examples/10_Thur_M2.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Copyright 2020 Marco Dal Molin et al.\n",
    "\n",
    "This file is part of SuperflexPy.\n",
    "\n",
    "SuperflexPy is free software: you can redistribute it and/or modify\n",
    "it under the terms of the GNU Lesser General Public License as published by\n",
    "the Free Software Foundation, either version 3 of the License, or\n",
    "(at your option) any later version.\n",
    "\n",
    "SuperflexPy is distributed in the hope that it will be useful,\n",
    "but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
    "MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n",
    "GNU Lesser General Public License for more details.\n",
    "\n",
    "You should have received a copy of the GNU Lesser General Public License\n",
    "along with SuperflexPy. If not, see <https://www.gnu.org/licenses/>.\n",
    "\n",
    "This file is part of the SuperflexPy modelling framework. For details about it,\n",
    "visit the page https://superflexpy.readthedocs.io\n",
    "\n",
    "CODED BY: Marco Dal Molin\n",
    "DESIGNED BY: Marco Dal Molin, Fabrizio Fenicia, Dmitri Kavetski"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SuperflexPy example 10: Implement M02 of Dal Molin et al., 2020\n",
    "\n",
    "Author: Marco Dal Molin\n",
    "\n",
    "Collaborators: Fabrizio Fenicia, Dmitri Kavetski"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What's in this example\n",
    "\n",
    "This example will cover the following aspects:\n",
    "- [Reproduce the semi distributed model M02 presented in Dal Molin et al., 2020](https://superflexpy.readthedocs.io/en/latest/case_studies.html#dal-molin-et-al-2020-hess)\n",
    "\n",
    "By clicking on the items you will be redirected to the documentation page (when available) that explains the arguments in a more detailed way. \n",
    "\n",
    "REFERENCE: Dal Molin, M., Schirmer, M., Zappa, M., and Fenicia, F.: Understanding dominant controls on streamflow spatial variability to set up a semi-distributed hydrological model: the case study of the Thur catchment, Hydrol. Earth Syst. Sci., 24, 1319–1345, https://doi.org/10.5194/hess-24-1319-2020, 2020."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What's not in this example\n",
    "\n",
    "The following aspects are already covered in other examples:\n",
    "\n",
    "- [Initialize a complete model](./06_init_complete_model.ipynb)\n",
    "- [Run the model](./01_run_simple_model.ipynb)\n",
    "- [Change states and parameters](./01_run_simple_model.ipynb)\n",
    "\n",
    "For this reason we will put only the code need, without further explanations. You can check the other examples by clicking on the items above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Do you want to use this example as a script?\n",
    "\n",
    "Examples of SuperflexPy are created and distributed using Jupyter notebooks because they enable to conjugate runnable code with text explanations. We have decided to not provide the content of the notebooks as script because this would mean duplicating the same content in multiple places, bringing to maintainability problems.\n",
    "\n",
    "If the user wants to download the content of this notebook as a python script, it is possible following the steps:\n",
    "1. If not already done, open this notebook using [Binder](https://mybinder.org/v2/gh/dalmo1991/superflexPy/master?filepath=examples%2F10_Thur_M2.ipynb)\n",
    "2. Go on File -> Download as -> Python (.py)\n",
    "3. Select the saving path\n",
    "\n",
    "The result is a Python script that contains all the markdown text as comment and the code contained inside the cells."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## STEP 1: Initialize the model\n",
    "\n",
    "The model M4 is composed by 2 elements in series: an unsaturated reservoir and a fast reservoir. Both elements have already been implemented and can be imported from `superflexpy.implementation.elements.thur_model_hess`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from superflexpy.implementation.root_finders.pegasus import PegasusPython\n",
