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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 #350052

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: Estimating basin-scale water budgets with SMAP soil moisture data

item KOSTER, R. - National Aeronautics Space Administration (NASA) - Jet Propulsion Laboratory
item Crow, Wade
item REICHLE, R. - National Aeronautics And Space Administration (NASA)
item MAHANAMA, S.P. - National Aeronautics And Space Administration (NASA)

Submitted to: Water Resources Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/24/2018
Publication Date: 7/1/2018
Citation: Koster, R., Crow, W.T., Reichle, R., Mahanama, S. 2018. Estimating basin-scale water budgets with SMAP soil moisture data. Water Resources Research. 54(7):4228-4244.

Interpretive Summary: Accurately tracking water resource availability in agricultural landscapes requires (among other things) high-quality information about (incoming) rainfall accumulation and (outgoing) stream flow totals. Over large areas of the globe, this information cannot be reliably obtained from available ground-based rain and stream gauge instrumentation. This paper introduces and validates a new approach for estimating 10-day rainfall and stream flow totals from satellite-based surface soil moisture retrievals. Since these retrievals are available over nearly all global agricultural areas, the approach expands our ability to accurately track water resource availability in data-poor areas of the world. Eventually, this technique will be used to improve our collective ability to globally monitor, and potentially mitigate, the social and agricultural impacts of drought.

Technical Abstract: Soil Moisture Active Passive (SMAP) Level-2 soil moisture retrievals collected during 2015-2017 are used in isolation to estimate 10-day warm season precipitation and streamflow totals within 148 medium-sized (2,000—10,000 km^2) unregulated watersheds in the conterminous United States. The precipitation estimation algorithm, derived from a well-documented approach, includes a new locally-tuned loss function component that significantly improves its performance. For the basin-scale water budget analysis, the precipitation and streamflow algorithms are calibrated with two years of SMAP retrievals in conjunction with observed precipitation and streamflow data in the studied basins and are then applied to SMAP retrievals alone during the third year. While estimation accuracy (as measured by the square of the correlation coefficient, r^2, between estimates and observations) varies by basin, the average r^2 for the basins considered is 0.53 for precipitation and 0.22 for streamflow, and for the subset of 22 basins that calibrate particularly well, the r^2 increases to 0.63 for precipitation and to 0.51 for streamflow. The magnitudes of the estimated variables are accurate as well, with sample pairs generally clustered about the 1:1 line. The chief limitation to the estimation procedure involves large and unavoidable biases induced during periods of large rainfall; the accuracy of the estimates increases significantly when periods of higher rainfall are not considered. The potential for transferability is also demonstrated by calibrating the streamflow estimation equation in one basin and then applying the equation, with some success, in another. Overall, the study demonstrates that SMAP retrievals contain, all by themselves, information that can be used to estimate large-scale water budgets.