mass_balance.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7a929a10-67ab-4f42-a36d-afd71e3d7712",
   "metadata": {},
   "source": [
    "# Notebook 3 - Mass Balance on the Watershed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22b26789-ee69-4a4f-bcb4-8a6878dc68a2",
   "metadata": {},
   "source": [
    "## Import necessary packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "805c4ebe-2f65-4eba-8c12-767c9733444b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import xarray as xr\n",
    "import os\n",
    "\n",
    "import hydrofunctions as hf\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7aa6d6f8-96ac-4fda-a4fc-b574cc117d0b",
   "metadata": {},
   "source": [
    "## Make list of dates\n",
    "* Make sure these match with the snow files and the precip files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5a2ad93e-0162-4d23-90d0-9fef935b9dba",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "start_date = \"2021-11-10\"\n",
    "end_date = \"2021-12-10\"\n",
    "date_list = pd.date_range(start_date, end_date).to_list()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18488f79-9157-40a4-b4bc-e4a803c6c7b4",
   "metadata": {},
   "source": [
    "## Open the datasets generated in snow_volume_calculations and data_download"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7fea053c-7336-4170-8e2d-b1f7639106f6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "precip = xr.open_dataset(f'climate_data/ppt_{start_date}-{end_date}.nc')\n",
    "tmean = xr.open_dataset(f'climate_data/tmean_{start_date}-{end_date}.nc')\n",
    "snow_accumulation = xr.open_dataset(f'climate_data/snow_{start_date}-{end_date}.nc')\n",
    "snow_change = xr.open_dataset(f'climate_data/snow_change_{start_date}-{end_date}.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b4015e9-e383-46da-980b-2c319fcfab70",
   "metadata": {},
   "source": [
    "## Calculate runoff/day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "865aa87b-e3b1-4661-a455-0124053ea90e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "total_runoff = []\n",
    "\n",
    "for i in range(len(date_list)):\n",
    "    # runoff = precipitation - snow accumulation/melt\n",
    "    runoff = precip['ppt'].isel(date=i) - snow_change['__xarray_dataarray_variable__'].isel(date=i)\n",
    "    runoff.coords['date'] = date_list[i]\n",
    "    total_runoff.append(runoff)\n",
    "\n",
    "# make dataarray of runoff (length) on each day\n",
    "total_runoff = xr.concat(total_runoff, 'date')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09cc8d9c-e114-4363-9664-61fb730cfb36",
   "metadata": {},
   "source": [
    "## Convert runoff length to runoff volume and plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5888c21b-3a8d-445e-986a-5055a8d4e28b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.plot.facetgrid.FacetGrid at 0x7f7016f9de10>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1300x2400 with 33 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# runoff volume per square = runoff * 10**-3 (converson to m) * prism grid cell size (4kmx4km) \n",
    "# gives runoff/cell/day in m3\n",
    "total_runoff_volume_gridded = total_runoff * (10**-3) * (4000*4000)\n",
    "total_runoff_volume_gridded.isel(date=slice(0, 31, 1)).plot.imshow(col = 'date', col_wrap = 4, vmin = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f9474bb8-cffe-4234-8139-e51d163ca0c8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# sum across entire watershed\n",
    "daily_runoff_volume = total_runoff_volume_gridded.sum(dim = ('lat', 'lon'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c287905e-f7c5-4634-a852-044ce1eed664",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (date: 31)&gt;\n",
       "array([2.60922389e+07, 9.23390416e+06, 6.57570350e+07, 5.79638940e+07,\n",
       "       7.33935779e+07, 2.07389686e+08, 1.09883150e+08, 3.36259208e+05,\n",
       "       4.65107005e+05, 1.42315363e+07, 1.67589413e+06, 3.22402365e+04,\n",
       "       1.45087296e+07, 2.21235184e+07, 8.08886909e+06, 9.87649668e+06,\n",
       "       8.85645027e+07, 2.59634199e+07, 1.33582884e+08, 1.14463107e+08,\n",
