Abstract:For basins characterized by long-sequence precipitation data but complex runoff generation mechanisms and a lack of flow process data, this study employed the rational formula method to estimate the early warning time duration for flood risk in small- and medium-sized rivers. Moreover, the Pearson Type Ⅲ frequency curve method, which is commonly used in hydrological frequency analysis, was applied to construct flood-triggering critical areal rainfall thresholds. Then, these thresholds were corrected based on nearly 10 years of flood peak discharge data from small- and medium-sized rivers in the middle reaches of Yellow River. Finally, this methodology was applied and tested in 15 flood events occurring from 2014 to 2024 within the upstream basin of the Dongwan Hydrological Station of the Yihe River, which lacked the measured hydrological data. The results demonstrated that correcting the critical thresholds based on the historical flood data and the same frequency method can significantly improve the hit rate of risk warnings to 71.8%, reducing the missing rate and false alarm rate to 20.0% and 29.4%, respectively. Its forecast accuracy is comparable to the current flood forecast standards in northern China. This method also performs well when applied in basins lacking hydrological characteristic values. Overall, the flood risk early warning method for small- and medium-sized rivers, corrected by the same frequency method, can effectively address the challenge of obtaining long-sequence hydrological data across different regions. It also fully leverages the advantage of meteorological departments that possess long-term precipitation records. This methodology can be further extended to small- and medium-sized watersheds without hydrological stations, and provide valuable technical references for meteorological flood disaster warning efforts in similar basins. Future work could involve classifying small- and medium-sized basins based on underlying surface conditions or establishing distinct correction models for basins dominated by saturation-excess runoff by categorizing soil moisture levels, so as to further enhance risk warning accuracy.