Towards an Extension of the Model Conditional Processor: Predictive Uncertainty Quantification of Monthly Streamflow via Gaussian Mixture Models and Clusters

[EN] This research develops an extension of the Model Conditional Processor (MCP), which merges clusters with Gaussian mixture models to offer an alternative solution to manage heteroscedastic errors. The new method is called the Gaussian mixture clustering post-processor (GMCP). The results of the...

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Detalles Bibliográficos
Autores: Romero-Cuellar, Jonathan, Gastulo-Tapia, Cristhian J., Hernández-López, Mario R., Prieto Sierra, Cristina, Francés, F.|||0000-0003-1173-4969
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/194039
Acceso en línea:https://riunet.upv.es/handle/10251/194039
Access Level:acceso abierto
Palabra clave:Uncertainty analysis
Water resources
Cluster analysis
Gaussian mixture model
Probabilistic prediction
INGENIERIA HIDRAULICA
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spelling Towards an Extension of the Model Conditional Processor: Predictive Uncertainty Quantification of Monthly Streamflow via Gaussian Mixture Models and ClustersRomero-Cuellar, JonathanGastulo-Tapia, Cristhian J.Hernández-López, Mario R.Prieto Sierra, CristinaFrancés, F.|||0000-0003-1173-4969Uncertainty analysisWater resourcesCluster analysisGaussian mixture modelProbabilistic predictionINGENIERIA HIDRAULICA[EN] This research develops an extension of the Model Conditional Processor (MCP), which merges clusters with Gaussian mixture models to offer an alternative solution to manage heteroscedastic errors. The new method is called the Gaussian mixture clustering post-processor (GMCP). The results of the proposed post-processor were compared to the traditional MCP and MCP using a truncated Normal distribution (MCPt) by applying multiple deterministic and probabilistic verification indices. This research also assesses the GMCP's capacity to estimate the predictive uncertainty of the monthly streamflow under different climate conditions in the "Second Workshop on Model Parameter Estimation Experiment" (MOPEX) catchments distributed in the SE part of the USA. The results indicate that all three post-processors showed promising results. However, the GMCP post-processor has shown significant potential in generating more reliable, sharp, and accurate monthly streamflow predictions than the MCP and MCPt methods, especially in dry catchments. Moreover, the MCP and MCPt provided similar performances for monthly streamflow and better performances in wet catchments than in dry catchments. The GMCP constitutes a promising solution to handle heteroscedastic errors in monthly streamflow, therefore moving towards a more realistic monthly hydrological prediction to support effective decision-making in planning and managing water resources.We are grateful to Qingyun Duan for information of the MOPEX experiment. We also are grateful to the editor and two anonymous reviewers for their thoughtful comments on this manuscript. This research was funded by the department of Huila Scholarship Program No. 677 (Colombia) and Colciencias, the Vice-Presidents Research and Social Work office of the Universidad Surcolombiana, the Spanish Ministry of Science and Innovation through research project TETISCHANGE (ref. RTI2018-093717-B-I00). Cristina Prieto acknowledges the financial support from the Government of Cantabria through the Fenix Program.MDPI AGDepartamento de Ingeniería Hidráulica y Medio AmbienteInstituto Universitario de Ingeniería del Agua y del Medio AmbienteEscuela Técnica Superior de Ingeniería de Caminos, Canales y PuertosGobierno de CantabriaAGENCIA ESTATAL DE INVESTIGACIONDepartamento Administrativo de Ciencia, Tecnología e Innovación, ColombiaRepositorio Institucional de la Universitat Politècnica de València Riunet20222022-04-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/194039reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 RTI2018-093717-B-I00 MEJORAS DEL CONOCIMIENTO Y DE LAS CAPACIDADES DE MODELIZACION PARA LA PROGNOSIS DE LOS EFECTOS DEL CAMBIO GLOBAL EN UNA CUENCA HIDROLOGICADepartamento Administrativo de Ciencia, Tecnología e Innovación, Colombia https://doi.org/10.13039/100007637 677open accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento (by)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/1940392026-06-13T07:49:27Z
dc.title.none.fl_str_mv Towards an Extension of the Model Conditional Processor: Predictive Uncertainty Quantification of Monthly Streamflow via Gaussian Mixture Models and Clusters
