{ "cells": [ { "cell_type": "markdown", "id": "0", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "# Use Case Example\n", "\n", "This example illustrates a use case that covers the essential steps involved in building a hydrological model and conducting a climate change analysis:\n", "\n", "- **Identification of the watershed and its key characteristics**\n", " - Beaurivage watershed in Southern Quebec, at the location of the 023401 streamflow gauge.\n", "- **Collection of observed data**\n", " - ERA5-Land and streamflow gauge data.\n", "- **Preparation and calibration of the hydrological model**\n", " - GR4JCN emulated by the Raven hydrological framework.\n", "- **Calculation of hydrological indicators**\n", " - Mean summer flow\n", " - Mean monthly flow\n", " - 20- and 100-year maximum flow\n", " - 2-year minimum 7-day average summer flow\n", "- **Assessment of the impact of climate change**\n", " - Bias-adjusted CMIP6 simulations from the ESPO-G6-R2 dataset\n", "\n", "## Identification of a watershed and its characteristics\n", "\n", "
INFO\n", "\n", "For more information on this section and available options, consult the [GIS notebook](gis.ipynb).\n", "\n", "
\n", "\n", "This first step is highly dependent on the hydrological model. Since we will use GR4JCN in our example, we need to obtain the drainage area, centroid coordinates, and elevation. We'll also need the watershed delineation to extract the meteorological data. All of these information can be acquired through the `xhydro.gis.watershed_to_raven_hru` function, which calls upon various functions of that module." ] }, { "cell_type": "code", "execution_count": 1, "id": "1", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ "hidden" ] }, "outputs": [], "source": [ "import warnings\n", "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", "warnings.filterwarnings(\"ignore\", category=FutureWarning)" ] }, { "cell_type": "code", "execution_count": 2, "id": "2", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [], "source": [ "from IPython.display import clear_output\n", "\n", "import xhydro.gis as xhgis\n", "\n", "clear_output(wait=False)" ] }, { "cell_type": "code", "execution_count": 3, "id": "3", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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HRU_IDarealatitudelongitudeelevationSubIdDowSubIdgeometry
07120365812585.58557746.452161-71.260464222.553651-1POLYGON ((-71.09758 46.40035, -71.09409 46.403...
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" ], "text/plain": [ " HRU_ID area latitude longitude elevation SubId DowSubId \\\n", "0 7120365812 585.585577 46.452161 -71.260464 222.55365 1 -1 \n", "\n", " geometry \n", "0 POLYGON ((-71.09758 46.40035, -71.09409 46.403... " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Watershed delineation\n", "coords = (-71.28878, 46.65692)\n", "gdf = xhgis.watershed_to_raven_hru(coords)\n", "gdf" ] }, { "cell_type": "markdown", "id": "4", "metadata": {}, "source": [ "Since `xhgis.watershed_delineation` extracts the nearest HydroBASINS polygon, the watershed might not exactly correspond to the requested coordinates. The 023401 streamflow gauge as an associated drainage area of 708 km², which differs from our results. Streamflow will have to be adjusted using an area scaling factor." ] }, { "cell_type": "code", "execution_count": 4, "id": "5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "np.float64(0.8270982719703007)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gauge_area = 708\n", "scaling_factor = gdf.iloc[0][\"area\"] / gauge_area\n", "scaling_factor" ] }, { "cell_type": "markdown", "id": "6", "metadata": {}, "source": [ "## Collection of observed data" ] }, { "cell_type": "code", "execution_count": 5, "id": "7", "metadata": {}, "outputs": [], "source": [ "import geopandas as gpd\n", "import matplotlib.pyplot as plt\n", "import xarray as xr\n", "\n", "# For easy access to the specific streamflow data used here\n", "import xdatasets\n", "import xscen" ] }, { "cell_type": "markdown", "id": "8", "metadata": {}, "source": [ "### Meteorological data\n", "\n", "
