{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0",
   "metadata": {},
   "source": [
    "## Update a Wflow model: landuse"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1",
   "metadata": {},
   "source": [
    "Once you have a **Wflow** model, you may want to update your model in order to use a new landuse map, change a parameter value, add sample locations, use different forcing data, create and run different scenarios etc.\n",
    "\n",
    "With HydroMT, you can easily read your model and update one or several components of your model using the **update** function of the command line interface (CLI). Here are the steps and some examples on how to **update the landuse map and parameters**.\n",
    "\n",
    "All lines in this notebook which starts with ! are executed from the command line. Within the notebook environment the logging messages are shown after completion. You can also copy these lines and paste them in your shell to get more direct feedback."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2",
   "metadata": {},
   "source": [
    "### Import packages"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3",
   "metadata": {},
   "source": [
    "In this notebook, we will use some functions of HydroMT to check available datasets and also to plot the landuse maps from the original and updated models. Here are the libraries to import to realize these steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import hydromt\n",
    "from hydromt_wflow import WflowSbmModel\n",
    "\n",
    "# for plotting\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "\n",
    "proj = ccrs.PlateCarree()  # plot projection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5",
   "metadata": {},
   "source": [
    "### Searching the data catalog for landuse"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6",
   "metadata": {},
   "source": [
    "In our previous notebook, we built a Wflow model using the *GlobCover* landuse classification. But we could as well have chosen another one. Let's what other landuse landcover data are available in HydroMT and choose another landuse classification for our model. For this we will open the data catalog.\n",
    "\n",
    "You can also directly open and search the HydroMT yaml library by downloading and opening the data_catalog.yml file in [hydromt-artifacts](https://github.com/DirkEilander/hydromt-artifacts/releases/tag/v0.0.3) or look at the list of data sources in the [documentation](https://deltares.github.io/hydromt/latest/user_guide/data_catalog/data_existing_cat.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the default data catalog of HydroMT\n",
    "data_catalog = hydromt.DataCatalog(\"artifact_data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check which landuse/lancover sources are available in the DataCatalog\n",
    "data_table = data_catalog._to_dataframe()\n",
    "data_table[data_table[\"category\"] == \"landuse & landcover\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9",
   "metadata": {},
   "source": [
    "Here we can see that we have five data sources in HydroMT related to landuse & landcover properties. Out of these, three are landuse classifications:\n",
    "\n",
    "- **globcover_2009** (already used in our current model)\n",
    "- **corine**\n",
    "- **vito_2015**\n",
    "\n",
    "The other datasets include a Leaf Area Index dataset (modis_lai) and a canopy height dataset (simard).\n",
    "\n",
    "Let's now see how to update our current model in one command line to use the **corine** classification."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10",
   "metadata": {},
   "source": [
    "### HydroMT CLI update interface"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11",
   "metadata": {},
   "source": [
    "Using the **HydroMT build** API, we can update one or several components of an already existing Wflow model. Let's get an overview of the available options:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Print the options available from the update command\n",
    "! hydromt update --help"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13",
   "metadata": {},
   "source": [
    "### Model setup configuration\n",
    "\n",
    "To let HydroMT know which setup method to call and the options, you can prepare and use a **configuration file** that includes every methods and settings that you want to do during your update.\n",
    "\n",
    "The HydroMT configuration file (YAML) contains the model setup configuration and determines which methods are updated and in which sequence and sets optional arguments for each method. This configuration is passed to hydromt using `-i <path_to_config_file>`.\n",
    "\n",
    "Each header (without indent) (e.g. `setup_lulcmaps:`) corresponds with a model method which are explained in the [docs (model methods)](https://deltares.github.io/hydromt_wflow/latest/user_guide/wflow_model_setup.html).\n",
    "\n",
    "Let's open the example configuration file **wflow_update_landuse.yml** from the model repository [examples folder] and have a look at the settings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14",
   "metadata": {},
   "outputs": [],
   "source": [
    "fn_config = \"wflow_update_landuse.yml\"\n",
