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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 #328380
Title: Dual assimilation of satellite soil moisture to improve flood prediction in ungauged catchments

Author
item ALVAREZ, C. - University Of Melbourne
item RYU, D. - University Of Melbourne
item WESTERN, A. - University Of Melbourne
item Crow, Wade
item SU, CHUN-HSU - University Of Melbourne
item ROBERTSON, D. - Collaborator

Submitted to: Water Resources Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/11/2016
Publication Date: 8/1/2016
Citation: Alvarez, C., Ryu, D., Western, A., Crow, W.T., Su, C., Robertson, D. 2016. Dual assimilation of satellite soil moisture to improve flood prediction in ungauged catchments. Water Resources Research. 52:5357–5375.

Interpretive Summary: The estimation of stream flow using rainfall-runoff models (and observed precipitation) is a valuable tool for water resource monitoring in agricultural watersheds. Recent research has focused on the potential for improving such estimates via the use of remotely-sensed surface soil moisture retrievals. This research presents a demonstration of a new approach for integrating remotely-sensed surface soil moisture estimations into a rainfall-runoff model within a series of Australian catchments. The approach is based on utilizing remotely-sensed surface soil moisture retrievals to simultaneously update both pre-storm soil moisture values (required to determine the infiltration capacity of the landscape) and within-storm precipitation totals. Preliminary results support the premise that this approach will eventually yield improved stream flow predictions for water resource (and water quality) management applications in agricultural watersheds. The Australian Bureau of Meteorology is interested in using this technique to improve their ability to monitoring stream flow within data poor areas of the Australian continent.

Technical Abstract: This paper explores the use of active and passive satellite soil moisture products for improving stream flow prediction within 4 large (>5,000km2) semi-arid catchments. We use the probability distributed model (PDM) under a data-scarce scenario and aim at correcting two key controlling factors in the stream flow generation: the rainfall forcing data and the catchment wetness condition. We use the soil moisture analysis rainfall tool (SMART, Crow et al., 2011) to correct a near-real time satellite rainfall product (forcing correction scheme) and an Ensemble Kalman filter to correct the PDM soil moisture state (state correction scheme). We combine these two schemes in a dual correction scheme and assess the relative improvements of each. Our results demonstrate that the quality of the satellite rainfall product is improved by SMART during mean-to-high daily rainfall events, which in turn leads to an improved stream flow prediction during high flows. The stream flow predictions after the stream flow correction scheme in most cases outperform the forcing correction scheme outputs, especially during low flows. In overall, the combined dual correction scheme further improves the stream flow predictions (reduction in root mean square error and false alarm ratio, and increase in correlation confident and Nash-Sutcliffe efficiency). Our results provide n evidence of the value of satellite soil moisture within data scarce regions. We also identify a number of challenges and limitations within the proposed schemes.