INFO\n", "\n", "Multiple libraries could be used to perform these steps. For simplicity, this example will use the [subset](https://xscen.readthedocs.io/en/latest/notebooks/2_getting_started.html#Defining-the-region) and [aggregate](https://xscen.readthedocs.io/en/latest/notebooks/2_getting_started.html#Spatial-mean) modules of the xscen library.\n", "\n", "
\n", "\n", "This example will use daily ERA5-Land data hosted on the PAVICS platform." ] }, { "cell_type": "code", "execution_count": 6, "id": "9", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset> Size: 454GB\n",
       "Dimensions:  (time: 27790, lat: 801, lon: 1700)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 222kB 1950-01-01 1950-01-02 ... 2026-01-31\n",
       "  * lat      (lat) float32 3kB 10.0 10.1 10.2 10.3 10.4 ... 89.7 89.8 89.9 90.0\n",
       "  * lon      (lon) float32 7kB -179.9 -179.8 -179.7 -179.6 ... -10.2 -10.1 -10.0\n",
       "Data variables:\n",
       "    pr       (time, lat, lon) float32 151GB dask.array<chunksize=(365, 50, 50), meta=np.ndarray>\n",
       "    tasmin   (time, lat, lon) float32 151GB dask.array<chunksize=(365, 50, 50), meta=np.ndarray>\n",
       "    tasmax   (time, lat, lon) float32 151GB dask.array<chunksize=(365, 50, 50), meta=np.ndarray>\n",
       "Attributes: (12/30)\n",
       "    Conventions:               CF-1.9\n",
       "    cell_methods:              time: mean (interval: 1 day)\n",
       "    doi:                       https://doi.org/10.24381/cds.e2161bac\n",
       "    domain:                    NAM\n",
       "    frequency:                 day\n",
       "    history:                   [2022-12-25 09:07:39.901698] Converted variabl...\n",
       "    ...                        ...\n",
       "    institute_id:              ECMWF\n",
       "    dataset_id:                ERA5-Land\n",
       "    abstract:                  ERA5-Land provides hourly high resolution info...\n",
       "    dataset_description:       https://www.ecmwf.int/en/era5-land\n",
       "    attribution:               Contains modified Copernicus Climate Change Se...\n",
       "    citation:                  Muñoz Sabater, J., (2021): ERA5-Land hourly da...
" ], "text/plain": [ " Size: 454GB\n", "Dimensions: (time: 27790, lat: 801, lon: 1700)\n", "Coordinates:\n", " * time (time) datetime64[ns] 222kB 1950-01-01 1950-01-02 ... 2026-01-31\n", " * lat (lat) float32 3kB 10.0 10.1 10.2 10.3 10.4 ... 89.7 89.8 89.9 90.0\n", " * lon (lon) float32 7kB -179.9 -179.8 -179.7 -179.6 ... -10.2 -10.1 -10.0\n", "Data variables:\n", " pr (time, lat, lon) float32 151GB dask.array\n", " tasmin (time, lat, lon) float32 151GB dask.array\n", " tasmax (time, lat, lon) float32 151GB dask.array\n", "Attributes: (12/30)\n", " Conventions: CF-1.9\n", " cell_methods: time: mean (interval: 1 day)\n", " doi: https://doi.org/10.24381/cds.e2161bac\n", " domain: NAM\n", " frequency: day\n", " history: [2022-12-25 09:07:39.901698] Converted variabl...\n", " ... ...\n", " institute_id: ECMWF\n", " dataset_id: ERA5-Land\n", " abstract: ERA5-Land provides hourly high resolution info...\n", " dataset_description: https://www.ecmwf.int/en/era5-land\n", " attribution: Contains modified Copernicus Climate Change Se...\n", " citation: Muñoz Sabater, J., (2021): ERA5-Land hourly da..." ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Extraction of ERA5-Land data\n", "meteo_ref = xr.open_dataset(\n", " \"https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/datasets/reanalyses/day_ERA5-Land_NAM.ncml\",\n", " engine=\"netcdf4\",\n", " chunks={\"time\": 365, \"lon\": 50, \"lat\": 50},\n", ")[[\"pr\", \"tasmin\", \"tasmax\"]]\n", "meteo_ref" ] }, { "cell_type": "markdown", "id": "10", "metadata": {}, "source": [ "That dataset covers the entire globe and has more than 70 years of data. The first step will thus be to subset the dataset both spatially and temporally. For the spatial subset, the GeoDataFrame obtained earlier can be used." ] }, { "cell_type": "code", "execution_count": 7, "id": "11", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset> Size: 9MB\n",
