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WaterNetwork.ipynb
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WaterNetwork.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `WaterNetwork` component uses the America's Water Network to describe how downstream flows depend on upstream flows and withdrawals.\n",
"\n",
"The America's Water Network is described in detail in its [working paper](https://www.overleaf.com/read/gftdkjjkdrsn). The data contained in the network and its loading are described in the sister [`waternet notebook`](https://github.com/AmericasWater/operational-problem/blob/master/docs/waternet.ipynb).\n",
"\n",
"The component definition is very simple:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"WaterNetwork"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"using Mimi\n",
"\n",
"@defcomp WaterNetwork begin\n",
" gauges = Index()\n",
"\n",
" # External\n",
" added = Parameter(index=[gauges, time], unit=\"1000 m^3\") # Water added at node from runoff\n",
" removed = Parameter(index=[gauges, time], unit=\"1000 m^3\") # Water removed from node\n",
" returned = Parameter(index=[gauges, time], unit=\"1000 m^3\") # Water returns to a node from canals\n",
"\n",
" inflows = Variable(index=[gauges, time], unit=\"1000 m^3\") # Sum of upstream outflows\n",
" outflows = Variable(index=[gauges, time], unit=\"1000 m^3\") # inflow + added - removed + returned\n",
"end"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"All of these values are defined for each `gauge`, or node on the river network. They present a water balance:\n",
"$$outflows_i(t) = \\sum_{j \\in N(i)} inflows_i(t) + added_i(t) - removed_i(t) + returned_i(t)$$\n",
"\n",
"Where $N(i)$ are the neighbors of gauge $i$, where neighbors are defined as the upstream sources of flow."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initialization\n",
"\n",
"The `added` parameter is set to the runoff calculated by VIC and loaded in `weather.jl`.\n",
"\n",
"In optimization mode, both the `returned` and `removed` parameters are set by the optimization to satisfy allocation demands.\n",
"\n",
"In simulation mode,\n",
"- The `removed` parameter is set to the contents of `data/cache/counties/extraction/withdrawals.jld`, if available.\n",
"- The `returned` parameter is set to the contents of `data/cache/counties/extraction/returns.jld`, if available.\n",
"\n",
"The `removed` and `returned` variables are generated by `optimize-surface.jl`, which finds plausible extractions to satisfy USGS demands."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Optimization\n",
"\n",
"The logic for optimization is complicated by the process of matching canal-based withdrawals to gauge-based flows. However, aside from this, the entire network follows two simple principes.\n",
"\n",
"- **Withdrawal applied to any gauge withdraws water from all downstream gauges.**\n",
"- **Runoff added to any guage increases the allottment at all downstream gauges.**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following shows these gauge-specific allotments, and some interesting caveats.\n",
"\n",
"First, the allotments take a while to calculate (which they are done by `constraintoffset_waternetwork_outflows(m);` in `optimize.jl` if need be), so they are cached in the file `data/partialhouse2.jld`."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"OptiMimi.LinearProgrammingHall(:WaterNetwork,:outflows,[NaN,NaN,NaN,NaN,1363.79,1975.22,NaN,NaN,14.7898,84.0744 … NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN])"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"using OptiMimi\n",
