Assessing climate change and human impacts on runoff and hydrological droughts in the Yellow River Basin using a machine learning-enhanced hydrological modeling approach

Analyzing the impacts of climate change (CC) and human activities (HA) on hydrological events is important for water resource management. This study quantifies the impacts of CC and HA on runoff and hydrological drought characteristics (HDC) in the Yellow River Basin (YRB) of China. Trends and abrup...

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Vydáno v:Journal of environmental management Ročník 380; s. 125091
Hlavní autoři: Wang, Lei, Li, Yi, Biswas, Asim, Zhao, Yong, Niu, Ben, Siddique, Kadambot.H.M.
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Elsevier Ltd 01.04.2025
Témata:
China
Climate Change
Contribution analysis
drought
Droughts
environmental management
Environmental Monitoring
Human activities
Humans
Hydrological drought
Hydrology
Machine Learning
Models, Theoretical
Random forest
Rivers
Runoff
Soil and Water Assessment Tool model
SWAT
temperature
water management
watersheds
Yellow River
China
Yellow River
SWAT
Climate change
Runoff
Contribution analysis
Hydrological drought
Human activities
Random forest
ISSN:0301-4797, 1095-8630, 1095-8630
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Shrnutí:Analyzing the impacts of climate change (CC) and human activities (HA) on hydrological events is important for water resource management. This study quantifies the impacts of CC and HA on runoff and hydrological drought characteristics (HDC) in the Yellow River Basin (YRB) of China. Trends and abrupt change points in runoff at 16 hydrological stations were detected. The Soil and Water Assessment Tool (SWAT), Random Forest (RF), and five bias-correction models, including SWAT_RF_bias3 which integrated SWAT outputs with RF using maximal precipitation and temperature inputs, were evaluated for their efficacy in monthly runoff simulation. The "simulated-observed" method was employed to assess the contributions of CC and HA to runoff and HDC variations. Results indicated a general decrease in runoff across the stations during 1961–2016. SWAT_RF_bias3 emerged as the superior model, highlighting the importance of high precipitation in the headwater region and near the main channel of the midstream for accurate runoff simulation. HA was found to contribute significantly more (68–95 %) to runoff reductions than CC. Additionally, CC predominantly influenced the frequency decrease in severe and extreme hydrological droughts, while HA was the main driver behind the increased magnitude and duration of extreme droughts. These findings underscore the complex interplay between CC and HA in water resource management and the effectiveness of bias-correction models in enhancing hydrological simulations in the YRB. •Enhanced runoff simulation of SWAT using Random Forest was obtained.•Human activities contributed 68–95 % to runoff reductions in the Yellow River Basin.•Climate change dominated the decreases in frequency of severe and extreme droughts.•Human activities led the increases in magnitude and duration of extreme droughts.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0301-4797
1095-8630
1095-8630
DOI:10.1016/j.jenvman.2025.125091