Upload VE Climate Data Preparation Script

analysis/abiotic/data_download/cdsapi_downloader.py

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+"""
+---
+title: Data downloader tools for ERA5
+
+description: |
+ This file defines the cdsapi_era5_downloader Python function, which automates the download of 
+ ERA5-Land monthly averaged datasets. It uses the Copernicus Climate Data Store (CDS) API.
+
+ reference:
+ Muñoz-Sabater, J. (2019). ERA5-Land monthly averaged data from 1981 to present.
+ Copernicus Climate Change Service (C3S) Climate Data Store (CDS).
+ https://doi.org/10.24381/cds.68d2bb30  (Last accessed: 11-09-2025)
+
+author:
+  - name: Lelavathy
+  - name: David Orme
+
+virtual_ecosystem_module: abiotic, abiotic_simple, hydrology
+
+status: final
+
+input_files:
+
+output_files:
+
+package_dependencies:
+  - cdsapi
+  - xarray
+
+usage_notes: |
+  Copernicus Data Store (CDS) Registration & API Key Setup:
+  Register at [](https://cds.climate.copernicus.eu/) and configure your `.cdsapirc`
+  file in your home directory. The Copernicus Climate Data Store provides
+  comprehensive guidance on setting up and using their API at
+  https://cds.climate.copernicus.eu/how-to-api
+
+  Example `.cdsapirc`:
+
+    url: https://cds.climate.copernicus.eu/api
+    key: your-api-key
+---
+"""  # noqa: D205, D212, D400, D415
+
+from pathlib import Path
+
+import cdsapi
+import xarray
+
+# List of required ERA5 climate variables
+REQUIRED_VARIABLES = [
+    "2m_temperature",  # abiotic variable
+    "2m_dewpoint_temperature",  # abiotic variable
+    "surface_pressure",  # abiotic variable
+    "10m_u_component_of_wind",  # abiotic variable
+    "total_precipitation",  # hydrological variable
+    "surface_solar_radiation_downwards", # plant variable
+]
+
+
+def cdsapi_era5_downloader(years: list[int], bbox: list[float], outfile: Path):
+    """ERA5 hourly data downloader.
+
+    Downloads a time series of the set of required variables from the CDS for the
+    "reanalysis-era5-land-monthly-means" dataset for the region within a provided
+    bounding box and for a set of years.
+
+
+    Args:
+        years: A list of years to download
+        bbox: The bounding box of the data to download in degrees,
+        outfile: An output path for the compiled data file.
+
+    """
+
+    # Each variable is downloaded to a separate file, so collect temporary filenames
+    downloaded_files = []
+
+    # Get the CDSAPI client
+    client = cdsapi.Client()
+
+    for var in REQUIRED_VARIABLES:
+        # Create the request dictionary - all hourly observations of the requested
+        # variable in the requested years
+        request = {
+            "product_type": ["monthly_averaged_reanalysis"],
+            "variable": var,
+            "year": [str(y) for y in years],
+            "month": [f"{i:02d}" for i in range(1, 13)],  # All months in a year
+            "time": [f"{i:02d}:00" for i in range(24)],  # All hours in a day
+            "data_format": "netcdf",
+            "download_format": "unarchived",
+            "area": bbox,
+        }
+
+        # Retrieve the request from the client. The file will download to a random UUID
+        # filename by default - we collect these to compile the data to a single file.
+        file = client.retrieve(
+            name="reanalysis-era5-land-monthly-means", request=request
+        ).download()
+        downloaded_files.append(file)
+
+    # Load the individual datafiles and then merge them into a single Dataset
+    netcdf_datasets = [xarray.load_dataarray(d) for d in downloaded_files]
+    compiled_data = xarray.merge(netcdf_datasets)
+
+    # Write the compiled data to the output file
+    compiled_data.to_netcdf(outfile)
+
+    # Tidy up the individual variable files
+    for file in downloaded_files:
+        Path(file).unlink()