"\n",
"cwro = deserialize(open(\"../data/partialhouse2.jld\", \"r\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is a Linear Programming Hall file, used for constraint vectors.\n",
"\n",
"Additional informatino on the gauges is stored in the `waternet.RData` file, generated outside of Julia."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"data-frame\"><tr><th></th><th>collection</th><th>colid</th><th>lat</th><th>lon</th><th>elev</th><th>nextpt</th><th>dist</th></tr><tr><th>1</th><td>rivdis</td><td>344</td><td>40.5</td><td>-124.1</td><td>11.0</td><td>17555</td><td>8646.0</td></tr><tr><th>2</th><td>rivdis</td><td>345</td><td>42.58</td><td>-124.06</td><td>35.0</td><td>NA</td><td>NA</td></tr><tr><th>3</th><td>rivdis</td><td>346</td><td>41.51</td><td>-123.96</td><td>2.0</td><td>18209</td><td>8794.0</td></tr><tr><th>4</th><td>rivdis</td><td>347</td><td>43.58</td><td>-123.56</td><td>28.0</td><td>19439</td><td>671.0</td></tr><tr><th>5</th><td>rivdis</td><td>348</td><td>46.93</td><td>-123.31</td><td>7.0</td><td>8278</td><td>927.0</td></tr><tr><th>6</th><td>rivdis</td><td>349</td><td>44.95</td><td>-123.03</td><td>32.0</td><td>20260</td><td>1583.0</td></tr><tr><th>7</th><td>rivdis</td><td>350</td><td>46.26</td><td>-122.91</td><td>6.0</td><td>20754</td><td>23709.0</td></tr><tr><th>8</th><td>rivdis</td><td>354</td><td>40.7</td><td>-118.2</td><td>1259.0</td><td>7462</td><td>1163.0</td></tr><tr><th>9</th><td>rivdis</td><td>356</td><td>46.43</td><td>-117.16</td><td>200.0</td><td>9293</td><td>38087.0</td></tr><tr><th>10</th><td>rivdis</td><td>357</td><td>48.18</td><td>-117.03</td><td>610.0</td><td>21929</td><td>927.0</td></tr><tr><th>11</th><td>rivdis</td><td>358</td><td>32.73</td><td>-114.63</td><td>30.0</td><td>13549</td><td>35958.0</td></tr><tr><th>12</th><td>rivdis</td><td>359</td><td>39.36</td><td>-112.03</td><td>1506.0</td><td>10625</td><td>2570.0</td></tr><tr><th>13</th><td>rivdis</td><td>360</td><td>36.86</td><td>-111.58</td><td>947.0</td><td>15436</td><td>3115.0</td></tr><tr><th>14</th><td>rivdis</td><td>361</td><td>38.98</td><td>-110.15</td><td>1231.0</td><td>16444</td><td>28841.0</td></tr><tr><th>15</th><td>rivdis</td><td>362</td><td>48.11</td><td>-104.46</td><td>574.0</td><td>21792</td><td>50852.0</td></tr><tr><th>16</th><td>rivdis</td><td>363</td><td>47.61</td><td>-104.16</td><td>573.0</td><td>21672</td><td>4412.0</td></tr><tr><th>17</th><td>rivdis</td><td>364</td><td>29.83</td><td>-101.38</td><td>353.0</td><td>12819</td><td>1608.0</td></tr><tr><th>18</th><td>rivdis</td><td>365</td><td>27.5</td><td>-99.5</td><td>106.0</td><td>12455</td><td>45890.0</td></tr><tr><th>19</th><td>rivdis</td><td>366</td><td>28.03</td><td>-97.85999</td><td>8.0</td><td>6502</td><td>927.0</td></tr><tr><th>20</th><td>rivdis</td><td>367</td><td>42.86</td><td>-97.39999</td><td>347.0</td><td>19018</td><td>13636.0</td></tr><tr><th>21</th><td>rivdis</td><td>368</td><td>47.93</td><td>-97.08002</td><td>237.0</td><td>21830</td><td>8906.0</td></tr><tr><th>22</th><td>rivdis</td><td>369</td><td>41.03</td><td>-96.29999</td><td>307.0</td><td>17916</td><td>42335.0</td></tr><tr><th>23</th><td>rivdis</td><td>370</td><td>29.3</td><td>-96.10001</td><td>20.0</td><td>12729</td><td>83028.0</td></tr><tr><th>24</th><td>rivdis</td><td>371</td><td>36.15</td><td>-96.0</td><td>188.0</td><td>15112</td><td>748.0</td></tr><tr><th>25</th><td>rivdis</td><td>372</td><td>40.66</td><td>-95.83002</