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dc.contributor.authorGarcía-Alén, Gonzalo
dc.contributor.authorHostache, Renaud
dc.contributor.authorCea, Luis
dc.contributor.authorPuertas, Jerónimo
dc.date.accessioned2023-10-11T17:48:11Z
dc.date.available2023-10-11T17:48:11Z
dc.date.issued2023
dc.identifier.citationGarcía-Alén, G., Hostache, R., Cea, L., Puertas, J. (2023). Joint assimilation of satellite soil moisture and streamflow data for the hydrological application of a two-dimensional shallow water model. Journal of Hydrology, 621, 129667. https://doi.org/10.1016/j.jhydrol.2023.129667es_ES
dc.identifier.urihttp://hdl.handle.net/2183/33760
dc.descriptionFinanciado para publicación en acceso aberto: Universidade da Coruña/CISUGes_ES
dc.description.abstract[Abstract:] Data assimilation (DA) in physically-based hydrodynamic models is conditioned by the difference in temporal and spatial scales of the observed data and the resolution of the model itself. In order to use remote sensing data in small-scale hydrodynamic modelling, it is necessary to explore innovative DA methods that can lead to a more plausible representation of the spatial variability of the parameters and processes involved. In the present study, satellite-derived soil moisture and in situ-observed streamflow data were jointly assimilated into a high-resolution hydrological-hydrodynamic model based on the Iber software, using the Tempered Particle Filter (TPF) for the dual estimation of model state variables and parameters. Twelve storm events occurring in a 199 km2 catchment located in NW Spain were used for testing the proposed approach. A 3-step procedure was followed: (1) sensitivity analysis of the model parameters; (2) joint assimilation of soil moisture and discharge data to estimate correlations between observations and model parameters; (3) joint assimilation of soil moisture and discharge data using an initial set of particles and parameter standard deviations derived from prior information. The numerical model correctly reproduces the observed data, with an average Nash-Sutcliffe efficiency (NSE) value of 0.74 over the 12 events when the prior information is used. The approach described is shown to be most efficient with storm events that produce isolated peak discharges.es_ES
dc.description.sponsorshipThe authors acknowledge the support of Augas de Galicia and the Galicia Meteorological Agency (Metogalicia). Gonzalo García-Alén acknowledge the support of the INDITEX-UDC 2021 and 2022 Predoctoral Grants. The research reported herein was funded by the Luxembourg National Research Fund through the CASCADE (grant no. C17/SR/11682050) Project. Funding for open access charge: Universidade da Coruña/CISUG.es_ES
dc.description.sponsorshipNational Research Fund of Luxembourg; C17/SR/11682050es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relation.urihttps://doi.org/10.1016/j.jhydrol.2023.129667es_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Españaes_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectHydrodynamic modellinges_ES
dc.subjectIber+es_ES
dc.subjectFlood forecastinges_ES
dc.subjectTempered particle filteres_ES
dc.subjectShallow water equationses_ES
dc.subjectData assimilationes_ES
dc.titleJoint assimilation of satellite soil moisture and streamflow data for the hydrological application of a two-dimensional shallow water modeles_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleJournal of Hydrologyes_ES
UDC.volume621es_ES
UDC.startPage129667es_ES
dc.identifier.doi10.1016/j.jhydrol.2023.129667


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