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

Abstract

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.

Atmospheric Circulation Patterns Associated with Extreme United States Floods Identified via Machine Learning

“…Proximate flood causes are driven by ultimate causes at various spatiotemporal scales, such as (extra)tropical cyclones, sea surface temperature anomalies, and preferred ridge and trough positions 6 . The importance of ultimate causes in generating flood events has been substantiated by global-scale studies of the correlation between floods and well-known climate patterns such as the El Niño-Southern Oscillation 7 , 8 , continental-scale studies which identify atmospheric circulation patterns associated with floods or further investigate the correlation between climate indices and floods 9 , 10 , and the myriad of regional- to local-scale studies that establish teleconnections between floods and oceanic-atmospheric patterns 11 – 13 .…”

Revealing Hidden Climate Indices from the Occurrence of Hydrologic Extremes

“…A hidden climate index (HCI) affecting the target hydrologic predictand data should leave a trace in it, and should therefore be identifiable directly from the hydrologic predictand. For instance, Grantz et al (), Zeng et al (), or Schlef et al () defined “customized” predictors (that can be viewed as HCIs) by means of some correlation analysis between the hydrologic predictand and large‐scale climate fields (e.g., sea surface temperatures or atmospheric pressures).…”

Changes in the exposure of California’s levee-protected critical infrastructure to flooding hazard in a warming climate

“…Using general circulation models (GCMs) to investigate possible changes in the flood hazard under the projected climate change scenarios is known as the top-down approach or predict-then-act method (e.g. Schlef et al 2018. GCMs can be used to investigate possible changes in the hydrological cycle under the projected climate change scenarios.…”