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

Abstract

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.

Landslides Triggered by the May 2017 Extreme Rainfall Event in the East Coast Northeast of Brazil

“…The ED and ID thresholds showed that short and long-duration rain events favor the occurrence of landslides in Jaboatão dos Guararapes (Figure 7), with the greatest constancy observed in shorter periods (D ≤ 10 days) for rain events above the threshold. This observation, indicating the best association of short periods with the occurrence of landslides, had similar results for different areas subject to landslides in other regions, showing that an accumulated rainfall of 3 days is sufficient to cause instability [10,59].…”

Confidence-Guided Learning Process for Continuous Classification of Time Series

“…It is the multivariate time series of 5 weather features. Rainfall warning is not only the demand of daily life, but also can help prevent natural disasters [38].…”

Rainfall-induced landslide early warning system based on corrected mesoscale numerical models: an application for the southern Andes

“…Due to new extreme rainfall events related to climate change, RIL events are increasing in the southern Andes and other parts of the world. To mitigate the impact of extremeprecipitation RILEWSs have gained interest to mitigate the impact of RILs using different approaches (Peres and Cancelliere, 2014;Tiranti et al, 2014;Sättele et al, 2015;Segoni et al, 2018;Cremonini and Tiranti, 2018;Fan et al, 2019;Tiranti et al, 2019;Thirugnanam et al, 2020;Bernard and Gregoretti, 2021;Lee et al, 2021). RILEWSs based on precipitation thresholds show good agreement but do not consider the effect of soil moisture, leading to bias in their predictive capacity (Marra et al, 2017;Zhao et al, 2019;Chikalamo et al, 2020).…”