ABSTRACT:

Reliable flood change projections under climate change are critical for water resources planning yet remain challenging due to uncertainty in climate-informed statistical models. Conventional approaches relate flood distribution parameters to climate variables using log-linear formulations, which can produce unrealistically large flood estimates. In this study, we develop a semi-parametric statistical framework that constrains flood variability using a bounded logistic link function and estimates parameters from hydroclimatically analogous samples. Using 548 natural basins across the coterminous United States, we evaluate the proposed approach against log-linear formulations from the entire sample. Model performances are assessed based on both the accuracy and sensitivity of flood quantiles to climate forcings. Results show that the proposed framework yields more stable and realistic flood quantile estimates, particularly under high precipitation conditions, and better preserves flood sensitivity. These findings highlight the value of bounded, locally informed statistical models for robust flood risk assessment under climate change.