Author(s): Thapthai Chaithong; Daisuke Komori
Linked Author(s):
Keywords: Satellite precipitation; Extreme rainfall; Landslide; Typhoon; Slope failure
Abstract: Precipitation data are fundamental to generating a landslide hazard map or for back-calculating landslide events. The spatial variation characteristic of precipitation data plays a critical role in the spatial analysis of landslides. Traditionally, observation data from rain-gauge stations and spatial interpolation techniques such as IDW or Kriging are used to generate the spatial data of precipitation. This method can analyze landslide events, but it is limited by the number of rain-gauge stations in the study area and the conditions for the spatial interpolation methods. In recent years, satellite precipitation data have been developed and collected by numerous organizations such as Global Precipitation Measurement (GPM) and the Tropical Rainfall Measuring Mission (TRMM). Satellite precipitation data can provide good regional information about the distribution of rainfall, especia lly when compared to rain-gauge data. However, there are disadvantages to satellite precipitation data such as coarse resolution and quest ionable accuracy. The purpose of this study is to propose a conceptual framework for spatial analysis of extreme rainfall-induced landslides using satellite precipitation data from a case study of tropical storm Nanmadol (2017) in the city of Asakura, Fukuoka prefecture, Japa n. The satellite precipitation data were calibrated and downsca led to obtain the final rainfall dataset. The rainfall -induced landslide model was developed based on a combination of the Green-Ampt infiltration model and the infinite slope model. According to our calculations, this conceptual framework using satellite precipitation data can identify landslide areas in the city of Asakura, Fukuoka prefecture, Japan.
Year: 2020