analysis/abiotic/data_download/maliau_era5_download_and_reproject.py

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+"""
+---
+title: ERA5 data download and reproject for the Maliau site
+
+description: |
+  This file uses the `cdsapi_era5_downloader` function to download a 3x3 grid at 0.1°
+  resolution from 2010 to 2020 around the Maliau basin data sites.
+
+  It then uses the `rasterio` package to reproject and interpolate those values to the
+  90m resolution grid defined in the Maliau basin site definition file. The script
+  currently uses the `nearest` interpolation strategy to map values from the 0.1° cells
+  to 90m cells, but we may want to revisit this.
+
+  Muñoz Sabater, J. (2019): ERA5-Land monthly averaged data from 1950 to present.
+  Copernicus Climate Change Service (C3S) Climate Data Store (CDS).
+  DOI: 10.24381/cds.68d2bb30 (Last accessed on xx-xx-2025)
+
+author:
+  - name: Lelavathy
+  - name: David Orme
+
+virtual_ecosystem_module: abiotic, abiotic_simple, hydrology
+
+status: final
+
+input_files:
+  - name: maliau_grid_definition.toml
+    path: analysis/core
+    description: Site grid definition for Maliau
+
+output_files:
+  - name: ERA5_Maliau_2010_2020.nc
+    path: data/primary/abiotic/era5_land_monthly
+    description: 2010-2020 ERA5 data for Maliau in original 0.1° resolution.
+  - name: ERA5_Maliau_2010_2020_UTM50N.nc
+    path: data/primary/abiotic/era5_land_monthly
+    description: 2010-2020 ERA5 data for Maliau reprojected to 90m UTM50N grid.
+
+package_dependencies:
+  - cdsapi
+  - xarray
+  - tomllib
+  - rasterio
+  - rioxarray
+
+usage_notes: |
+  Run as `python maliau_era5_download_and_reproject.py`
+
+---
+"""  # noqa: D205, D212, D400, D415
+
+from pathlib import Path
+
+import tomllib
+import xarray
+from cdsapi_downloader import cdsapi_era5_downloader
+from rasterio import Affine
+from rasterio.crs import CRS
+from rasterio.warp import Resampling
+
+# Define output directory and filename
+output_dir = Path("../../../data/primary/abiotic/era5_land_monthly")
+output_dir.mkdir(parents=True, exist_ok=True)
+
+output_filename = output_dir / "ERA5_Maliau_2010_2020.nc"
+
+# Run the downloader tool
+cdsapi_era5_downloader(
+    years=list(range(2010, 2021)),
+    bbox=[4.6, 116.8, 4.8, 117.0],
+    outfile=output_filename,
+)
+
+# Define the projections to be used
+wgs84_crs = CRS.from_epsg(4326)
+utm50N_crs = CRS.from_epsg(32650)
+
+# Load the destination grid details
+with open("../../../sites/maliau_site_definition.toml", "rb") as maliau_grid_file:
+    utm50N_grid_details = tomllib.load(maliau_grid_file)
+
+# Define the XY shape of the data in the destination dataset
+dest_shape = (
+    utm50N_grid_details["cell_nx"],
+    utm50N_grid_details["cell_ny"],
+)
+
+# Define the affine matrix giving the coordinates of pixels in the destination dataset
+dest_transform = Affine(
+    utm50N_grid_details["res"],
+    0,
+    utm50N_grid_details["ll_x"],
+    0,
+    utm50N_grid_details["res"],
+    utm50N_grid_details["ll_y"],
+)
+
+# Open the ERA5 dataset in WGS84 and and set the CRS manually because it is not
+# set in the file. Do not decode times from CF to np.datetime64, because we'd have
+# to convert back to write the file.
+era5_data_WGS84 = xarray.open_dataset(
+    output_filename,
+    engine="rasterio",
+    decode_times=False,
+)
+era5_data_WGS84 = era5_data_WGS84.rio.write_crs(wgs84_crs)
+
+# Use the rasterio accessor tools to reproject the data
+
+# NOTE: The reprojection is automatically applying downscaling to the 90m grid
+# definition. The interpolation mapping used here at the moment is the `nearest`
+# method of rasterio. Other methods are available:
+#
+# https://rasterio.readthedocs.io/en/stable/api/rasterio.enums.html#rasterio.enums.Resampling
+#
+# We might want to use a different interpolation strategy to give a smooth surface -
+# something like a cubic interpolation or a spline - but it isn't clear to me right now
+# if we'd be better off doing that or simply taking the nearest value and then doing
+# something more scientifically informed from that baseline.
+
+era5_data_UTM50N = era5_data_WGS84.rio.reproject(
+    dst_crs=utm50N_crs,
+    shape=dest_shape,
+    transform=dest_transform,
+    resampling=Resampling.nearest,
+)
+
+# Save the reprojected data to file
+era5_data_UTM50N.to_netcdf(output_dir / "ERA5_Maliau_2010_2020_UTM50N.nc")