Late-fall satellite-based soil moisture observations show clear connections to subsequent spring streamflow

Randal D. Koster (), Qing Liu, Wade T. Crow and Rolf H. Reichle
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Randal D. Koster: Global Modeling and Assimilation Office, NASA Goddard Space Flight Center
Qing Liu: Global Modeling and Assimilation Office, NASA Goddard Space Flight Center
Wade T. Crow: U.S. Department of Agriculture, Agricultural Research Service, Hydrology and Remote Sensing Laboratory
Rolf H. Reichle: Global Modeling and Assimilation Office, NASA Goddard Space Flight Center

Nature Communications, 2023, vol. 14, issue 1, 1-7

Abstract: Abstract Because runoff production is more efficient over wetter soils, and because soil moisture has an intrinsic memory, soil moisture information can potentially contribute to the accuracy of streamflow predictions at seasonal leads. In this work, we use surface (0–5 cm) soil moisture retrievals obtained with the National Aeronautics and Space Administration’s Soil Moisture Active Passive satellite instrument in conjunction with streamflow measurements taken within 236 intermediate-scale (2000–10,000 km2) unregulated river basins in the conterminous United States to show that late-fall satellite-based surface soil moisture estimates are indeed strongly connected to subsequent springtime streamflow. We thus show that the satellite-based soil moisture retrievals, all by themselves, have the potential to produce skillful seasonal streamflow predictions several months in advance. In poorly instrumented regions, they could perform better than reanalysis soil moisture products in this regard.

Date: 2023
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DOI: 10.1038/s41467-023-39318-3

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