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dc.contributor.author
Zhao, Xiaoen
dc.contributor.author
Fang, Keyan
dc.contributor.author
Chen, Feng
dc.contributor.author
Hadad, Martín Ariel
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Roig Junent, Fidel Alejandro
dc.date.available
2024-02-19T14:07:04Z
dc.date.issued
2023-04
dc.identifier.citation
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
dc.identifier.issn
0022-1694
dc.identifier.uri
http://hdl.handle.net/11336/227413
dc.description.abstract
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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier Science
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
JING RIVER
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MACHINE LEARNING
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STREAMFLOW RECONSTRUCTION
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TREE RINGS
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Otras Ciencias de la Tierra y relacionadas con el Medio Ambiente
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Ciencias de la Tierra y relacionadas con el Medio Ambiente
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
Reconstructed Jing River streamflow from western China: A 399-year perspective for hydrological changes in the Loess Plateau
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2024-02-19T10:19:13Z
dc.journal.volume
621
dc.journal.pagination
1-40
dc.journal.pais
Países Bajos
dc.journal.ciudad
Amsterdam
dc.description.fil
Fil: Zhao, Xiaoen. Yunnan University; China
dc.description.fil
Fil: Fang, Keyan. Fujian Normal University; China
dc.description.fil
Fil: Chen, Feng. Yunnan University; China
dc.description.fil
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
dc.description.fil
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
dc.journal.title
Journal of Hydrology
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.jhydrol.2023.129573
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