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WeaveDoc.html
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<!DOCTYPE html>
<HTML lang = "en">
<HEAD>
<meta charset="UTF-8"/>
<meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
<title>DRIP Water Resource Allocation Tool</title>
<script type="text/x-mathjax-config">
MathJax.Hub.Config({
tex2jax: {inlineMath: [['$','$'], ['\\(','\\)']]}
});
</script>
<script src='https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
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font-weight: 600;
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/*
* Skeleton V2.0.4
* Copyright 2014, Dave Gamache
* www.getskeleton.com
* Free to use under the MIT license.
* http://www.opensource.org/licenses/mit-license.php
* 12/29/2014
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<BODY>
<div class ="container">
<div class = "row">
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<div class="title">
<h1 class="title">DRIP Water Resource Allocation Tool</h1>
<h5>Lisa Rennels and Nick Depsky</h5>
<h5>11th May 2018</h5>
</div>
<h1>I. Intro</h1>
<p>DRIP is a water resource allocation tool, modeled after <a href="http://weap21.org">WEAP</a> from the <a href="https://www.sei.org/centres/us/">Stockholm Environment Institute's U.S. Center</a>. WEAP can be summarized as follows:</p>
<p>*WEAP ("Water Evaluation And Planning" system) is a user-friendly software tool that takes an integrated approach to water resources planning.</p>
<p>Freshwater management challenges are increasingly common. Allocation of limited water resources between agricultural, municipal and environmental uses now requires the full integration of supply, demand, water quality and ecological considerations. The Water Evaluation and Planning system, or WEAP, aims to incorporate these issues into a practical yet robust tool for integrated water resources planning. WEAP is developed by the Stockholm Environment Institute's U.S. Center.*</p>
<p>The source code for DRIP is available on <a href="https://github.com/lrennels/ER290A-finalproject">GitHub</a> and includes several script files as well as input data files to run a small example. In addition to the modeling in Julia, we created a small model in <code>WEAP</code> for testing purposes. The required software is proprietary, so it is not included in Github.</p>
<h1>II. User Guide and Example</h1>
<h4>1. Setup</h4>
<p>In order to use DRIP, you will first want to include the helper functions as follows:</p>
<pre class='hljl'>
<span class='hljl-k'>using</span><span class='hljl-t'> </span><span class='hljl-n'>Weave</span><span class='hljl-t'>
</span><span class='hljl-nf'>include</span><span class='hljl-p'>(</span><span class='hljl-s'>"helper_functions.jl"</span><span class='hljl-p'>)</span>
</pre>
<pre class="hljl">
(::#8) (generic function with 1 method)
</pre>
<p>Next, set up some basic date information:</p>
<pre class='hljl'>
<span class='hljl-kd'>const</span><span class='hljl-t'> </span><span class='hljl-n'>start_year</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-ni'>1990</span><span class='hljl-t'>
</span><span class='hljl-kd'>const</span><span class='hljl-t'> </span><span class='hljl-n'>stop_year</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-ni'>2017</span><span class='hljl-t'>
</span><span class='hljl-kd'>const</span><span class='hljl-t'> </span><span class='hljl-n'>tstep</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-ni'>12</span><span class='hljl-t'>
</span><span class='hljl-kd'>const</span><span class='hljl-t'> </span><span class='hljl-n'>modays</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-p'>[</span><span class='hljl-ni'>31</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-ni'>28</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-ni'>31</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-ni'>30</span><span class='hljl-t'> </span><span class='hljl-p'>,</span><span class='hljl-ni'>31</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-ni'>30</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-ni'>31</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-ni'>31</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-ni'>30</span><span class='hljl-t'> </span><span class='hljl-p'>,</span><span class='hljl-ni'>31</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-ni'>30</span><span class='hljl-t'> </span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-ni'>31</span><span class='hljl-p'>];</span>
