Articles | Volume 24, issue 10
https://doi.org/10.5194/hess-24-4793-2020
https://doi.org/10.5194/hess-24-4793-2020
Research article
 | 
12 Oct 2020
Research article |  | 12 Oct 2020

Assimilation of Soil Moisture and Ocean Salinity (SMOS) brightness temperature into a large-scale distributed conceptual hydrological model to improve soil moisture predictions: the Murray–Darling basin in Australia as a test case

Renaud Hostache, Dominik Rains, Kaniska Mallick, Marco Chini, Ramona Pelich, Hans Lievens, Fabrizio Fenicia, Giovanni Corato, Niko E. C. Verhoest, and Patrick Matgen

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Cited articles

Al Bitar, A., Leroux, D., Kerr, Y. H., Merlin, O., Richaume, P., Sahoo, A., and Wood, E. F.: Evaluation of SMOS Soil Moisture Products Over Continental U.S. Using the SCAN/SNOTEL Network, IEEE T. Geosci. Remote, 50, 1572–1586, 2012. a
Al-Yaari, A., Wigneron, J.-P., Kerr, Y., Rodriguez-Fernandez, N., O'Neill, P., Jackson, T., Lannoy, G. D., Bitar, A. A., Mialon, A., Richaume, P., Walker, J., Mahmoodi, A., and Yueh, S.: Evaluating soil moisture retrievals from ESA's SMOS and NASA's SMAP brightness temperature datasets, Remote Sens. Environ., 193, 257–273, 2017. a
Andreadis, K. and Schumann, G.-P.: Estimating the impact of satellite observations on the predictability of large-scale hydraulic models, Adv. Water Resour., 73, 44–54, 2014. a
Bergström, S.: Development and application of a conceptual runoff model for Scandinavian catchments, SMHI Report RHO 7, Norrköping, Tech. rep., SMHI, 1976. a
Brocca, L., Melone, F., Moramarco, T., Wagner, W., Naeimi, V., Bartalis, Z., and Hasenauer, S.: Improving runoff prediction through the assimilation of the ASCAT soil moisture product, Hydrol. Earth Syst. Sci., 14, 1881–1893, https://doi.org/10.5194/hess-14-1881-2010, 2010. a
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Short summary
Our objective is to investigate how satellite microwave sensors, particularly Soil Moisture and Ocean Salinity (SMOS), may help to reduce errors and uncertainties in soil moisture simulations with a large-scale conceptual hydro-meteorological model. We assimilated a long time series of SMOS observations into a hydro-meteorological model and showed that this helps to improve model predictions. This work therefore contributes to the development of faster and more accurate drought prediction tools.
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