td><td>276.0</td><td>17652</td><td>4659.0</td></tr><tr><th>26</th><td>rivdis</td><td>373</td><td>29.58</td><td>-95.75</td><td>12.0</td><td>11718</td><td>58740.0</td></tr><tr><th>27</th><td>rivdis</td><td>374</td><td>35.26</td><td>-95.23001</td><td>146.0</td><td>14713</td><td>29808.0</td></tr><tr><th>28</th><td>rivdis</td><td>375</td><td>38.9</td><td>-94.88</td><td>231.0</td><td>5237</td><td>10977.0</td></tr><tr><th>29</th><td>rivdis</td><td>376</td><td>30.43</td><td>-94.85001</td><td>11.0</td><td>12843</td><td>86805.46218764059</td></tr><tr><th>30</th><td>rivdis</td><td>377</td><td>30.35</td><td>-94.10001</td><td>3.0</td><td>12908</td><td>14759.0</td></tr><tr><th>⋮</th><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr></table>"
],
"text/plain": [
"22619×7 DataFrames.DataFrame\n",
"│ Row │ collection │ colid │ lat │ lon │ elev │ nextpt │\n",
"├───────┼────────────┼────────────┼─────────┼──────────┼────────┼────────┤\n",
"│ 1 │ \"rivdis\" │ \"344\" │ 40.5 │ -124.1 │ 11.0 │ 17555 │\n",
"│ 2 │ \"rivdis\" │ \"345\" │ 42.58 │ -124.06 │ 35.0 │ NA │\n",
"│ 3 │ \"rivdis\" │ \"346\" │ 41.51 │ -123.96 │ 2.0 │ 18209 │\n",
"│ 4 │ \"rivdis\" │ \"347\" │ 43.58 │ -123.56 │ 28.0 │ 19439 │\n",
"│ 5 │ \"rivdis\" │ \"348\" │ 46.93 │ -123.31 │ 7.0 │ 8278 │\n",
"│ 6 │ \"rivdis\" │ \"349\" │ 44.95 │ -123.03 │ 32.0 │ 20260 │\n",
"│ 7 │ \"rivdis\" │ \"350\" │ 46.26 │ -122.91 │ 6.0 │ 20754 │\n",
"│ 8 │ \"rivdis\" │ \"354\" │ 40.7 │ -118.2 │ 1259.0 │ 7462 │\n",
"│ 9 │ \"rivdis\" │ \"356\" │ 46.43 │ -117.16 │ 200.0 │ 9293 │\n",
"│ 10 │ \"rivdis\" │ \"357\" │ 48.18 │ -117.03 │ 610.0 │ 21929 │\n",
"│ 11 │ \"rivdis\" │ \"358\" │ 32.73 │ -114.63 │ 30.0 │ 13549 │\n",
"⋮\n",
"│ 22608 │ \"junction\" │ \"22594-dn\" │ 56.1292 │ -96.2792 │ NA │ 22609 │\n",
"│ 22609 │ \"junction\" │ \"22595-dn\" │ 56.2875 │ -95.9292 │ NA │ 22610 │\n",
"│ 22610 │ \"junction\" │ \"22596-dn\" │ 56.3792 │ -95.1042 │ NA │ 22611 │\n",
"│ 22611 │ \"junction\" │ \"22597-dn\" │ 56.3792 │ -94.9958 │ NA │ 22612 │\n",
"│ 22612 │ \"junction\" │ \"22598-dn\" │ 56.3875 │ -94.5708 │ NA │ 22613 │\n",
"│ 22613 │ \"junction\" │ \"22599-dn\" │ 56.5125 │ -94.1042 │ NA │ 22614 │\n",
"│ 22614 │ \"junction\" │ \"22600-dn\" │ 56.7625 │ -93.6125 │ NA │ 22615 │\n",
"│ 22615 │ \"junction\" │ \"22601-dn\" │ 56.9125 │ -93.3292 │ NA │ 22616 │\n",
"│ 22616 │ \"junction\" │ \"22602-dn\" │ 56.9125 │ -92.8375 │ NA │ 22617 │\n",
"│ 22617 │ \"junction\" │ \"22603-dn\" │ 56.9375 │ -92.7458 │ NA │ NA │\n",
"│ 22618 │ \"junction\" │ \"22632-dn\" │ 40.3875 │ -73.9708 │ NA │ NA │\n",
"│ 22619 │ \"junction\" │ \"22652-dn\" │ 27.9375 │ -82.4625 │ NA │ NA │\n",
"\n",
"│ Row │ dist │\n",
"├───────┼─────────┤\n",
"│ 1 │ 8646.0 │\n",
"│ 2 │ NA │\n",
"│ 3 │ 8794.0 │\n",
"│ 4 │ 671.0 │\n",
"│ 5 │ 927.0 │\n",
"│ 6 │ 1583.0 │\n",
"│ 7 │ 23709.0 │\n",
"│ 8 │ 1163.0 │\n",
"│ 9 │ 38087.0 │\n",
"│ 10 │ 927.0 │\n",
"│ 11 │ 35958.0 │\n",
"⋮\n",
"│ 22608 │ 32761.0 │\n",
"│ 22609 │ 62341.0 │\n",
"│ 22610 │ 6670.0 │\n",
"│ 22611 │ 27802.0 │\n",
"│ 22612 │ 38762.0 │\n",
"│ 22613 │ 46887.0 │\n",
"│ 22614 │ 27871.0 │\n",
"│ 22615 │ 32042.0 │\n",
"│ 22616 │ 7213.0 │\n",
"│ 22617 │ NA │\n",
"│ 22618 │ NA │\n",
"│ 22619 │ NA │"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"using DataFrames\n",
"\n",
"stations = read_rda(\"../data/waternet.RData\", convertdataframes=true)[\"network\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Unfortunately, the allotment entries in the optimization problem are neither in the same order (they're in vertex order), nor have all of the entries that are in the water network."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"22619\n",
