11 citations as recorded by crossref.

1. Hydrological impact of widespread afforestation in Great Britain using a large ensemble of modelled scenarios M. Buechel et al. https://doi.org/10.1038/s43247-021-00334-0
2. Agricultural drought assessment in dry zones of Tolima, Colombia, using an approach based on water balance and vegetation water stress J. Hernández-López et al. https://doi.org/10.1016/j.scitotenv.2024.171144
3. Retrieving Soil Physical Properties by Assimilating SMAP Brightness Temperature Observations into the Community Land Model H. Zhao et al. https://doi.org/10.3390/s23052620
4. Future increases in soil moisture drought frequency at UK monitoring sites: merging the JULES land model with observations and convection-permitting UK climate projections M. Szczykulska et al. https://doi.org/10.1088/1748-9326/ad7045
5. A new approach for joint assimilation of cosmic-ray neutron soil moisture and groundwater level data into an integrated terrestrial model F. Li et al. https://doi.org/10.5194/hess-29-6419-2025
6. pedon: A Python package for analyzing unsaturated soil hydraulic properties M. Vonk & A. Peche https://doi.org/10.21105/joss.10409
7. Comparison of Met Office regional model soil moisture with COSMOS‐UK field‐scale in situ observations E. Cooper et al. https://doi.org/10.1002/asl.1236
8. Towards the assimilation of atmospheric CO2 concentration data in a land surface model using adjoint-free variational methods S. Beylat et al. https://doi.org/10.5194/gmd-18-7501-2025
9. Soil moisture dynamics near a gully head in relation to the trigger of collapse in granite red soil slope in southern China Z. Zou et al. https://doi.org/10.1016/j.geomorph.2022.108493
10. Optimising ensemble streamflow predictions with bias correction and data assimilation techniques M. Tanguy et al. https://doi.org/10.5194/hess-29-1587-2025
11. Soil hydraulic and hydrological data from seven field sites in the Thames catchment, UK, 2021 J. Robotham et al. https://doi.org/10.5194/essd-17-4277-2025