Construction of a daily streamflow dataset for Peru using a similarity-based regionalization approach and a hybrid hydrological modeling framework

Descripción del Articulo

This paper aims to develop a national hydrological model using physiographic and climatic characteristics to identify donor and receptor sub-catchments (sub-zones). Therefore, we use the hydrometeorological PISCO dataset (0.1º x 0.1º) to drive a sub-catchment conceptual rainfall-runoff (ARNO/VIC) mo...

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Detalles Bibliográficos
Autores: Llauca, Harold, Leon, Karen, Lavado-Casimiro, W.
Formato: artículo
Fecha de Publicación:2023
Institución:Servicio Nacional de Meteorología e Hidrología del Perú
Repositorio:SENAMHI-Institucional
Lenguaje:español
OAI Identifier:oai:repositorio.senamhi.gob.pe:20.500.12542/2894
Enlace del recurso:https://hdl.handle.net/20.500.12542/2894
https://doi.org/10.1016/j.ejrh.2023.101381
Nivel de acceso:acceso abierto
Materia:PISCO
Modelamiento Hidrológico
Modelos y Simulación
Modelos Regionales
https://purl.org/pe-repo/ocde/ford#1.05.11
datos y estadisticas ambientales - Gestión, Fiscalización y Participación Ciudadana Ambiental
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dc.title.es_PE.fl_str_mv Construction of a daily streamflow dataset for Peru using a similarity-based regionalization approach and a hybrid hydrological modeling framework
title Construction of a daily streamflow dataset for Peru using a similarity-based regionalization approach and a hybrid hydrological modeling framework
spellingShingle Construction of a daily streamflow dataset for Peru using a similarity-based regionalization approach and a hybrid hydrological modeling framework
Llauca, Harold
PISCO
Modelamiento Hidrológico
Modelos y Simulación
Modelos Regionales
https://purl.org/pe-repo/ocde/ford#1.05.11
datos y estadisticas ambientales - Gestión, Fiscalización y Participación Ciudadana Ambiental
title_short Construction of a daily streamflow dataset for Peru using a similarity-based regionalization approach and a hybrid hydrological modeling framework
title_full Construction of a daily streamflow dataset for Peru using a similarity-based regionalization approach and a hybrid hydrological modeling framework
title_fullStr Construction of a daily streamflow dataset for Peru using a similarity-based regionalization approach and a hybrid hydrological modeling framework
title_full_unstemmed Construction of a daily streamflow dataset for Peru using a similarity-based regionalization approach and a hybrid hydrological modeling framework
title_sort Construction of a daily streamflow dataset for Peru using a similarity-based regionalization approach and a hybrid hydrological modeling framework
author Llauca, Harold
author_facet Llauca, Harold
Leon, Karen
Lavado-Casimiro, W.
author_role author
author2 Leon, Karen
Lavado-Casimiro, W.
author2_role author
author
dc.contributor.author.fl_str_mv Llauca, Harold
Leon, Karen
Lavado-Casimiro, W.
dc.subject.es_PE.fl_str_mv PISCO
Modelamiento Hidrológico
Modelos y Simulación
Modelos Regionales
topic PISCO
Modelamiento Hidrológico
Modelos y Simulación
Modelos Regionales
https://purl.org/pe-repo/ocde/ford#1.05.11
datos y estadisticas ambientales - Gestión, Fiscalización y Participación Ciudadana Ambiental
dc.subject.ocde.es_PE.fl_str_mv https://purl.org/pe-repo/ocde/ford#1.05.11
dc.subject.sinia.es_PE.fl_str_mv datos y estadisticas ambientales - Gestión, Fiscalización y Participación Ciudadana Ambiental
description This paper aims to develop a national hydrological model using physiographic and climatic characteristics to identify donor and receptor sub-catchments (sub-zones). Therefore, we use the hydrometeorological PISCO dataset (0.1º x 0.1º) to drive a sub-catchment conceptual rainfall-runoff (ARNO/VIC) model and a river-routing (RAPID) model in thousands of river reaches. We identify 43 hydrological zones (with 122 sub-zones) to run the hybrid hydrological modeling framework (ARNO/VIC+RAPID) with previously calibrated and validated parameters with 43 fluviometric stations for 1981–2020. Simulated flow series show a higher performance at daily scale (KGE ≥ 0.75, NSEsqrt ≥ 0.65, MARE ≤ 1, and −25% ≤ PBIAS ≤ 25%) for catchments located at the Pacific coast and the Andes-Amazon transition, and good representation (R≥0.75) of seasonal and interannual variability.
publishDate 2023
dc.date.accessioned.none.fl_str_mv 2023-08-24T19:51:24Z
dc.date.available.none.fl_str_mv 2023-08-24T19:51:24Z
dc.date.issued.fl_str_mv 2023
dc.type.es_PE.fl_str_mv info:eu-repo/semantics/article
dc.type.sinia.es_PE.fl_str_mv text/publicacion cientifica
format article
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12542/2894
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1016/j.ejrh.2023.101381
dc.identifier.journal.none.fl_str_mv Journal of Hydrology: Regional Studies
dc.identifier.url.none.fl_str_mv https://hdl.handle.net/20.500.12542/2894
url https://hdl.handle.net/20.500.12542/2894
https://doi.org/10.1016/j.ejrh.2023.101381
identifier_str_mv Journal of Hydrology: Regional Studies
dc.language.iso.es_PE.fl_str_mv spa
language spa
dc.relation.ispartof.none.fl_str_mv urn:issn:2214-5818
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dc.rights.es_PE.fl_str_mv info:eu-repo/semantics/openAccess
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eu_rights_str_mv openAccess
rights_invalid_str_mv Reconocimiento - No comercial - Sin obra derivada (CC BY-NC-ND)
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dc.format.es_PE.fl_str_mv application/pdf
dc.publisher.es_PE.fl_str_mv Elsevier
dc.source.es_PE.fl_str_mv Repositorio Institucional - SENAMHI
Servicio Nacional de Meteorología e Hidrología del Perú
dc.source.none.fl_str_mv reponame:SENAMHI-Institucional
instname:Servicio Nacional de Meteorología e Hidrología del Perú
instacron:SENAMHI
instname_str Servicio Nacional de Meteorología e Hidrología del Perú
instacron_str SENAMHI
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spelling Llauca, HaroldLeon, KarenLavado-Casimiro, W.2023-08-24T19:51:24Z2023-08-24T19:51:24Z2023https://hdl.handle.net/20.500.12542/2894https://doi.org/10.1016/j.ejrh.2023.101381Journal of Hydrology: Regional Studieshttps://hdl.handle.net/20.500.12542/2894This paper aims to develop a national hydrological model using physiographic and climatic characteristics to identify donor and receptor sub-catchments (sub-zones). Therefore, we use the hydrometeorological PISCO dataset (0.1º x 0.1º) to drive a sub-catchment conceptual rainfall-runoff (ARNO/VIC) model and a river-routing (RAPID) model in thousands of river reaches. We identify 43 hydrological zones (with 122 sub-zones) to run the hybrid hydrological modeling framework (ARNO/VIC+RAPID) with previously calibrated and validated parameters with 43 fluviometric stations for 1981–2020. 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