Reconstructed Jing River streamflow from western China: A 399-year perspective for hydrological changes in the Loess Plateau

Autores
Zhao, Xiaoen; Fang, Keyan; Chen, Feng; Hadad, Martín Ariel; Roig Junent, Fidel Alejandro
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The Jing River is a secondary tributary of the Yellow River, which flows through the middle of the Loess Plateau in China. Severe water scarcity and soil erosion in the basin have threatened sustainable social and economic development. To assess and solve the region's water resource problems, it is important to understand its historical hydrological climate change. Accordingly, we used five machine learning models and simple linear regression to reconstruct the January-June streamflow of the Jing River based on the tree ring width of Pinus tabulaeformis and Pinus armandii. By combining six models into an ensemble streamflow reconstruction, we obtained a more accurate reconstruction and streamflow variability information than with a single model. Over the past nearly four centuries, the Jing River has experienced seven high streamflow periods and ten low streamflow periods. The main atmospheric forcing factors driving the streamflow variability are the Pacific Decadal Oscillation and the El Niño-Southern Oscillation, which regulate the climate and hydrology of the region by affecting water vapor fluxes and the Asian monsoon. The different climate scenarios revealed the continued reduction in the future Jing River streamflow and a worsening water resource situation. This new streamflow reconstruction can serve as a valuable reference for analyzing regional hydrology and informing water resource management and policy formulations.
Fil: Zhao, Xiaoen. Yunnan University; China
Fil: Fang, Keyan. Fujian Normal University; China
Fil: Chen, Feng. Yunnan University; China
Fil: Hadad, Martín Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Centro de Investigaciones de la Geosfera y Biosfera. Universidad Nacional de San Juan. Facultad de Ciencias Exactas Físicas y Naturales. Centro de Investigaciones de la Geosfera y Biosfera; Argentina
Fil: Roig Junent, Fidel Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; Argentina
Materia
JING RIVER
MACHINE LEARNING
STREAMFLOW RECONSTRUCTION
TREE RINGS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/227413
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Reconstructed Jing River streamflow from western China: A 399-year perspective for hydrological changes in the Loess PlateauZhao, XiaoenFang, KeyanChen, FengHadad, Martín ArielRoig Junent, Fidel AlejandroJING RIVERMACHINE LEARNINGSTREAMFLOW RECONSTRUCTIONTREE RINGShttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1The Jing River is a secondary tributary of the Yellow River, which flows through the middle of the Loess Plateau in China. Severe water scarcity and soil erosion in the basin have threatened sustainable social and economic development. To assess and solve the region's water resource problems, it is important to understand its historical hydrological climate change. Accordingly, we used five machine learning models and simple linear regression to reconstruct the January-June streamflow of the Jing River based on the tree ring width of Pinus tabulaeformis and Pinus armandii. By combining six models into an ensemble streamflow reconstruction, we obtained a more accurate reconstruction and streamflow variability information than with a single model. Over the past nearly four centuries, the Jing River has experienced seven high streamflow periods and ten low streamflow periods. The main atmospheric forcing factors driving the streamflow variability are the Pacific Decadal Oscillation and the El Niño-Southern Oscillation, which regulate the climate and hydrology of the region by affecting water vapor fluxes and the Asian monsoon. The different climate scenarios revealed the continued reduction in the future Jing River streamflow and a worsening water resource situation. This new streamflow reconstruction can serve as a valuable reference for analyzing regional hydrology and informing water resource management and policy formulations.Fil: Zhao, Xiaoen. Yunnan University; ChinaFil: Fang, Keyan. Fujian Normal University; ChinaFil: Chen, Feng. Yunnan University; ChinaFil: Hadad, Martín Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Centro de Investigaciones de la Geosfera y Biosfera. Universidad Nacional de San Juan. Facultad de Ciencias Exactas Físicas y Naturales. Centro de Investigaciones de la Geosfera y Biosfera; ArgentinaFil: Roig Junent, Fidel Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; ArgentinaElsevier Science2023-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/227413Zhao, Xiaoen; Fang, Keyan; Chen, Feng; Hadad, Martín Ariel; Roig Junent, Fidel Alejandro; Reconstructed Jing River streamflow from western China: A 399-year perspective for hydrological changes in the Loess Plateau; Elsevier Science; Journal of Hydrology; 621; 4-2023; 1-400022-1694CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.jhydrol.2023.129573info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:17:24Zoai:ri.conicet.gov.ar:11336/227413instacron: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:34982025-09-29 10:17:25.271CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Reconstructed Jing River streamflow from western China: A 399-year perspective for hydrological changes in the Loess Plateau
title Reconstructed Jing River streamflow from western China: A 399-year perspective for hydrological changes in the Loess Plateau
spellingShingle Reconstructed Jing River streamflow from western China: A 399-year perspective for hydrological changes in the Loess Plateau
Zhao, Xiaoen
JING RIVER
MACHINE LEARNING
