Reconstruction of Rainfall Events and Empirical Rainfall Threshold Modeling for Landslide and Debris Flow Forecasting

Rainfall-induced landslides and debris flow as one of the most common geohazards, causing significant societal and economic impacts. To enhance the accuracy of early warning systems and reduce the risks associated with these events, it is essential to establish precise and regionally adaptive rainfall thresholds. This study addresses the challenges in defining rainfall thresholds by integrating rainfall data with landslide datasets that include large-scale landslides caused by Typhoon Morakot, which are defined as those with an area larger than 10 hectares, a volume exceeding 100,000 cubic meters, or a depth greater than 10 meters, small-to-moderate sized (defined as those with a sliding area of less than 10 hectares, a soil volume of less than 100,000 cubic meters, and a sliding depth of less than 10 meters) landslides triggered by Typhoons Sinlaku and Kongrey, and recent debris flow events (2019–2023) in the Putunpunas River area of Kaohsiung, Taiwan. By incorporating diverse landslide magnitudes and climatic conditions, this study seeks to improve the reliability and adaptability of rainfall thresholds.
For rainfall data preprocessing, the first step was to determine the climatic season (cold season: October to April; warm season: May to September) for each rainfall record. Based on the season, rainfall events were initially separated using intervals of 3 hours (warm season) or 6 hours (cold season). Hourly rainfall measurements below 0.2 mm were excluded (set to 0). Subsequently, rainfall events were reconstructed using adjusted interval criteria of 6 hours for the warm season and 12 hours for the cool season. Valid rainfall events were required to have cumulative rainfall greater than 1 mm. Only events meeting this condition were further processed. Finally, rainfall events were redefined based on adjusted intervals of 5 hours for the warm season and 10 hours for the cool season to better capture event continuity.
This study employed the bootstrap technique to estimate rainfall thresholds under various exceedance probabilities (0.005% to 50%). The threshold curve is expressed as E=(α±∆α)∙D^(γ±∆γ) , where α represents the baseline proportional constant between cumulative rainfall (E,unit:mm) and event duration(D,unit:hr), reflecting the vertical shift of the threshold under different probability conditions. ∆α represents the standard deviation of α , quantifying its uncertainty. Additionally,γ=-β+1, where β is the average slope of the best-fit line (T₅₀), and ∆γ is the standard deviation of γ . These parameters effectively describe the uncertainty range of the thresholds across different probabilities.
The results show that α and γ under different exceedance probabilities provide a reliable description of rainfall thresholds, which can be adjusted regionally based on local topographical and different scales and corresponding event types conditions, Typhoon Gaemi in 2023 can serve as a validation case. Ultimately, this study provides a robust scientific foundation for rainfall threshold estimation, supporting the implementation of early warning systems for rainfall-induced landslides and contributing to regional risk management and disaster mitigation strategies.

Key words: Landslide、Rainfall、Debris flow、Empirical rainfall thresholds