Optimization of CMIP6 Precipitation Projection Based on Bayesian Model Averaging Approach and Future Urban Precipitation Risk Assessment

A Case Study of Shanghai

Bibliographic Data

ID13114261
AuthorsYifeng Qin (0000-0002-3765-6818, Shanghai Jiao Tong University), Caihua Yang (Shanghai Jiao Tong University), Hao Wu (0000-0001-5323-516X, Shanghai Jiao Tong University), Changkun Xie (0000-0003-1215-4958, Shanghai Jiao Tong University), Afshin Afshari (0000-0002-3237-2162, Fraunhofer Institute for Building Physics), Veselin Krustev (Agricultural Academy), Shengbing He (0000-0002-0753-4860, Shanghai Jiao Tong University), Shengquan Che (0000-0003-3210-0818, Shanghai Jiao Tong University, corresponding author)
Year2025
Volume9
Issue9
Pages331-331
Publication date2025-08-25
Open AccessYes
TypeARTICLE
VenueUrban Science (JOURNAL)
Journal identifiersISSN: 2413-8851 • E-ISSN: 2413-8851
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/urbansci9090331
OpenAlexW4413669363
LanguageEN
References cited55

Urban flooding, intensified by climate change, poses significant threats to sustainable development, necessitating accurate precipitation projections for effective risk management. This study utilized Bayesian Model Averaging (BMA) to optimize CMIP6 multi-model ensemble precipitation projections for Shanghai, integrating Delta statistical downscaling with observational data to enhance spatial accuracy and reduce uncertainty. After downscaling, RMSE values of daily precipitation for individual models range from 10.158 to 12.512, with correlation coefficients between −0.009 and 0.0047. The BMA exhibits an RMSE of 8.105 and a correlation coefficient of 0.056, demonstrating better accuracy compared to individual models. The BMA-weighted projections, coupled with Soil Conservation Service Curve Number (SCS-CN) hydrological model and drainage capacity constraints, reveal spatiotemporal flood risk patterns under Shared Socioeconomic Pathway (SSP) 245 and SSP585 scenarios. Key findings indicate that while SSP245 shows stable extreme precipitation intensity, SSP585 drives substantial increases—particularly for 50-year and 100-year return periods, with late 21st century maximums rising by 24.9% and 32.6%, respectively, compared to mid-century. Spatially, flood risk concentrates in peripheral districts due to higher precipitation exposure and average drainage capacity, contrasting with the lower-risk central urban core. This study establishes a watershed-based risk assessment framework linking climate projections directly to urban drainage planning, proposing differentiated strategies: green infrastructure for runoff reduction in high-risk areas, drainage system integration for vulnerable suburbs, and ecological restoration for coastal zones. This integrated methodology provides a replicable approach for climate-resilient urban flood management, demonstrating that effective adaptation requires scenario-specific spatial targeting

Algorithm · Bayesian inference · Bayesian probability · Climatology · Geography · Meteorology · Precipitation · Projection (relational algebra · Climate variability and models · Computer Science · Environmental Science · Meteorological Phenomena and Simulations · Precipitation Measurement and Analysis · Artificial Intelligence · Geology

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