Precipitation data and their uncertainty as input for rainfall-induced shallow landslide models
Yueli CHEN1, Linna ZHAO1, Ying WANG2(), Qingu JIANG3, Dan QI3
1. State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China 2. China Institute of Water Resources and Hydropower Research, Beijing 100038, China 3. National Meteorological Center, Beijing 100081, China
Physical models used to forecast the temporal occurrence of rainfall-induced shallow landslides are based on deterministic laws. Owing to the existing measuring technology and our knowledge of the physical laws controlling landslide initiation, model uncertainties are due to an inability to accurately quantify the model input parameters and rainfall forcing data. An uncertainty analysis of slope instability prediction provides a rationale for refining the geotechnical models. The Transient Rainfall Infiltration and Grid-based Regional Slope Stability-Probabilistic (TRIGRS-P) model adopts a probabilistic approach to compute the changes in the Factor of Safety (FS) due to rainfall infiltration. Slope Infiltration Distributed Equilibrium (SLIDE) is a simplified physical model for landslide prediction. The new code (SLIDE-P) is also modified by adopting the same probabilistic approach to allow values of the SLIDE model input parameters to be sampled randomly. This study examines the relative importance of rainfall variability and the uncertainty in the other variables that determine slope stability. The precipitation data from weather stations, China Meteorological Administration Land Assimilation System 2.0 (CLDAS2.0), China Meteorological Forcing Data set precipitation (CMFD), and China geological hazard bulletin are used to drive TRIGRS, SLIDE, TRIGRS-P and SLIDE-P models. The TRIGRS-P and SLIDE-P models are used to generate the input samples and to calculate the values of FS. The outputs of several model runs with varied input parameters and rainfall forcings are analyzed statistically. A comparison suggests that there are significant differences in the simulations of the TRIGRS-P and SLIDE-P models. Although different precipitation data sets are used, the simulation results of TRIGRS-P are more concentrated. This study can inform the potential use of numerical models to forecast the spatial and temporal occurrence of regional rainfall-induced shallow landslides.
. [J]. Frontiers of Earth Science, 2019, 13(4): 695-704.
Yueli CHEN, Linna ZHAO, Ying WANG, Qingu JIANG, Dan QI. Precipitation data and their uncertainty as input for rainfall-induced shallow landslide models. Front. Earth Sci., 2019, 13(4): 695-704.
CLDAS 2.0, CMFD, Xinyi station, Yangchun station, Report data
Fixed input parameters
Exp2-1
SLIDE-P
Xinyi station
Uniform distribution
Exp2-2
SLIDE-P
Xinyi station
Normal distribution
Exp2-3
SLIDE-P
CMFD
Uniform distribution
Exp2-4
SLIDE-P
CMFD
Normal distribution
Exp3-1
TRIGRS-P
Xinyi station
Uniform distribution
Exp3-2
TRIGRS-P
Xinyi station
Normal distribution
Exp3-3
TRIGRS-P
CMFD
Uniform distribution
Exp3-4
TRIGRS-P
CMFD
Normal distribution
Tab.2
Fig.3
Fig.4
Fig.5
Fig.6
Fig.7
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