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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 #329566
Title: Improving streamflow prediction using remotely-sensed soil moisture and snow depth

Author
item LU, HAISHEN - Hohai University
item Crow, Wade
item ZHU, YONGHUA - Hohai University
item OUYANG, FEN - Hohai University
item SU, JIANBIN - Hohai University

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/10/2016
Publication Date: 6/15/2016
Citation: Lu, H., Crow, W.T., Zhu, Y., Ouyang, F., Su, J. 2016. Improving streamflow prediction using remotely-sensed soil moisture and snow depth. Remote Sensing. 8(6):503.

Interpretive Summary: Water availability in agricultural areas is frequently determined by stream flow levels in upstream headwater basins. Ground-based instrumentation in such basins is often lacking; therefore, remotely-sensed soil moisture and snow cover products are of potentially great value for improving our ability to monitor such areas. However, in order for this value to be realized, we need to develop modeling and data assimilation tools which convert these remotely-sensed products into actual stream flow estimates. This paper describes the development and application of such tools. In particular, in describes how remotely-sensed soil moisture and snow cover products can be optimally merged with a hydrologic model to enhance the accuracy of outlet stream flow predictions within mountainous headwater catchments. Better monitoring of outlet stream flow from such catchments allows for better water resource decision making in downstream agricultural areas. Eventually, this research will be used by agricultural water resource managers to optimize the allocation of water for competing agricultural and municipal uses.

Technical Abstract: The monitoring of both cold and warm season hydrologic processes in headwater watersheds is critical for accurate water resource monitoring in many alpine regions. This work presents a new method that explores the simultaneous use of remotely sensed surface soil moisture (SM) and snow depth (SD) retrievals to improve hydrological modeling in such areas. In particular, remotely sensed SM and SD retrievals are applied to filter errors present in both solid and liquid phase precipitation accumulation products acquired from satellite remote sensing. Simultaneously, SM and SD retrievals are also used to correct antecedent SM and SD states within a hydrological model. In synthetic data assimilation experiments, results suggest that the simultaneous correction of both precipitation forcing and SM/SD antecedent conditions is more efficient at improving streamflow simulation than data assimilation techniques which focus solely on the constraint of antecedent SM or SD conditions. In a real assimilation case, results demonstrate the potential benefits of remotely sensed SM and SD retrievals for improving the representation of hydrological processes in a headwater basin. In particular, it is demonstrated that dual precipitation/state correction represents an efficient strategy for improving the simulation of cold-region hydrologic processes.