</pre>
<h4>2. Define Demand</h4>
<p>The <code>DefineDemand.jl</code> script allows a user to define specific <strong>demand</strong> nodes of which can be of several categories including agricultural, municipal, industrial, and instream flow requirements (IFRs). Each demand nodes has several metadata requirements including</p>
<ul>
<li><p>months - a list of months (<em>12 by 1 array of Strings</em>)</p>
</li>
<li><p>node_type - type of node (<em>String</em>)</p>
</li>
<li><p>name - name of the node (<em>String</em>)</p>
</li>
<li><p>size - size of the node (<em>Float64</em>)</p>
</li>
<li><p>rate - monthly rate of flow through node (<em>12 by 1 array of Float64</em>) </p>
</li>
<li><p>size_units - units of size variable (<em>String</em>)</p>
</li>
<li><p>demand_units = units of rate variable (<em>String</em>)</p>
</li>
<li><p>priority - demand priority (<em>Int64</em>)</p>
</li>
<li><p>Loc- location (or position) in list of all nodes (<em>Int64</em>)</p>
</li>
</ul>
<p>Once all demand nodes are defined, you can create the structures with an <code>include</code> statement and convert them to a DataFrame structure using the <code>rdf</code> function as follows:</p>
<pre class='hljl'>
<span class='hljl-nf'>include</span><span class='hljl-p'>(</span><span class='hljl-s'>"DefineDemand.jl"</span><span class='hljl-p'>)</span><span class='hljl-t'>
</span><span class='hljl-n'>demand</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-nf'>ddf</span><span class='hljl-p'>(</span><span class='hljl-n'>demand_nodes</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-n'>start_year</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-n'>stop_year</span><span class='hljl-p'>)</span><span class='hljl-t'>
</span><span class='hljl-nf'>head</span><span class='hljl-p'>(</span><span class='hljl-n'>demand</span><span class='hljl-p'>)</span>
</pre>
<table class="data-frame"><thead><tr><th></th><th>Date</th><th>Name</th><th>Quantity</th><th>Units</th><th>Loc</th></tr></thead><tbody><tr><th>1</th><td>1990-01-01</td><td>City: Berkeley</td><td>1.2e9</td><td>MM3</td><td>1</td></tr><tr><th>2</th><td>1990-02-01</td><td>City: Berkeley</td><td>1.2e9</td><td>MM3</td><td>1</td></tr><tr><th>3</th><td>1990-03-01</td><td>City: Berkeley</td><td>1.2e9</td><td>MM3</td><td>1</td></tr><tr><th>4</th><td>1990-04-01</td><td>City: Berkeley</td><td>1.2e9</td><td>MM3</td><td>1</td></tr><tr><th>5</th><td>1990-05-01</td><td>City: Berkeley</td><td>1.2e9</td><td>MM3</td><td>1</td></tr><tr><th>6</th><td>1990-06-01</td><td>City: Berkeley</td><td>1.2e9</td><td>MM3</td><td>1</td></tr></tbody></table>
<p><em>The <code>include</code> call will return a list of dictionaries, one for each node, and the <code>rdf</code> call will convert this data structure into a <code>DataFrame</code> for use by the main DRIP script.</em></p>
<p>You can also graphically view the demand time series using <code>dplot</code></p>
<pre class='hljl'>
<span class='hljl-nf'>dplot</span><span class='hljl-p'>(</span><span class='hljl-n'>demand</span><span class='hljl-p'>,</span><span class='hljl-ni'>1990</span><span class='hljl-p'>,</span><span class='hljl-ni'>1990</span><span class='hljl-p'>)</span>
</pre>
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" />
<h4>3. Define Supply</h4>
<p>The <code>DefineSupply.jl</code> script allows a user to define specific <strong>supply</strong> nodes of which can be of several categories including tributary inflow, catchment inflows, return flows, and groundwater. Each supply nodes has several metadata requirements including</p>
<ul>
