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A regional-scale landslide early warning methodology applying statistical and physically based approaches in sequence
- Park, Joon-Young, Lee, Seung-Rae, Lee, Deuk-Hwan, Kim, Yun-Tae, Lee, Ji-Sung
- Engineering geology 2019 v.260 pp. 105193
- algorithms, disasters, hazard characterization, landslides, meteorological data, models, rain, spatial data, Korean Peninsula
- In response to the sharp increase in geological disasters resulting from localized extreme rainfall events in Korea, a landslide early warning methodology is proposed. The method is based on sequential applications of the statistical and physically based hazard evaluation approaches and devised by combining the strengths of these two mutually complementary approaches. Following the decision algorithm for five phases of the warning level, the statistical evaluation was set to be applied first by using two different rainfall thresholds and one fixed geo-property (landslide susceptibility) threshold to determine a preliminary conservative warning level. To assess whether higher warning levels with higher certainties should be assigned, the physically based evaluation was set for application in the precondition of the preliminary warning stage. This was accomplished by using a rainfall threshold related to slope instability based on an advanced analysis using the physical modeling of landslide-triggering mechanisms. Consequently, a landslide early warning model based on the sequential evaluation approach was developed. The model ran through from transforming raw rainfall data to generating a series of geographic information system-based landslide early warning level maps. The spatially-discriminating capability and temporal applicability of the model on the distributed landslide events of 2009 were discussed by comparing the simulated results with landslide historical data while contemplating the adequacy of the durations and lead times of early warning levels. As a result, several advantages were identified for both spatial and temporal landslide early warning performances in the proposed methodology.