Streamflow_Evaluation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "695a3f6a-dce5-4900-9580-ba9bb622aff9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rjohnson18\\Anaconda3\\lib\\site-packages\\xarray\\backends\\cfgrib_.py:27: UserWarning: Failed to load cfgrib - most likely there is a problem accessing the ecCodes library. Try `import cfgrib` to get the full error message\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import Streamflow_Eval\n",
    "import os\n",
    "\n",
    "\n",
    "#set path directory\n",
    "cwd = os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "be588977-7e56-4966-b4cb-73b2324f3cc6",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#site information\n",
    "#set up for Provo River at river road bridge !!! these sites may not be currently colocated!!!\n",
    "NWISsite = '10163000'\n",
    "NWMsegment = 4909714 #Can we drive this value as a function of the NWIS site?\n",
    "startDT ='2020-07-01'\n",
    "endDT ='2020-07-31'\n",
    "freq = 'D'\n",
    "\n",
    "#Run parameter setting function\n",
    "Streamflow = Streamflow_Eval.Reach_Eval(NWISsite, NWMsegment, startDT, endDT, freq, cwd)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "50de8513",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating the summary statistics of the catchment\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NWIS_site_id</th>\n",
       "      <th>Drainage_area_mi2</th>\n",
       "      <th>Mean_Basin_Elev_ft</th>\n",
       "      <th>Perc_Forest</th>\n",
       "      <th>Perc_Develop</th>\n",
       "      <th>Perc_Imperv</th>\n",
       "      <th>Perc_Herbace</th>\n",
       "      <th>Perc_Slop_30</th>\n",
       "      <th>Mean_Ann_Precip_in</th>\n",
       "      <th>Ann_low_cfs</th>\n",
       "      <th>Ann_mean_cfs</th>\n",
       "      <th>Ann_hi_cfs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10163000</td>\n",
       "      <td>0.0988</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>8.25</td>\n",
       "      <td>71.2</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.16</td>\n",
       "      <td>12.7</td>\n",
       "      <td>34.6</td>\n",
       "      <td>191.0</td>\n",
       "      <td>606.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  NWIS_site_id Drainage_area_mi2 Mean_Basin_Elev_ft Perc_Forest Perc_Develop  \\\n",
       "0     10163000            0.0988             4500.0        8.25         71.2   \n",
       "\n",
       "  Perc_Imperv Perc_Herbace Perc_Slop_30 Mean_Ann_Precip_in Ann_low_cfs  \\\n",
       "0        36.8          0.0         1.16               12.7        34.6   \n",
       "\n",
       "  Ann_mean_cfs Ann_hi_cfs  \n",
       "0        191.0      606.6  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Get a Summary of the catchment\n",
    "Streamflow.get_StreamStats()\n",
    "\n",
    "#Plot interactive map of catchment\n",
    "Streamflow.WatershedMap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c6c5f570",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retrieving USGS site  10163000  data\n",
      "Calculating the summary statistics of the catchment\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NWIS_site_id</th>\n",
       "      <th>Drainage_area_mi2</th>\n",
       "      <th>Mean_Basin_Elev_ft</th>\n",
       "      <th>Perc_Forest</th>\n",
       "      <th>Perc_Develop</th>\n",
       "      <th>Perc_Imperv</th>\n",
       "      <th>Perc_Herbace</th>\n",
       "      <th>Perc_Slop_30</th>\n",
       "      <th>Mean_Ann_Precip_in</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10163000</td>\n",
       "      <td>0.0988</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>8.25</td>\n",
       "      <td>71.2</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.16</td>\n",
       "      <td>12.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  NWIS_site_id Drainage_area_mi2 Mean_Basin_Elev_ft Perc_Forest Perc_Develop  \\\n",
       "0     10163000            0.0988             4500.0        8.25         71.2   \n",
       "\n",
       "  Perc_Imperv Perc_Herbace Perc_Slop_30 Mean_Ann_Precip_in  \n",
       "0        36.8          0.0         1.16               12.7  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retrieving NWM reach  4909714  data\n",
      "The NWM demonstrates the following model performance\n",
      "R2 =  -66.72956442406755\n",
      "RMSE =  51.903214093264005 ft3/s\n",
      "Maximum error =  68.99330675180477 ft3/s\n",
      "Mean Absolute Percentage Error =  92.03464416437814 %\n",
      "Kling-Gupta Efficiency =  -0.4581394720229153\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Get NWIS data\n",
    "Streamflow.NWIS_retrieve()\n",
    "\n",
    "#Get NWM data\n",
    "Streamflow.NWM_retrieve()\n",
    "\n",
    "#Compare NWM performance with USGS observations\n",
    "Streamflow.NWM_Eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2f490f6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88312366",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "510a5aca",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0% |                                                                        |\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating the summary statistics of the catchment for  PROVO RIVER AT PROVO, UT , USGS:  10163000\n",
      "Calculating the summary statistics of the catchment for  DOLORES RIVER NEAR CISCO, UT , USGS:  09180000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  1% |                                                                        |\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating the summary statistics of the catchment for  COLORADO RIVER NEAR CISCO, UT , USGS:  09180500\n",
      "Calculating the summary statistics of the catchment for  MILL CREEK AT SHELEY TUNNEL, NEAR MOAB, UT , USGS:  09183500\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  2% |#                                                                       |\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating the summary statistics of the catchment for  MILL CREEK BELOW SHELEY TUNNEL, NEAR MOAB, UT , USGS:  09183600\n",
      "Calculating the summary statistics of the catchment for  PACK CREEK AT PACK CREEK ROAD BRIDGE NEAR MOAB, UT , USGS:  09184600\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  4% |##                                                                      |\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating the summary statistics of the catchment for  MILL CREEK BLW PACK CREEK NR MOUTH AT MOAB, UT , USGS:  09185100\n",
      "Calculating the summary statistics of the catchment for  COLORADO RIVER AT POTASH, UT , USGS:  09185600\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  5% |###                                                                     |\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating the summary statistics of the catchment for  GREEN RIVER NEAR GREENDALE, UT , USGS:  09234500\n",
