Small streams exhibit rapid and nonlinear flood responses due to steep slopes, short flow paths, and limited storage capacity, making real-time flood prediction difficult under both computational and data constraints. This study presents a measurement-based flood prediction framework for real-time estimation of flood discharge and depth in small-stream basins. Conventional approaches, such as physically based hydrodynamic models, require detailed boundary conditions and high computational cost, while data-driven models often lack physical interpretability. The proposed framework integrates high-frequency monitoring data from the Small-Stream Smart Monitoring System, short-term rainfall nowcasting from the MAPLE system, and nonlinear regression-based hydraulic relationships within a unified operational structure. Rainfall–discharge nomographs and depth–discharge rating curves were developed using a four-parameter logistic regression model based on long-term observations from 12 small streams in Korea. Additional comparisons with alternative regression forms confirmed the suitability of the 4PL model for representing nonlinear hydrological responses. Forecast rainfall was used to estimate discharge, which was subsequently converted to flood depth through calibrated rating curves. For ungauged reaches, depth–discharge relationships were derived using HEC–RAS-based scenario simulations and the Manning equation to enable spatially continuous prediction along stream networks. Model performance was evaluated using independent validation events, showing mean prediction accuracies of approximately 89% for discharge and 90% for flood depth. The framework reduces computational demand by relying on pre-established relationships while maintaining physically interpretable structures. The results indicate that the proposed approach can support real-time flood prediction in small streams under conditions like those examined in this study, although its applicability to other regions requires site-specific calibration and further validation.
Cheong et al. (Tue,) studied this question.