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 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.