An Early Warning System for Regional Rain-Induced Landslide Hazard
- 1 China Institute of Geo-Environment Monitoring, Beijing, China
- 2 China Institute of Geo-Environment Monitoring, Beijing, China
- 3 China Institute of Geo-Environment Monitoring, Beijing, China
- 4 China Institute of Geo-Environment Monitoring, Beijing, China
Abstract
Landslide in alpine regions often causes heavy losses of both human lives and properties, most of the landslides are induced by heavy rainfall. In this paper, we put forward an early warning system of rain-induced landslide. From 2002, we carried on the demonstrative work of landslide monitoring and early warning in Yaan, Sichuan Province, China, and constructed the first county-scale landslide monitoring and early warning region. Yucheng District of Yaan City is lo cated in the west of the Sichuan Basin, right in the intersection of SichuanBasin and the Tibetan Plateau. The slopes are made of Mesozoic sedimentary rock, sandstone inter-bedded with mudstone. Yucheng District has the title “sky funnel” because of the high precipitation, the annual precipitation is about 1750 mm. We carried out detailed landslide survey, and obtained the location, scale, characteristics, influence and triggering factors of the landslides. Then we assessed the regional landslide susceptibility. Based on the evolution law of the landslides, we selected ten factors to study the rela tionship between the factors and landslide. Using the bi - variate statistics method, we calculated the contribut ion to land slide from each factor, classified the susceptibility into four categories. We set up the regional rainfall monitoring net work with 13 automatic CAWS600R rain gauges. Using the landslide survey data, we studied the rainfall influencing of the regional landslides. The one-day and three-day rainfall controls the occurrence of regional landslide. We also classi fied the triggering effect of rainfall into four categories. We presented a method to calculate the landslide danger degree using the susceptibility and triggering category. Utilizing the predicted rainfall data and real-time monitored rainfall data, together with the landslide susceptibility map, we developed a WebGIS-based landslide warning system, which greatly strengthened the capability for geohazard control.
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