\n",
       "      <td>0.621792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>233.00</td>\n",
       "      <td>2.05</td>\n",
       "      <td>0.624840</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.597825</td>\n",
       "      <td>0.624840</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   INDEP  SHIFT     DEP  INDEP_SHIFT   INDEP_m  SHIFT_m   DEP_cms  \\\n",
       "0   2.01    0.0  223.00         2.01  0.612648      0.0  6.314657   \n",
       "1   2.02    0.0  226.14         2.02  0.615696      0.0  6.403572   \n",
       "2   2.03    0.0  228.00         2.03  0.618744      0.0  6.456241   \n",
       "3   2.04    0.0  230.80         2.04  0.621792      0.0  6.535528   \n",
       "4   2.05    0.0  233.00         2.05  0.624840      0.0  6.597825   \n",
       "\n",
       "   INDEP_SHIFT_m  \n",
       "0       0.612648  \n",
       "1       0.615696  \n",
       "2       0.618744  \n",
       "3       0.621792  \n",
       "4       0.624840  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "USGS_rcs = get_USGS_rc(comid)\n",
    "print(USGS_rcs[0])\n",
    "USGS_rcs[1].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply it to the stream flow data to convert it to stage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>stage_usgs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-08-02 00:00:00</th>\n",
       "      <td>0.975155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-02 01:00:00</th>\n",
       "      <td>0.986826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-02 02:00:00</th>\n",
       "      <td>0.996148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-02 03:00:00</th>\n",
       "      <td>1.004343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-02 04:00:00</th>\n",
       "      <td>1.010814</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     stage_usgs\n",
       "2019-08-02 00:00:00    0.975155\n",
       "2019-08-02 01:00:00    0.986826\n",
       "2019-08-02 02:00:00    0.996148\n",
       "2019-08-02 03:00:00    1.004343\n",
       "2019-08-02 04:00:00    1.010814"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stage_df_usgs = sr_df.apply(USGS_rcs[0]).rename(columns={'streamflow': 'stage_usgs'})\n",
    "stage_df_usgs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot them and compare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 = stage_df_usgs.plot(figsize=(20,6), title='Short-range 18-hour forecast')\n",
    "ax = stage_df.plot(ax=ax1, figsize=(20,6), title='Short-range 18-hour forecast')\n",
    "ax.grid(True, which=\"both\")\n",
    "ax.set(xlabel='Date', ylabel='Depth (m)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# USGS rating curve equates to a higher stage than the NWM rating curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:gis]",
   "language": "python",
   "name": "conda-env-gis-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}