Articles | Volume 24, issue 10
https://doi.org/10.5194/hess-24-4793-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-24-4793-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
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
Department Environmental Research and Innovation, Luxembourg Institute of Science and Technology (LIST), Belvaux, Luxembourg
Dominik Rains
Department of Environment, Ghent University, Ghent, Belgium
Department of Physics and Astronomy, Earth Observation Science, University of Leicester, Leicester, UK
Kaniska Mallick
Department Environmental Research and Innovation, Luxembourg Institute of Science and Technology (LIST), Belvaux, Luxembourg
Marco Chini
Department Environmental Research and Innovation, Luxembourg Institute of Science and Technology (LIST), Belvaux, Luxembourg
Ramona Pelich
Department Environmental Research and Innovation, Luxembourg Institute of Science and Technology (LIST), Belvaux, Luxembourg
Hans Lievens
Department of Environment, Ghent University, Ghent, Belgium
Department of Earth and Environmental Sciences, Katholieke Universiteit Leuven, Heverlee, Belgium
Fabrizio Fenicia
Department of Systems Analysis, Integrated Assessment and Modelling, Swiss Federal Institute of Aquatic Science and Technology (EAWAG),
Dübendorf, Switzerland
Giovanni Corato
Department Environmental Research and Innovation, Luxembourg Institute of Science and Technology (LIST), Belvaux, Luxembourg
Niko E. C. Verhoest
Department of Environment, Ghent University, Ghent, Belgium
Patrick Matgen
Department Environmental Research and Innovation, Luxembourg Institute of Science and Technology (LIST), Belvaux, Luxembourg
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This study evaluates how the sequential assimilation of flood extent derived from synthetic aperture radar data can help improve flood forecasting. In particular, we carried out twin experiments based on a synthetically generated dataset with controlled uncertainty. Our empirical results demonstrate the efficiency of the proposed data assimilation framework, as forecasting errors are substantially reduced as a result of the assimilation.
Hongkai Gao, Chuntan Han, Rensheng Chen, Zijing Feng, Kang Wang, Fabrizio Fenicia, and Hubert Savenije
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Laurène J. E. Bouaziz, Fabrizio Fenicia, Guillaume Thirel, Tanja de Boer-Euser, Joost Buitink, Claudia C. Brauer, Jan De Niel, Benjamin J. Dewals, Gilles Drogue, Benjamin Grelier, Lieke A. Melsen, Sotirios Moustakas, Jiri Nossent, Fernando Pereira, Eric Sprokkereef, Jasper Stam, Albrecht H. Weerts, Patrick Willems, Hubert H. G. Savenije, and Markus Hrachowitz
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Theresa C. van Hateren, Marco Chini, Patrick Matgen, and Adriaan J. Teuling
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-583, https://doi.org/10.5194/hess-2020-583, 2020
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Brecht Martens, Dominik L. Schumacher, Hendrik Wouters, Joaquín Muñoz-Sabater, Niko E. C. Verhoest, and Diego G. Miralles
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Anne J. Hoek van Dijke, Kaniska Mallick, Martin Schlerf, Miriam Machwitz, Martin Herold, and Adriaan J. Teuling
Biogeosciences, 17, 4443–4457, https://doi.org/10.5194/bg-17-4443-2020, https://doi.org/10.5194/bg-17-4443-2020, 2020
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We investigated the link between the vegetation leaf area index (LAI) and the land–atmosphere exchange of water, energy, and carbon fluxes. We show that the correlation between the LAI and water and energy fluxes depends on the vegetation type and aridity. For carbon fluxes, however, the correlation with the LAI was strong and independent of vegetation and aridity. This study provides insight into when the vegetation LAI can be used to model or extrapolate land–atmosphere fluxes.
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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.
Our objective is to investigate how satellite microwave sensors, particularly Soil Moisture and...