       "       2.80380469e+07, 9.13157740e+07, 3.65923722e+07, 5.45564658e+05,\n",
       "       3.06306254e+06, 1.76927039e+06, 1.93978853e+06, 2.53072198e+06,\n",
       "       3.86471865e+07, 8.42535196e+06, 2.85363734e+06])\n",
       "Coordinates:\n",
       "  * date     (date) datetime64[ns] 2021-11-10 2021-11-11 ... 2021-12-10</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>date</span>: 31</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-d67358c2-8dac-4125-921e-e5d990b566ba' class='xr-array-in' type='checkbox' checked><label for='section-d67358c2-8dac-4125-921e-e5d990b566ba' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>2.609e+07 9.234e+06 6.576e+07 ... 3.865e+07 8.425e+06 2.854e+06</span></div><div class='xr-array-data'><pre>array([2.60922389e+07, 9.23390416e+06, 6.57570350e+07, 5.79638940e+07,\n",
       "       7.33935779e+07, 2.07389686e+08, 1.09883150e+08, 3.36259208e+05,\n",
       "       4.65107005e+05, 1.42315363e+07, 1.67589413e+06, 3.22402365e+04,\n",
       "       1.45087296e+07, 2.21235184e+07, 8.08886909e+06, 9.87649668e+06,\n",
       "       8.85645027e+07, 2.59634199e+07, 1.33582884e+08, 1.14463107e+08,\n",
       "       2.80380469e+07, 9.13157740e+07, 3.65923722e+07, 5.45564658e+05,\n",
       "       3.06306254e+06, 1.76927039e+06, 1.93978853e+06, 2.53072198e+06,\n",
       "       3.86471865e+07, 8.42535196e+06, 2.85363734e+06])</pre></div></div></li><li class='xr-section-item'><input id='section-d712d4b6-f6fb-46a1-965b-37cec923e2e4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d712d4b6-f6fb-46a1-965b-37cec923e2e4' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>date</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2021-11-10 ... 2021-12-10</div><input id='attrs-505c6823-3c51-4c28-9da1-e65ad0d80832' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-505c6823-3c51-4c28-9da1-e65ad0d80832' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d6a54bc-6f98-4200-acfd-1e10993dd570' class='xr-var-data-in' type='checkbox'><label for='data-3d6a54bc-6f98-4200-acfd-1e10993dd570' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2021-11-10T00:00:00.000000000&#x27;, &#x27;2021-11-11T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-11-12T00:00:00.000000000&#x27;, &#x27;2021-11-13T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-11-14T00:00:00.000000000&#x27;, &#x27;2021-11-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-11-16T00:00:00.000000000&#x27;, &#x27;2021-11-17T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-11-18T00:00:00.000000000&#x27;, &#x27;2021-11-19T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-11-20T00:00:00.000000000&#x27;, &#x27;2021-11-21T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-11-22T00:00:00.000000000&#x27;, &#x27;2021-11-23T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-11-24T00:00:00.000000000&#x27;, &#x27;2021-11-25T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-11-26T00:00:00.000000000&#x27;, &#x27;2021-11-27T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-11-28T00:00:00.000000000&#x27;, &#x27;2021-11-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-11-30T00:00:00.000000000&#x27;, &#x27;2021-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-12-02T00:00:00.000000000&#x27;, &#x27;2021-12-03T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-12-04T00:00:00.000000000&#x27;, &#x27;2021-12-05T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-12-06T00:00:00.000000000&#x27;, &#x27;2021-12-07T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-12-08T00:00:00.000000000&#x27;, &#x27;2021-12-09T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-12-10T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cd523eba-83de-43d0-8700-5e612c4c91c6' class='xr-section-summary-in' type='checkbox'  ><label for='section-cd523eba-83de-43d0-8700-5e612c4c91c6' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>date</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-84eec508-f804-450d-b3db-24d5401cbb62' class='xr-index-data-in' type='checkbox'/><label for='index-84eec508-f804-450d-b3db-24d5401cbb62' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2021-11-10&#x27;, &#x27;2021-11-11&#x27;, &#x27;2021-11-12&#x27;, &#x27;2021-11-13&#x27;,\n",
