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

最小化 最大化

Vol13 No.10: 1735-1745

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

Author】CHEN Yue-li1,2,3*; CHEN De-hui3; LI Ze-chun3; HUANG Jun-bao4

Addresses】1 College of Atmospheric Science, Nanjing University of Information Science &Technology, Nanjing 210044, China; 2 State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China; 3 Numerical Weather Prediction Center, China Meteorological Administration, Beijing 100081, China; 4 Fujian Monitoring Center of Geological Environment, Fuzhou 350002, China

Corresponding author】chenyl@camscma.cn

Citation】Chen YL, Chen DH, Li ZC, et al. (2016) Preliminary studies on the dynamic prediction method of rainfall-triggered landslide. Journal of Mountain Science 13(10). DOI: 10.1007/s11629-014-3110-5

DOI】10.1007/s11629-014-3110-5

Abstract】Rainfall-triggered landslides have posed significant threats to human lives and property each year in China. This paper proposed a meteorological-geotechnical early warning system GRAPES-LFM (GRAPES: Global and Regional Assimilation and PrEdiction 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.

Keywords】Landslide; Precipitation; Early warning system; Landslide predicting model