Assimilating high resolution remotely sensed soil moisture into a distributed hydrologic model to improve runoff prediction

Mason, D. ORCID: https://orcid.org/0000-0001-6092-6081, Garcia Pintado, J., Cloke, H. L. ORCID: https://orcid.org/0000-0002-1472-868X, Dance, S. ORCID: https://orcid.org/0000-0003-1690-3338 and Munoz-Sabater, J., (2020) Assimilating high resolution remotely sensed soil moisture into a distributed hydrologic model to improve runoff prediction. ECMWF Technical Memoranda. 867. Technical Report. ECMWF, Reading, UK. doi: 10.21957/5isuz4a91

Abstract/Summary

The susceptibility of a catchment to flooding during an extreme rainfall event is affected by its soil moisture condition prior to the event. A study to improve the state vector of a distributed hydrologic model by assimilating high resolution remotely sensed soil moisture is described. The launch of Sentinel-1 has stimulated interest in measuring soil moisture at high resolution suitable for hydrological studies using Synthetic Aperture Radars (SARs). The advantages of using SAR soil moisture in conjunction with land cover data are considered. These include the ability to reduce contamination of the surface soil signal due to vegetation, radar artefacts, mixed pixels and land cover classes not providing meaningful soil moistures. Results for 2008 using ASAR data showed that the assimilation of ASAR soil moisture values improved the predicted flows for all images. The improvement was less marked for 2007, probably because the antecedent soil moisture conditions were of reduced importance during the extreme flooding that occurred then. Particularly for 2008, the higher resolution of ASAR data improved predicted flows compared to low resolution ASCAT data that were not disaggregated and limited to the temporal frequency of ASAR. The method is likely to give better results with Sentinel-1 rather than ASAR data due to its higher temporal resolution.

Item Type	Report (Technical Report)
URI	https://reading-clone.eprints-hosting.org/id/eprint/91408
Identification Number/DOI	10.21957/5isuz4a91
Refereed	Yes
Divisions	Science > School of Archaeology, Geography and Environmental Science > Department of Geography and Environmental Science Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	ECMWF
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