<li><p>filepath - the file path of th e.csv file containing flow information (<em>String</em>)</p>
</li>
<li><p>name - name of supply node (<em>String</em>)</p>
</li>
<li><p>supply_units - units of rate variable (<em>String</em>)</p>
</li>
<li><p>Loc- location (or position) in list of all nodes (<em>Int64</em>)</p>
</li>
</ul>
<p>Note that supply information is assumed to be stored in a .csv file in the format of month (Date format) in the first column and Supply (number format) in the second column. Example files can be found in this folder eg. <em>NYuba_Inflow_Month</em>.</p>
<p>Once all supply nodes are defined, you can create the structures with an <code>include</code> statement and convert them to a DataFrame structure using the <code>rdf</code> function as follows:</p>
<pre class='hljl'>
<span class='hljl-nf'>include</span><span class='hljl-p'>(</span><span class='hljl-s'>"DefineSupply.jl"</span><span class='hljl-p'>)</span><span class='hljl-t'>
</span><span class='hljl-n'>supply</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-nf'>sdf</span><span class='hljl-p'>(</span><span class='hljl-n'>supply_nodes</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-n'>start_year</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-n'>stop_year</span><span class='hljl-p'>)</span><span class='hljl-t'>
</span><span class='hljl-nf'>head</span><span class='hljl-p'>(</span><span class='hljl-n'>supply</span><span class='hljl-p'>)</span>
</pre>
<table class="data-frame"><thead><tr><th></th><th>Date</th><th>Name</th><th>Quantity</th><th>Units</th><th>Loc</th></tr></thead><tbody><tr><th>1</th><td>1990-01-01</td><td>Headflow</td><td>11.603494113119</td><td>CMS</td><td>1</td></tr><tr><th>2</th><td>1990-02-01</td><td>Headflow</td><td>9.22421291025</td><td>CMS</td><td>1</td></tr><tr><th>3</th><td>1990-03-01</td><td>Headflow</td><td>22.4477366842534</td><td>CMS</td><td>1</td></tr><tr><th>4</th><td>1990-04-01</td><td>Headflow</td><td>30.919810751272</td><td>CMS</td><td>1</td></tr><tr><th>5</th><td>1990-05-01</td><td>Headflow</td><td>22.4766483000266</td><td>CMS</td><td>1</td></tr><tr><th>6</th><td>1990-06-01</td><td>Headflow</td><td>17.521922805016</td><td>CMS</td><td>1</td></tr></tbody></table>
<p><em>The <code>include</code> call will return a list of dictionaries, one for each node, and the <code>sdf</code> call will convert this data structure into a <code>DataFrame</code> for use by the main DRIP script.</em></p>
<p>You can also graphically view the demand time series using <code>splot</code></p>
<pre class='hljl'>
<span class='hljl-nf'>splot</span><span class='hljl-p'>(</span><span class='hljl-n'>supply</span><span class='hljl-p'>,</span><span class='hljl-ni'>1990</span><span class='hljl-p'>,</span><span class='hljl-ni'>2017</span><span class='hljl-p'>)</span>
</pre>
<img 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" />
<h4>4. Define Infrastructure</h4>
<p>The <code>DefineInfrastructure.jl</code> script allows a user to define specific <strong>infrastructure</strong> nodes, which as of now are simply reservoirs. Each infrastructure node has several metadata requirements including</p>
<ul>
<li><p>name - name of reservoir node (<em>String</em>)</p>
</li>
<li><p>storage_capacity - capacity of reservoir (<em>Float64</em>)</p>
</li>
<li><p>init_storage - initial storage of reservoir (<em>Float64</em>)</p>
</li>
<li><p>top_of_conservation - (<em>12 by 1 array of Float64</em>) </p>
</li>
<li><p>storage_units - units of storage (<em>String</em>)</p>
</li>
<li><p>Loc - location (or position) in list of all nodes (<em>Int64</em>)</p>
</li>
</ul>
<p>Note that supply information is assumed to be stored in a .csv file in the format of month (Date format) in the first column and Supply (number format) in the second column. Example files can be found in this folder eg. <em>NYuba_Inflow_Month</em>.</p>
<p>Once all supply nodes are defined, you can create the structures with an <code>include</code> statement and convert them to a DataFrame structure using the <code>rdf</code> function as follows:</p>
<pre class='hljl'>
