# GFM maximum flood extent with STAC This notebook will demonstrate how to find data using STAC, load it into a xarray object and calculate a result. As an example, we will calculate the maximum flood extent of a certain time range over an area of interest in Morocco. In February 2026, the area suffered from severe weather and floods. ## Prepare Python environment In this notebook, we are using Python 3.12.11. First, let's install some necessary Python packages. We also need to install the "ipykernel" packge to enable Jupyter notebooks to run Python code. ```python !pip install pyproj xarray shapely pystac_client odc-stac matplotlib rioxarray ipykernel ``` ## First some imports ```python import pyproj import rioxarray # noqa import xarray as xr from datetime import datetime from shapely.geometry import box from pystac_client import Client from odc import stac as odc_stac from pathlib import Path ``` ## Search and load data We will define our area (AOI) and time range of interest for which we want to calculate the maximum flood extent for. For defining a bounding box, you can use [this web tool](http://bboxfinder.com). All GFM data is registered as a [STAC](https://stacspec.org/en/) collection. Please find more information about STAC in our [documentation](https://docs.eodc.eu/services/stac.html). ```python # Define the API URL api_url = "https://stac.eodc.eu/api/v1" # Define the STAC collection ID collection_id = "GFM" # Define the area of interest (AOI) as a bounding box # Use portal.gfm.eodc.eu to create an AOI and retrieve the coordinates or another tool like bboxfinder.com # aoi = box(min_lon, min_lat, max_lon, max_lat) aoi = box(-6.725286048,34.093280072,-4.792626025,36.073018993) # Define the time range for the search time_range = (datetime(2026, 2, 1), datetime(2026, 2, 7)) # Open the STAC catalog using the specified API URL eodc_catalog = Client.open(api_url) # Perform a search in the catalog with the specified parameters search = eodc_catalog.search( max_items=1000, # Maximum number of items to return collections=collection_id, # The collection to search within intersects=aoi, # The area of interest datetime=time_range # The time range for the search ) # Collect the found items into an item collection items = search.item_collection() print(f"On EODC we found {len(items)} items for the given search query") ``` On EODC we found 39 items for the given search query We will use the found STAC items to load the data into a xarray.Dataset object. In order to achieve this, we need to specify the bands which we want to load. To calculate the maximum flood extent, we are interested in the "ensemble_flood_extent" layer of each GFM item. Furthermore, we need to specify the coordinate reference system (CRS) as well as the resolution of the data. All necessary metadata is saved in each STAC item. Depending on your Internet connection, running this cell will take some time (around 1min30s). Please be patient and wait until the cell finishes executing. Once the data is loaded, you can proceed with further analysis or visualization. ```python # Extract the coordinate reference system (CRS) from the first item's properties crs = pyproj.CRS.from_wkt(items[0].properties["proj:wkt2"]) # Set the resolution of the data resolution = items[0].properties['gsd'] # Specify the bands to load bands = ["ensemble_flood_extent"] # Load the data using odc-stac with the specified parameters xx = odc_stac.load( items, bbox=aoi.bounds, # Define the bounding box for the area of interest crs=crs, # Set the coordinate reference system bands=bands, # Specify the bands to load resolution=resolution, # Set the resolution of the data dtype='uint8', # Define the data type groupby="solar_day", # fail_on_error=False, ) xx ``` 
<xarray.Dataset> Size: 718MB
Dimensions:                (y: 12534, x: 11448, time: 5)
Coordinates:
  * y                      (y) float64 100kB 9.249e+06 9.249e+06 ... 8.998e+06
  * x                      (x) float64 92kB 2.944e+06 2.944e+06 ... 3.173e+06
  * time                   (time) datetime64[us] 40B 2026-02-01T18:32:49 ... ...
    spatial_ref            int32 4B 0
Data variables:
    ensemble_flood_extent  (time, y, x) uint8 717MB 255 255 255 ... 255 255 255
## Process locally First, we filter the data to exclude invalid values and calculate the sum along the time dimension. The maximum flood extent refers to the largest area covered by flooded pixels during the specified time range. Therefore, we convert the result to a binary mask where each pixel is set to 1 if it was flooded during the specified time range, and 0 if it was not. Then we start the computation on the cluster and save the result as a compressed TIFF file. This file can be visualized in e.g. QGIS. ```python # Create output directory output = Path("./output") output.mkdir(exist_ok=True) fname = "max_flood_morocco_202602.tif" # Filter the data to exclude values of 255 (nodata) and 0 (no-flood), then sum # along the "time" dimension data = xx["ensemble_flood_extent"] filtered_data = data.where((data != 255) & (data != 0)) result = filtered_data.sum(dim="time") # Convert the result to binary (1 where the sum is greater than 0, otherwise 0) # and set the data type to uint8 binary_result = xr.where(result > 0, 1, 0).astype("uint8") # Save the computed result to a GeoTIFF file with LZW compression output_path = output.joinpath(fname) binary_result = binary_result.rio.write_crs(crs) binary_result.rio.to_raster( output_path, compress="LZW", tiled=True, blockxsize=512, blockysize=512 ) ``` ## Plot with matplotlib Additionally, we can plot a part of the result with the Python library matplotlib. ```python import matplotlib.pyplot as plt from matplotlib.colors import ListedColormap cmap = ListedColormap(['none', 'blue']) plt.figure() plt.imshow(binary_result, cmap=cmap) plt.title("GFM Maximum Flood Extent") plt.show() ``` ![png](gfm-flood-extent-local_files/gfm-flood-extent-local_12_0.png)