Establishing flood thresholds for sea level rise impact communication.

Autor: Mahmoudi S; Center for Complex Hydrosystems Research, The University of Alabama, Tuscaloosa, AL, USA. smahmoudikouhi@crimson.ua.edu.; Department of Civil, Construction, and Environmental Engineering, The University of Alabama, Tuscaloosa, AL, USA. smahmoudikouhi@crimson.ua.edu., Moftakhari H; Center for Complex Hydrosystems Research, The University of Alabama, Tuscaloosa, AL, USA. hmoftakhari@ua.edu.; Department of Civil, Construction, and Environmental Engineering, The University of Alabama, Tuscaloosa, AL, USA. hmoftakhari@ua.edu., Muñoz DF; Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, VA, USA., Sweet W; NOAA/National Ocean Service, Silver Spring, MD, USA., Moradkhani H; Center for Complex Hydrosystems Research, The University of Alabama, Tuscaloosa, AL, USA.; Department of Civil, Construction, and Environmental Engineering, The University of Alabama, Tuscaloosa, AL, USA.
Jazyk: angličtina
Zdroj: Nature communications [Nat Commun] 2024 May 18; Vol. 15 (1), pp. 4251. Date of Electronic Publication: 2024 May 18.
DOI: 10.1038/s41467-024-48545-1
Abstrakt: Sea level rise (SLR) affects coastal flood regimes and poses serious challenges to flood risk management, particularly on ungauged coasts. To address the challenge of monitoring SLR at local scales, we propose a high tide flood (HTF) thresholding system that leverages machine learning (ML) techniques to estimate SLR and HTF thresholds at a relatively fine spatial resolution (10 km) along the United States' coastlines. The proposed system, complementing conventional linear- and point-based estimations of HTF thresholds and SLR rates, can estimate these values at ungauged stretches of the coast. Trained and validated against National Oceanic and Atmospheric Administration (NOAA) gauge data, our system demonstrates promising skills with an average Kling-Gupta Efficiency (KGE) of 0.77. The results can raise community awareness about SLR impacts by documenting the chronic signal of HTF and providing useful information for adaptation planning. The findings encourage further application of ML in achieving spatially distributed thresholds.
(© 2024. The Author(s).)
Databáze: MEDLINE
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