Articles | Volume 29, issue 2
https://doi.org/10.5194/hess-29-547-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-547-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Jan 2025
Research article |
| 29 Jan 2025
| 29 Jan 2025
Do land models miss key soil hydrological processes controlling soil moisture memory?
Mohammad A. Farmani
CORRESPONDING AUTHOR
Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA
Ali Behrangi
Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA
Department of Geosciences, University of Arizona, Tucson, AZ, USA
Aniket Gupta
Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA
Ahmad Tavakoly
US Army Engineer Research and Development Center, Coastal and Hydraulics Laboratory, Vicksburg, MS, USA
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
Matthew Geheran
US Army Engineer Research and Development Center, Coastal and Hydraulics Laboratory, Vicksburg, MS, USA
Guo-Yue Niu
CORRESPONDING AUTHOR
Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA
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Short summary
Soil moisture memory (SMM) shows how long soil stays moist after rain, impacting climate and ecosystems. Current models often overestimate SMM, causing inaccuracies in evaporation predictions. We enhanced a land model, Noah-MP, to include better water flow and ponding processes, and we tested it against satellite and field data. This improved model reduced overestimations and enhanced short-term predictions, helping create more accurate climate and weather forecasts.
Soil moisture memory (SMM) shows how long soil stays moist after rain, impacting climate and...
