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dc.contributor.authorMontero, Rodolfo Alvarado
dc.contributor.authorSchwanenberg, Dirk
dc.contributor.authorKrahe, Peter
dc.contributor.authorLisniak, Dmytro
dc.contributor.authorŞensoy, Aynur
dc.contributor.authorŞorman, Ali Arda
dc.contributor.authorAkkol, Bulut
dc.descriptionWOS: 000375945600020en_US
dc.description.abstractRemote sensing information has been extensively developed over the past few years including spatially distributed data for hydrological applications at high resolution. The implementation of these products in operational flow forecasting systems is still an active field of research, wherein data assimilation plays a vital role on the improvement of initial conditions of streamflow forecasts. We present a novel implementation of a variational method based on Moving Horizon Estimation (MHE), in application to the conceptual rainfall-runoff model HBV, to simultaneously assimilate remotely sensed snow covered area (SCA), snow water equivalent (SWE), soil moisture (SM) and in situ measurements of streamflow data using large assimilation windows of up to one year. This innovative application of the MHE approach allows to simultaneously update precipitation, temperature, soil moisture as well as upper and lower zones water storages of the conceptual model, within the assimilation window, without an explicit formulation of error covariance matrixes and it enables a highly flexible formulation of distance metrics for the agreement of simulated and observed variables. The framework is tested in two data-dense sites in Germany and one data-sparse environment in Turkey. Results show a potential improvement of the lead time performance of streamflow forecasts by using perfect time series of state variables generated by the simulation of the conceptual rainfall-runoff model itself. The framework is also tested using new operational data products from the Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) of EUMETSAT. This study is the first application of H-SAF products to hydrological forecasting systems and it verifies their added value. Results from assimilating H-SAF observations lead to a slight reduction of the streamflow forecast skill in all three cases compared to the assimilation of streamflow data only. On the other hand, the forecast skill of soil moisture shows a significant improvementen_US
dc.description.sponsorshipGerman Federal Institute of Hydrology (BfG); H-SAF project [13_03]en_US
dc.description.sponsorshipThis project was funded by the German Federal Institute of Hydrology (BfG) and supported by the visiting scientist activity No. 13_03 from the H-SAF project. We thank both organizations for providing the data for all three catchments.en_US
dc.publisherElsevier Sci LTDen_US
dc.subjectHydrological Modellingen_US
dc.subjectRemote Sensingen_US
dc.subjectData Assimilationen_US
dc.subjectMoving Horizon Estimationen_US
dc.subjectVariational Methodsen_US
dc.titleMoving horizon estimation for assimilating H-SAF remote sensing data into the HBV hydrological modelen_US
dc.relation.journalAdvances in Water Resourcesen_US
dc.contributor.departmentAnadolu Üniversitesi, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümüen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.contributor.institutionauthorŞensoy, Aynur
dc.contributor.institutionauthorŞorman, Ali Arda

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