title Towards an Extension of the Model Conditional Processor: Predictive Uncertainty Quantification of Monthly Streamflow via Gaussian Mixture Models and Clusters
spellingShingle Towards an Extension of the Model Conditional Processor: Predictive Uncertainty Quantification of Monthly Streamflow via Gaussian Mixture Models and Clusters
Romero-Cuellar, Jonathan
Uncertainty analysis
Water resources
Cluster analysis
Gaussian mixture model
Probabilistic prediction
INGENIERIA HIDRAULICA
title_short Towards an Extension of the Model Conditional Processor: Predictive Uncertainty Quantification of Monthly Streamflow via Gaussian Mixture Models and Clusters
title_full Towards an Extension of the Model Conditional Processor: Predictive Uncertainty Quantification of Monthly Streamflow via Gaussian Mixture Models and Clusters
title_fullStr Towards an Extension of the Model Conditional Processor: Predictive Uncertainty Quantification of Monthly Streamflow via Gaussian Mixture Models and Clusters
title_full_unstemmed Towards an Extension of the Model Conditional Processor: Predictive Uncertainty Quantification of Monthly Streamflow via Gaussian Mixture Models and Clusters
title_sort Towards an Extension of the Model Conditional Processor: Predictive Uncertainty Quantification of Monthly Streamflow via Gaussian Mixture Models and Clusters
dc.creator.none.fl_str_mv Romero-Cuellar, Jonathan
Gastulo-Tapia, Cristhian J.
Hernández-López, Mario R.
Prieto Sierra, Cristina
Francés, F.|||0000-0003-1173-4969
author Romero-Cuellar, Jonathan
author_facet Romero-Cuellar, Jonathan
Gastulo-Tapia, Cristhian J.
Hernández-López, Mario R.
Prieto Sierra, Cristina
Francés, F.|||0000-0003-1173-4969
author_role author
author2 Gastulo-Tapia, Cristhian J.
Hernández-López, Mario R.
Prieto Sierra, Cristina
Francés, F.|||0000-0003-1173-4969
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Departamento de Ingeniería Hidráulica y Medio Ambiente
Instituto Universitario de Ingeniería del Agua y del Medio Ambiente
Escuela Técnica Superior de Ingeniería de Caminos, Canales y Puertos
Gobierno de Cantabria
AGENCIA ESTATAL DE INVESTIGACION
Departamento Administrativo de Ciencia, Tecnología e Innovación, Colombia
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Uncertainty analysis
Water resources
Cluster analysis
Gaussian mixture model
Probabilistic prediction
INGENIERIA HIDRAULICA
topic Uncertainty analysis
Water resources
Cluster analysis
Gaussian mixture model
Probabilistic prediction
INGENIERIA HIDRAULICA
description [EN] This research develops an extension of the Model Conditional Processor (MCP), which merges clusters with Gaussian mixture models to offer an alternative solution to manage heteroscedastic errors. The new method is called the Gaussian mixture clustering post-processor (GMCP). The results of the proposed post-processor were compared to the traditional MCP and MCP using a truncated Normal distribution (MCPt) by applying multiple deterministic and probabilistic verification indices. This research also assesses the GMCP's capacity to estimate the predictive uncertainty of the monthly streamflow under different climate conditions in the "Second Workshop on Model Parameter Estimation Experiment" (MOPEX) catchments distributed in the SE part of the USA. The results indicate that all three post-processors showed promising results. However, the GMCP post-processor has shown significant potential in generating more reliable, sharp, and accurate monthly streamflow predictions than the MCP and MCPt methods, especially in dry catchments. Moreover, the MCP and MCPt provided similar performances for monthly streamflow and better performances in wet catchments than in dry catchments. The GMCP constitutes a promising solution to handle heteroscedastic errors in monthly streamflow, therefore moving towards a more realistic monthly hydrological prediction to support effective decision-making in planning and managing water resources.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-04-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/194039
url https://riunet.upv.es/handle/10251/194039
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 RTI2018-093717-B-I00 MEJORAS DEL CONOCIMIENTO Y DE LAS CAPACIDADES DE MODELIZACION PARA LA PROGNOSIS DE LOS EFECTOS DEL CAMBIO GLOBAL EN UNA CUENCA HIDROLOGICA
Departamento Administrativo de Ciencia, Tecnología e Innovación, Colombia https://doi.org/10.13039/100007637 677
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI AG
publisher.none.fl_str_mv MDPI AG
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.301603

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