    "from superflexpy.implementation.numerical_approximators.implicit_euler import ImplicitEulerPython\n",
    "from superflexpy.implementation.elements.thur_model_hess import SnowReservoir, UnsaturatedReservoir, PowerReservoir, HalfTriangularLag\n",
    "from superflexpy.implementation.elements.structure_elements import Transparent, Junction, Splitter\n",
    "from superflexpy.framework.unit import Unit\n",
    "from superflexpy.framework.node import Node\n",
    "from superflexpy.framework.network import Network\n",
    "\n",
    "solver = PegasusPython()\n",
    "approximator = ImplicitEulerPython(root_finder=solver)\n",
    "\n",
    "# Fluxes in the order P, T, PET\n",
    "upper_splitter = Splitter(\n",
    "    direction=[\n",
    "        [0, 1, None],    # P and T go to the snow reservoir\n",
    "        [2, None, None]  # PET goes to the transparent element\n",
    "    ],\n",
    "    weight=[\n",
    "        [1.0, 1.0, 0.0],\n",
    "        [0.0, 0.0, 1.0]\n",
    "    ],\n",
    "    id='upper-splitter'\n",
    ")\n",
    "\n",
    "snow = SnowReservoir(\n",
    "    parameters={'t0': 0.0, 'k': 0.01, 'm': 2.0},\n",
    "    states={'S0': 0.0},\n",
    "    approximation=approximator,\n",
    "    id='snow'\n",
    ")\n",
    "\n",
    "upper_transparent = Transparent(\n",
    "    id='upper-transparent'\n",
    ")\n",
    "\n",
    "upper_junction = Junction(\n",
    "    direction=[\n",
    "        [0, None],\n",
    "        [None, 0]\n",
    "    ],\n",
    "    id='upper-junction'\n",
    ")\n",
    "\n",
    "unsaturated = UnsaturatedReservoir(\n",
    "    parameters={'Smax': 50.0, 'Ce': 1.0, 'm': 0.01, 'beta': 2.0},\n",
    "    states={'S0': 10.0},\n",
    "    approximation=approximator,\n",
    "    id='unsaturated'\n",
    ")\n",
    "\n",
    "lower_splitter = Splitter(\n",
    "    direction=[\n",
    "        [0],\n",
    "        [0]\n",
    "    ],\n",
    "    weight=[\n",
    "        [0.3],   # Portion to slow reservoir\n",
    "        [0.7]    # Portion to fast reservoir\n",
    "    ],\n",
    "    id='lower-splitter'\n",
    ")\n",
    "\n",
    "lag_fun = HalfTriangularLag(\n",
    "    parameters={'lag-time': 2.0},\n",
    "    states={'lag': None},\n",
    "    id='lag-fun'\n",
    ")\n",
    "\n",
    "fast = PowerReservoir(\n",
    "    parameters={'k': 0.01, 'alpha': 3.0},\n",
    "    states={'S0': 0.0},\n",
    "    approximation=approximator,\n",
    "    id='fast'\n",
    ")\n",
    "\n",
    "slow = PowerReservoir(\n",
    "    parameters={'k': 1e-4, 'alpha': 1.0},\n",
    "    states={'S0': 0.0},\n",
    "    approximation=approximator,\n",
    "    id='slow'\n",
    ")\n",
    "\n",
    "lower_transparent = Transparent(\n",
    "    id='lower-transparent'\n",
    ")\n",
    "\n",
    "lower_junction = Junction(\n",
    "    direction=[\n",
    "        [0, 0]\n",
    "    ],\n",
    "    id='lower-junction'\n",
    ")\n",
    "\n",
    "consolidated = Unit(\n",
    "    layers=[\n",
    "        [upper_splitter],\n",
    "        [snow, upper_transparent],\n",
    "        [upper_junction],\n",
    "        [unsaturated],\n",
    "        [lower_splitter],\n",
    "        [slow, lag_fun],\n",
    "        [lower_transparent, fast],\n",
    "        [lower_junction],\n",
    "    ],\n",
    "    id='consolidated'\n",
    ")\n",
    "\n",
    "unconsolidated = Unit(\n",
    "    layers=[\n",
    "        [upper_splitter],\n",
    "        [snow, upper_transparent],\n",
    "        [upper_junction],\n",
    "        [unsaturated],\n",
    "        [lower_splitter],\n",
    "        [slow, lag_fun],\n",
    "        [lower_transparent, fast],\n",
    "        [lower_junction],\n",
    "    ],\n",
    "    id='unconsolidated'\n",
    ")\n",
    "\n",
    "andelfingen = Node(\n",
    "    units=[consolidated, unconsolidated],\n",
    "    weights=[0.24, 0.76],\n",
    "    area=403.3,\n",
    "    id='andelfingen'\n",
