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Effect of rainfall uncertainty on the performance of physically based rainfall–runoff models
dc.contributor.author | Fraga, Ignacio | |
dc.contributor.author | Cea, Luis | |
dc.contributor.author | Puertas, Jerónimo | |
dc.contributor.other | Enxeñaría da Auga e do Medio Ambiente (GEAMA) | es_ES |
dc.date.accessioned | 2024-02-07T20:32:25Z | |
dc.date.available | 2024-02-07T20:32:25Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Fraga, I., Cea, L., & Puertas, J. (2019). Effect of rainfall uncertainty on the performance of physically based rainfall–runoff models. Hydrological Processes, 33(1), 160-173. https://doi.org/10.1002/hyp.13319 | es_ES |
dc.identifier.uri | http://hdl.handle.net/2183/35504 | |
dc.description | Versión aceptada de https://doi.org/10.1002/hyp.13319 | es_ES |
dc.description.abstract | [Abstract:] This paper analyses the effect of rain data uncertainty on the performance of two hydrological models with different spatial structures: a semidistributed and a fully distributed model. The study is performed on a small catchment of 19.6 km2 located in the north-west of Spain, where the arrival of low pressure fronts from the Atlantic Ocean causes highly variable rainfall events. The rainfall fields in this catchment during a series of storm events are estimated using rainfall point measurements. The uncertainty of the estimated fields is quantified using a conditional simulation technique. Discharge and rain data, including the uncertainty of the estimated rainfall fields, are then used to calibrate and validate both hydrological models following the generalized likelihood uncertainty estimation (GLUE) methodology. In the storm events analysed, the two models show similar performance. In all cases, results show that the calibrated distribution of the input parameters narrows when the rain uncertainty is included in the analysis. Otherwise, when rain uncertainty is not considered, the calibration of the input parameters must account for all uncertainty in the rainfall–runoff transformation process. Also, in both models, the uncertainty of the predicted discharges increase in similar magnitude when the uncertainty of rainfall input increase. | es_ES |
dc.description.sponsorship | This study was supported by the Spanish Ministerio de Economía y Competitividad through the CAPRI project (Probabilistic Flood Prediction with High Resolution Hydrologic Models from Radar Rainfall Estimates; Reference CGL2013-46245-R). Ignacio Fraga received financial support from the Xunta de Galicia (Centro Singular de Investigación, Galicia, accreditation 2016–2019) and the European Regional Development Fund. The authors would also like to thank MeteoGalicia (Galician Regional Weather Service) for the rainfall data provided for this study. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Wiley | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/CGL2013-46245-R/ES/CALCULO PROBABILISTICO DE INUNDACIONES CON MODELOS HIDROLOGICOS DE ALTA RESOLUCION ESPACIAL A PARTIR DE ESTIMACIONES DE PRECIPITACION DE RADAR | es_ES |
dc.relation.uri | https://doi.org/10.1002/hyp.13319 | es_ES |
dc.rights | © 2018 John Wiley & Sons, Ltd. | es_ES |
dc.subject | Distributed and semidistributed hydrological models | es_ES |
dc.subject | GLUE | es_ES |
dc.subject | Quantitative precipitation estimation | es_ES |
dc.subject | Rainfall uncertainty | es_ES |
dc.subject | Rainfall–runoff modelling | es_ES |
dc.title | Effect of rainfall uncertainty on the performance of physically based rainfall–runoff models | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Hydrological Processes | es_ES |
UDC.volume | 33 | es_ES |
UDC.startPage | 160 | es_ES |
UDC.endPage | 173 | es_ES |
dc.identifier.doi | 10.1002/hyp.13319 |