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

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')