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Landslide hazard probability and risk assessment at the community level: a case of western Hubei, China

危害 自然灾害 危害分析 地质灾害 中国 地理 环境科学 脆弱性(计算)
作者
Sheng Fu,Lixia Chen,Tsehaie Woldai,Kunlong Yin,Lei Gui,Deying Li,Juan Du,Chao Zhou,Yong Xu,Zhipeng Lian
出处
期刊:Natural Hazards and Earth System Sciences [Copernicus Publications]
卷期号:20 (2): 581-601 被引量:6
标识
DOI:10.5194/nhess-20-581-2020
摘要

Abstract. Small communities living in mountainous terrain in Hubei province are often affected by landslides. Previous studies by the China Geological Survey focused on the 1:100 000  scale. Therefore, a more detailed assessment, especially at the community level, is urgently required by local governments for risk management. In this study, we conducted a more detailed semiquantitative landslide and risk assessment at the community level using a scale of  1:10 000 . We applied the probabilistic method to assess landslide spatial, temporal, and size probabilities, while the hazard and risk assessment were considered for four return periods (5, 10, 20, and 50 years) and two size scenarios (landslide volume). The spatial probability from susceptibility mapping with an accuracy of 84 % indicates that the major controlling factors are Quaternary deposits and weathered eluvium from Ordovician limestones. This study revealed that most building areas in hazard maps are at the foot of major slopes with very high hazard probabilities, and therefore we computed the potential loss of life and property for each slope. The results reveal that 1530 people and USD 18 million worth of property were at risk of landslides within a 50-year return period and a landslide volume of 50 000 m 3 . The longer the return period is, the higher the hazard probability is. Compared with the classic inverse gamma and power law distribution of landslide magnitude and frequency, the function by the ordinary least squares method is more suitable for landslide size probability analysis of the study area. According to these methods, the proposed procedure of landslide risk assessment proves more useful than the existing data from the 1:100 000  scale in western Hubei, China.

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