Research on early warning for meteorological risks of rainfall-induced landslide hazards in typical areas of southwest Yunnan
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摘要: 基于易发性评价和降雨阈值分析，建立降雨诱发的滑坡危险性预警模型，能有效为区域滑坡灾害风险预警工作提供支持。文章以滑坡灾害频发的滇西南隆阳区—芒市段为研究对象，采用随机森林模型进行滑坡灾害易发性评价，分析降雨与滑坡之间的耦合关系，进而构建降雨阈值模型，通过时空等级耦合分析划分气象风险预警等级，经实际灾情数据验证，模型有较好的可靠性。Abstract:
The area of Longyang district–Mangshi section is located in the middle and lower reaches of the Nujiang river and the Daying river. The landform in this area is dominated by medium mountains, wide valleys, and basins. This area is characterized by a southern subtropical monsoon climate and a southern subtropical mountain monsoon climate. The overall structural features within the area are composed of a series of near north-south faults, and their derived secondary transverse tensile faults as well as tight folds. Lithology is mainly composed of metamorphic rock strata in the Proterozoic Gaoligong mountains, strata from the Paleozoic to the Mesozoic, and clay, fragments, gravel from the Quaternary. The study area is typically concentrated and developed with rainfall-induced landslides, and geological disasters mainly include collapses, landslides, debris flows and ground subsidence. Among these disasters, there are 1,175 landslide events, with rainfall-induced landslides accounting for over 95%. This type of disaster is the most significant in the study area. Water systems are intensively distributed in this area, with large depths of river valleys, and a pronounced variation in terrain. Landslide hazards are mainly distributed along valleys on both sides of rivers. Based on the assessment of vulnerability and the analysis of rainfall thresholds, an early warning model for rainfall-induced landslide risks has been established. This model can effectively support early warnings for regional landslide risks. The study area is located in the section of Longyang district–Mangshi section in southwest Yunnan Province, which is prone to frequent landslide hazards. This study employed Pearson correlation coefficient to analyze the relationships among various evaluation factors. A total of 13 evaluation factors, including elevation, slope and slope direction, were selected, and grid units measuring 100 m×100 m were divided. The random forest model was employed to evaluate the vulnerability of landslide hazards and the area under curve (AUC) was utilized to verify the accuracy of the model. Then, by analyzing the coupling relationship between landslides and rainfall, the early effective rainfall intensity (EI) was calculated. The EI and rainfall duration (D) were used as the horizontal and vertical coordinates respectively, to create a double-logarithmic coordinate system. This system illustrated the scatter distribution of the probability of landslide occurrence time and allowed for the fitting of the EI-D threshold curve according to the classification standards for landslide warning grades. Subsequently, a rainfall threshold model was created. Finally, the susceptibility classification region and the EI-D rainfall threshold were superimposed and combined to establish early warning levels for meteorological risks of rainfall-induced landslides based on the EI-D rainfall threshold. Research findings indicate that the AUC value of the training results for the random forest model is 0.84. This suggests that the model selection is appropriate and that the susceptibility evaluation results are reliable. An EI-D rainfall threshold model has been developed, and four EI-D rainfall threshold curves have been fitted. In conjunction with the evaluation results of the vulnerability of the study area, a dynamic evaluation model for landslide risks based on the EI-D rainfall threshold has been established. This model can serve as a reference for early warning evaluations of meteorological risks in the study area. -
Key words:
- random forest /
- landslide /
- rainfall /
- susceptibility evaluation /
- risk warning /
- southwest Yunnan
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图 6 研究区月平均降雨与滑坡发生频次图
Figure 6. Monthly average rainfall and frequency of landslides in the study area
图 7 连续降雨天数与灾害发生次数关系图
Figure 7. Relationship between the number of continuous rainfall days and the number of disasters
图 9 2020年8月19日各降雨阈值级别及预警图
Figure 9. Each rainfall threshold and map of early warning on 19 August, 2020
图 10 2021年7月25日各降雨阈值级别及预警图
Figure 10. Each rainfall threshold and map of early warning on 25 July, 2021
表 1 训练得到的最优超参数值
Table 1. Optimal hyperparameter values obtained by training
超参数 含义 n_estimators 70 criterion gini min_samples_split 8 max_depths 22 max_features 3 min_samples_leaf 8 bootstrap True 
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表 2 滑坡事件当日降雨级别分布
Table 2. Distribution of rainfall levels on the day of landslide occurrence
雨量分级/mm 无雨
（0）小雨
（<10）中雨
（10~25）大雨
（25~50）暴雨
（≥50）滑坡发生占比/% 3% 29% 23% 20% 25%
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表 3 降雨阈值等级划分表
Table 3. Classification of rainfall threshold levels
等级划分 T1 T2 T3 T4 T5 滑坡发生概率/% ≤20 20~40 40~60 60~80 >80
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表 4 气象风险预警分级表
Table 4. Grading of early warning for meteorological risks
预警级别 T1 T2 T3 T4 T5 S1 风险低 风险低 风险低 风险低 有一定风险 S2 风险低 风险低 有一定风险 有一定风险 风险较高 S3 风险低 有一定风险 风险较高 风险较高 风险高 S4 风险低 有一定风险 风险较高 风险高 风险很高 S5 有一定风险 风险较高 风险高 风险很高 风险很高
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表 5 各降雨事件下的气象风险预警等级
Table 5. Early warning levels of meteorological risk in each rainfall event
发生时间 D EI EI-D降雨阈值等级 易发性等级 气象风险预警等级 2020/8/19 12 14.59 T4 S5 风险高 11 13.39 T4 S5 风险很高 3 34.66 T5 S5 风险很高 2021/7/25 7 15.67 T4 S5 风险很高 1 99.14 T5 S5 风险很高
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