"45118\n"
]
}
],
"source": [
"println(nrow(stations))\n",
"println(length(cwro.f))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We need to use the `wateridverts` dictionary, which maps from gauge IDs to vertices in the network."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Dict{UTF8String,Graphs.ExVertex} with 22559 entries:\n",
" \"usgs.02456000\" => vertex [3630] \"usgs.02456000\"\n",
" \"junction.13805-dn\" => vertex [11658] \"junction.13805-dn\"\n",
" \"junction.11142-up\" => vertex [12918] \"junction.11142-up\"\n",
" \"usgs.03488000\" => vertex [5098] \"usgs.03488000\"\n",
" \"usgs.12113347\" => vertex [13374] \"usgs.12113347\"\n",
" \"junction.13981-up\" => vertex [20305] \"junction.13981-up\"\n",
" \"junction.12762-up\" => vertex [4676] \"junction.12762-up\"\n",
" \"usgs.0214627970\" => vertex [2418] \"usgs.0214627970\"\n",
" \"usgs.01396800\" => vertex [975] \"usgs.01396800\"\n",
" \"usgs.05532000\" => vertex [6898] \"usgs.05532000\"\n",
" \"junction.19865-dn\" => vertex [7569] \"junction.19865-dn\"\n",
" \"junction.9399-dn\" => vertex [4855] \"junction.9399-dn\"\n",
" \"usgs.03025000\" => vertex [3794] \"usgs.03025000\"\n",
" \"usgs.09067200\" => vertex [10866] \"usgs.09067200\"\n",
" \"usgs.08261000\" => vertex [10597] \"usgs.08261000\"\n",
" \"usgs.04286000\" => vertex [6117] \"usgs.04286000\"\n",
" \"junction.22562-up\" => vertex [22524] \"junction.22562-up\"\n",
" \"junction.7795-dn\" => vertex [16679] \"junction.7795-dn\"\n",
" \"usgs.11313472\" => vertex [12777] \"usgs.11313472\"\n",
" \"usgs.14337800\" => vertex [14571] \"usgs.14337800\"\n",
" \"reservoir.2566\" => vertex [17636] \"reservoir.2566\"\n",
" \"junction.12093-dn\" => vertex [20077] \"junction.12093-dn\"\n",
" \"junction.14022-up\" => vertex [20314] \"junction.14022-up\"\n",
" \"canal.09528800\" => vertex [18159] \"canal.09528800\"\n",
" \"usgs.11433200\" => vertex [13026] \"usgs.11433200\"\n",
" ⋮ => ⋮"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"using Graphs\n",
"\n",
"wateridverts = deserialize(open(\"../data/cache/wateridverts.jld\", \"r\"))\n",
"wateridverts\n",
"#waternet = deserialize(open(\"../data/cache/waternet.jld\", \"r\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And now we can collect all of the information to produce a map."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lats = []\n",
"lons = []\n",
"runoff = []\n",
"for ii in 1:nrow(stations)\n",
" gaugeid = \"$(stations[ii, :collection]).$(stations[ii, :colid])\"\n",
" if gaugeid in keys(wateridverts)\n",
" runoff = [runoff; cwro.f[vertex_index(wateridverts[gaugeid])]]\n",
" lats = [lats; stations[ii, :lat]]\n",
" lons = [lons; stations[ii, :lon]]\n",
" end\n",
"end\n",
"\n",
"runoff[isna(runoff)] = 0"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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"
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"RCall.RObject{RCall.NilSxp}\n",
"NULL\n"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"using RCall\n",
"\n",
"cex = convert(Vector{Float64}, runoff) / 1e9\n",
"\n",
"R\"library(maps)\"\n",
"R\"map('usa', xlim=c(-140, -60), ylim=c(20, 50))\"\n",
"R\"points($lons, $lats, cex=$cex)\""
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 0.4.0",
"language": "julia",
"name": "julia-0.4"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "0.4.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}