Neglect of Potential Seasonal Streamflow Forecasting Skill in the United States National Water Model

Author:

Crow Wade T.1ORCID,Koster Randal D.2ORCID,Reichle Rolf H.2ORCID,Chen Fan13ORCID,Liu Qing23ORCID

Affiliation:

1. USDA ARS Hydrology and Remote Sensing Laboratory Beltsville MD USA

2. NASA Global Modelling and Assimilation Office Goddard Space Flight Center Greenbelt MD USA

3. Science Systems and Applications, Inc. Greenbelt MD USA

Abstract

AbstractUsing data from the NASA Soil Moisture Active/Passive mission, Koster et al. (2023, https://doi.org/10.1038/s41467‐023‐39318‐3) conclude that, for medium‐scale basins in the contiguous United States, a quarter of interannual variability in springtime streamflow is explained by interannual anomalies in late‐fall soil moisture. This lagged relationship can be leveraged for seasonal hydrologic forecasting, but only if effectively captured by existing prediction models. Here, we extend the analysis in Koster et al. (2023, https://doi.org/10.1038/s41467‐023‐39318‐3) to diagnose systematic errors present in the United States National Water Model (NWM). Results demonstrate that the NWM tends to underestimate both the trans‐winter temporal memory of 0–1 m soil moisture as well as the correlation between 0 and 1 m soil moisture and streamflow—thereby reducing the NWM's ability to leverage vertically averaged soil moisture as a source of hydrologic predictability.

Funder

Earth Sciences Division

Publisher

American Geophysical Union (AGU)

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