A multi-scenario ensemble approach incorporating stepwise cluster analysis to reduce uncertainty in large-scale watershed precipitation projections: a case study of Pearl River Basin, South China

Assessing and selecting climate models with lower uncertainty is necessary to predict future climate and hydrological risks at the watershed scale. In this study, we integrated stepwise cluster analysis (SCA) to propose a multi-model ensemble downscaling framework aimed at reducing the uncertainty o...

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Bibliographic Details
Published in:Environmental science and pollution research international 2024-10, Vol.31 (49), p.59342-59362
Main Authors: Qi, Zixuan, Cai, Yanpeng, Xie, Yulei, Zhang, Pingping, Zhang, Xiaodong, Zhou, Wenjie
Format: Article
Language:English
Subjects:
Annual precipitation
Aquatic Pollution
Atmospheric Protection/Air Quality Control/Air Pollution
China
Climate
Climate Change
Climate models
Climate prediction
Cluster Analysis
Cold season
Earth and Environmental Science
Ecotoxicology
Environment
Environmental Chemistry
Environmental Health
Extreme weather
Heterogeneity
Hydrologic regime
Hydrology
Models, Theoretical
Precipitation
Rain
Research Article
River basins
Rivers
Seasons
Spatial heterogeneity
Uncertainty
Uncertainty analysis
Waste Water Technology
Water Management
Water Pollution Control
Watersheds
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Summary:Assessing and selecting climate models with lower uncertainty is necessary to predict future climate and hydrological risks at the watershed scale. In this study, we integrated stepwise cluster analysis (SCA) to propose a multi-model ensemble downscaling framework aimed at reducing the uncertainty of GCM-based precipitation projections in large-scale watersheds. The Pearl River Basin (PRB) in southern China was selected as the study area to validate the reliability of this framework. Spatially, we investigated the features of terrain-related spatial heterogeneity in precipitation simulation of different climate models using a stepwise cluster zoning approach. The spatial performance of most CMIP6 models was effective in capturing the annual mean precipitation from the source region to the downstream of the PRB. To further evaluate the model's skill in simulating precipitation patterns, we conducted a seasonal analysis for different periods throughout the year. However, the seasonal precipitation cycle exhibited a wet bias during cold seasons, and the most significant deviation of precipitation percentage intervals occurred during winter. The TSS ranking of CMIP6 models was used to select the top-performing models to construct an improved multi-model ensemble mean (MEM5), resulting in a more accurate precipitation simulation for PRB. Results showed consistent precipitation increases ( p  
ISSN:1614-7499
0944-1344
1614-7499
DOI:10.1007/s11356-024-35013-y