Development of Continuous Rainfall-Based Citywide Landslide Early Warning Model

박, 준영; 이, 승래; 이, 득환; 김, 윤태; 오, 세붕; 박, 혁진

doi:10.9798/kosham.2018.18.1.99

Cited by 7 publications

(5 citation statements)

References 16 publications

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“…However, this cannot be easily achieved due to the coexistence of different rainfall patterns from various heavy rain types (i.e., typhoons, monsoon fronts, or localized convective rainstorms) in Korea (Kim et al 2015), as noted by Rosi et al (2012). Therefore, by following the methods of several previous domestic and international studies (Ministry of Land, Infrastructure and Transport 2004; National Institute for Disaster Prevention 2005; Saito et al 2010;Park et al 2018), the start of the landslide-triggering rainfall event was delimited by at least 24 h of no rainfall (Fig. 4).…”

Section: Definition Of a Landslide-triggering Rainfall Eventmentioning

confidence: 99%

Effect of antecedent rainfall conditions and their variations on shallow landslide-triggering rainfall thresholds in South Korea

et al. 2020

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The intensity-duration (I-D) threshold is considered an effective indicator for landslides triggered by short-term highintensity rainfall and long-term low-intensity rainfall. However, previous studies have not considered the influence of antecedent rainfall. Herein, we analyzed hourly rainfall data for 613 shallow landslides that occurred from 1963 to 2018 in South Korea to obtain rainfall thresholds and investigated the effect of antecedent rainfall conditions on threshold variations. The I-D and I MAP -D, which is normalized by dividing I by mean annual precipitation (MAP), thresholds were determined to be I = 10.40D -0.31 and I MAP = 0.006D -0.26 (4 ≤ D (h) ≤ 84), respectively, at the 2nd percentile level through quantile regression analysis. These thresholds were lower than other local thresholds (i.e., excluding global and regional thresholds), suggesting that the southern region of the Korean Peninsula was more susceptible to rainfall-induced landslides. Although the effective length of antecedent rainfall was not presented herein, the I-D threshold of landslide-triggering rainfall was confirmed to be low for the absolute and/or calibrated antecedent rainfalls greater than event cumulative rainfall at 5, 7, 10, and 20 days prior to the event. Such differences in I can be greater at shorter durations; however, these differences gradually decrease as D increases, suggesting that they lose their effect as soil water content increases. The results of the current study can improve the understanding of the effect of antecedent rainfall conditions on landslide occurrence and should be further tested with respect to the hydrologic response of hillslopes by considering regional climate and local site conditions.

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Section: Definition Of a Landslide-triggering Rainfall Eventmentioning

confidence: 99%

Effect of antecedent rainfall conditions and their variations on shallow landslide-triggering rainfall thresholds in South Korea

et al. 2020

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“…A rainfall event is divided by a nonrainfall period in an objective way to separate the case whether the rainfall event affects the landslide. In general, studies distinguish rainfall events based on a 24-h non-rainfall period (Guzzetti et al 2008;Saito et al 2010;Chen et al 2015;Kim et al 2013;Oh and Park 2014;Hong et al 2016;Kang and Kim 2016;Lee and Kim 2017;Park et al 2018). Brunetti et al (2010) separated rainfall events based on a non-rainfall period to account for different meteorological regimes, 48-h periods without rainfall during the dry and warm season (May-September), and 96-h periods without rainfall during the wet and cold season (October-April).…”

Section: Introductionmentioning

confidence: 99%

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

2021

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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.

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“…However, this is difficult to achieve because multiple rainfall patterns coexist [41] and landslides are triggered by various types of heavy rain in South Korea [29]. Therefore, we determined the start of the landslide-triggering rainfall event for each landslide by delimiting it by at least 24 h of no rainfall (Figure 3), as has been conducted in several studies [15,[44][45][46]. Using this method, we defined an independent landslide-triggering rainfall event as a series of events from the beginning of the rainfall to the landslide occurrence.…”

Section: Definition Of Landslide-triggering Rainfallmentioning

confidence: 99%

The Effects of Different Geological Conditions on Landslide-Triggering Rainfall Conditions in South Korea

Lee

Cho

Kim

et al. 2022

Water

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How landslide-triggering rainfall conditions vary with geology is unclear. The effects of three different geological conditions (gneiss, GN; granite, GR; sedimentary rock, SR) on variations in intensity–duration (I-D) conditions and rainfall characteristics responsible for initiating shallow landslides were examined using data from 476 landslides in South Korea from 1963 to 2018 and detailed statistical analyses. Results from quantile regression and one-way analysis of variance analyses clearly showed that impermeable SR slopes result in smaller critical rainfall than permeable GN and GR slopes do, indicating a relatively high occurrence exceedance probability and susceptibility to landslides in SR slope. These findings suggest that geological conditions, particularly the relatively high susceptibility of SR slopes, should be considered when establishing rainfall information-based landslide warning criteria for South Korea. Our findings can contribute to the assessment of landslide susceptibility and probability based on geological conditions; however, they should be further investigated through in situ observations.

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Development of Continuous Rainfall-Based Citywide Landslide Early Warning Model

Cited by 7 publications

References 16 publications

Effect of antecedent rainfall conditions and their variations on shallow landslide-triggering rainfall thresholds in South Korea

Effect of antecedent rainfall conditions and their variations on shallow landslide-triggering rainfall thresholds in South Korea

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

The Effects of Different Geological Conditions on Landslide-Triggering Rainfall Conditions in South Korea

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