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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #340195
Title: L-band microwave remote sensing and land data assimilation improve the representation of pre-storm soil moisture conditions for hydrologic forecasting

Author
item Crow, Wade
item CHEN, F. - Collaborator
item REICHLE, R. - Goddard Space Flight Center
item LIU, Q. - Goddard Space Flight Center

Submitted to: Geophysical Research Letters
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/1/2017
Publication Date: 6/17/2017
Citation: Crow, W.T., Chen, F., Reichle, R., Liu, Q. 2017. L-band microwave remote sensing and land data assimilation improve the representation of pre-storm soil moisture conditions for hydrologic forecasting. Geophysical Research Letters. 44(11). https://doi.org/10.1002/2017GL073642.
DOI: https://doi.org/10.1002/2017GL073642

Interpretive Summary: Forecasting stream flow conditions is important for minimizing loss of life and property during flooding and adequately planning for low stream flow conditions accompanying drought. One way to improve these forecasts is measuring the amount of water in the soil - since soil moisture conditions determine what fraction of rainfall will run off horizontally into stream channels (versus vertically infiltrating into the soil column). Within the past five years, there have been important advances in our ability to monitor soil moisture over large scales using both satellite-based sensors and the application of new modeling techniques. This paper illustrates that these advances have significantly improved our capacity to forecast how much stream flow will be generated by future precipitation events. These results will eventually be used by operational forecasters (such as the National Weather Service) to improve flash flood forecasting and agricultural water use management.

Technical Abstract: Recent advances in remote sensing and land data assimilation purport to improve the quality of antecedent soil moisture information available for operational hydrologic forecasting. We objectively validate this claim by calculating the strength of the relationship between storm-scale runoff ratio (i.e., total stream flow divided by total rainfall accumulation in depth units) and pre-storm surface soil moisture estimates provided by a range of surface soil moisture products Results demonstrate that both satellite-based, L-band microwave radiometry and the application of land data assimilation techniques have significantly improved the utility of surface soil moisture data sets for forecasting the land surface response to future rainfall events.