Analysis of instability disaster of rainfall induced shallow landslides at the regional scale based on the modified Green Ampt model
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摘要: 由于具有类似的工程地质和水文地质条件, 在高度相关的降雨作用下, 同一个区域中的降雨诱发浅层斜坡失稳灾害常成群出现。在区域尺度预测浅层斜坡失稳灾害对滑坡灾害的防灾减灾工作具有重要的意义。为此, 提出了一种基于力学原理的降雨诱发浅层斜坡失稳灾害预测新模型RARIL。该模型采用修正Green-Ampt模型进行降雨入渗分析, 采用无限体边坡模型进行安全系数计算, 利用可靠度原理考虑区域斜坡稳定性分析中的参数不确定性。该模型具有可考虑降雨诱发浅层斜坡的失稳力学机理、可考虑区域内斜坡土体参数不确定性, 以及计算效率高、易于在GIS平台上实现等优点。案例分析表明, RARIL模型较为准确地预测了2010年8月12日11∶00至2010年8月14日9∶00期间强降雨在四川省汶川县映秀镇附近的303省道K0-K20段沿线区域引发的滑坡灾害, 研究结果证明RARIL模型在预测降雨诱发区域斜坡失稳灾害方面有很好的应用前景。Abstract: Due to similar engineering and hydrogeological conditions, landslides in the same area often occur in groups under highly correlated rainfall. As a result, predicting shallow landslide instability disasters at the regional scale is of great significance to disaster prevention and mitigation work. This paper suggests a new prediction model, the regional assessment of rainfall induced landslides (RARIL), in which the modified Green-Ampt model is used to analyse rainfall infiltration, the infinite-slope model is used to calculate the safety factor, and the reliability principle is used to consider the parameter uncertainty in regional landslide stability analysis. The model has the advantages of considering the instability mechanism of rainfall-induced shallow landslides, the uncertainty of the slope soil parameters in the area, and high computational efficiency and can be easily implemented in GIS. The case study shows that the RARIL model can accurately predict the landslide disaster caused by heavy rainfall in the region along the 303 provincial Highway K0-K20 section from 11∶00 on August 12, 2010, to 9∶00 on August 14, 2010. Therefore, it has good application prospects in predicting regional landslide instability disasters induced by rainfall.
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Key words:
- Green-Ampt model /
- Monte Carlo method /
- regional landslide /
- unsteady rainfall /
- reliability /
- shallow landslide
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图 1 SHALSTAB模型滑动面示意图
Figure 1. Schematic diagram of the sliding surface of the SHALSTAB model
图 3 降雨强度-入渗率关系图
Figure 3. Relationship between rainfall intensity and infiltration rate
图 8 2010年8月12日降雨后的滑坡分布
Figure 8. Distribution of landslides after rainfall on August 12, 2010
图 9 降雨后(t=46 h)失稳区计算结果
Figure 9. Calculation results of unstable area after rainfall(t=46 h)
表 1 研究区岩土体物理力学参数
Table 1. Pysical and mechanical parameters of rock and soil
土类 c/kPa φ/(°) ks/
(m·
s-1)θs θi γs/
(kN·
m-3)μ COV/% μ COV/% 植被覆盖土层 10.5 5 37 15 1×10-6 0.40 0.25 21 基岩 - - - - 0 - - - 松散土体层 4.0 50 37 15 1×10-5 0.42 0.18 21 河床 - - - - 1×10-3 - - - 注：μ为平均值；COV为变异系数 
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表 2 模型计算结果
Table 2. Calculation results of model
风险等级 稳定 潜在不稳定 不稳定 极不稳定 总面积/
km2pf < 5% 5 < pf < 10% 10% < pf < 50% 50% < pf 模型预测的不同状态斜坡面积(km2)及占比 104.6(63.6%) 35.0(21.3%) 22.7(13.8%) 2.1(1.3%) 164.5 不同区域内滑坡实际发生的面积(km2)及占比 1.3(1.2%) 3.9(11.1%) 9.1(40.1%) 1.9(90.1%) 16.2
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表 3 受试者工作曲线混淆矩阵
Table 3. Confusion matrices of ROC
预测结果 滑坡 非滑坡 实际结果 滑坡 TP(true positive，真阳性) FP(false positive，假阳性) 非滑坡 FN(false negative，假阴性) TN(true negative，真阴性) ACC=(TP+TN)/N TPR=TP/(TP+FN) FPR=FN/(TN+FN) 注：ACC.模型准确度；TPR.真阳性率；FPR.假阳性率
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