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Persistent Identifier
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perma:LIST.U7BPAZ |
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Publication Date
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2026-07-06 |
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Title
| A joint assimilation of satellite soil moisture and flood extent maps to improve a flood hazard modelling. [* Cross-Reference *] |
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Other Identifier
| https://doi.org/10.5194/iahs2022-359
OpenAlex ID: https://openalex.org/W4297001344 |
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Author
| Renaud Hostache (Luxembourg Institute of Science and Technology, Institut de Recherche pour le Développement) - ORCID: https://orcid.org/0000-0002-8109-6010
Patrick Matgen (Luxembourg Institute of Science and Technology) - ORCID: https://orcid.org/0000-0001-6668-4693
Peter Jan van Leeuwen (University of Reading, Colorado State University) - ORCID: https://orcid.org/0000-0003-2325-5340
Nancy Nichols (University of Reading) - ORCID: https://orcid.org/0000-0003-1133-5220
Marco Chini (Luxembourg Institute of Science and Technology) - ORCID: https://orcid.org/0000-0002-9094-0367
Ramona Pelich (Luxembourg Institute of Science and Technology) - ORCID: https://orcid.org/0000-0002-4313-3116
Carole Delenne (Centre National de la Recherche Scientifique, Institut national de recherche en sciences et technologies du numérique, Université de Montpellier, Laboratoire HydroSciences Montpellier, Institut de Recherche pour le Développement) - ORCID: https://orcid.org/0000-0001-6683-4399 |
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Point of Contact
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Use email button above to contact.
LIST QDKM (LIST) |
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Description
| <p>The main objective of this study is to investigate how innovative satellite Earth observation techniques that allow for the estimation of soil moisture and the mapping of flood extents can help in reducing errors and uncertainties in hydro-meteorological modelling especially in ungauged areas where potentially no or limited runoff records are available. A conceptual hydrological model is loosely coupled with a shallow water model allowing for the simulation of soil moisture and flood extent. Using as forcing of this model rainfall and air temperature time series provided in the globally and freely available ERA5 database it is then possible to carry out long-term simulations of soil moisture, discharge and flood extent. Next, time series of soil moisture and flood extent observations derived from freely available satellite image databases are jointly assimilated into the hydrological model in order to retrieve optimal parameter sets. For this assimilation experiment, we take benefit of recently introduced Particle Filters with tempering that circumvent some of the usual particle filter limitations such as degeneracy and sample impoverishment. As a proof of concept, we set up an identical twin experiment based on synthetically generated observations and we evaluate the performance of the calibrated model.</p> (2022-09-23)
***This entry has been automatically imported via OpenAlex by LIST harvest scripts. Please refer to https://doi.org/10.5194/iahs2022-359 for the original and latest version of the publication*** (2026-07-01) |
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Subject
| Astronomy and Astrophysics; Earth and Environmental Sciences; Physics |
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Keyword
| Flood myth
Environmental science
Satellite
Data assimilation
Forcing (mathematics)
Surface runoff
Water content
Flood forecasting
Meteorology
Hydrology (agriculture)
Climatology
Geography
Geology |
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Topic Classification
| Geophysics and Gravity Measurements
Hydrology and Watershed Management Studies
Soil Moisture and Remote Sensing |
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Deposit Date
| 2022-09-23 |
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Data Type
| Preprint |