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On the assimilation set-up of ASCAT soil moisture data for improving streamflow catchment simulation
dc.creator | Loizu Maeztu, Javier | es_ES |
dc.creator | Massari, Christian | es_ES |
dc.creator | Álvarez-Mozos, Jesús | es_ES |
dc.creator | Tarpanelli, Angelica | es_ES |
dc.creator | Brocca, Luca | es_ES |
dc.creator | Casalí Sarasíbar, Javier | es_ES |
dc.date.accessioned | 2019-02-07T16:05:43Z | |
dc.date.available | 2020-11-03T00:00:16Z | |
dc.date.issued | 2018 | |
dc.identifier.uri | https://hdl.handle.net/2454/32203 | |
dc.description.abstract | Assimilation of remotely sensed surface soil moisture (SSM) data into hydrological catchment models has been identified as a means to improve stream flow simulations, but reported results vary markedly depending on the particular model, catchment and assimilation procedure used. In this study, the in fluence of key aspects, such as the type of model, re-scaling technique and SSM observation error considered, were evaluated. For this aim, Advanced SCATterometer ASCAT-SSM observations were assimilated through the ensemble Kalman filter into two hydrological models of different complexity namely MISDc and TOPLATS) run on two Mediterranean catchments of similar size (750 km2). Three different re-scaling techniques were evaluated (linear re-scaling, variance matching and cumulative distribution function matching), and SSM observation error values ranging from 0.01% to 20% were considered. Four different efficiency measures were used for evaluating the results. Increases in Nash-Sutcliffe efficiency (0.03–0.15) and efficiency indices (10–45%) were obtained, especially when linear re-scaling and observation errors within 4-6% were considered. This study found out that there is a potential to improve stream flow prediction through data assimilation of remotely sensed SSM in catchments of different characteristics and with hydrological models of different conceptualizations schemes, but for that, a careful evaluation of the observation error and re-scaling technique set-up utilized is required. | en |
dc.description.sponsorship | This study was partially funded by the Spanish Ministry of Science and Innovation (Project CGL2011-24336), the Spanish Ministry of Innovation and Competitiveness (Project CGL2015-64284-C2-1-R MINECO/FEDER) and by the Public University of Navarre through a pre-doctorate research scholarship to the first author. | en |
dc.format.mimetype | application/pdf | en |
dc.language.iso | eng | en |
dc.publisher | Elsevier | en |
dc.relation.ispartof | Advances in Water Resources, volume 111, january 2018, pp. 86-104 | en |
dc.rights | © 2017 Elsevier Ltd. The manuscript version is made available under the CC BY-NC-ND 4.0 license. | en |
dc.subject | Data assimilation | en |
dc.subject | ASCAT | en |
dc.subject | Surface soil moisture | en |
dc.subject | Ensemble Kalman Filter | en |
dc.subject | Stream flow simulation | en |
dc.subject | Hydrological catchment models | en |
dc.title | On the assimilation set-up of ASCAT soil moisture data for improving streamflow catchment simulation | en |
dc.type | Artículo / Artikulua | es |
dc.type | info:eu-repo/semantics/article | en |
dc.contributor.department | Proyectos e Ingeniería Rural | es_ES |
dc.contributor.department | Landa Ingeniaritza eta Proiektuak | eu |
dc.rights.accessRights | Acceso abierto / Sarbide irekia | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | en |
dc.embargo.terms | 2020-11-03 | |
dc.identifier.doi | 10.1016/j.advwatres.2017.10.034 | |
dc.relation.projectID | info:eu-repo/grantAgreement/MICINN//CGL2011-24336/ES/ | en |
dc.relation.projectID | info:eu-repo/grantAgreement/MINECO//CGL2015-64284-C2-1-R/ES/ | en |
dc.relation.publisherversion | https://doi.org/10.1016/j.advwatres.2017.10.034 | |
dc.type.version | Versión aceptada / Onetsi den bertsioa | es |
dc.type.version | info:eu-repo/semantics/acceptedVersion | en |