Time-Variant Instantaneous Unit Hydrograph Based on Machine Learning Pretraining and Rainfall Spatiotemporal Patterns

The hydrological response of a watershed is strongly influenced by the spatiotemporal dynamics of rainfall. Rainfall events of similar magnitude can produce markedly different flood processes due to variations in the spatiotemporal patterns of rainfall, posing significant challenges for flood forecasting under complex rainfall scenarios. Traditional methods typically rely on high-resolution or synthetic rainfall data to characterize the scale, direction and velocity of rainstorms, in order to analyze their impact on the flood process. These studies have shown that storms traveling along the main river channel tend to exert the greatest impact on flood processes. Therefore, tracking the movement of the rainfall center along the flow direction, especially when only rain gauge data are available, can reduce model complexity while maintaining forecast accuracy and improving model applicability. This study proposes a machine learning-based time-variable instantaneous unit hydrograph that integrates rainfall spatiotemporal dynamics using quantitative spatial indicators. To overcome limitations of traditional variable unit hydrograph methods, a pre-training and fine-tuning strategy is employed to link the unit hydrograph S-curve with rainfall spatial distribution. First, synthetic pre-training data were used to enable the machine learning model to learn the shape of the S-curve and its general pattern of variation with rainfall spatial distribution. Then, real flood data were employed to learn the actual runoff routing characteristics of the study area. The improved model allows the unit hydrograph to adapt dynamically to rainfall evolution during the flood event, effectively capturing hydrological responses under varying spatiotemporal patterns. The case study shows that the improved model exhibits superior performance across all runoff routing metrics under spatiotemporal rainfall variability. The improved model increased the simulation qualified rate for historical flood events, with significant rainfall center movement during the event from 63% to 90%. This study deepens the understanding of how rainfall dynamics influence watershed response and enhances hourly-scale flood forecasting, providing support for disaster early warning with strong theoretical and practical significance.