Early warning system for rainfall- and snowmelt-induced slope failure in seasonally cold regions

In 2005, the Japanese government launched a new nationwide early warning system for predicting debris flow and slope failure disasters based on rainfall intensity and the Soil Water Index (SWI). However, the Japanese government has not set early warning criteria in many mountain areas. In addition, the existing early warning criteria in some areas are much higher than realistic ones, and snowmelt water is not considered in the calculation of the SWI. These two factors have been the cause of many slope failures in seasonally cold regions, induced by rainfall and/or snowmelt, which were not predicted. Therefore, this study attempts to propose a new determination method for setting early warning criteria for rainfall- and/or snowmelt-induced slope failures in seasonally cold regions. For this purpose, the study firstly proposes a combination model for estimating snow density that incorporates the hourly snowmelt water into the Japanese early warning system more accurately by using meteorological monitoring data and modeled snow density. Next, based on case studies and parametric analyses for slope stability assessment, new early warning criteria are proposed for predicting three different patterns of slope failures under two typical types of precipitation (rainfall and snowmelt) conditions. Finally, a new determination method for setting early warning criteria in seasonally cold regions is proposed by referring to the existing early warning criteria near the target area, in accordance with the precipitation types and the local ground conditions of the slopes. Since the existing early warning criteria near the target area already take the effects of the variations in local geology and geography into account, the new determination method for early warning criteria can be applied to arbitrary areas in seasonally cold regions, without directly considering the local soil properties, in the actual design and maintenance works.