Precipitation data and their uncertainty as input for rainfall-induced shallow landslide models

doi:10.1007/s11707-019-0791-7

Front. Earth Sci.

2019, Vol. 13

Issue (4) : 695-704 https://doi.org/10.1007/s11707-019-0791-7

RESEARCH ARTICLE

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

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Abstract

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.

Keywords rainfall-induced landslide SLIDE TRIGRS FS

Corresponding Author(s): Ying WANG

Just Accepted Date: 15 October 2019 Online First Date: 25 November 2019 Issue Date: 30 December 2019

Cite this article:

Yueli CHEN,Linna ZHAO,Ying WANG, et al. Precipitation data and their uncertainty as input for rainfall-induced shallow landslide models[J]. Front. Earth Sci., 2019, 13(4): 695-704.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-019-0791-7
https://academic.hep.com.cn/fesci/EN/Y2019/V13/I4/695

Fig.1 Location of the landslide and the path of Typhoon Fanapi.

Fig.2 Landslide triggered by heavy rainfall from Typhoon Fanapi in September 2010 in Liutang village (^{The Ministry of Land and Resources, 2010}).

Properties	Slope angel	Soil type	Parameter shear	Friction angle	Cohesion	Coefficient
Symbol (Unit)	$δ$ (Deg)	1-16	A (Unitless)	$φ$ (Deg)	$c$ (kPa)	$λ, ∂$ (Unitless)
	26	12	100	22	10	0.4, 3.4

Properties	Unit weight of soil	porosity	Water content	Degree of saturation	Hydrologic conductivity	capillary
Symbol (Unit)	$γ s$ (kN/m³)	N (Unitless)	$θ s, θ r$ (Unitless)	$S r$ (Unitless)	$K s$ (m/s)	$H c$ (m)
	12.5	0.40	0.4, 0.9	0.39	1.8 × 10^-⁵	500

Tab.1 Geotechnical parameters for the model

Tab.2 Summary of Numerical Experiments

Fig.3 Comparison of the hourly rainfall intensity for Xinyi station, Yangchun station, CLDAS, CMFD and report data. (Xinyi station: precipitation data from Xinyi station; Yangchun station: precipitation data from Yangchun station; CLDAS: precipitation data from the China Meteorological Administration Land Assimilation System (CLDAS) version 2.0; CMFD: China Meteorological Forcing Data set (CMFD); Report data: precipitation from the China geological hazard bulletin. (Hereinafter the same)).

Fig.4 (a) FS using the TRIGRS model for Typhoon Fanapi; (b) FS using the SLIDE model for Typhoon Fanapi.

Fig.5 Distribution graphs of strength parameters: (a) friction angle from the normal distribution function; (b) soil cohesion from the normal distribution function.

Fig.6 Probability of landslide occurrence using the SLIDE-P model in Exp2.

Fig.7 Probability of the time of landslide occurrence using the TRIGRS-P model in Exp3.

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