A General Methodology for Climate‐Informed Approaches to Long‐Term Flood Projection—Illustrated With the Ohio River Basin

Estimating future hydrologic floods under nonstationary climate is a key challenge for flood management. Climate‐informed approaches to long‐term flood projection are an appealing alternative to traditional modeling chains. This work formalizes climate‐informed approaches into a general methodology...

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Vydáno v:Water resources research Ročník 54; číslo 11; s. 9321 - 9341
Hlavní autoři: Schlef, Katherine E., François, Baptiste, Robertson, Andrew W., Brown, Casey
Médium: Journal Article
Jazyk:angličtina
Vydáno: Washington John Wiley & Sons, Inc 01.11.2018
Témata:
Bayesian analysis
Bayesian theory
Case studies
Climate
climate change
Climate models
climate‐informed
Dynamic height
flood
Flood control
Flood magnitude
Flood management
Floods
Geopotential
Geopotential height
Hydrology
Literature reviews
Mathematical models
Methods
Modelling
nonstationary
Ohio River
Ohio River Basin
Probability theory
Projection
regression analysis
Regression models
River basins
Rivers
Soil
Soil moisture
soil water
Statistical analysis
Statistical models
Transparency (optical)
watersheds
United States > US
Ohio River
ISSN:0043-1397, 1944-7973
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Shrnutí:Estimating future hydrologic floods under nonstationary climate is a key challenge for flood management. Climate‐informed approaches to long‐term flood projection are an appealing alternative to traditional modeling chains. This work formalizes climate‐informed approaches into a general methodology consisting of four steps: (1) selection of predictand representing extreme events, (2) identification of credible large‐scale predictors that mechanistically control the occurrence and magnitude of the predictand, (3) development of a statistical model relating the predictors to the predictand, and (4) projection of the predictand by forcing the model with predictor projections. These four steps, developed from a review of the current literature, are demonstrated for multiple gages in the northwest Ohio River Basin in the United States Midwest as a case study. Floods are defined as annual maximum series events in January through April and are linked to geopotential height and soil moisture predictors in a Bayesian linear regression model. The projections generally show a slight decrease in future flood magnitude and demonstrate the transparency of the climate‐informed approach. An initial step for more general application across the United States and remaining challenges associated with climate‐informed flood projection are discussed. Key Points A formal four step methodology is proposed for climate‐informed approaches to long‐term flood projection The methodology is applied to the Ohio River Basin as a case study, and projected change is attributed to specific predictands An initial step to general applicability across the United States and remaining challenges are discussed
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ISSN:0043-1397
1944-7973
DOI:10.1029/2018WR023209