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Hydrological post-processing based on approximate Bayesian computation (ABC)

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Hydrological post-processing based on approximate Bayesian computation (ABC)

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dc.contributor.author Romero-Cuellar, Jonathan es_ES
dc.contributor.author Abbruzzo, Antonino es_ES
dc.contributor.author Adelfio, Giada es_ES
dc.contributor.author Francés, F. es_ES
dc.date.accessioned 2019-10-16T20:00:44Z
dc.date.available 2019-10-16T20:00:44Z
dc.date.issued 2019 es_ES
dc.identifier.issn 1436-3240 es_ES
dc.identifier.uri http://hdl.handle.net/10251/128735
dc.description.abstract [EN] This study introduces a method to quantify the conditional predictive uncertainty in hydrological post-processing contexts when it is cumbersome to calculate the likelihood (intractable likelihood). Sometimes, it can be difficult to calculate the likelihood itself in hydrological modelling, specially working with complex models or with ungauged catchments. Therefore, we propose the ABC post-processor that exchanges the requirement of calculating the likelihood function by the use of some sufficient summary statistics and synthetic datasets. The aim is to show that the conditional predictive distribution is qualitatively similar produced by the exact predictive (MCMC post-processor) or the approximate predictive (ABC post-processor). We also use MCMC post-processor as a benchmark to make results more comparable with the proposed method. We test the ABC post-processor in two scenarios: (1) the Aipe catchment with tropical climate and a spatially-lumped hydrological model (Colombia) and (2) the Oria catchment with oceanic climate and a spatially-distributed hydrological model (Spain). The main finding of the study is that the approximate (ABC post-processor) conditional predictive uncertainty is almost equivalent to the exact predictive (MCMC post-processor) in both scenarios. es_ES
dc.description.sponsorship This study was partially supported by the Departamento del Huila Scholarship Program No. 677 (Colombia) and Colciencias, by the Spanish Research Project TETIS-MED (ref. CGL2014-58127-C3-3-R) and TETIS-CHANGE (ref.RTI2018-093717-B-I00). Also, G. Adelfio's research has been supported by the national grant of the Italian Ministry of Education University and Research (MIUR) for the PRIN-2015 program, "Complex space-time modelling and functional analysis for probabilistic forecast of seismic events'. The authors also wish to thank the editor and the two anonymous reviewers for their thoughtful comments for the revision of the manuscript. es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof Stochastic Environmental Research and Risk Assessment es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Free-likelihood approach es_ES
dc.subject Probabilistic modelling es_ES
dc.subject Uncertainty analysis es_ES
dc.subject Hydrological forecasting es_ES
dc.subject Summary statistics es_ES
dc.subject.classification INGENIERIA HIDRAULICA es_ES
dc.title Hydrological post-processing based on approximate Bayesian computation (ABC) es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s00477-019-01694-y es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//CGL2014-58127-C3-3-R/ES/MEJORAS BIOGEOQUIMICAS EN EL MODELO TETIS Y SU EXPLOTACION EN EL ANALISIS DEL IMPACTO DEL CAMBIO GLOBAL EN LOS CICLOS DEL AGUA, CALIDAD Y SEDIMENTOS EN CUENCAS MEDITERRANEAS/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-093717-B-I00/ES/MEJORAS DEL CONOCIMIENTO Y DE LAS CAPACIDADES DE MODELIZACION PARA LA PROGNOSIS DE LOS EFECTOS DEL CAMBIO GLOBAL EN UNA CUENCA HIDROLOGICA/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Hidráulica y Medio Ambiente - Departament d'Enginyeria Hidràulica i Medi Ambient es_ES
dc.description.bibliographicCitation Romero-Cuellar, J.; Abbruzzo, A.; Adelfio, G.; Francés, F. (2019). Hydrological post-processing based on approximate Bayesian computation (ABC). Stochastic Environmental Research and Risk Assessment. 33(7):1361-1373. https://doi.org/10.1007/s00477-019-01694-y es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://doi.org/10.1007/s00477-019-01694-y es_ES
dc.description.upvformatpinicio 1361 es_ES
dc.description.upvformatpfin 1373 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 33 es_ES
dc.description.issue 7 es_ES
dc.relation.pasarela S\390053 es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.contributor.funder Ministerio de Economía y Empresa es_ES
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