Abstract：Rainfall plays a crucial role in triggering these disasters. Traditional studies utilizing statistical analysis and machine learning to forecast these disasters primarily rely on historical data, which results in models with limited generalizability. To enhance the simulation of rainfall-induced disaster processes, this study focuses on Muping Town and develops an integrated hydrological model based on the Macro Dynamic Propagation Model (MDPM) to simulate rainfall-runoff evolution in the region. By incorporating raster data, the study area is transformed into a network structure, abstracting the rainfall propagation process as a network fluid flow problem. Additionally, based on the mechanisms underlying rainfall-induced disasters, the model is divided into five computational modules: surface runoff propagation, groundwater propagation, vegetation interception, infiltration, and runoff erosion. The rainfall simulation results are represented using water argument and groundwater argument data. The study designed four rainfall scenarios for simulation: single-peak rainfall (peak intensity of 100 mm/h), single-peak rainfall (peak intensity of 200 mm/h), constant rainfall (50 mm/h), and constant rainfall (100 mm/h). The model's simulation results are visualized and compared with historical disaster data from the study area. The findings indicate that the model effectively captures the formation processes of natural phenomena, such as groundwater accumulation and surface runoff generation. It vividly illustrates the temporal and spatial evolution of groundwater and surface runoff during rainfall, successfully identifying high-risk zones susceptible to landslides and debris flow disasters within the study area. This has practical significance for the prevention and control of geological disasters.
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2026, Vol. 45 
