Probabilistic forecasting of drought events using Markov chain- and Bayesian network-based models: A case study of an Andean regulated river basin

The scarcity of water resources in mountain areas can distort normal water application patterns with among other effects, a negative impact on water supply and river ecosystems. Knowing the probability of droughts might help to optimize a priori the planning and management of the water resources in...

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Autores: Aviles Anazco, Alex Manuel, Celleri Alvear, Rolando Enrique
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2016
País:Ecuador
Institución:Universidad de Cuenca
Repositorio:Repositorio Universidad de Cuenca
OAI Identifier:oai:dspace.ucuenca.edu.ec:123456789/29129
Acceso en línea:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84960093877&doi=10.3390%2fw8020037&partnerID=40&md5=60ee68be59fdeb2caed89bf246e76c53
http://dspace.ucuenca.edu.ec/handle/123456789/29129
Access Level:acceso abierto
Palabra clave:Andean Watersheds
Bayesian Networks
Copulas
Drought Index
Markov Chains
Probabilistic Drought Forecasting
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spelling Probabilistic forecasting of drought events using Markov chain- and Bayesian network-based models: A case study of an Andean regulated river basinAviles Anazco, Alex ManuelCelleri Alvear, Rolando EnriqueAndean WatershedsBayesian NetworksCopulasDrought IndexMarkov ChainsProbabilistic Drought ForecastingThe scarcity of water resources in mountain areas can distort normal water application patterns with among other effects, a negative impact on water supply and river ecosystems. Knowing the probability of droughts might help to optimize a priori the planning and management of the water resources in general and of the Andean watersheds in particular. This study compares Markov chain- (MC) and Bayesian network- (BN) based models in drought forecasting using a recently developed drought index with respect to their capability to characterize different drought severity states. The copula functions were used to solve the BNs and the ranked probability skill score (RPSS) to evaluate the performance of the models. Monthly rainfall and streamflow data of the Chulco River basin, located in Southern Ecuador, were used to assess the performance of both approaches. Global evaluation results revealed that the MC-based models predict better wet and dry periods, and BN-based models generate slightly more accurately forecasts of the most severe droughts. However, evaluation of monthly results reveals that, for each month of the hydrological year, either the MC- or BN-based model provides better forecasts. The presented approach could be of assistance to water managers to ensure that timely decision-making on drought response is undertaken.MDPI AG2018-01-11T16:47:29Z2018-01-11T16:47:29Z2016-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdf20734441https://www.scopus.com/inward/record.uri?eid=2-s2.0-84960093877&doi=10.3390%2fw8020037&partnerID=40&md5=60ee68be59fdeb2caed89bf246e76c53http://dspace.ucuenca.edu.ec/handle/123456789/2912910.3390/w8020037Water (Switzerland)reponame:Repositorio Universidad de Cuencainstname:Universidad de Cuencainstacron:UCUENCAen_USinfo:eu-repo/semantics/openAccess2020-08-01T01:14:59Zoai:dspace.ucuenca.edu.ec:123456789/29129Institucionalhttp://dspace.ucuenca.edu.ec/Universidad públicahttps://www.ucuenca.edu.ec/http://dspace.ucuenca.edu.ec/oai.Ecuador...opendoar:41862020-08-01T01:14:59Repositorio Universidad de Cuenca - Universidad de Cuencafalse
dc.title.none.fl_str_mv Probabilistic forecasting of drought events using Markov chain- and Bayesian network-based models: A case study of an Andean regulated river basin
title Probabilistic forecasting of drought events using Markov chain- and Bayesian network-based models: A case study of an Andean regulated river basin
spellingShingle Probabilistic forecasting of drought events using Markov chain- and Bayesian network-based models: A case study of an Andean regulated river basin
Aviles Anazco, Alex Manuel
Andean Watersheds
Bayesian Networks
Copulas
Drought Index
Markov Chains
Probabilistic Drought Forecasting
title_short Probabilistic forecasting of drought events using Markov chain- and Bayesian network-based models: A case study of an Andean regulated river basin
title_full Probabilistic forecasting of drought events using Markov chain- and Bayesian network-based models: A case study of an Andean regulated river basin
title_fullStr Probabilistic forecasting of drought events using Markov chain- and Bayesian network-based models: A case study of an Andean regulated river basin
title_full_unstemmed Probabilistic forecasting of drought events using Markov chain- and Bayesian network-based models: A case study of an Andean regulated river basin
title_sort Probabilistic forecasting of drought events using Markov chain- and Bayesian network-based models: A case study of an Andean regulated river basin
dc.creator.none.fl_str_mv Aviles Anazco, Alex Manuel
Celleri Alvear, Rolando Enrique
author Aviles Anazco, Alex Manuel
author_facet Aviles Anazco, Alex Manuel
Celleri Alvear, Rolando Enrique
author_role author
author2 Celleri Alvear, Rolando Enrique
author2_role author
dc.subject.none.fl_str_mv Andean Watersheds
Bayesian Networks
Copulas
Drought Index
Markov Chains
Probabilistic Drought Forecasting
topic Andean Watersheds
Bayesian Networks
Copulas
Drought Index
Markov Chains
Probabilistic Drought Forecasting
description The scarcity of water resources in mountain areas can distort normal water application patterns with among other effects, a negative impact on water supply and river ecosystems. Knowing the probability of droughts might help to optimize a priori the planning and management of the water resources in general and of the Andean watersheds in particular. This study compares Markov chain- (MC) and Bayesian network- (BN) based models in drought forecasting using a recently developed drought index with respect to their capability to characterize different drought severity states. The copula functions were used to solve the BNs and the ranked probability skill score (RPSS) to evaluate the performance of the models. Monthly rainfall and streamflow data of the Chulco River basin, located in Southern Ecuador, were used to assess the performance of both approaches. Global evaluation results revealed that the MC-based models predict better wet and dry periods, and BN-based models generate slightly more accurately forecasts of the most severe droughts. However, evaluation of monthly results reveals that, for each month of the hydrological year, either the MC- or BN-based model provides better forecasts. The presented approach could be of assistance to water managers to ensure that timely decision-making on drought response is undertaken.
publishDate 2016
dc.date.none.fl_str_mv 2016-01-01
2018-01-11T16:47:29Z
2018-01-11T16:47:29Z
dc.type.none.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv 20734441
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84960093877&doi=10.3390%2fw8020037&partnerID=40&md5=60ee68be59fdeb2caed89bf246e76c53
http://dspace.ucuenca.edu.ec/handle/123456789/29129
10.3390/w8020037
identifier_str_mv 20734441
10.3390/w8020037
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84960093877&doi=10.3390%2fw8020037&partnerID=40&md5=60ee68be59fdeb2caed89bf246e76c53
http://dspace.ucuenca.edu.ec/handle/123456789/29129
dc.language.none.fl_str_mv en_US
language_invalid_str_mv en_US
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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 Water (Switzerland)
reponame:Repositorio Universidad de Cuenca
instname:Universidad de Cuenca
instacron:UCUENCA
instname_str Universidad de Cuenca
instacron_str UCUENCA
institution UCUENCA
reponame_str Repositorio Universidad de Cuenca
collection Repositorio Universidad de Cuenca
repository.name.fl_str_mv Repositorio Universidad de Cuenca - Universidad de Cuenca
repository.mail.fl_str_mv .
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