      "Calculating the summary statistics of the catchment for  BLACKS FORK NEAR ROBERTSON, WY , USGS:  09217900\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  6% |####                                                                    |\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating the summary statistics of the catchment for  WHITE RIVER NEAR WATSON, UTAH , USGS:  09306500\n",
      "Calculating the summary statistics of the catchment for  GREEN RIVER NEAR JENSEN, UT , USGS:  09261000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  8% |#####                                                                   |\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating the summary statistics of the catchment for  GREEN RIVER AT OURAY, UTAH , USGS:  09272400\n",
      "Calculating the summary statistics of the catchment for  BIG BRUSH CRK ABV RED FLEET RES, NR VERNAL, UT , USGS:  09261700\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  9% |######                                                                  |\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating the summary statistics of the catchment for  ASHLEY CREEK NEAR VERNAL, UT , USGS:  09266500\n",
      "Calculating the summary statistics of the catchment for  DUCHESNE RIVER NEAR TABIONA, UT , USGS:  09277500\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 10% |#######                                                                 |\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating the summary statistics of the catchment for  ROCK CREEK NEAR MOUNTAIN HOME, UT , USGS:  09279000\n",
      "Calculating the summary statistics of the catchment for  LAKE FORK RIVER AB MOON LAKE, NR MOUNTAIN HOME, UT , USGS:  09289500\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 12% |########                                                                |\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating the summary statistics of the catchment for  LAKE FORK RIVER BL MOON LAKE NR MOUNTAIN HOME, UT , USGS:  09291000\n",
      "Calculating the summary statistics of the catchment for  YELLOWSTONE R @ BRIDGE CMPGRND NR ALTONAH, UT , USGS:  09292000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 13% |#########                                                               |\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating the summary statistics of the catchment for  YELLOWSTONE RIVER NEAR ALTONAH, UT , USGS:  09292500\n",
      "Calculating the summary statistics of the catchment for  DUCHESNE RIVER AT MYTON, UT , USGS:  09295000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 14% |##########                                                              |\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating the summary statistics of the catchment for  DUCHESNE RIV ABV UINTA RIV, NR RANDLETT, UT , USGS:  09295100\n",
      "Calculating the summary statistics of the catchment for  UINTA R BLW POWERPLANT DIVERSION, NR NEOLA, UT , USGS:  09296800\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 16% |###########                                                             |\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating the summary statistics of the catchment for  WHITEROCKS RIVER NEAR WHITEROCKS, UT , USGS:  09299500\n",
      "Calculating the summary statistics of the catchment for  UINTA RIVER AT RANDLETT, UT , USGS:  09301500\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 17% |############                                                            |\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating the summary statistics of the catchment for  DUCHESNE RIVER NEAR RANDLETT, UT , USGS:  09302000\n",
      "Calculating the summary statistics of the catchment for  STRAWBERRY RIVER AT PINNACLES NEAR FRUITLAND, UT , USGS:  09285900\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 18% |#############                                                           |\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating the summary statistics of the catchment for  CURRANT CREEK NEAR FRUITLAND, UT , USGS:  09288000\n",
      "Calculating the summary statistics of the catchment for  STRAWBERRY RIVER NEAR DUCHESNE, UT , USGS:  09288180\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 20% |##############                                                          |\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating the summary statistics of the catchment for  FAIRVIEW TUNNEL NEAR FAIRVIEW, UT , USGS:  09309600\n",
      "Calculating the summary statistics of the catchment for  FISH CREEK ABOVE RESERVOIR, NEAR SCOFIELD, UT , USGS:  09310500\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 21% |###############                                                         |\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating the summary statistics of the catchment for  MUD CRK BL WINTER QUARTERS CYN @ SCOFIELD, UT , USGS:  09310700\n",
      "Calculating the summary statistics of the catchment for  WHITE R BL TABBYUNE CRK NR SOLDIER SUMMIT, UT , USGS:  09312600\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 22% |################                                                        |\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating the summary statistics of the catchment for  PRICE RIVER AT WOODSIDE, UT , USGS:  09314500\n",
      "Calculating the summary statistics of the catchment for  GREEN RIVER AT GREEN RIVER, UT , USGS:  09315000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 24% |#################                                                       |\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating the summary statistics of the catchment for  GREEN RIVER AT MINERAL BOTTOM NR CYNLNDS NTL PARK , USGS:  09328920\n",
      "Calculating the summary statistics of the catchment for  EPHRAIM TUNNEL NEAR EPHRAIM, UT , USGS:  09319000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 25% |##################                                                      |\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating the summary statistics of the catchment for  FERRON CREEK (UPPER STATION) NEAR FERRON, UT , USGS:  09326500\n"
     ]
    }
   ],
   "source": [
    "#This functions gets all of the USGS sites per state, gets the summary statistics for all sites, and then saves to csv\n",
    "\n",
    "state = 'ut'\n",
    "\n",
    "Streamflow.get_USGS_site_info(state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f42bd992",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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