       "Dimensions:  (time: 10958, lat: 8, lon: 8)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 88kB 1991-01-01 1991-01-02 ... 2020-12-31\n",
       "  * lat      (lat) float32 32B 46.1 46.2 46.3 46.4 46.5 46.6 46.7 46.8\n",
       "  * lon      (lon) float32 32B -71.6 -71.5 -71.4 -71.3 -71.2 -71.1 -71.0 -70.9\n",
       "Data variables:\n",
       "    pr       (time, lat, lon) float32 3MB dask.array<chunksize=(355, 8, 8), meta=np.ndarray>\n",
       "    tasmin   (time, lat, lon) float32 3MB dask.array<chunksize=(355, 8, 8), meta=np.ndarray>\n",
       "    tasmax   (time, lat, lon) float32 3MB dask.array<chunksize=(355, 8, 8), meta=np.ndarray>\n",
       "Attributes: (12/30)\n",
       "    Conventions:               CF-1.9\n",
       "    cell_methods:              time: mean (interval: 1 day)\n",
       "    doi:                       https://doi.org/10.24381/cds.e2161bac\n",
       "    domain:                    NAM\n",
       "    frequency:                 day\n",
       "    history:                   [2026-03-31 14:35:41] shape spatial subsetting...\n",
       "    ...                        ...\n",
       "    institute_id:              ECMWF\n",
       "    dataset_id:                ERA5-Land\n",
       "    abstract:                  ERA5-Land provides hourly high resolution info...\n",
       "    dataset_description:       https://www.ecmwf.int/en/era5-land\n",
       "    attribution:               Contains modified Copernicus Climate Change Se...\n",
       "    citation:                  Muñoz Sabater, J., (2021): ERA5-Land hourly da...
" ], "text/plain": [ " Size: 9MB\n", "Dimensions: (time: 10958, lat: 8, lon: 8)\n", "Coordinates:\n", " * time (time) datetime64[ns] 88kB 1991-01-01 1991-01-02 ... 2020-12-31\n", " * lat (lat) float32 32B 46.1 46.2 46.3 46.4 46.5 46.6 46.7 46.8\n", " * lon (lon) float32 32B -71.6 -71.5 -71.4 -71.3 -71.2 -71.1 -71.0 -70.9\n", "Data variables:\n", " pr (time, lat, lon) float32 3MB dask.array\n", " tasmin (time, lat, lon) float32 3MB dask.array\n", " tasmax (time, lat, lon) float32 3MB dask.array\n", "Attributes: (12/30)\n", " Conventions: CF-1.9\n", " cell_methods: time: mean (interval: 1 day)\n", " doi: https://doi.org/10.24381/cds.e2161bac\n", " domain: NAM\n", " frequency: day\n", " history: [2026-03-31 14:35:41] shape spatial subsetting...\n", " ... ...\n", " institute_id: ECMWF\n", " dataset_id: ERA5-Land\n", " abstract: ERA5-Land provides hourly high resolution info...\n", " dataset_description: https://www.ecmwf.int/en/era5-land\n", " attribution: Contains modified Copernicus Climate Change Se...\n", " citation: Muñoz Sabater, J., (2021): ERA5-Land hourly da..." ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "meteo_ref = meteo_ref.sel(time=slice(\"1991\", \"2020\")) # Temporal subsetting\n", "meteo_ref = xscen.spatial.subset(\n", " meteo_ref, method=\"shape\", tile_buffer=2, shape=gdf\n", ") # Spatial subsetting, with a buffer of 2 grid cells\n", "meteo_ref" ] }, { "cell_type": "code", "execution_count": 8, "id": "12", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = plt.subplot(1, 1, 1)\n", "meteo_ref.tasmin.isel(time=0).plot(ax=ax)\n", "gdf.plot(ax=ax)" ] }, { "cell_type": "markdown", "id": "13", "metadata": {}, "source": [ "Raven expects temperatures in Celsius and precipitation in millimetres, but they currently are in CF-compliant Kelvin and kg m-2 s-1, respectively. The `xhydro.modelling.format_input` function can be used to prepare data for Raven modelling. It handles unit conversion, variable renaming, and coordinate formatting to ensure compatibility with `RavenPy`. In the case of gridded meteorological data—as in this example—`xHydro` calls functions available in `RavenPy` to assign weights to each grid cell based on the portion that overlaps with the watershed. Alternatively, the data could be aggregated manually before being passed to the model.\n", "\n", "For simplification matters, the grid's elevation will be set at a flat 450 m. Computing grid cell elevation in ERA5-Land is not always trivial and is not within the scope of this example." ] }, { "cell_type": "code", "execution_count": 9, "id": "14", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset> Size: 9MB\n",
