Quantitative Precipitation Forecast with a Radar Data Assimilation Method for Rainfall-induced Shallow Landslide Predictions

Abstract

Abstract Taiwan is prone to hillslope disasters in mountain areas because of its special topographical, geological, and hydrological conditions. During typhoons and rainstorms, severe shallow landslides frequently occur. To mitigate the impact of shallow landslides, not only the structural measures are necessary, but also adequate warning systems and contingency measures must be executed. Hence, precise precipitation forecasts and landslide prediction are the most important measures in practice. To account for inherent weather uncertainties precipitation forecasts based on quantitative precipitation forecast model with radar data assimilation method and ensemble mean prediction were adopted in this study instead of using a single model output. The SIMTOP model based on infinite-slope model and TOPMODEL was developed. In considering detail topographic characteristics of the subcatchments, the proposed model can estimate the variation of saturated water level during rainstorms, and then link with the slope instability analysis to clarify whether shallow landslides would occur in the subcatchment. Two area vulnerable to landslide in Taiwan were collected to test the applicability of the model for landslide prediction. Six typhoons events were used to verify the applicability of the proposed model. Four indexes including the probability of detection (POD), false alarm ratio (FAR), threat score (TS), and accuracy (ACC) were adopted to assess the performance of two kinds of very short-term rainfall forecasts with SIMTOP model. The results indicate that quantitative precipitation forecast model with radar data assimilation with SIMTOP model was better than ensemble rainfall forecasts with SIMTOP model. It is promising to apply the proposed model for landslide early warnings to reduce the magnitude of the loss of lives and properties.