Measurement-Based Framework for Real-Time Flood Prediction in Small Streams Using Rainfall–Discharge Nomographs and Depth–Discharge Rating Curves

Small streams exhibit rapid and nonlinear flood responses due to steep slopes, short flow paths, and limited storage capacity, making real-time flood prediction challenging under both computational and data constraints. This study proposes an integrated, measurement-based flood prediction framework that enables real-time estimation of flood dis-charge and flood depth in small-stream basins. Unlike conventional approaches that rely on either computationally intensive hydrodynamic models or standalone data-driven methods, the proposed framework combines high-frequency monitoring data, rainfall nowcasting, and nonlinear regression-based hydraulic relationships into a unified system. Rainfall–discharge nomographs and depth–discharge rating curves were developed using a nonlinear four-parameter logistic (4PL) regression model based on long-term observations from twelve representative basins in Korea. Forecast rainfall from the Korea Meteorological Administration MAPLE nowcasting system was used to estimate discharge, which was subsequently transformed into flood depth through calibrated rating curves. To extend prediction capability beyond monitoring locations, additional depth–discharge relationships were derived for ungauged reaches using hybrid approaches based on HEC-RAS scenario simulations and the Manning equation. Validation against major flood events showed strong agreement between observed and predicted values, with mean prediction accuracies of approximately 89% for discharge and 90% for flood depth. The proposed framework effectively captures nonlinear rainfall–runoff behavior while significantly reducing computational complexity compared with conventional hydrodynamic models. These results demonstrate that the framework provides a practical and scalable solution for real-time flood prediction and early warning in small-stream environments, particularly by enabling spatially continuous flood-depth estimation across both gauged and ungauged reaches.