       "Dimensions:    (time: 10958, latitude: 8, longitude: 8)\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 88kB 1991-01-01 1991-01-02 ... 2020-12-31\n",
       "  * latitude   (latitude) float32 32B 46.1 46.2 46.3 46.4 46.5 46.6 46.7 46.8\n",
       "  * longitude  (longitude) float32 32B -71.6 -71.5 -71.4 ... -71.1 -71.0 -70.9\n",
       "    elevation  (latitude, longitude) float32 256B dask.array<chunksize=(8, 8), meta=np.ndarray>\n",
       "Data variables:\n",
       "    pr         (time, latitude, longitude) float32 3MB dask.array<chunksize=(355, 8, 8), meta=np.ndarray>\n",
       "    tasmin     (time, latitude, longitude) float32 3MB dask.array<chunksize=(355, 8, 8), meta=np.ndarray>\n",
       "    tasmax     (time, latitude, longitude) float32 3MB dask.array<chunksize=(355, 8, 8), meta=np.ndarray>\n",
       "Attributes: (12/30)\n",
       "    Conventions:               CF-1.9\n",
       "    cell_methods:              time: mean (interval: 1 day)\n",
       "    doi:                       https://doi.org/10.24381/cds.e2161bac\n",
       "    domain:                    NAM\n",
       "    frequency:                 day\n",
       "    history:                   [2026-03-31 14:35:41] shape spatial subsetting...\n",
       "    ...                        ...\n",
       "    institute_id:              ECMWF\n",
       "    dataset_id:                ERA5-Land\n",
       "    abstract:                  ERA5-Land provides hourly high resolution info...\n",
       "    dataset_description:       https://www.ecmwf.int/en/era5-land\n",
       "    attribution:               Contains modified Copernicus Climate Change Se...\n",
       "    citation:                  Muñoz Sabater, J., (2021): ERA5-Land hourly da...
" ], "text/plain": [ " Size: 9MB\n", "Dimensions: (time: 10958, latitude: 8, longitude: 8)\n", "Coordinates:\n", " * time (time) datetime64[ns] 88kB 1991-01-01 1991-01-02 ... 2020-12-31\n", " * latitude (latitude) float32 32B 46.1 46.2 46.3 46.4 46.5 46.6 46.7 46.8\n", " * longitude (longitude) float32 32B -71.6 -71.5 -71.4 ... -71.1 -71.0 -70.9\n", " elevation (latitude, longitude) float32 256B dask.array\n", "Data variables:\n", " pr (time, latitude, longitude) float32 3MB dask.array\n", " tasmin (time, latitude, longitude) float32 3MB dask.array\n", " tasmax (time, latitude, longitude) float32 3MB dask.array\n", "Attributes: (12/30)\n", " Conventions: CF-1.9\n", " cell_methods: time: mean (interval: 1 day)\n", " doi: https://doi.org/10.24381/cds.e2161bac\n", " domain: NAM\n", " frequency: day\n", " history: [2026-03-31 14:35:41] shape spatial subsetting...\n", " ... ...\n", " institute_id: ECMWF\n", " dataset_id: ERA5-Land\n", " abstract: ERA5-Land provides hourly high resolution info...\n", " dataset_description: https://www.ecmwf.int/en/era5-land\n", " attribution: Contains modified Copernicus Climate Change Se...\n", " citation: Muñoz Sabater, J., (2021): ERA5-Land hourly da..." ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pathlib import Path\n", "import tempfile\n", "notebook_folder = Path(tempfile.TemporaryDirectory().name)\n", "\n", "import xhydro as xh\n", "\n", "# Add altitude data\n", "meteo_ref = meteo_ref.assign_coords(\n", " {\"elevation\": xr.ones_like(meteo_ref.pr.isel(time=0).drop_vars(\"time\")) * 450}\n", ")\n", "meteo_ref[\"elevation\"].attrs = {\"units\": \"m\"}\n", "\n", "meteo_ref, config_meteo_ref = xh.modelling.format_input(\n", " meteo_ref, model=\"GR4JCN\", save_as=notebook_folder / \"_data\" / \"meteo.nc\"\n", ")\n", "meteo_ref" ] }, { "cell_type": "markdown", "id": "15", "metadata": {}, "source": [ "That function also returns information that will be used later to instanciate the hydrological model:" ] }, { "cell_type": "code", "execution_count": 10, "id": "16", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'data_type': ['TEMP_MAX', 'TEMP_MIN', 'PRECIP'],\n", " 'alt_names_meteo': {'TEMP_MAX': 'tasmax',\n", " 'TEMP_MIN': 'tasmin',\n", " 'PRECIP': 'pr'},\n", " 