{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "*This notebook contains material from [Controlling Natural Watersheds](https://jckantor.github.io/Controlling-Natural-Watersheds);\n", "content is available [on Github](https://github.com/jckantor/Controlling-Natural-Watersheds.git).*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "< [USGS Surface Water Daily Data](http://nbviewer.jupyter.org/github/jckantor/Controlling-Natural-Watersheds/blob/master/notebooks/A.07-USGS_Surface_Water_Daily_Data.ipynb) | [Contents](toc.ipynb) | [Rule Curves for Rainy and Namakan Lakes](http://nbviewer.jupyter.org/github/jckantor/Controlling-Natural-Watersheds/blob/master/notebooks/A.09-Rule_Curves_for_Rainy_and_Namakan_Lakes.ipynb) >
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Stage-Volume Relationships for Rainy and Namakan Lakes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Stage-Volume relationships expresses the relationship between lake surface elevation and the amount of water stored in the lake. For most flow and rule curve calculations, the most important feature of the stage-volume relationship is the change in lake volume associated with a change in lake volume.\n",
"\n",
"This notebook displays stage-volume relationship data for Rainy Lake and Namakan Reservoir obtained from\n",
"\n",
">Thompson, A.F. (2013). Rainy and Namakan Hydrologic Response Model Documentation. Prepared for the Evaluation of the International Joint Commission 2000 Order for Rainy and Namakan Lakes and Rainy River.\n",
"\n",
"The data is fitted to a polynomial function to create a dictionary of interpolation functions \n",
"\n",
"```\n",
"volume[key]()\n",
"```\n",
"\n",
"and differentiated to create a dictionary of area functions \n",
"\n",
"```\n",
"area[key]()\n",
"```\n",
"\n",
"These are pickled and stored in the data directory in files of the same name."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initialization"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Initilization of the graphics system and computational modules used in this IPython notebook."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Display graphics inline with the notebook\n",
"%matplotlib inline\n",
"\n",
"# Standard Python modules\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import os\n",
"import datetime\n",
"import seaborn as sns\n",
"sns.set_context('talk')\n",
"\n",
"# Modules to display images and data tables\n",
"from IPython.display import Image\n",
"from IPython.core.display import display\n",
"\n",
"# Data directory\n",
"dir = '../data/'\n",
"img = '../images/'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Stage-Volume Data - Thompson"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following data is extracted from Tables 1 and 2 of the Thompson report. Evidently these data were extracted from a 1932 stage/volume curve derived from earlier hydrographic measurements of the lakes.\n",
"\n",
"The data is stored here in a dictionary constructed as key, dataframe pairs. (this is the old data. We want to add a new data set, then compare new versus old estimates of area as function of lake level.)."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data = {}\n",
"\n",
"data['Rainy Lake'] = pd.DataFrame(\n",
" [[335.0, 112.67],\n",
" [336.0, 798.00],\n",
" [336.5, 1176.42],\n",
" [337.0, 1577.25],\n",
" [337.5, 2002.06],\n",
" [338.0, 2450.57],\n",
" [339.0, 3416.85],\n",
" [340.0, 4458.97]],\n",
" columns = ['stage','volume'])\n",
"\n",
"data['Namakan Reservoir'] = pd.DataFrame(\n",
" [[337.0, 65.33],\n",
" [338.0, 259.95],\n",
" [338.5, 364.20],\n",
" [339.0, 475.58],\n",
" [339.5, 592.46],\n",
" [340.0, 712.28],\n",
" [340.5, 836.56],\n",
" [341.0, 966.17],\n",
" [341.5, 1099.79],\n",
" [342.0, 1239.68],\n",
" [343.0, 1540.75]],\n",
" columns = ['stage','volume'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Everything from this point on should be automatic and require no editing."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
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7/Pbbb9DT00PJkiUBAKGhoXmu79NeMyB7/0aOHImyZcvi8OHDsLa2hkgkwuLFi/HkyROV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\nSUVORK5CYII=\n",
"text/plain": [
" "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}