Primary establishment of an early warning model of debris flow hazards in Nyingchi City of Tibetan autonomous region based on raster runoff simulation
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摘要:
西藏林芝市泥石流灾害频发，亟需建立泥石流灾害预警模型，预测林芝市泥石流灾害可能发生的区域，减少泥石流灾害导致的损失。文章提出了一种基于栅格径流汇流的林芝市泥石流灾害预警模型，从栅格像元尺度上模拟流域各位置上的水深，以提高泥石流预警的空间针对性。该模型将泥石流致灾因子分为背景因子和激发因子。通过林芝市裸岩率、河床纵比降等因子的逻辑回归，获取林芝市泥石流灾害概率，作为泥石流预警模型的背景因子；引入栅格径流汇流模型，以站点降水和雪水当量为模型的水量输入，模拟预警时段内的流域各位置上的模型水深，作为泥石流预警模型的激发因子。利用二元逻辑回归的方法计算背景因子和激发因子的权重，建立泥石流预警模型。利用2011—2020年18次历史灾害对模型进行验证，落入预警区内的灾害点占比64.4%，预警精度较高，对于林芝市泥石流灾害预警具有一定的指导意义。
Abstract:Debris flow disasters occur frequently in Nyingchi City, Tibet. There is an urgent need to establish an early warning model to predict the possible areas of debris flow disasters in Nyingchi City and reduce the losses caused by those disasters. This paper presented an early warning model based on raster runoff simulation in Nyingchi City, which can simulate the water depth at each location in the watershed and improve the spatial pertinence of debris flow early warning. In this model, the disaster factors of debris flow are divided into background factor and excitation factor. The probability of debris flow disaster in Nyingchi City is obtained by logistic regression of many factors such as bare rock rate, vertical slope of riverbed and so on, which is used as the background factor. The raster runoff simulation model is introduced to simulate the predicted water depth at each position of the basin during the early warning period by importing the precipitation and snow water equivalent data. Using binary logistic regression method to calculate the weight of background factor and excitation factor, the final model is obtained. 18 historical disasters from 2011 to 2020 were used to verify the model. The disaster points falling into the early warning area accounted for 64.4%, which has certain guiding significance for the early warning of debris flow disaster in Nyingchi City.
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Key words:
- Nyingchi City /
- debris flow disaster /
- runoff simulation
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表 1 逻辑回归分析结果
Table 1. Results of logistic regression analysis
指标因子 B S.E Wals df Sig. exp（B） 裸岩信息 0.925 0.456 4.119 1 0.042 2.523 流域面积 0.880 0.394 5.000 1 0.025 2.412 沟床纵比降 1.042 0.186 31.348 1 0 2.834 河流 1.000 0.122 66.763 1 0 2.717 道路 0.879 0.105 69.489 1 0 2.409 断层密度 0.995 0.442 5.059 1 0.024 2.704 土地利用 0.541 0.245 4.888 1 0.027 1.718 土壤类型 0.603 0.112 18.239 1 0 1.828 隐患点密度 0.896 0.297 63.593 1 0 2.449 沟谷密度 1.295 0.211 19.043 1 0 3.650 年降水量 1.064 0.281 25.459 1 0 2.897 常量 −1.485 0.281 27.852 1 0 0.226 注：B为逻辑回归系数；S.E.为标准误差；Wals为卡方值统计量；df为自由度；Sig.为显著性。 
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表 2 泥石流灾害的概率占比
Table 2. Probability proportion of debris flow disaster
级别 取值区间 灾害点数量 百分比/% 一级 [0, 0.2) 8 6.25 二级 [0.2, 0.4) 5 3.90 三级 [0.4, 0.6) 2 1.56 四级 [0.6, 0.8) 9 7.03 五级 [0.8, 1) 104 81.25
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表 3 径流水深分级
Table 3. Classification of runoff depth
级别 水深区间/m 1 (0, 0.01] 2 (0.01, 0.05] 3 (0.05, 0.1] 4 (0.1, 0.3] 5 >0.3
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表 4 逻辑回归方程表
Table 4. Logistic regression equation table
B S.E. Wald df Sig. exp（B） P 0.790 0.257 9.486 1 0.002 2.204 D 0.834 0.297 7.910 1 0.005 2.303 常量 −5.330 1.432 13.857 1 0.000 0.005 注：B为逻辑回归系数；S.E.为标准误差；Wald为卡方值；df为自由度；Sig.为显著性。
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表 5 泥石流灾害预警验证结果
Table 5. Validation results of debris flow disaster early warning
序号 灾害日期 灾害点数/个 预警区内灾害点数/个 1 2011年7月14日 2 1 2 2013年7月6日 1 1 3 2014年4月16日 1 0 4 2014年7月18日 1 0 5 2015年8月6日 5 1 6 2015年8月19日 12 7 7 2015年8月20日 2 2 8 2015年8月21日 3 3 9 2016年4月25日 1 1 10 2016年7月26日 3 3 11 2016年7月27日 2 1 12 2017年8月3日 1 0 13 2017年7月21日 1 1 14 2018年9月9日 1 1 15 2019年7月7日 2 1 16 2020年7月10日 2 2 17 2020年7月11日 3 2 18 2020年8月15日 2 2 合计 45 29
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