'meteo_file': '/tmp/tmpbnxtefbc/_data/meteo.nc'}" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "config_meteo_ref" ] }, { "cell_type": "markdown", "id": "17", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "### Hydrometric data\n", "\n", "Gauge streamflow data from the Quebec government can be accessed through the `xdatasets` library." ] }, { "cell_type": "code", "execution_count": 11, "id": "18", "metadata": {}, "outputs": [], "source": [ "qobs = (\n", " xdatasets.Query(\n", " **{\n", " \"datasets\": {\n", " \"deh\": {\n", " \"id\": [\"023401\"],\n", " \"variables\": [\"streamflow\"],\n", " }\n", " },\n", " \"time\": {\"start\": \"1991-01-01\", \"end\": \"2020-12-31\"},\n", " }\n", " )\n", " .data.squeeze()\n", " .load()\n", ").sel(spatial_agg=\"watershed\")" ] }, { "cell_type": "code", "execution_count": 12, "id": "19", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset> Size: 132kB\n",
       "Dimensions:        (time: 10958, station_id: 1)\n",
       "Coordinates: (12/15)\n",
       "  * time           (time) datetime64[ns] 88kB 1991-01-01 ... 2020-12-31\n",
       "  * station_id     (station_id) <U6 24B '023401'\n",
       "    drainage_area  (station_id) float32 4B 708.0\n",
       "    end_date       (station_id) datetime64[ns] 8B 2020-12-31\n",
       "    latitude       (station_id) float32 4B 46.66\n",
       "    longitude      (station_id) float32 4B -71.29\n",
       "    ...             ...\n",
       "    source         (station_id) <U102 408B 'Ministère de l’Environnement, de ...\n",
       "    spatial_agg    (station_id) <U9 36B 'watershed'\n",
       "    start_date     (station_id) datetime64[ns] 8B 1991-01-01\n",
       "    variable       (station_id) <U10 40B 'streamflow'\n",
       "    time_agg       <U4 16B 'mean'\n",
       "    timestep       <U1 4B 'D'\n",
       "Data variables:\n",
       "    q              (time) float32 44kB 46.0 33.9 26.2 21.0 ... 19.98 15.76 12.93
" ], "text/plain": [ " Size: 132kB\n", "Dimensions: (time: 10958, station_id: 1)\n", "Coordinates: (12/15)\n", " * time (time) datetime64[ns] 88kB 1991-01-01 ... 2020-12-31\n", " * station_id (station_id) ]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=[10, 5])\n", "qobs.q.plot()" ] }, { "cell_type": "markdown", "id": "21", "metadata": {}, "source": [ "As specified earlier, streamflow observations need to be modified to account for the differences in watershed sizes between the gauge and the polygon." ] }, { "cell_type": "code", "execution_count": 14, "id": "22", "metadata": {}, "outputs": [], "source": [ "with xr.set_options(keep_attrs=True):\n", " qobs[\"q\"] = qobs[\"q\"] * scaling_factor" ] }, { "cell_type": "code", "execution_count": 15, "id": "23", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [], "source": [ "# Save (necessary for model calibration)\n", "qobs.to_netcdf(notebook_folder / \"_data\" / \"qobs.nc\")" ] }, { "cell_type": "markdown", "id": "24", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "## Preparation and calibration of the hydrological model (xhydro.modelling)\n", "\n", "
INFO\n", "\n", "For more information on this section and available options, consult the [Hydrological modelling notebook](hydrological_modelling_raven.ipynb).\n", "\n", "
" ] }, { "cell_type": "code", "execution_count": 16, "id": "25", "metadata": {}, "outputs": [], "source": [ "import xhydro as xh\n", "from xhydro.modelling.calibration import perform_calibration\n", "from xhydro.modelling.obj_funcs import get_objective_function" ] }, { "cell_type": "markdown", "id": "26", "metadata": {}, "source": [ "The `perform_calibration` function requires a `model_config` argument that allows it to build the corresponding hydrological model. All the required information has been acquired in previous sections, so it is only a matter of filling in the entries of the RavenPy/GR4JCN model.