The optimal rainfall thresholds and probabilistic rainfall conditions for a landslide early warning system for Chuncheon, Republic of Korea

The purpose of this study is to establish the criteria for a landslide early warning system (LEWS). We accomplished this by deriving optimal thresholds for the cumulative event rainfall–duration (ED) and identifying the characteristics of the rainfall variables associated with a high probability of landslide occurrence via a Bayesian model. We have established these system criteria using rainfall and landslide data for Chuncheon, Republic of Korea. Heavy rainfall is the leading cause of landslides in Chuncheon; thus, it is crucial to determine the rainfall conditions that trigger landslides. Hourly rainfall data spanning 1999 to 2017 from seven gauging stations were utilized to establish the ED thresholds and the Bayesian model. We used three different calibration periods of rainfall events split by 12, 24, 48, and 96 non-rainfall hours to calibrate the ED thresholds. Finally, the optimal threshold was determined by comparing the results of the contingency table and the skill scores that maximize the probability of detection (POD) score and minimize the probability of false detection (POFD) score. In the LEWS, by considering the first level as “normal,” we developed subsequent step-by-step warning levels based on the Bayesian model as well as the ED thresholds. We propose the second level, “watch,” when the rainfall condition is above the ED thresholds. We then adopt the third level, “warning,” and the fourth level, “severe warning,” based on the probability of landslide occurrence determined via a Bayesian model that considers several factors including the rainfall conditions of landslide vs. non-landslide and various rainfall variables such as hourly maximum rainfall and 3-day antecedent rainfall conditions. The proposed alert level predicted a total of 98.2% of the landslide occurrences at the levels of “severe warning” and “warning” as a result of the model fitness verification. The false alarm rate is 0% for the severe warning level and 47.4% for the warning level. We propose using the optimal ED thresholds to forecast when landslides are likely to occur in the local region. Additionally, we propose the ranges of rainfall variables that represent a high landslide probability based on the Bayesian model to set the landslide warning standard that fits the local area’s characteristics. © 2021, The Author(s)

The morphological changes after hillslope erosion and the stability of the Dokdo

본 연구에서는 수심데이터를 이용하여 동해해저에 있는 독도해산의 형태적 특성을 분석하고 지형변화의 요인을 밝히는데 목적이 있다. 독도해산의 지형변화는 독도주변해저와 해산에 남아있는 암설류 잔재와 현재 해산사면의 침식지형을 통해 알 수 있다. 독도해산의 정상부에는 수심 30 ~ 40m 까지 과거 독도에서 붕괴된 암설류로 추정되는 불규칙한 돌출지형들이 나타나고, 해산의 기저부에도 다양한 규모의 암설류가 존재한다. 이는 독도가 매스무브먼트에 의해 붕괴가 일어나고 있음을 뜻한다. 해산의 사면에는 대규모의 매스무브먼트와 함께 소규모의 해저수로가 발달되어 있어 독도의 사면후퇴를 가속시키고 있다. 특히 해산의 남, 북사면은 대규모 매스무브먼트 지형과 소규모 해저수로가 발달되어 있고 서-북서사면은 불규칙한 돌출지형과 사이에 길고 폭이 좁은 해저수로가 발달되어 있다.2