气候变化领域集成服务门户(CCPortal): Downscaling of real-time coastal flooding predictions for decision support

CCPortal

DOI	10.1007/s11069-021-04634-8
	Downscaling of real-time coastal flooding predictions for decision support
	Rucker C.A.; Tull N.; Dietrich J.C.; Langan T.E.; Mitasova H.; Blanton B.O.; Fleming J.G.; Luettich R.A.; Jr.
发表日期	2021
ISSN	0921030X
起始页码	1341
结束页码	1369
卷号	107 期号:2
英文摘要	During coastal storms, forecasters and researchers use numerical models to predict the magnitude and extent of coastal flooding. These models must represent the large regions that may be affected by a storm, and thus, they can be computationally costly and may not use the highest geospatial resolution. However, predicted flood extents can be downscaled (by increasing resolution) as a post-processing step. Existing downscaling methods use either a static extrapolation of the flooding as a flat surface, or rely on subsequent simulations with nested, full-physics models at higher resolution. This research explores a middle way, in which the downscaling includes simplified physics to improve accuracy. Using results from a state-of-the-art model, we downscale its flood predictions with three methods: (1) static, in which the water surface elevations are extrapolated horizontally until they intersect the ground surface; (2) slopes, in which the gradient of the water surface is used; and (3) head loss, which accounts for energy losses due to land cover characteristics. The downscaling methods are then evaluated for forecasts and hindcasts of Hurricane Florence (2018), which caused widespread flooding in North Carolina. The static and slopes methods tend to over-estimate the flood extents. However, the head loss method generates a downscaled flooding extent that is a close match to the predictions from a higher-resolution, full-physics model. These results are encouraging for the use of these downscaling methods to support decision-making during coastal storms. © 2021, The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature.
关键词	ADCIRC Carteret County GRASS GIS North Carolina Storm Surge
英文关键词	coastal zone management; computer simulation; decision support system; downscaling; flooding; GIS; numerical model; prediction; real time; storm surge; North Carolina; United States; Rosa carolina carolina
语种	英语
来源期刊	Natural Hazards
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/206527
作者单位	Dep’t of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, NC, United States; U.S. Army Corps of Engineers, Wilmington District, Wilmington, NC, United States; University of Texas at Austin, Austin, TX, United States; North Carolina Floodplain Mapping Program, NC Emergency Management, Raleigh, NC, United States; Center for Geospatial Analytics, North Carolina State University, Raleigh, NC, United States; Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States; Seahorse Coastal Consulting, Morehead City, NC, United States; Institute of Marine Science, University of North Carolina at Chapel Hill, Morehead City, NC, United States
推荐引用方式 GB/T 7714	Rucker C.A.,Tull N.,Dietrich J.C.,et al. Downscaling of real-time coastal flooding predictions for decision support[J],2021,107(2).
APA	Rucker C.A..,Tull N..,Dietrich J.C..,Langan T.E..,Mitasova H..,...&Jr..(2021).Downscaling of real-time coastal flooding predictions for decision support.Natural Hazards,107(2).
MLA	Rucker C.A.,et al."Downscaling of real-time coastal flooding predictions for decision support".Natural Hazards 107.2(2021).