\n", "\n", "For simplification matters, as snow water equivalent is not currently available on PAVICS' database, \"AVG_ANNUAL_SNOW\" was roughly estimated using [Brown & Brasnett (2010)](https://ccin.ca/ccw/snow/overview/references)." ] }, { "cell_type": "code", "execution_count": 17, "id": "27", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [], "source": [ "# Model configuration\n", "model_config = {\n", " \"model_name\": \"GR4JCN\",\n", " \"workdir\": notebook_folder / \"model\",\n", " \"overwrite\": True,\n", " \"parameters\": [0.529, -3.396, 407.29, 1.072, 16.9, 0.947],\n", " \"hru\": gdf,\n", " \"start_date\": \"1991-01-01\",\n", " \"end_date\": \"2020-12-31\",\n", " \"rain_snow_fraction\": \"RAINSNOW_DINGMAN\",\n", " \"evaporation\": \"PET_HARGREAVES_1985\",\n", " \"global_parameter\": {\"AVG_ANNUAL_SNOW\": 100.00},\n", " **config_meteo_ref, # Reuse information gathered earlier\n", "}\n", "\n", "# Parameter bounds for GR4JCN\n", "bounds_low = [0.01, -15.0, 10.0, 0.0, 1.0, 0.0]\n", "bounds_high = [2.5, 10.0, 700.0, 7.0, 30.0, 1.0]" ] }, { "cell_type": "code", "execution_count": 18, "id": "28", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Initializing the Dynamically Dimensioned Search (DDS) algorithm with 8 repetitions\n", "The objective function will be maximized\n", "Starting the DDS algorithm with 8 repetitions...\n", "Finding best starting point for trial 1 using 5 random samples.\n", "1 of 8, maximal objective function=0.129327, time remaining: 00:00:10\n", "Initialize database...\n", "['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']\n", "2 of 8, maximal objective function=0.129327, time remaining: 00:00:11\n", "3 of 8, maximal objective function=0.293959, time remaining: 00:00:09\n", "4 of 8, maximal objective function=0.293959, time remaining: 00:00:08\n", "5 of 8, maximal objective function=0.293959, time remaining: 00:00:05\n", "6 of 8, maximal objective function=0.293959, time remaining: 00:00:03\n", "7 of 8, maximal objective function=0.390197, time remaining: 00:00:00\n", "8 of 8, maximal objective function=0.393656, time remaining: 23:59:57\n", "Best solution found has obj function value of 0.3936562927318237 at 5\n", "\n", "\n", "\n", "*** Final SPOTPY summary ***\n", "Total Duration: 24.88 seconds\n", "Total Repetitions: 8\n", "Maximal objective value: 0.393656\n", "Corresponding parameter setting:\n", "param0: 1.13242\n", "param1: -2.46413\n", "param2: 43.5229\n", "param3: 1.33209\n", "param4: 24.153\n", "param5: 0.0766439\n", "******************************\n", "\n", "Best parameter set:\n", "param0=1.132418270728551, param1=-2.464133107201744, param2=43.522924628974806, param3=1.3320909887768697, param4=24.153016559360484, param5=0.07664389863678882\n", "Run number 7 has the highest objectivefunction with: 0.3937\n" ] } ], "source": [ "# Calibration / validation period\n", "mask_calib = xr.where(qobs.time.dt.year <= 2010, 1, 0).values\n", "mask_valid = xr.where(qobs.time.dt.year > 2010, 1, 0).values\n", "\n", "# Model calibration\n", "best_parameters, best_simulation, best_objfun = perform_calibration(\n", " model_config,\n", " \"kge\",\n", " qobs=notebook_folder / \"_data\" / \"qobs.nc\",\n", " bounds_low=bounds_low,\n", " bounds_high=bounds_high,\n", " evaluations=8,\n", " algorithm=\"DDS\",\n", " mask=mask_calib,\n", " sampler_kwargs=dict(trials=1),\n", ")" ] }, { "cell_type": "markdown", "id": "29", "metadata": {}, "source": [ "To reduce computation times for this example, only 10 steps were used for the calibration function, which is well below the recommended number. The parameters below were obtained by running the code above with 150 evaluations." ] }, { "cell_type": "code", "execution_count": 19, "id": "30", "metadata": {}, "outputs": [], "source": [ "# Replace the results with parameters obtained using 150 evaluations\n", "best_parameters = [\n", " 0.3580270511815579,\n", " -2.187141388684563,\n", " 24.012067980309702,\n", " 0.000781,\n", " 1.9330212374187332,\n", " 0.5491789347783598,\n", "]\n", "model_config[\"parameters\"] = best_parameters\n", "\n", "best_simulation = xh.modelling.hydrological_model(model_config).run()" ] }, { "cell_type": "markdown", "id": "31", "metadata": {}, "source": [ "The real KGE should be computed from a validation period, using `get_objective_function`." ] }, { "cell_type": "code", "execution_count": 