Direct parameterization of paired rainfall-runoff events for peak discharge estimation: an event-based framework for flood risk assessment

Abstract Accurate estimation of event-scale peak discharge is essential for flood risk assessment and infrastructure design, yet conventional hydrologic modelling approaches often require extensive calibration procedures and computationally demanding optimization. This study presents a parsimonious event-based framework for estimating hourly runoff hydrographs and peak discharges through direct parameter estimation from physically consistent rainfall-runoff event pairs. The framework integrates precipitation event identification, streamflow baseflow separation, and event pairing with a process-based hydrologic model combining the Natural Resources Conservation Service Curve Number method for precipitation loss estimation and the Natural Resources Conservation Service Unit Hydrograph method for direct runoff transformation. Model parameters, including catchment lag time, peak rate factor, and seasonal Curve NumberPrecipitation relationships, are estimated directly from observed rainfall-runoff events, eliminating the need for inverse numerical optimization against an explicit likelihood or objective function. The framework is evaluated across seven catchments with varying drainage areas within the Housatonic River Basin, using a leave-one-out cross-validation approach. Results demonstrate consistent performance across catchments, with negligible bias and similar residual error characteristics between parameter estimation and evaluation datasets, indicating robust performance for unseen events. Compared with a geometric mean estimator, the proposed framework reduces the standard deviation of the logarithmic residuals by roughly 40 to 60 percent, providing a computationally efficient and physically interpretable tool for large-scale event-based peak discharge estimation.