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Remote Sens. 2016, 8(6), 503; doi:10.3390/rs8060503

Improving Streamflow Prediction Using Remotely-Sensed Soil Moisture and Snow Depth

1
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
2
USDA Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705-2350, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Gabriel Senay, Magaly Koch and Prasad S. Thenkabail
Received: 14 March 2016 / Revised: 21 May 2016 / Accepted: 7 June 2016 / Published: 15 June 2016
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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 hydrological processes. View Full-Text
Keywords: soil moisture; snow depth; precipitation; streamflow; data assimilation soil moisture; snow depth; precipitation; streamflow; data assimilation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Lü, H.; Crow, W.T.; Zhu, Y.; Ouyang, F.; Su, J. Improving Streamflow Prediction Using Remotely-Sensed Soil Moisture and Snow Depth. Remote Sens. 2016, 8, 503.

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