<span class='hljl-nf'>include</span><span class='hljl-p'>(</span><span class='hljl-s'>"DefineInfrastructure.jl"</span><span class='hljl-p'>)</span><span class='hljl-t'>
</span><span class='hljl-n'>reservoirs</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-nf'>rdf</span><span class='hljl-p'>(</span><span class='hljl-n'>reservoir_nodes</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-n'>start_year</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-n'>stop_year</span><span class='hljl-p'>)</span>
</pre>
<p><em>The <code>include</code> call will return a list of dictionaries, one for each node, and the <code>rdf</code> call will convert this data structure into a <code>DataFrame</code> for use by the main DRIP script.</em></p>
<h4>5. Run DRIP</h4>
<p>Once you have set up the demand, supply, and reservoir nodes, you can run the main DRIP allocation routine! This allocation script will run the allocation algorithm and return <code>results</code>, which is a vector of values between 0 and 1 indicating the fraction of demand fulfilled for that specific demand node. </p>
<pre class='hljl'>
<span class='hljl-nf'>include</span><span class='hljl-p'>(</span><span class='hljl-s'>"DRIP_allocation_routine.jl"</span><span class='hljl-p'>)</span><span class='hljl-t'>
</span><span class='hljl-n'>results</span><span class='hljl-t'> </span><span class='hljl-oB'>=</span><span class='hljl-t'> </span><span class='hljl-nf'>DRIP_allocation_routine</span><span class='hljl-p'>(</span><span class='hljl-n'>demand_nodes</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-n'>supply_nodes</span><span class='hljl-p'>,</span><span class='hljl-t'> </span><span class='hljl-n'>reservoir_nodes</span><span class='hljl-p'>,</span><span class='hljl-t'>
</span><span class='hljl-p'>(</span><span class='hljl-n'>stop_year</span><span class='hljl-t'> </span><span class='hljl-oB'>-</span><span class='hljl-t'> </span><span class='hljl-n'>start_year</span><span class='hljl-t'> </span><span class='hljl-oB'>+</span><span class='hljl-t'> </span><span class='hljl-ni'>1</span><span class='hljl-p'>))</span><span class='hljl-t'>
</span><span class='hljl-n'>results</span>
</pre>
<pre class="hljl">
4-element Array{Float64,1}:
0.0129429
0.0129429
0.0
0.0129429
</pre>
<h1>III. WEAP Model</h1>
<p>In order to benchmark our model against the original WEAP software, we built a small dummy WEAP model of the system constructed in our model. It consists of a single river with headflow represented as a monthly average streamflow time series taken from the USGS hydrologic gauge data repository for the North Yuba River in the California Sierra Nevada. There is a single reservoir with a pre-defined storage capacity and initial storage. Currently there is also a single hydropower plant, a city (Berkeley) demand, a farm demand (Anaya's Farm), and a minimum instream flow requirement (IFR). A schema of the model in WEAP is shown below.</p>
<p><img src="WEAP_schema1.png" alt="" /></p>
<p>This model was run from 1990 to 2017 at a monthly timestep in order to produce result upon which we want to compare out model outputs. However, given the nascent stage of our model, we were yet unable to totally replicate the WEAP outputs, and are currently thinking through the ways in which WEAP likely obtains its optimzation outputs.</p>
<p>Some data inputs: <img src="WEAP_input1.png" alt="" /></p>
<p>Here are some results of the reservoir storage and demand shortage: Reservoir Shortage <img src="WEAP_results_res.png" alt="" /></p>
<p>Demand Shortage <img src="WEAP_results_shortage.png" alt="" /></p>
<h1>IV. Future Work</h1>
<p>Nick and I are interested in expanding upon this work, and have begun to do so in the <code>DRIP_allocation_routine_WIP.jl</code> file. The most significant changes will be to the the allocation algorithms, and include an optimisation component that mirrors the one used in WEAP. A small model of the inner portion of this optimisation can be found in <code>Excel_Optim_Model.xlsx</code>.</p>
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