    ")\n",
    "\n",
    "appenzell = Node(\n",
    "    units=[consolidated, unconsolidated],\n",
    "    weights=[0.92, 0.08],\n",
    "    area=74.4,\n",
    "    id='appenzell'\n",
    ")\n",
    "\n",
    "frauenfeld = Node(\n",
    "    units=[consolidated, unconsolidated],\n",
    "    weights=[0.49, 0.51],\n",
    "    area=134.4,\n",
    "    id='frauenfeld'\n",
    ")\n",
    "\n",
    "halden = Node(\n",
    "    units=[consolidated, unconsolidated],\n",
    "    weights=[0.34, 0.66],\n",
    "    area=314.3,\n",
    "    id='halden'\n",
    ")\n",
    "\n",
    "herisau = Node(\n",
    "    units=[consolidated, unconsolidated],\n",
    "    weights=[0.88, 0.12],\n",
    "    area=16.7,\n",
    "    id='herisau'\n",
    ")\n",
    "\n",
    "jonschwil = Node(\n",
    "    units=[consolidated, unconsolidated],\n",
    "    weights=[0.9, 0.1],\n",
    "    area=401.6,\n",
    "    id='jonschwil'\n",
    ")\n",
    "\n",
    "mogelsberg = Node(\n",
    "    units=[consolidated, unconsolidated],\n",
    "    weights=[0.92, 0.08],\n",
    "    area=88.1,\n",
    "    id='mogelsberg'\n",
    ")\n",
    "\n",
    "mosnang = Node(\n",
    "    units=[consolidated],\n",
    "    weights=[1.0],\n",
    "    area=3.1,\n",
    "    id='mosnang'\n",
    ")\n",
    "\n",
    "stgallen = Node(\n",
    "    units=[consolidated, unconsolidated],\n",
    "    weights=[0.87, 0.13],\n",
    "    area=186.6,\n",
    "    id='stgallen'\n",
    ")\n",
    "\n",
    "waengi = Node(\n",
    "    units=[consolidated, unconsolidated],\n",
    "    weights=[0.63, 0.37],\n",
    "    area=78.9,\n",
    "    id='waengi'\n",
    ")\n",
    "\n",
    "model = Network(\n",
    "    nodes=[\n",
    "        andelfingen,\n",
    "        appenzell,\n",
    "        frauenfeld,\n",
    "        halden,\n",
    "        herisau,\n",
    "        jonschwil,\n",
    "        mogelsberg,\n",
    "        mosnang,\n",
    "        stgallen,\n",
    "        waengi,\n",
    "    ],\n",
    "    topology={\n",
    "        'andelfingen': None,\n",
    "        'appenzell': 'stgallen',\n",
    "        'frauenfeld': 'andelfingen',\n",
    "        'halden': 'andelfingen',\n",
    "        'herisau': 'halden',\n",
    "        'jonschwil': 'halden',\n",
    "        'mogelsberg': 'jonschwil',\n",
    "        'mosnang': 'jonschwil',\n",
    "        'stgallen': 'halden',\n",
    "        'waengi': 'frauenfeld',\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## STEP 2: Run the model\n",
    "\n",
    "Now that the mode has been initialized, it can be run. This can be done running the code in the following cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams.update({'font.size': 20})\n",
    "\n",
    "# Fix the seed\n",
    "SEED = 2\n",
    "rng = np.random.RandomState(seed=SEED)\n",
    "\n",
    "catchments = [\n",
    "    andelfingen,\n",
    "    appenzell,\n",
    "    frauenfeld,\n",
    "    halden,\n",
    "    herisau,\n",
    "    jonschwil,\n",
    "    mogelsberg,\n",
    "    mosnang,\n",
    "    stgallen,\n",
    "    waengi\n",
    "]\n",
    "\n",
    "for cat in catchments:\n",
    "    # Generate the input\n",
    "    P = np.zeros(100)\n",
    "    P[:10] = rng.randint(10, size=10)\n",
    "    P[25:30] = rng.randint(20, size=5)\n",
    "    P[40:60] = rng.randint(5, size=20)\n",
    "    P[80:83] = rng.randint(30, 50, size=3)\n",
    "    E = np.ones_like(P) * 2.0 # We use a constant PET\n",
    "    T = (rng.rand(100) - 0.2) * 20\n",
    "    cat.set_input([P, T, E])\n",
    "\n",
    "# Set the timestep\n",
    "model.set_timestep(1.0)\n",
    "\n",
    "# Run the model\n",
    "model.reset_states()\n",
    "output = model.get_output()\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(20, 8))\n",
    "for cat in output:\n",
    "    ax.plot(np.arange(len(P)), output[cat][0], label=cat)\n",
    "\n",
    "ax.set_xlabel('Time [days]')\n",
    "ax.set_ylabel('Flows [mm/day]')\n",
    "ax.legend()\n",
    "ax.grid(True)"
   ]
  }
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