20, "id": "32", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(0.68497379)" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_objective_function(\n", " qobs=qobs.q,\n", " qsim=best_simulation,\n", " obj_func=\"kge\",\n", " mask=mask_valid,\n", ").values" ] }, { "cell_type": "code", "execution_count": 21, "id": "33", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = plt.figure(figsize=(10, 5))\n", "qobs.q.plot(color=\"k\", linewidth=3)\n", "best_simulation.q.plot(color=\"r\")" ] }, { "cell_type": "markdown", "id": "34", "metadata": {}, "source": [ "## Calculation of hydroclimatological indicators" ] }, { "cell_type": "code", "execution_count": 22, "id": "35", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import xclim\n", "\n", "import xhydro.frequency_analysis as xhfa" ] }, { "cell_type": "markdown", "id": "36", "metadata": {}, "source": [ "### Non-frequential indicators\n", "\n", "
INFO\n", "\n", "For more information on this section and available options, consult the [Climate change analysis notebook](pavics_notebooks/climate_change.ipynb).\n", "\n", "
\n", "\n", "
INFO\n", "\n", "Custom indicators in `xHydro` are built by following the YAML formatting required by `xclim`.\n", "\n", "A custom indicator built using `xclim.core.indicator.Indicator.from_dict` will need these elements:\n", "\n", "- \"data\": A dictionary with the following information:\n", " - \"base\": The \"YAML ID\" obtained from [here](https://xclim.readthedocs.io/en/stable/indicators.html).\n", " - \"input\": A dictionary linking the default xclim input to the name of your variable. Needed only if it is different. In the link above, they are the string following \"Uses:\".\n", " - \"parameters\": A dictionary containing all other parameters for a given indicator. In the link above, the easiest way to access them is by clicking the link in the top-right corner of the box describing a given indicator.\n", " - More entries can be used here, as described [in the xclim documentation](https://xclim.readthedocs.io/en/latest/api.html#yaml-file-structure) under \"identifier\".\n", "- \"identifier\": A custom name for your indicator. This will be the name returned in the results.\n", "- \"module\": Needed, but can be anything. To prevent an accidental overwriting of `xclim` indicators, it is best to use something different from: [\"atmos\", \"land\", \"generic\"].\n", "\n", "
\n", "\n", "For a climate change impact analysis, the typical process to compute non-frequential indicators would be to:\n", "\n", "1. Define the indicators either through the `xclim` functionalities shown below or through a [YAML file](https://xclim.readthedocs.io/en/latest/api.html#yaml-file-structure).\n", "2. Call `xhydro.indicators.compute_indicators`, which would produce annual results through a dictionary, where each key represents the requested frequencies.\n", "3. Call `xhydro.cc.climatological_op` on each entry of the dictionary to compute the 30-year average.\n", "4. Recombine the datasets.\n", "\n", "However, if the annual results are not required, `xhydro.cc.produce_horizon` can bypass steps 2 to 4 and alleviate a lot of hassle. It accomplishes that by removing the `time` axis and replacing it for a `horizon` dimension that represents a slice of time. In the case of seasonal or monthly indicators, a corresponding `season` or `month` dimension is also added.\n", "\n", "We will compute the mean summer flow (an annual indicator) and mean monthly flows." ] }, { "cell_type": "code", "execution_count": 23, "id": "37", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset> Size: 316B\n",
       "Dimensions:             (horizon: 1, month: 12)\n",
       "Coordinates:\n",
       "  * horizon             (horizon) <U9 36B '1991-2020'\n",
       "  * month               (month) <U3 144B 'JAN' 'FEB' 'MAR' ... 'OCT' 'NOV' 'DEC'\n",
       "    subbasin_id         <U1 4B '1'\n",
       "    elevation           float32 4B 222.6\n",
       "    drainage_area       float64 8B 585.6\n",
       "    centroid_longitude  float64 8B -71.26\n",
       "    centroid_latitude   float64 8B 46.45\n",
       "Data variables:\n",
       "    qmoy_summer         (horizon) float64 8B 9.589\n",
