Probability analysis of shallow landslides in varying vegetation zones with random soil grain-size distribution

IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Hu Jiang , Qiang Zou , Yong Li , Yao Jiang , Junfang Cui , Bin Zhou , Wentao Zhou , Siyu Chen , Zihao Zeng
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引用次数: 0

Abstract

The physically-based landslide susceptibility models are widely used to guide disaster prevention and mitigation in mountainous areas due to their significant predictive capability. However, this method faces limitations in regions with complex topography and vegetation types, primarily due to a lack of consideration for the spatial uncertainty of planted soil caused by variations in soil particle size composition. Therefore, a new model is established to predict shallow landslide occurrence considering the impact of the uncertainty of soil particle size composition on soil shear strength parameters. This model optimizes the assignment strategy for soil physical strength parameters with the support of the random soil grain-size field theory. Subsequently, it organically integrates the impact of plants on slope stability, involving root reinforcing, moisture regulation (preferential flow and root water uptake), and the canopy's interception and weight loading effects, based on the infinite slope model. The model is validated in a region with significant vegetation zonality in Sichuan Province, China. The results show: (i) the testing indicator AUC values range from 0.862 to 0.873, indicating that the model can effectively predict the spatial occurrence probability of shallow landslides, (ii) the proposed LSM-VEG-GSD model exceeds by 17.50% the traditional pseudo-static model according to the AUC score, and (iii) regardless of water height ratio interval, the probability of slope failure in different vegetation zones increases with slope angle, following an S-shaped curve regression pattern. Overall, the findings of this study contribute to predicting the stability of shallow landslides in terrain transition zones with high potential landslide concealment and uncertainty under the influence of vegetation.
不同植被带中土壤粒度随机分布的浅层滑坡概率分析
基于物理的山体滑坡易发性模型因其强大的预测能力而被广泛用于指导山区的防灾减灾工作。然而,在地形和植被类型复杂的地区,这种方法面临着一定的局限性,主要原因是没有考虑到土壤颗粒大小组成的变化所造成的种植土壤的空间不确定性。因此,考虑到土壤粒径组成的不确定性对土壤抗剪强度参数的影响,建立了一个新的模型来预测浅层滑坡的发生。该模型在随机土壤粒度场理论的支持下,优化了土壤物理强度参数的赋值策略。随后,该模型以无限坡度模型为基础,有机整合了植物对边坡稳定性的影响,包括根系加固、水分调节(优先流和根系吸水)以及冠层的截流和重量负荷效应。该模型在中国四川省植被分带明显的地区进行了验证。结果表明:(i) 测试指标 AUC 值在 0.862 至 0.873 之间,表明该模型可有效预测浅层滑坡的空间发生概率;(ii) 根据 AUC 值,所提出的 LSM-VEG-GSD 模型比传统的伪静态模型高出 17.50%;(iii) 无论水高比间隔如何,不同植被带的边坡崩塌概率随边坡角的增加而增加,呈 S 型曲线回归模式。总之,本研究结果有助于预测植被影响下潜在滑坡隐蔽性和不确定性较高的地形过渡带浅层滑坡的稳定性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Environmental Modelling & Software
Environmental Modelling & Software 工程技术-工程:环境
CiteScore
9.30
自引率
8.20%
发文量
241
审稿时长
60 days
期刊介绍: Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.
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