       "               &#x27;2021-11-14&#x27;, &#x27;2021-11-15&#x27;, &#x27;2021-11-16&#x27;, &#x27;2021-11-17&#x27;,\n",
       "               &#x27;2021-11-18&#x27;, &#x27;2021-11-19&#x27;, &#x27;2021-11-20&#x27;, &#x27;2021-11-21&#x27;,\n",
       "               &#x27;2021-11-22&#x27;, &#x27;2021-11-23&#x27;, &#x27;2021-11-24&#x27;, &#x27;2021-11-25&#x27;,\n",
       "               &#x27;2021-11-26&#x27;, &#x27;2021-11-27&#x27;, &#x27;2021-11-28&#x27;, &#x27;2021-11-29&#x27;,\n",
       "               &#x27;2021-11-30&#x27;, &#x27;2021-12-01&#x27;, &#x27;2021-12-02&#x27;, &#x27;2021-12-03&#x27;,\n",
       "               &#x27;2021-12-04&#x27;, &#x27;2021-12-05&#x27;, &#x27;2021-12-06&#x27;, &#x27;2021-12-07&#x27;,\n",
       "               &#x27;2021-12-08&#x27;, &#x27;2021-12-09&#x27;, &#x27;2021-12-10&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;date&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e09d6aa3-53c2-44d5-9245-104cebf526b1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e09d6aa3-53c2-44d5-9245-104cebf526b1' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (date: 31)>\n",
       "array([2.60922389e+07, 9.23390416e+06, 6.57570350e+07, 5.79638940e+07,\n",
       "       7.33935779e+07, 2.07389686e+08, 1.09883150e+08, 3.36259208e+05,\n",
       "       4.65107005e+05, 1.42315363e+07, 1.67589413e+06, 3.22402365e+04,\n",
       "       1.45087296e+07, 2.21235184e+07, 8.08886909e+06, 9.87649668e+06,\n",
       "       8.85645027e+07, 2.59634199e+07, 1.33582884e+08, 1.14463107e+08,\n",
       "       2.80380469e+07, 9.13157740e+07, 3.65923722e+07, 5.45564658e+05,\n",
       "       3.06306254e+06, 1.76927039e+06, 1.93978853e+06, 2.53072198e+06,\n",
       "       3.86471865e+07, 8.42535196e+06, 2.85363734e+06])\n",
       "Coordinates:\n",
       "  * date     (date) datetime64[ns] 2021-11-10 2021-11-11 ... 2021-12-10"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total runoff volume / day\n",
    "daily_runoff_volume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4950965e-3676-4c2d-9593-0062ecdbcd83",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# read in discharge\n",
    "discharge_df = pd.read_parquet('discharge_data/everson.parquet', engine = 'pyarrow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "aef6af0b-1b0b-450d-9bd7-c6c3a2690b6f",
   "metadata": {
    "tags": []
   },
   "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>USGS:12211200:00060:00003</th>\n",
       "      <th>USGS:12211200:00060:00003_qualifiers</th>\n",
       "      <th>USGS:12211200:00065:00003</th>\n",
       "      <th>USGS:12211200:00065:00003_qualifiers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetimeUTC</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-11-10 00:00:00+00:00</th>\n",
       "      <td>4850.0</td>\n",
       "      <td>A</td>\n",
       "      <td>75.21</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-11 00:00:00+00:00</th>\n",
       "      <td>4520.0</td>\n",
       "      <td>A</td>\n",
       "      <td>75.03</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-12 00:00:00+00:00</th>\n",
       "      <td>12500.0</td>\n",
       "      <td>A</td>\n",
       "      <td>78.27</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-13 00:00:00+00:00</th>\n",
       "      <td>10900.0</td>\n",
       "      <td>A</td>\n",
       "      <td>77.84</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-14 00:00:00+00:00</th>\n",
       "      <td>23000.0</td>\n",
       "      <td>A</td>\n",
       "      <td>81.07</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           USGS:12211200:00060:00003  \\\n",
       "datetimeUTC                                            \n",
       "2021-11-10 00:00:00+00:00                     4850.0   \n",
       "2021-11-11 00:00:00+00:00                     4520.0   \n",
       "2021-11-12 00:00:00+00:00                    12500.0   \n",
       "2021-11-13 00:00:00+00:00                    10900.0   \n",
       "2021-11-14 00:00:00+00:00                    23000.0   \n",
       "\n",
