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Improving hydrological post-processing for assessing the conditional predictive uncertainty of monthly streamflows

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Improving hydrological post-processing for assessing the conditional predictive uncertainty of monthly streamflows

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Romero Cuellar, J. (2019). Improving hydrological post-processing for assessing the conditional predictive uncertainty of monthly streamflows [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/133999

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/133999

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Title: Improving hydrological post-processing for assessing the conditional predictive uncertainty of monthly streamflows
Author:
Director(s): Francés García, Félix Ramón
UPV Unit: Universitat Politècnica de València. Departamento de Ingeniería Hidráulica y Medio Ambiente - Departament d'Enginyeria Hidràulica i Medi Ambient
Read date / Event date:
2019-11-29
Issued date:
Abstract:
[ES] La cuantificación de la incertidumbre predictiva es de vital importancia para producir predicciones hidrológicas confiables que soporten y apoyen la toma de decisiones en el marco de la gestión de los recursos hídricos. ...[+]


[CAT] La quantificació de la incertesa predictiva és de vital importància per a produir prediccions hidrològiques confiables que suporten i recolzen la presa de decisions en el marc de la gestió dels recursos hídrics. Els ...[+]


[EN] The predictive uncertainty quantification in monthly streamflows is crucial to make reliable hydrological predictions that help and support decision-making in water resources management. Hydrological post-processing ...[+]
Subjects: Probabilistic uncertainty quantification , Statistical post-processor , Water resources systems , Approximate Bayesian computation , Uncertainty analysis , Hydrological modelling
Copyrigths: Reserva de todos los derechos
DOI: 10.4995/Thesis/10251/133999
Type: Tesis doctoral

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