气候变化领域集成服务门户(CCPortal): Understanding the Impact of Precipitation Bias-Correction and Statistical Downscaling Methods on Projected Changes in Flood Extremes

CCPortal

DOI	10.1029/2023EF004179
	Understanding the Impact of Precipitation Bias-Correction and Statistical Downscaling Methods on Projected Changes in Flood Extremes
	Michalek, Alexander T.; Villarini, Gabriele; Kim, Taereem
发表日期	2024
EISSN	2328-4277
起始页码	12
结束页码	3
卷号	12 期号:3
英文摘要	This study evaluates five bias correction and statistical downscaling (BCSD) techniques for daily precipitation and examines their impacts on the projected changes in flood extremes (i.e., 1%, 0.5%, and 0.2% floods). We use climate model outputs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to conduct hydrologic simulations across watersheds in Iowa and determine historical and future flood extreme estimates based on generalized extreme value distribution fitting. Projected changes in these extremes are examined with respect to four Shared Socioeconomic Pathways (SSPs) alongside five BCSD techniques. We find the magnitude of the estimates of future annual exceedance probabilities (AEPs) are expected to increase under all SSPs, especially for the emission scenarios with higher greenhouse gases concentrations (i.e., SSP370 and SSP585). Our results also suggest the choice of BCSD impacts the magnitude of the projected changes, with the SSPs that play a more limited role compared to the choice of downscaling method. The variability in projected flood changes across Iowa is similar across the downscaling technique but increases as the AEP increases. Our findings provide insights into the impact of downscaling techniques on flood extremes' projections and useful information for climate planning across the state. This study examines how different methods of spatial downscaling of gridded precipitation by climate model impact the projected changes of extreme flood events under different greenhouse gas emission scenarios. We focus on 44 locations within the state of Iowa (central United States) and use a hydrologic model to estimate the changes in flood extremes under current climate and four future emissions scenarios for different downscaling methods. Our results suggest that the choice of downscaling method impacts the magnitude of the projected changes, while the selected emission scenario plays a smaller role on the projected changes. Therefore, we encourage stakeholders to incorporate the impact of downscaling methods within water resource planning and design when considering climate change. All emission scenarios and downscaling techniques produce a projected increase in flood extremes across Iowa Discrepancies among downscaling techniques become more pronounced with higher emission scenarios Projected increases in flood extremes become greater as the annual exceedance probability decreases
英文关键词	CMIP6; flood frequency; projections; hydrologic modeling; Iowa
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Meteorology & Atmospheric Sciences
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:001179193800001
来源期刊	EARTHS FUTURE
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/305627
作者单位	Princeton University; Princeton University
推荐引用方式 GB/T 7714	Michalek, Alexander T.,Villarini, Gabriele,Kim, Taereem. Understanding the Impact of Precipitation Bias-Correction and Statistical Downscaling Methods on Projected Changes in Flood Extremes[J],2024,12(3).
APA	Michalek, Alexander T.,Villarini, Gabriele,&Kim, Taereem.(2024).Understanding the Impact of Precipitation Bias-Correction and Statistical Downscaling Methods on Projected Changes in Flood Extremes.EARTHS FUTURE,12(3).
MLA	Michalek, Alexander T.,et al."Understanding the Impact of Precipitation Bias-Correction and Statistical Downscaling Methods on Projected Changes in Flood Extremes".EARTHS FUTURE 12.3(2024).