Bias in Peak Flood Discharges: Are Our Bridges and Culverts Undersized?

ABSTRACT Reliable methods for peak discharge predictions at ungaged locations are required for infrastructure design and floodplain management. For decades, a standard practice in the United States has been to utilize US Geological Survey regional regression equations (StreamStats) as a singular method. However, implementation of multiple methods, such as streamgage‐based transfers, rainfall‐runoff modeling, and downscaled climate modeling, provides a more rigorous approach. The Flood Potential Portal ( https://floodpotential.erams.com/ ) Watershed Analysis module provides three streamgage‐based methods for predictions, facilitating comparative evaluations of prediction bias across the United States. Our findings indicate that regional regression equations, overall, significantly underpredict 100‐year (1% chance of exceedance; Q 100 ) peak discharges by −26.6%. In contrast, index flood frequency ( Q 100 ) and expected flood potential discharge ( Q efp ) show minimal bias of +4.4% and 0%, respectively. Within the 18 analyzed watersheds, regional regression equations underpredict in 15 (−55.1% to −1.8%, average: −27.8%), predict best in two, and overpredict in one. The other methods yield far less relative bias. Underprediction of −55.1% results in road‐stream crossings having less than half the needed capacity, and an average bias of −27.8% indicates that crossings may be commonly undersized by more than a quarter—use of only regional regression equations for peak discharge predictions for bridges and culverts may result in infrastructure having insufficient flood resilience. A case study for a watershed impacted by the remnants of Hurricane Helene reveals underprediction of up to −53.2%; peak discharge bias could result in undersized structures. Resilient recovery requires the use of multiple predictive methods.