长江流域资源与环境 >> 2019, Vol. 28 >> Issue (10): 2419-2428.doi: 10.11870/cjlyzyyhj201910014

• 自然资源 • 上一篇    下一篇

基于小流域单元的怒江州泥石流易发性评价

李益敏1,2,杨蕾1,魏苏杭1,3   

  1. (1. 云南大学 资源环境与地球科学学院,云南 昆明,650091;2. 云南省地理研究所高原山地灾害与
    环境研究中心, 云南 昆明 650223;3. 昆明市国土规划勘察测绘研究院,云南 昆明 650041)
  • 出版日期:2019-10-20 发布日期:2019-11-05

Susceptibility Assessment of Debris Flow in Nujiang Befecture Based on the Catchment

LI Yi-min1,2,YANG Lei1,WEI Su-hang1,3   

  1. (1.College of Resource Environment and Earth Science, Yunnan University, Kunming 650091,China;
    2.Tableland Mountain Hazards and Environment Research Center, Institute of Geography, Kunming 650223,China;
    3. Kunming Land Planning, Surveying and Mapping Research Institute,Kunming 650041,China)
  • Online:2019-10-20 Published:2019-11-05

摘要: 开展泥石流灾害易发性评价,了解区域泥石流易发程度的整体特征和空间异质性, 为区域宏观层面国土空间布局及地质灾害防治规划等提供依据。基于小流域评价单元,选择距断裂带距离、岩性、melton比率、流域延伸率、流域高差率、河流弯曲系数、流域水系密度、平均植被覆盖度、年均降水量、距道路距离、距居民点距离等11个评价指标,采用确定性系数模型CF和多因子叠加权重确定法开展怒江州泥石流易发性评价研究。将易发性评价结果划分为5个等级,并以研究区历史泥石流灾害对易发性评价结果验证。结果表明:极高易发区面积仅占研究区总面积的8.31%,但发生泥石流灾害的数量占泥石流总数的29.75%;高易发区的面积占总面积的12.29%,发生泥石流的数量占22.59%。最后通过独立样本验证,易发性评价模型性能(AUC=0.742)良好。怒江州泥石流灾害易发性评价结果与历史泥石流灾害点空间分布较为吻合,表明选取的易发性评价指标和评价方法可行,评价结果可为怒江州防灾减灾工作提供参考。

Abstract: In order to learn the overall characteristics and spatial heterogeneity of regional debris flow, this article investigates the susceptibility of debris flow based on catchments and 434 debris flows point in NuJiang Prefecture. Firstly, the study area is devided into 1 414 catchments. Secondly, 11 feature factors including distance to fracture, melton rate, catchment elongation, catchment relief ratio, river tortuosity, drainage density of catchment, average VFC, Annual average precipitation, distance to road and villages are selcected to Certainly Factor model(CF) and CF-based multi-factor overlay method to assessment the susceptibility of debris. And then, the susceptibility assessment results be dividied into five grades and verified it by independent validation samples(20% historical debris flow points in the study area). The results shows that 8.31% of the study area is the extremely high-prone area involving 29.75% debris flow points and 12.29% of the study area is high-prone area involving 22.59% debris flow points. Finally ,the model predicted well (AUC=0.742) through the test of independent validation samples. Therefore, the susceptibility evaluation results is similar to debris flow’s spatial distribution in NuJiang Prefecture that indicates the evaluation index and evaluation method are feasible, and the evaluation results is a reference for the disaster prevention and mitigation work in Nujiang Prefecture.

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