A statistical tool for a hydrometeorological forecast in the lower La Plata Basin

Autores
Meis, Melanie; Llano, Maria Paula; Rodríguez, Daniela
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Extreme discharge events in the La Plata Basin need to be prevented. Simple approaches to the forecast problem such as SARIMA models usually predict average values correctly but fail to anticipate extreme events. As an approach to this problem, we used copula methods to model the distribution of the NIÑO 3.4 index and river streamflow pair. We used this to build a six-months forecast for streamflow 95% percentile using observed index values. We added this forecast as an exogenous variable in a SARIMAX model to predict discharge. Given that NIÑO events are usually correlated with extreme discharge events, we expected this model to improve the SARIMA model in predicting extreme events. When comparing both models, we effectively found that SARIMAX model is better than a SARIMA model both for 6- and 12-month discharge forecasts in periods when an El Niño event occurs, while it retains the same performance level when evaluated on all the span of the time series. This model emerges as a lightweight and easily implementable option for decision makers to anticipate extreme events and reduce the negative impacts that they generate.
Fil: Meis, Melanie. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; Argentina
Fil: Llano, Maria Paula. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; Argentina
Fil: Rodríguez, Daniela. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina
Materia
ENSO
DISCHARGE
EXTREME EVENTS
COPULA METHODS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/162685
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling A statistical tool for a hydrometeorological forecast in the lower La Plata BasinMeis, MelanieLlano, Maria PaulaRodríguez, DanielaENSODISCHARGEEXTREME EVENTSCOPULA METHODShttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1Extreme discharge events in the La Plata Basin need to be prevented. Simple approaches to the forecast problem such as SARIMA models usually predict average values correctly but fail to anticipate extreme events. As an approach to this problem, we used copula methods to model the distribution of the NIÑO 3.4 index and river streamflow pair. We used this to build a six-months forecast for streamflow 95% percentile using observed index values. We added this forecast as an exogenous variable in a SARIMAX model to predict discharge. Given that NIÑO events are usually correlated with extreme discharge events, we expected this model to improve the SARIMA model in predicting extreme events. When comparing both models, we effectively found that SARIMAX model is better than a SARIMA model both for 6- and 12-month discharge forecasts in periods when an El Niño event occurs, while it retains the same performance level when evaluated on all the span of the time series. This model emerges as a lightweight and easily implementable option for decision makers to anticipate extreme events and reduce the negative impacts that they generate.Fil: Meis, Melanie. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; ArgentinaFil: Llano, Maria Paula. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; ArgentinaFil: Rodríguez, Daniela. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; ArgentinaTaylor & Francis2022-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/162685Meis, Melanie; Llano, Maria Paula; Rodríguez, Daniela; A statistical tool for a hydrometeorological forecast in the lower La Plata Basin; Taylor & Francis; International Journal of River Basin Management; 2022; 5-2022; 1-391571-51241814-2060CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.tandfonline.com/doi/abs/10.1080/15715124.2022.2079657info:eu-repo/semantics/altIdentifier/doi/10.1080/15715124.2022.2079657info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:01:58Zoai:ri.conicet.gov.ar:11336/162685instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 10:01:59.06CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A statistical tool for a hydrometeorological forecast in the lower La Plata Basin
title A statistical tool for a hydrometeorological forecast in the lower La Plata Basin
spellingShingle A statistical tool for a hydrometeorological forecast in the lower La Plata Basin
Meis, Melanie
ENSO
DISCHARGE
EXTREME EVENTS
COPULA METHODS
title_short A statistical tool for a hydrometeorological forecast in the lower La Plata Basin
title_full A statistical tool for a hydrometeorological forecast in the lower La Plata Basin
title_fullStr A statistical tool for a hydrometeorological forecast in the lower La Plata Basin
title_full_unstemmed A statistical tool for a hydrometeorological forecast in the lower La Plata Basin
title_sort A statistical tool for a hydrometeorological forecast in the lower La Plata Basin
dc.creator.none.fl_str_mv Meis, Melanie
Llano, Maria Paula
Rodríguez, Daniela
author Meis, Melanie
author_facet Meis, Melanie
Llano, Maria Paula
Rodríguez, Daniela
author_role author
author2 Llano, Maria Paula
Rodríguez, Daniela
author2_role author
author
dc.subject.none.fl_str_mv ENSO
DISCHARGE
EXTREME EVENTS
COPULA METHODS
topic ENSO
DISCHARGE
EXTREME EVENTS
COPULA METHODS
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Extreme discharge events in the La Plata Basin need to be prevented. Simple approaches to the forecast problem such as SARIMA models usually predict average values correctly but fail to anticipate extreme events. As an approach to this problem, we used copula methods to model the distribution of the NIÑO 3.4 index and river streamflow pair. We used this to build a six-months forecast for streamflow 95% percentile using observed index values. We added this forecast as an exogenous variable in a SARIMAX model to predict discharge. Given that NIÑO events are usually correlated with extreme discharge events, we expected this model to improve the SARIMA model in predicting extreme events. When comparing both models, we effectively found that SARIMAX model is better than a SARIMA model both for 6- and 12-month discharge forecasts in periods when an El Niño event occurs, while it retains the same performance level when evaluated on all the span of the time series. This model emerges as a lightweight and easily implementable option for decision makers to anticipate extreme events and reduce the negative impacts that they generate.
Fil: Meis, Melanie. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; Argentina
Fil: Llano, Maria Paula. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; Argentina
Fil: Rodríguez, Daniela. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina
description Extreme discharge events in the La Plata Basin need to be prevented. Simple approaches to the forecast problem such as SARIMA models usually predict average values correctly but fail to anticipate extreme events. As an approach to this problem, we used copula methods to model the distribution of the NIÑO 3.4 index and river streamflow pair. We used this to build a six-months forecast for streamflow 95% percentile using observed index values. We added this forecast as an exogenous variable in a SARIMAX model to predict discharge. Given that NIÑO events are usually correlated with extreme discharge events, we expected this model to improve the SARIMA model in predicting extreme events. When comparing both models, we effectively found that SARIMAX model is better than a SARIMA model both for 6- and 12-month discharge forecasts in periods when an El Niño event occurs, while it retains the same performance level when evaluated on all the span of the time series. This model emerges as a lightweight and easily implementable option for decision makers to anticipate extreme events and reduce the negative impacts that they generate.
publishDate 2022
dc.date.none.fl_str_mv 2022-05
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/162685
Meis, Melanie; Llano, Maria Paula; Rodríguez, Daniela; A statistical tool for a hydrometeorological forecast in the lower La Plata Basin; Taylor & Francis; International Journal of River Basin Management; 2022; 5-2022; 1-39
1571-5124
1814-2060
CONICET Digital
CONICET
url http://hdl.handle.net/11336/162685
identifier_str_mv Meis, Melanie; Llano, Maria Paula; Rodríguez, Daniela; A statistical tool for a hydrometeorological forecast in the lower La Plata Basin; Taylor & Francis; International Journal of River Basin Management; 2022; 5-2022; 1-39
1571-5124
1814-2060
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.tandfonline.com/doi/abs/10.1080/15715124.2022.2079657
info:eu-repo/semantics/altIdentifier/doi/10.1080/15715124.2022.2079657
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Taylor & Francis
publisher.none.fl_str_mv Taylor & Francis
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1842269727947227136
score 13.13397

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