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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 #347127

Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: SMAP Level 4 soil moisture estimates reveal possible bias in the runoff response of land surface models

Author
item Crow, Wade
item Chen, F. - Science Systems And Applications, Inc.
item Reiche, R. - Goddard Space Flight Center
item Xia - National Oceanic & Atmospheric Administration (NOAA)
item Liu, Q. - Goddard Space Flight Center

Submitted to: Geophysical Research Letters
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/27/2018
Publication Date: 6/14/2018
Citation: Crow, W.T., Chen, F., Reiche, R., Xia, Liu, Q. 2018. SMAP Level 4 soil moisture estimates reveal possible bias in the runoff response of land surface models. Geophysical Research Letters. 45(10):4869-4878. https://doi.org/10.1016/j.rse.2018.05.008.
DOI: https://doi.org/10.1016/j.rse.2018.05.008

Interpretive Summary: Hydrologic models attempt to predict the fraction of incoming rainfall which is converted into runoff (versus infiltrated into the soil). If accurate, these predictions are valuable for wide range of agricultural water use and water management applications. The ability of the land surface to infiltrate rainfall is largely dependent on the amount of water present in the soil column prior to the start of precipitation. Using a new satellite-based soil moisture data product, this paper examines whether existing hydrologic models can reproduce the correct relationship between pre-storm soil moisture and rainfall infiltration. Results indicate the models tend to underestimate the strength of this relationship and, therefore, under utilize available soil moisture information for predicting the land surface response to future rainfall events. Information in this paper will eventually be used to correct for this bias and enhance our ability to predict stream flow extremes associated with floods and droughts.

Technical Abstract: The accurate partitioning of precipitation into infiltration and runoff is a fundamental objective of land surface models tasked with characterizing the surface water and energy balance. Temporal variability in this partitioning is due, in part, to changes in pre-storm soil moisture which determine soil infiltration capacity. Utilizing surface and root-zone soil moisture estimates from the NASA Soil Moisture Active Passive (SMAP) Level 4 soil moisture product, we demonstrate that land surface models tend to underestimate the strength of the relationship between pre-storm soil moisture and subsequent storm-scale runoff generation efficiency. This implies that land models neglect a portion of hydrologic predictability afforded by knowledge of pre-storm soil moisture conditions.