National-Scale Rainfall-Triggered Landslide Susceptibility and Exposure in Nepal
National-Scale Rainfall-Triggered Landslide Susceptibility and Exposure in Nepal
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尼泊尔全国范围内降雨引发的山体滑坡敏感性和暴露度
DOI：
10.1029/2023ef004102
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发表时间：
2024
期刊：
影响因子：
--
通讯作者：
Kincey M
中科院分区：
文献类型：
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作者：
Kincey M
尼泊尔是世界上最容易发生山体滑坡的国家之一，年复一年的影响导致生命损失，并对可持续生计造成长期障碍。越来越多的人每天都要面对滑坡，因此确定滑坡灾害和风险的性质至关重要。在这里，我们为尼泊尔开发了一个滑坡易感性模型，并使用它来生成一个全国性的降雨引发滑坡的地理概况。我们使用基于免费提供的地形数据的模糊叠加方法对滑坡敏感性进行建模，并在绘制的滑坡清单上进行训练，然后将其与高分辨率人口和建筑数据结合起来，联合收割机来描述滑坡暴露的空间分布。我们发现，虽然滑坡的易感性是最高的高喜马拉雅山，暴露是最高的中间山，但这是高度空间变化和平均值相对较低的倾斜。大约有4 × 106尼泊尔人（占人口的15%）生活在被认为是中等或更高程度的滑坡（>最大值的0.25）的地区，而且这个数字对滑坡敏感性的微小变化非常敏感。我们的研究结果表明，山体滑坡和建筑物之间的复杂关系，这意味着更广泛的复杂性之间的关联物理暴露于山体滑坡和贫困。这一分析第一次将重点放在尼泊尔滑坡暴露和风险案例负荷的地理位置上，并展示了根据以往事件的有限记录评估未来风险的局限性。
Nepal is one of the most landslide‐prone countries in the world, with year‐on‐year impacts resulting in loss of life and imposing a chronic impediment to sustainable livelihoods. Living with landslides is a daily reality for an increasing number of people, so establishing the nature of landslide hazard and risk is essential. Here we develop a model of landslide susceptibility for Nepal and use this to generate a nationwide geographical profile of exposure to rainfall‐triggered landslides. We model landslide susceptibility using a fuzzy overlay approach based on freely‐available topographic data, trained on an inventory of mapped landslides, and combine this with high resolution population and building data to describe the spatial distribution of exposure to landslides. We find that whilst landslide susceptibility is highest in the High Himalaya, exposure is highest within the Middle Hills, but this is highly spatially variable and skewed to on average relatively low values. Around 4 × 106Nepalis (∼15% of the population) live in areas considered to be at moderate or higher degree of exposure to landsliding (>0.25 of the maximum), and critically this number is highly sensitive to even small variations in landslide susceptibility. Our results show a complex relationship between landslides and buildings, that implies wider complexity in the association between physical exposure to landslides and poverty. This analysis for the first time brings into focus the geography of the landslide exposure and risk case load in Nepal, and demonstrates limitations of assessing future risk based on limited records of previous events.