Soil moisture (open loop)

Produced by
CNRM
Data type
Gridded
Horizontal coverage
Europe (west=25.0° W, east=45.0° E, south=30.0° N, north=70.0° N)
Horizontal resolution
0.1° x 0.1°
Temporal resolution
Hourly
Temporal coverage
2018-2022
Model
SURFEX land surface model
File format
Zarr

Download the data

Time-chunked

https://data.seedsproject.eu/seeds_wg_cnrm_20180101-20221231_surfex_ol_v1/slices.zarr

Geo-chunked

https://data.seedsproject.eu/seeds_wg_cnrm_20180101-20221231_surfex_ol_v1/dices.zarr
How to download the data?

Variables

Click on a variable to see it on the viewer.

Soil liquid water content (surface)
wg2_isba
Soil liquid water content (root zone)
wg5_isba
Soil solid water content (surface)
wgi2_isba
Soil solid water content (root zone)
wgi5_isba
Total volumetric water content
wgtot_isba

Description

Leaf area index (LAI) is unitless measure of the total area of leaves per unit ground area (e.g., an LAI of 1 equates to 1 m2 of leaf area above 1 m2 of ground area), which can be a vital variable for predicting primary production of plants and evapotranspiration. The LAI data in SEEDS are calculated by the SURFEX land surface model. The seasonal changes in vegetation growth and senescence are simulated in a dynamic way by the model taking into account vegetation type, sunlight, and meteorological conditions that affect the growing season. The LAI is calculated over different vegetation types separately and are then spatially averaged to combine them into a single LAI for all natural surface types in the SURFEX model.

Citation

CNRM. (2024). Soil moisture (open loop) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10731823

Download instructions

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This sample Python code shows how to download this dataset, create subsets of it and save them as NetCDF, using the Xarray library.

import xarray as xr import fsspec # Open remote dataset ds = xr.open_zarr( store=fsspec.get_mapper("https://data.seedsproject.eu/seeds_wg_cnrm_20180101-20221231_surfex_ol_v1/slices.zarr") ) # Subset a time range, save as NetCDF time_subset = ds.wg2_isba.sel(time=slice('2019-06-01', '2019-07-01')) time_subset.to_netcdf('./wg2_isba_time_subset.nc') # Subset a time and geographical range, save as NetCDF time_geo_subset = ds.wg2_isba.sel(time=slice('2019-06-01', '2019-07-01'), latitude=slice(40, 43), longitude=slice(0,3.5)) time_geo_subset.to_netcdf('./wg2_isba_time_geo_subset.nc')