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Analysis of spatio-temporal dependence of in flow time series through Bayesian causal modelling

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Analysis of spatio-temporal dependence of in flow time series through Bayesian causal modelling

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dc.contributor.author Macian-Sorribes, Hector es_ES
dc.contributor.author Molina González, José Luis es_ES
dc.contributor.author Zazo-Del Dedo, Santiago es_ES
dc.contributor.author Pulido-Velazquez, M. es_ES
dc.date.accessioned 2021-05-13T03:31:37Z
dc.date.available 2021-05-13T03:31:37Z
dc.date.issued 2021-06 es_ES
dc.identifier.issn 0022-1694 es_ES
dc.identifier.uri http://hdl.handle.net/10251/166256
dc.description.abstract [EN] This paper aims to assess fully the spatio-temporal dependence dimensions of inflow across two adjacent and parallel basins and among different time steps through Causality. This is addressed from the perspective of Causal Reasoning, supported by Bayesian modelling, under a novel framework named Bayesian Causal Modelling (BCM). This is applied, through a "concept-proof", to the Jucar River Basin (the second largest basin of Eastern Spain, characterized by long and severe drought conditions). In this ¿concept-proof¿ a double goal is evaluated; first dedicated to a lumped analysis of dependence and second a specific one over dry periods focused on time-horizon of the Jucar basin typical drought (3 years). These challenges comprise the development of two fully connected Bayesian Networks (BNs), one for each challenge populated/trained from historical-inflow records. BNs were designed at a season-scale and consequently, time was upscaled and grouped into Irrigation and Non-Irrigation periods, according to Jucar River Basin Authority operational practices. Results achieved showed that BCM framework satisfactorily captured the spatio-temporal dependencies of systems. Furthermore, BCM is able to answer to some key questions over interdependencies between adjacent and parallel subbasins. Those questions may comprise, the amount of spatial dependences among time series, the temporarily conditionality among subbasins and the spatio-temporal dependence among basins. This provides a relevant insight on the intrinsic spatio-temporal dependence structure of inflow time series in complex basins systems. This approach could be very valuable for water resources planning and management, due to its application power for predicting extreme events (e.g. droughts) as well as improving and optimizing the reservoirs operation rules. es_ES
dc.description.sponsorship This work was supported by the by the IMPADAPT project (CGL2013-48424-C2-1-R) with Spanish MINECO (Ministerio de Economía y Competitividad) and European FEDER funds; and the European Union's Horizon 2020 research and innovation programme under the IMPREX project (GA 641.811). es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Journal of Hydrology es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Causality es_ES
dc.subject Causal reasoning es_ES
dc.subject Bayesian spatio-temporal dependence es_ES
dc.subject Stochastic hydrology es_ES
dc.subject Jucar river basin es_ES
dc.subject Historical inflow time series es_ES
dc.subject.classification INGENIERIA HIDRAULICA es_ES
dc.title Analysis of spatio-temporal dependence of in flow time series through Bayesian causal modelling es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.jhydrol.2020.125722 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/641811/EU/IMproving PRedictions and management of hydrological EXtremes/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//CGL2013-48424-C2-1-R/ES/ADAPTACION AL CAMBIO GLOBAL EN SISTEMAS DE RECURSOS HIDRICOS/ 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 Macian-Sorribes, H.; Molina González, JL.; Zazo-Del Dedo, S.; Pulido-Velazquez, M. (2021). Analysis of spatio-temporal dependence of in flow time series through Bayesian causal modelling. Journal of Hydrology. 597:1-14. https://doi.org/10.1016/j.jhydrol.2020.125722 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.jhydrol.2020.125722 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 14 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 597 es_ES
dc.relation.pasarela S\425096 es_ES
dc.contributor.funder European Commission es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
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dc.subject.ods 06.- Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos es_ES
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