       "                          USGS:12211200:00060:00003_qualifiers  \\\n",
       "datetimeUTC                                                      \n",
       "2021-11-10 00:00:00+00:00                                    A   \n",
       "2021-11-11 00:00:00+00:00                                    A   \n",
       "2021-11-12 00:00:00+00:00                                    A   \n",
       "2021-11-13 00:00:00+00:00                                    A   \n",
       "2021-11-14 00:00:00+00:00                                    A   \n",
       "\n",
       "                           USGS:12211200:00065:00003  \\\n",
       "datetimeUTC                                            \n",
       "2021-11-10 00:00:00+00:00                      75.21   \n",
       "2021-11-11 00:00:00+00:00                      75.03   \n",
       "2021-11-12 00:00:00+00:00                      78.27   \n",
       "2021-11-13 00:00:00+00:00                      77.84   \n",
       "2021-11-14 00:00:00+00:00                      81.07   \n",
       "\n",
       "                          USGS:12211200:00065:00003_qualifiers  \n",
       "datetimeUTC                                                     \n",
       "2021-11-10 00:00:00+00:00                                    A  \n",
       "2021-11-11 00:00:00+00:00                                    A  \n",
       "2021-11-12 00:00:00+00:00                                    A  \n",
       "2021-11-13 00:00:00+00:00                                    A  \n",
       "2021-11-14 00:00:00+00:00                                    A  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "discharge_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e0fc2fda-427d-4d3d-84c5-9b7f8c38893f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# convert daily mean streamflow from cfs to m3/d\n",
    "daily_discharge_volume = discharge_df['USGS:12211200:00060:00003']*60*60*24/35.314666212661"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "676f8d7d-dca8-4aaa-83bb-78e90d0fca3b",
   "metadata": {},
   "source": [
    "## Calculate the difference between runoff and streamflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "48f38131-a3a9-45e1-ada2-066627dc0696",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "daily_streamflow_difference = []\n",
    "daily_streamflow_percent = []\n",
    "\n",
    "for i in range(len(date_list)):\n",
    "    # daily volume difference between total runoff and total discharge\n",
    "    daily_delta = daily_runoff_volume.isel(date=i).values - daily_discharge_volume.iloc[i]\n",
    "    \n",
    "    # percent volume difference\n",
    "    daily_percent = 100*daily_delta/daily_discharge_volume.iloc[i]\n",
    "    daily_streamflow_difference.append(daily_delta)\n",
    "    daily_streamflow_percent.append(daily_percent)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75364a27-00a2-401d-a880-08d66a275437",
   "metadata": {},
   "source": [
    "## Plot the % difference\n",
    "Wow! They're super off from each other. Anecdotally from professors, PRISM tends to overestimate precipitation in this region, although this error does seem to be too high. Investigating the snow volume calculations a little bit more, as well as using CO-OP station,  might help me understand where all of this is going wrong. Another thing would be to subtract baseflow from discharge, although that would make this error larger for the most part."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "92ec680a-d2cd-4e9c-a3ed-50787769e636",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '% Difference between Calculated Runoff and Measured Streamflow')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, a = plt.subplots(figsize = (8, 4))\n",
    "a.plot(date_list, daily_streamflow_percent)\n",
    "a.xaxis.set_ticks(date_list[::5])\n",
    "a.axhline(0, color = 'k')\n",
    "a.set_title('% Difference between Calculated Runoff and Measured Streamflow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a2919c3-80d5-4e3a-89da-2fb6af3a50d5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ccffe941-df8f-4ad2-8084-894331762acc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}