Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: comparison between lumped and semi-distributed schemes
C Alvarez-Garreton, D Ryu, AW Western, C-H Su, WT Crow, DE Robertson, C Leahy
HYDROLOGY AND EARTH SYSTEM SCIENCES | COPERNICUS GESELLSCHAFT MBH | Published : 2015
Assimilation of remotely sensed soil moisture data (SM-DA) to correct soil water stores of rainfall-runoff models has shown skill in improving streamflow prediction. In the case of large and sparsely monitored catchments, SM-DA is a particularly attractive tool. Within this context, we assimilate satellite soil moisture (SM) retrievals from the Advanced Microwave Scanning Radiometer (AMSR-E), the Advanced Scatterometer (ASCAT) and the Soil Moisture and Ocean Salinity (SMOS) instrument, using an Ensemble Kalman filter to improve operational flood prediction within a large (> 40 000 km2) semi-arid catchment in Australia. We assess the importance of accounting for channel routing and the spatia..View full abstract
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Awarded by Australian Research Council (ARC Linkage Project)
The authors wish to thank one anonymous reviewer, Uwe Ehret and the Chief-Executive Editor Erwin Zehe for their constructive comments and suggestions on the earlier draft of the paper. This research was conducted with financial support from the Australian Research Council (ARC Linkage Project No. LP110200520) and the Australian Bureau of Meteorology. C. Alvarez-Garreton was supported by Becas Chile scholarship.