Probabilistic modelling of rainfall induced landslide hazard assessment

Abstract

Abstract.To evaluate the frequency and distribution of landslides hazards over Japan, this study uses a probabilistic model based on multiple logistic regression analysis. Study particular concerns several important physical parameters such as hydraulic parameters, geographical parameters and the geological parameters which are considered to be influential in the occurrence of landslides. Sensitivity analysis confirmed that hydrological parameter (hydraulic gradient) is the most influential factor in the occurrence of landslides. Therefore, the hydraulic gradient is used as the main hydraulic parameter; dynamic factor which includes the effect of heavy rainfall and their return period. Using the constructed spatial data-sets, a multiple logistic regression model is applied and landslide hazard probability maps are produced showing the spatial-temporal distribution of landslide hazard probability over Japan. To represent the landslide hazard in different temporal scales, extreme precipitation in 5 years, 30 years, and 100 years return periods are used for the evaluation. The results show that the highest landslide hazard probability exists in the mountain ranges on the western side of Japan (Japan Sea side), including the Hida and Kiso, Iide and the Asahi mountainous range, the south side of Chugoku mountainous range, the south side of Kyusu mountainous and the Dewa mountainous range and the Hokuriku region. The developed landslide hazard probability maps in this study will assist authorities, policy makers and decision makers, who are responsible for infrastructural planning and development, as they can identify landslide-susceptible areas and thus decrease landslide damage through proper preparation.

Distributed specific sediment yield estimations in Japan attributed to extreme-rainfall-induced slope failures under a changing climate

“…Use of the probability model developed by Kawagoe et al (2010) for the relationship between the slope failure hazard and triggering parameters, including spatially distributed extreme rainfall, 2. Development of a regression relationship between the probability of slope failure and subsequent sediment yield and 3.…”

“…Nevertheless, it requires a large computational effort as compared to other triggering parameters. To estimate the hydraulic gradient attributed to extreme rainfall at a 1-km resolution, we followed the method previously developed by Kawagoe et al (2010). The two-dimensional form of Richard's equation was employed to obtain the hydraulic gradient, which was numerically solved by considering soil data, the slope angle and extreme rainfall as the independent input variables in each grid cell (more details can be found in Kawagoe et al, 2010).…”

“…To estimate the hydraulic gradient attributed to extreme rainfall at a 1-km resolution, we followed the method previously developed by Kawagoe et al (2010). The two-dimensional form of Richard's equation was employed to obtain the hydraulic gradient, which was numerically solved by considering soil data, the slope angle and extreme rainfall as the independent input variables in each grid cell (more details can be found in Kawagoe et al, 2010). To estimate extreme rainfall events, 24-hour maximum rainfall data covering 21 years from the Automated Meteorological Data Acquisition System (AMeDAS) were employed with the Generalized Extreme Value (GEV) probability distribution function.…”

“…To develop a statistically better relationship, the AMeDAS stations were categorized into three seasonal classes, winter (December-February), spring-summer (March-August) and autumn (September-November) based on the probability of an extreme rainfall event. As an example, the mountain areas at the seaside receive their maximum rainfall during the winter, while the south islands of Japan receive the most rainfall in the spring-summer category (Kawagoe et al, 2010). Therefore, three separate regression analyses were performed to obtain the relationship between the extreme rainfall and maximum monthly rainfall from the Mesh Climate Data 2000, and later extreme rainfall values were distributed with a 1-km grid resolution based on the maximum monthly rainfall values from the Mesh Climate Data 2000 at each grid box.…”

Groundwater Overexploitation Causing Land Subsidence: Hazard Risk Assessment Using Field Observation and Spatial Modelling

“…(Hürlimann et al 2006;Bilgot and Parriaux 2009;Kawagoe et al 2010;Wu et al 2010;Kappes et al 2011). But report about hazard risk assessment of land subsidence is rare.…”