       "    qmoy_monthly        (horizon, month) float64 96B 5.919 4.565 ... 12.27 9.125\n",
       "Attributes:\n",
       "    Conventions:           CF-1.6\n",
       "    featureType:           timeSeries\n",
       "    history:               Created on 2026-03-31T14:36:41 by Raven 4.1\n",
       "    description:           Standard Output\n",
       "    references:            Craig J.R. and the Raven Development Team Raven us...\n",
       "    model_id:              GR4JCN\n",
       "    Raven_version:         4.1\n",
       "    RavenPy_version:       0.20.0\n",
       "    cat:xrfreq:            fx\n",
       "    cat:frequency:         fx\n",
       "    cat:processing_level:  horizons\n",
       "    cat:variable:          ('qmoy_monthly',)
" ], "text/plain": [ " Size: 316B\n", "Dimensions: (horizon: 1, month: 12)\n", "Coordinates:\n", " * horizon (horizon) INFO\n", "\n", "For more information on this section and available options, consult the [Local frequency analysis notebook](local_frequency_analysis.ipynb).\n", "\n", "\n", "\n", "A frequency analysis typically follows these steps:\n", "\n", "1. Get the raw data needed for the analysis, such as annual maximums, through `xhydro.indicators.get_yearly_op`.\n", "2. Call `xhfa.local.fit` to obtain the parameters for a specified number of distributions, such as Gumbel, GEV, and Pearson-III.\n", "3. (Optional) Call `xhfa.local.criteria` to obtain goodness-of-fit parameters.\n", "4. Call `xhfa.local.parametric_quantiles` to obtain the return levels.\n", "\n", "We will compute the 20 and 100-year annual maximums, as well as the 2-year minimum 7-day summer flow." ] }, { "cell_type": "code", "execution_count": 24, "id": "39", "metadata": {}, "outputs": [], "source": [ "qref_max = xh.indicators.get_yearly_op(\n", " best_simulation,\n", " op=\"max\",\n", " timeargs={\"annual\": {}},\n", " missing=\"pct\",\n", " missing_options={\"tolerance\": 0.15},\n", ")\n", "qref_min = xh.indicators.get_yearly_op(\n", " best_simulation,\n", " op=\"min\",\n", " window=7,\n", " timeargs={\"summer\": {\"date_bounds\": [\"05-01\", \"11-30\"]}},\n", " missing=\"pct\",\n", " missing_options={\"tolerance\": 0.15},\n", ")" ] }, { "cell_type": "code", "execution_count": 25, "id": "40", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = plt.figure(figsize=(10, 5))\n", "qref_max.q_max_annual.dropna(\"time\", how=\"all\").plot()\n", "qref_min.q7_min_summer.dropna(\"time\", how=\"all\").plot()" ] }, { "cell_type": "code", "execution_count": 26, "id": "41", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset> Size: 436B\n",
       "Dimensions:             (scipy_dist: 4, dparams: 4)\n",
       "Coordinates:\n",
       "  * scipy_dist          (scipy_dist) <U10 160B 'genextreme' ... 'pearson3'\n",
       "  * dparams             (dparams) <U5 80B 'c' 'skew' 'loc' 'scale'\n",
       "    horizon             <U9 36B '1991-2020'\n",
       "    subbasin_id         <U1 4B '1'\n",
       "    elevation           float32 4B 222.6\n",
       "    drainage_area       float64 8B 585.6\n",
       "    centroid_longitude  float64 8B -71.26\n",
       "    centroid_latitude   float64 8B 46.45\n",
       "Data variables:\n",
       "    q_max_annual        (scipy_dist, dparams) float64 128B 0.08161 nan ... 87.46\n",
       "Attributes: (12/13)\n",
       "    Conventions:           CF-1.6\n",
       "    featureType:           timeSeries\n",
       "    history:               Created on 2026-03-31T14:36:41 by Raven 4.1\n",
       "    description:           Standard Output\n",
       "    references:            Craig J.R. and the Raven Development Team Raven us...\n",
       "    model_id:              GR4JCN\n",
       "    ...                    ...\n",
       "    RavenPy_version:       0.20.0\n",
       "    cat:xrfreq:            YS-JAN\n",
       "    cat:frequency:         yr\n",
       "    cat:processing_level:  indicators\n",
       "    cat:variable:          ('q_max_annual',)\n",
       "    cat:id:                
" ], "text/plain": [ " Size: 436B\n", "Dimensions: (scipy_dist: 4, dparams: 4)\n", "Coordinates:\n", " * scipy_dist (scipy_dist)