    "with open(fn_config, \"r\") as f:\n",
    "    txt = f.read()\n",
    "print(txt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15",
   "metadata": {},
   "source": [
    "Here we can see that to fully update wflow forcing, we will run one methods of **Wflow**:\n",
    "\n",
    "- **setup_lulcmaps**: prepares landuse map and parameters based on a landuse map `lulc_fn` and a mapping table `lulc_mapping_fn` to convert from specific landuse classes (urban, forest) to hydrological parameters (roughness, depth of the vegetation roots etc).\n",
    "\n",
    "Here, we can see that we will update the landuse with the classification from CORINE and using the HydroMT default mapping table for the parameter creation (HydroMT supports a few of these including \"globcover\", \"corine\", \"vito\" and \"esa_worldcover\"). If you want to use a different classification or update the default values (eg for calibration), you can create and catalog your own table.\n",
    "\n",
    "We will also here save the different maps with the suffix \"corine\" via the `output_names_suffix` options. If this option is not provided (or None), the new maps will overwrite previously existing ones. Here we will keep both the globcover maps and the new maps from CORINE in the updated model.\n",
    "\n",
    "You can find more information about the different options in the [docs (setup lulcmaps)](https://deltares.github.io/hydromt_wflow/latest/_generated/hydromt_wflow.WflowBaseModel.setup_lulcmaps.html#hydromt_wflow.WflowBaseModel.setup_lulcmaps)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16",
   "metadata": {},
   "source": [
    "### Update Wflow landuse layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17",
   "metadata": {},
   "outputs": [],
   "source": [
    "# NOTE: copy this line (without !) to your shell for more direct feedback\n",
    "! hydromt update wflow_sbm wflow_piave_subbasin -o ./wflow_piave_corine -i wflow_update_landuse.yml -d artifact_data -v"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18",
   "metadata": {},
   "source": [
    "The example above means the following: run **hydromt** with:\n",
    "\n",
    "- `update wflow`: i.e. update a wflow model\n",
    "- `wflow_piave_subbasin`: original model folder\n",
    "- `-o ./wflow_piave_corine`: output updated model folder\n",
    "- `-i wflow_update_landuse.yml`: setup configuration file containing the components to update and their different options\n",
    "- `-d artifact_data`: specify to use the artifact_data catalog\n",
    "- `-v`: give some extra verbosity (1 * v) to display feedback on screen. Now INFO messages are provided."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19",
   "metadata": {},
   "source": [
    "### Model comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20",
   "metadata": {},
   "source": [
    "From the information above, you can see that not only was the landuse map updated but also all wflow landuse-related parameters with it: Kext, N, PathFrac, RootingDepth, Sl, Swood and WaterFrac.\n",
    "\n",
    "Let's now have a look at the different landuse maps of our two models and check that they were indeed updated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load both models with hydromt\n",
    "model = WflowSbmModel(root=\"wflow_piave_corine\", mode=\"r\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"./legends/GLOBCOVER_2009_QGIS.txt\", header=None, index_col=0)\n",
    "\n",
    "# plot  globcover map\n",
    "levels = df.index\n",
    "colors = (df.iloc[:-1, :4] / 255).values\n",
    "ticklabs = df.iloc[:-1, 4].values\n",
    "cmap, norm = mpl.colors.from_levels_and_colors(levels, colors)\n",
    "ticks = np.array(levels[:-1]) + np.diff(levels) / 2.0\n",
    "\n",
    "# create new figure\n",
    "fig = plt.figure(figsize=(14, 7))\n",
    "ax = fig.add_subplot(projection=proj)\n",
    "# plot globcover landuse\n",
    "mask = model.staticmaps.data[\"subcatchment\"] > 0\n",
    "p = (\n",
    "    model.staticmaps.data[\"meta_landuse\"]\n",
    "    .raster.mask_nodata()\n",
    "    .plot(ax=ax, cmap=cmap, norm=norm, cbar_kwargs=dict(ticks=ticks))\n",
    ")\n",
    "p.axes.set_title(\"GlobCover LULC\")\n",
    "_ = p.colorbar.ax.set_yticklabels(ticklabs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot  corine map\n",
    "df = pd.read_csv(\n",
    "    \"./legends/CLC2018_CLC2018_V2018_20_QGIS.txt\", header=None, index_col=0\n",
    ")\n",
    "\n",
    "# plot  corine map\n",
    "levels = df.index\n",
    "colors = (df.iloc[:-1, :4] / 255).values\n",
    "ticklabs = df.iloc[:-1, 4].values\n",
    "cmap, norm = mpl.colors.from_levels_and_colors(levels, colors)\n",
    "ticks = np.array(levels[:-1]) + np.diff(levels) / 2.0\n",
    "\n",
    "# create new figure\n",
    "fig = plt.figure(figsize=(14, 7))\n",
    "ax = fig.add_subplot(projection=proj)\n",
    "# plot corine landuse\n",
    "p = (\n",
    "    model.staticmaps.data[\"meta_landuse_corine\"]\n",
    "    .raster.mask_nodata()\n",
    "    .plot(ax=ax, cmap=cmap, norm=norm, cbar_kwargs=dict(ticks=ticks))\n",
    ")\n",
    "p.axes.set_title(\"Corine LULC\")\n",
    "_ = p.colorbar.ax.set_yticklabels(ticklabs)"
   ]
  }
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