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...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.展开更多
In order to provide wind profiles for the microscale numerical simulation of wind farm with complex terrain,using the 100 m tower atmospheric turbulence observation experiment data in 2010 in Hebei Province offered by...In order to provide wind profiles for the microscale numerical simulation of wind farm with complex terrain,using the 100 m tower atmospheric turbulence observation experiment data in 2010 in Hebei Province offered by National Climate Center, the variation characteristics of wind profile under the different atmospheric stability conditions are analyzed, and the wind profile expression based on the local similarity theory is established. The results show that:(1) In spring, the occurrence probability of unstable stratification in the Hebei coastal area is as high as 28%, and the probability of stable stratification is more than 43% while, in summer, the probability of occurrence of unstable stratification is as high as 80% with a lower probability for stable stratification; and(2) for stable stratification, the characteristics of atmosphere change is dramatic in terms of the vertical direction, which need to be treated layer by layer.According to the atmospheric turbulence observation experiment data above, under stable stratification, the relationship between the dimensionless velocity gradient and the stability ζ can be expressed as 1 +βmζ, with βm changing with the height: βm takes 4.1-4.3 under 30 m, βm takes 4.6-4.7 between 30-50 m, and βm takes 6.3-6.7 over 50 m.展开更多
基金supported by The National Basic Research Program of China(973)(Grant No.2013CB430106)National Natural Science Foundation of China(Grant No.41375108)Scientific Research&Innovation Projects for Academic Degree students of ordinary Universities of Jiangsu(Grant No.CXLX13_474)
文摘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.
基金Public Welfare Industry(Meteorological Sector) Special Funds for Scientific Research Projects(GYHY20120626)
文摘In order to provide wind profiles for the microscale numerical simulation of wind farm with complex terrain,using the 100 m tower atmospheric turbulence observation experiment data in 2010 in Hebei Province offered by National Climate Center, the variation characteristics of wind profile under the different atmospheric stability conditions are analyzed, and the wind profile expression based on the local similarity theory is established. The results show that:(1) In spring, the occurrence probability of unstable stratification in the Hebei coastal area is as high as 28%, and the probability of stable stratification is more than 43% while, in summer, the probability of occurrence of unstable stratification is as high as 80% with a lower probability for stable stratification; and(2) for stable stratification, the characteristics of atmosphere change is dramatic in terms of the vertical direction, which need to be treated layer by layer.According to the atmospheric turbulence observation experiment data above, under stable stratification, the relationship between the dimensionless velocity gradient and the stability ζ can be expressed as 1 +βmζ, with βm changing with the height: βm takes 4.1-4.3 under 30 m, βm takes 4.6-4.7 between 30-50 m, and βm takes 6.3-6.7 over 50 m.
