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dc.contributor.authorFleischmann, Ayan Santospt_BR
dc.contributor.authorOliveira, Aline Meyerpt_BR
dc.contributor.authorSiqueira, Vinícius Alencarpt_BR
dc.contributor.authorColossi, Bibiana Rodriguespt_BR
dc.contributor.authorPaiva, Rodrigo Cauduro Dias dept_BR
dc.contributor.authorKerr, Yannpt_BR
dc.contributor.authorRuhoff, Anderson Luispt_BR
dc.contributor.authorFan, Fernando Mainardipt_BR
dc.contributor.authorPontes, Paulo Rógenes Monteiropt_BR
dc.contributor.authorCollischonn, Walterpt_BR
dc.contributor.authorAl Bitar, Ahmadpt_BR
dc.date.accessioned2021-12-11T04:46:19Zpt_BR
dc.date.issued2021pt_BR
dc.identifier.issn2072-4292pt_BR
dc.identifier.urihttp://hdl.handle.net/10183/232840pt_BR
dc.description.abstractHydrological models are useful tools for water resources studies, yet their calibration is still a challenge, especially if aiming at improved estimates of multiple components of the water cycle. This has led the hydrologic community to look for ways to constrain models with multiple variables. Remote sensing estimates of soil moisture are very promising in this sense, especially in large areas for which field observations may be unevenly distributed. However, the use of such data to calibrate hydrological models in a synergistic way is still not well understood, especially in tropical humid areas such as those found in South America. Here, we perform multiple scenarios of multiobjective model optimization with in situ discharge and the SMOS L4 root zone soil moisture product for the Upper Paraná River Basin in South America (drainage area > 900,000 km2), for which discharge data for 136 river gauges are used. An additional scenario is used to compare the relative impacts of using all river gauges and a small subset containing nine gauges only. Across the basin, the joint calibration (CAL-DS) using discharge and soil moisture leads to improved precision and accuracy for both variables. The discharges estimated by CAL-DS (median KGE improvement for discharge was 0.14) are as accurate as those obtained with the calibration with discharge only (median equal to 0.14), while the CAL-DS soil moisture retrieval is practically as accurate (median KGE improvement for soil moisture was 0.11) as that estimated using the calibration with soil moisture only (median equal to 0.13). Nonetheless, the individual calibration with discharge rates is not able to retrieve satisfactory soil moisture estimates, and vice versa. These results show the complementarity between these two variables in the model calibration and highlight the benefits of considering multiple variables in the calibration framework. It is also shown that, by considering only nine gauges insteen
dc.format.mimetypeapplication/pdfpt_BR
dc.language.isoengpt_BR
dc.relation.ispartofRemote Sensing. Basel. Vol. 13, n. 16 (Aug, 2021) [Article] 3256, 20 p.pt_BR
dc.rightsOpen Accessen
dc.subjectOptimal calibrationen
dc.subjectModelos hidrológicospt_BR
dc.subjectSMOSen
dc.subjectCalibraçãopt_BR
dc.subjectSoil moistureen
dc.subjectUmidade do solopt_BR
dc.subjectSouth America hydrologyen
dc.subjectSensoriamento remotopt_BR
dc.subjectLarge-scale hydrologyen
dc.subjectParaná, Rio, Bacia dopt_BR
dc.titleSynergistic calibration of a hydrological model using discharge and remotely sensed soil moisture in the Paraná river basinpt_BR
dc.typeArtigo de periódicopt_BR
dc.identifier.nrb001131828pt_BR
dc.type.originEstrangeiropt_BR


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