Causal Reasoning: Towards Dynamic Predictive Models for Runoff Temporal Behavior of High Dependence Rivers

[ES] Nowadays, a noteworthy temporal alteration of traditional hydrological patterns is being observed, producing a higher variability and more unpredictable extreme events worldwide. This is largely due to global warming, which is generating a growing uncertainty over water system behavior, especia...

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Autores: Molina González, José Luis, Zazo del Dedo, Santiago, Martín Casado, Ana María
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2019
País:España
Institución:Universidad de Salamanca (USAL)
Repositorio:GREDOS. Repositorio Institucional de la Universidad de Salamanca
OAI Identifier:oai:gredos.usal.es:10366/162146
Acceso en línea:http://hdl.handle.net/10366/162146
Access Level:acceso abierto
Palabra clave:Causality
Causal reasoning
Runoff fractions
Hydrological time series
Dynamic temporal dependence propagation
Predictive models
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spelling Causal Reasoning: Towards Dynamic Predictive Models for Runoff Temporal Behavior of High Dependence RiversMolina González, José LuisZazo del Dedo, SantiagoMartín Casado, Ana MaríaCausalityCausal reasoningRunoff fractionsHydrological time seriesDynamic temporal dependence propagationPredictive models[ES] Nowadays, a noteworthy temporal alteration of traditional hydrological patterns is being observed, producing a higher variability and more unpredictable extreme events worldwide. This is largely due to global warming, which is generating a growing uncertainty over water system behavior, especially river runoff. Understanding these modifications is a crucial and not trivial challenge that requires new analytical strategies like Causality, addressed by Causal Reasoning. Through Causality over runo series, the hydrological memory and its logical time-dependency structure have been dynamically/stochastically discovered and characterized. This is done in terms of the runoff dependence strength over time. This has allowed determining and quantifying two opposite temporal-fractions within runoff: Temporally Conditioned/Non-conditioned Runo (TCR/TNCR). Finally, a successful predictive model is proposed and applied to an unregulated stretch, Mijares river catchment (Jucar river basin, Spain), with a very high time-dependency behavior. This research may have important implications over the knowledge of historical rivers´ behavior and their adaptation. Furthermore, it lays the foundations for reaching an optimum reservoir dimensioning through the building of predictive models of runo behavior. Regarding reservoir capacity, this research would imply substantial economic/environmental savings. Also, a more sustainable management of river basins through more reliable control reservoirs’ operation is expected to be achieved.202520252019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10366/162146reponame:GREDOS. Repositorio Institucional de la Universidad de Salamancainstname:Universidad de Salamanca (USAL)InglésAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:gredos.usal.es:10366/1621462026-06-07T06:28:51Z
dc.title.none.fl_str_mv Causal Reasoning: Towards Dynamic Predictive Models for Runoff Temporal Behavior of High Dependence Rivers
title Causal Reasoning: Towards Dynamic Predictive Models for Runoff Temporal Behavior of High Dependence Rivers
spellingShingle Causal Reasoning: Towards Dynamic Predictive Models for Runoff Temporal Behavior of High Dependence Rivers
Molina González, José Luis
Causality
Causal reasoning
Runoff fractions
Hydrological time series
Dynamic temporal dependence propagation
Predictive models
title_short Causal Reasoning: Towards Dynamic Predictive Models for Runoff Temporal Behavior of High Dependence Rivers
title_full Causal Reasoning: Towards Dynamic Predictive Models for Runoff Temporal Behavior of High Dependence Rivers
title_fullStr Causal Reasoning: Towards Dynamic Predictive Models for Runoff Temporal Behavior of High Dependence Rivers
title_full_unstemmed Causal Reasoning: Towards Dynamic Predictive Models for Runoff Temporal Behavior of High Dependence Rivers
title_sort Causal Reasoning: Towards Dynamic Predictive Models for Runoff Temporal Behavior of High Dependence Rivers
dc.creator.none.fl_str_mv Molina González, José Luis
Zazo del Dedo, Santiago
Martín Casado, Ana María
author Molina González, José Luis
author_facet Molina González, José Luis
Zazo del Dedo, Santiago
Martín Casado, Ana María
author_role author
author2 Zazo del Dedo, Santiago
Martín Casado, Ana María
author2_role author
author
dc.subject.none.fl_str_mv Causality
Causal reasoning
Runoff fractions
Hydrological time series
Dynamic temporal dependence propagation
Predictive models
topic Causality
Causal reasoning
Runoff fractions
Hydrological time series
Dynamic temporal dependence propagation
Predictive models
description [ES] Nowadays, a noteworthy temporal alteration of traditional hydrological patterns is being observed, producing a higher variability and more unpredictable extreme events worldwide. This is largely due to global warming, which is generating a growing uncertainty over water system behavior, especially river runoff. Understanding these modifications is a crucial and not trivial challenge that requires new analytical strategies like Causality, addressed by Causal Reasoning. Through Causality over runo series, the hydrological memory and its logical time-dependency structure have been dynamically/stochastically discovered and characterized. This is done in terms of the runoff dependence strength over time. This has allowed determining and quantifying two opposite temporal-fractions within runoff: Temporally Conditioned/Non-conditioned Runo (TCR/TNCR). Finally, a successful predictive model is proposed and applied to an unregulated stretch, Mijares river catchment (Jucar river basin, Spain), with a very high time-dependency behavior. This research may have important implications over the knowledge of historical rivers´ behavior and their adaptation. Furthermore, it lays the foundations for reaching an optimum reservoir dimensioning through the building of predictive models of runo behavior. Regarding reservoir capacity, this research would imply substantial economic/environmental savings. Also, a more sustainable management of river basins through more reliable control reservoirs’ operation is expected to be achieved.
publishDate 2019
dc.date.none.fl_str_mv 2019
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10366/162146
url http://hdl.handle.net/10366/162146
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:GREDOS. Repositorio Institucional de la Universidad de Salamanca
instname:Universidad de Salamanca (USAL)
instname_str Universidad de Salamanca (USAL)
reponame_str GREDOS. Repositorio Institucional de la Universidad de Salamanca
collection GREDOS. Repositorio Institucional de la Universidad de Salamanca
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.81155

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