Articles | Volume 30, issue 4
https://doi.org/10.5194/hess-30-1097-2026
© Author(s) 2026. 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-30-1097-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Feb 2026
Research article |
| 24 Feb 2026
| 24 Feb 2026
Skills in sub-seasonal to seasonal terrestrial water storage forecasting: insights from the FEWS NET land data assimilation system
ESSIC University of Maryland, College Park, MD 20740, USA
Abheera Hazra
ESSIC University of Maryland, College Park, MD 20740, USA
Amy McNally
NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
Kimberly Slinski
ESSIC University of Maryland, College Park, MD 20740, USA
Shraddhanand Shukla
University of California at Santa Barbara, Santa Barbara, CA 93106, USA
Weston Anderson
Department of Geography, University of Maryland, College Park, MD 20740, USA
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Short summary
Accurate prediction of terrestrial water storage (TWS) changes is essential for disaster response. This study evaluates TWS forecast skill over Africa using observations from the Gravity Recovery and Climate Experiment (GRACE). Results show that the NASA Catchment Land Surface Model (CLSM) outperforms Noah with Multi-Parameterization (Noah-MP) across 1-6 months lead times, owing to more accurate reanalysis-based initial conditions and stronger representation of TWS interannual variability.
Accurate prediction of terrestrial water storage (TWS) changes is essential for disaster...