STREAMFLOW RECONSTRUCTION
TREE RINGS
title_short Reconstructed Jing River streamflow from western China: A 399-year perspective for hydrological changes in the Loess Plateau
title_full Reconstructed Jing River streamflow from western China: A 399-year perspective for hydrological changes in the Loess Plateau
title_fullStr Reconstructed Jing River streamflow from western China: A 399-year perspective for hydrological changes in the Loess Plateau
title_full_unstemmed Reconstructed Jing River streamflow from western China: A 399-year perspective for hydrological changes in the Loess Plateau
title_sort Reconstructed Jing River streamflow from western China: A 399-year perspective for hydrological changes in the Loess Plateau
dc.creator.none.fl_str_mv Zhao, Xiaoen
Fang, Keyan
Chen, Feng
Hadad, Martín Ariel
Roig Junent, Fidel Alejandro
author Zhao, Xiaoen
author_facet Zhao, Xiaoen
Fang, Keyan
Chen, Feng
Hadad, Martín Ariel
Roig Junent, Fidel Alejandro
author_role author
author2 Fang, Keyan
Chen, Feng
Hadad, Martín Ariel
Roig Junent, Fidel Alejandro
author2_role author
author
author
author
dc.subject.none.fl_str_mv JING RIVER
MACHINE LEARNING
STREAMFLOW RECONSTRUCTION
TREE RINGS
topic JING RIVER
MACHINE LEARNING
STREAMFLOW RECONSTRUCTION
TREE RINGS
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The Jing River is a secondary tributary of the Yellow River, which flows through the middle of the Loess Plateau in China. Severe water scarcity and soil erosion in the basin have threatened sustainable social and economic development. To assess and solve the region's water resource problems, it is important to understand its historical hydrological climate change. Accordingly, we used five machine learning models and simple linear regression to reconstruct the January-June streamflow of the Jing River based on the tree ring width of Pinus tabulaeformis and Pinus armandii. By combining six models into an ensemble streamflow reconstruction, we obtained a more accurate reconstruction and streamflow variability information than with a single model. Over the past nearly four centuries, the Jing River has experienced seven high streamflow periods and ten low streamflow periods. The main atmospheric forcing factors driving the streamflow variability are the Pacific Decadal Oscillation and the El Niño-Southern Oscillation, which regulate the climate and hydrology of the region by affecting water vapor fluxes and the Asian monsoon. The different climate scenarios revealed the continued reduction in the future Jing River streamflow and a worsening water resource situation. This new streamflow reconstruction can serve as a valuable reference for analyzing regional hydrology and informing water resource management and policy formulations.
Fil: Zhao, Xiaoen. Yunnan University; China
Fil: Fang, Keyan. Fujian Normal University; China
Fil: Chen, Feng. Yunnan University; China
Fil: Hadad, Martín Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Centro de Investigaciones de la Geosfera y Biosfera. Universidad Nacional de San Juan. Facultad de Ciencias Exactas Físicas y Naturales. Centro de Investigaciones de la Geosfera y Biosfera; Argentina
Fil: Roig Junent, Fidel Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; Argentina
description The Jing River is a secondary tributary of the Yellow River, which flows through the middle of the Loess Plateau in China. Severe water scarcity and soil erosion in the basin have threatened sustainable social and economic development. To assess and solve the region's water resource problems, it is important to understand its historical hydrological climate change. Accordingly, we used five machine learning models and simple linear regression to reconstruct the January-June streamflow of the Jing River based on the tree ring width of Pinus tabulaeformis and Pinus armandii. By combining six models into an ensemble streamflow reconstruction, we obtained a more accurate reconstruction and streamflow variability information than with a single model. Over the past nearly four centuries, the Jing River has experienced seven high streamflow periods and ten low streamflow periods. The main atmospheric forcing factors driving the streamflow variability are the Pacific Decadal Oscillation and the El Niño-Southern Oscillation, which regulate the climate and hydrology of the region by affecting water vapor fluxes and the Asian monsoon. The different climate scenarios revealed the continued reduction in the future Jing River streamflow and a worsening water resource situation. This new streamflow reconstruction can serve as a valuable reference for analyzing regional hydrology and informing water resource management and policy formulations.
publishDate 2023
dc.date.none.fl_str_mv 2023-04
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/11336/227413
Zhao, Xiaoen; Fang, Keyan; Chen, Feng; Hadad, Martín Ariel; Roig Junent, Fidel Alejandro; Reconstructed Jing River streamflow from western China: A 399-year perspective for hydrological changes in the Loess Plateau; Elsevier Science; Journal of Hydrology; 621; 4-2023; 1-40
0022-1694
CONICET Digital
CONICET
url http://hdl.handle.net/11336/227413
identifier_str_mv Zhao, Xiaoen; Fang, Keyan; Chen, Feng; Hadad, Martín Ariel; Roig Junent, Fidel Alejandro; Reconstructed Jing River streamflow from western China: A 399-year perspective for hydrological changes in the Loess Plateau; Elsevier Science; Journal of Hydrology; 621; 4-2023; 1-40
0022-1694
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jhydrol.2023.129573
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
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
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score 13.070432

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