Preliminary studies on the dynamic prediction method of rainfall-triggered landslide

• Published in: Journal of mountain science 2016-10, Vol.13 (10), p.1735-1745
• Main Authors: Chen, Yue-li, Chen, De-hui, Li, Ze-chun, Huang, Jun-bao
• Format: Article
• Language: English
• Subjects: Earth and Environmental Science; Earth Sciences; Ecology; Environment; Fruits; Geography; Landslides; Landslides & mudslides; Meteorology; Predictions; Rain; Rainfall infiltration; Typhoons; Vitaceae
• ISSN: 1672-6316; 1993-0321; 1008-2786
• DOI: 10.1007/s11629-014-3110-5

Rainfall-triggered landslides have posed significant threats to human lives and property each year in China. This paper proposed a meteorologicalgeotechnical early warning system GRAPES-LFM（GRAPES： Global and Regional Assimilation and Pr Ediction System; LFM： Landslide Forecast Model）,basing on the GRAPES model and the landslide predicting model TRIGRS（Transient Rainfall Infiltration and Grid-based Regional Slope-Stability Model） for predicting rainfall-triggered landslides.This integrated system is evaluated in Dehua County,Fujian Province, where typhoon Bilis triggered widespread landslides in July 2006. The GRAPES model runs in 5 km×5 km horizontal resolution, and the initial fields and lateral boundaries are provided by NCEP（National Centers for Environmental Prediction） FNL（Final） Operational Global Analysis data. Quantitative precipitation forecasting products of the GRAPES model are downscaled to 25 m×25 m horizontal resolution by bilinear interpolation to drive the TRIGRS model. Results show that the observed areas locate in the high risk areas, and the GRAPES-LFM model could capture about 74% of the historical landslides with the rainfall intense 30mm/h. Meanwhile, this paper illustrates the relationship between the factor of safety（FS） and different rainfall patterns. GRAPES-LFM model enables us to further develop a regional, early warning dynamic prediction tool of rainfall-induced landslides.