Three-dimensional hydrological thresholds to predict shallow landslides

Abstract

Abstract Past studies have focused on the importance of hydrological variables in analyzing landslide initiation condition. Even though precipitation is the main driver of shallow landslides and debris flows, use of only rainfall-based parameters has shown some limitations. Soil moisture has been used widely to improve threshold detection capabilities. Since soil moisture directly reflects the wetness status of the ground, it can be used to identify pore pressure fluctuations more effectively. This study used rainfall and soil moisture simultaneously to capture landslide initiation conditions in detail. Results showed that continued rainfall on the day of landslide leaded to a sudden increase in soil moisture, and that soil moisture increments (∆SM) were positive in 155 out of 170 landslide cases (91%). Two simple thresholds (daily precipitation over 40 mm, ∆SM over 0) and daily precipitation (P), Antecedent Precipitation Index (API), ∆SM-based three-dimensional threshold planes having 5%, 20% probability levels were applied and compared. With respect to false alarms (FA), P-based threshold was most effective among the single thresholds (FA ranging from 24 to 28 from September 2016 to December 2019 at five validation locations). Combining P- and ∆SM-based thresholds, FA reduced without compromising the detection accuracy (2 to 3 reduction in FA). Additionally combining three-dimensional threshold with 20% probability level, FA reduced significantly (ranging from 12 to 16), at the cost of two detection failures. These findings demonstrate the need for combining precipitation and soil moisture to determine landslide thresholds.