13 citations as recorded by crossref.

1. 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
2. Historical memory in remotely sensed soil moisture can enhance flash flood modeling for headwater catchments in Germany Y. Liu et al. https://doi.org/10.1016/j.jhydrol.2024.132395
3. Editorial: Impact of anthropogenic disturbances on agroforestry ecosystems P. Nasta et al. https://doi.org/10.3389/frwa.2023.1207654
4. Seasonal soil moisture and crop yield prediction with fifth-generation seasonal forecasting system (SEAS5) long-range meteorological forecasts in a land surface modelling approach T. Boas et al. https://doi.org/10.5194/hess-27-3143-2023
5. Evapotranspiration prediction for European forest sites does not improve with assimilation of in situ soil water content data L. Strebel et al. https://doi.org/10.5194/hess-28-1001-2024
6. NAQPMS-PDAF v2.0: a novel hybrid nonlinear data assimilation system for improved simulation of PM2.5 chemical components H. Li et al. https://doi.org/10.5194/gmd-17-8495-2024
7. Can a Sparse Network of Cosmic Ray Neutron Sensors Improve Soil Moisture and Evapotranspiration Estimation at the Larger Catchment Scale? F. Li et al. https://doi.org/10.1029/2023WR035056
8. Isolating the effects of land use land cover change and inter-decadal climate variations on the water and energy cycles over India, 1981–2010 N. Ghodichore et al. https://doi.org/10.1016/j.jhydrol.2022.128267
9. Resolving the limits of MJO forecast skill: large-sample-based ensemble optimization in the IAP-CAS S2S model Y. Liu et al. https://doi.org/10.3389/fclim.2026.1828808
10. Identical hierarchy of physical drought types for climate change signals and uncertainty P. Hosseinzadehtalaei et al. https://doi.org/10.1016/j.wace.2023.100573
11. Improving the representation of major Indian crops in the Community Land Model version 5.0 (CLM5) using site-scale crop data N. Kangari et al. https://doi.org/10.5194/gmd-18-763-2025
12. A Python interface to the Fortran-based Parallel Data Assimilation Framework: pyPDAF v1.0.2 Y. Chen et al. https://doi.org/10.5194/gmd-18-8235-2025
13. Spatiotemporal upscaling of sparse air-sea pCO2 data via physics-informed transfer learning S. Kim et al. https://doi.org/10.1038/s41597-024-03959-w