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

机译:降雨数据及其不确定性作为降雨诱发的浅层滑坡模型的输入

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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.
机译:用来预测降雨引起的浅层滑坡的时间发生的物理模型是基于确定性定律的。由于现有的测量技术以及我们对控制滑坡发生的物理定律的了解,模型不确定性是由于无法准确量化模型输入参数和降雨强迫数据而引起的。边坡失稳预测的不确定性分析为完善岩土模型提供了理论依据。瞬态降雨入渗和基于网格的区域边坡稳定性概率模型(TRIGRS-P)采用概率方法来计算由于降雨入渗引起的安全系数(FS)的变化。边坡入渗分布均衡(SLIDE)是用于滑坡预测的简化物理模型。还通过采用相同的概率方法修改了新代码(SLIDE-P),以允许对SLIDE模型输入参数的值进行随机采样。这项研究考察了降雨变化的相对重要性以及其他决定斜坡稳定性的变量的不确定性。来自气象站,中国气象局土地同化系统2.0(CLDAS2.0),中国气象强迫数据集降水量(CMFD)和中国地质灾害公告的降水数据被用于驱动TRIGRS,SLIDE,TRIGRS-P和SLIDE-P楷模。 TRIGRS-P和SLIDE-P模型用于生成输入样本并计算FS值。几个具有不同输入参数的模型运行的输出,并对降雨强迫进行统计分析。比较表明,TRIGRS-P和SLIDE-P模型的仿真存在显着差异。尽管使用了不同的降水数据集,但TRIGRS-P的模拟结果更加集中。这项研究可以为潜在的数值模型预测区域降雨引起的浅层滑坡的时空分布提供参考。

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