Performance and topographic preferences of dynamic and steady models for shallow landslide prediction in a small catchment
Performance and topographic preferences of dynamic and steady models for shallow landslide prediction in a small catchment
复制标题
小流域浅层滑坡预测动态和稳定模型的性能和地形偏好
DOI：
10.1007/s10346-021-01771-w
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发表时间：
2021
期刊：
影响因子：
6.7
通讯作者：
Uchida Taro
中科院分区：
文献类型：
--
作者：
Liang Wei-Li;Uchida Taro
基于物理的稳定性模型用于减少降雨引起的滑坡造成的损害并建立灾害预警系统，是评估浅层滑坡敏感性的空间或时间模式的有效工具。此类综合模型可分为动态和稳态水文模型，它们将动态或稳态地下饱和度生成的模拟与边坡稳定性分析结合起来。尽管已经提出了许多动态和稳定模型并针对实际滑坡进行了验证，但缺乏对动态模型和稳定模型之间地形特征的性能和检测偏好（以下简称地形偏好）的全面比较。本研究基于土壤-基岩界面地形的详细观测数据，将动态和稳定模型的数值预测与小流域降雨引起的浅层滑坡的情况进行了比较。动态模型基于使用三维理查兹方程的地下饱和度的时变生成，稳态模型基于地下饱和度的稳态分布。结果表明，动态模型和稳态模型通常都可以检测滑坡地区存在失效风险的单元。然而，动态模型的精度大约是稳定模型的两倍。降雨模式对滑坡发生的时间和地点有显着影响，延迟的降雨峰值模式最容易发生滑坡，这一点在动态模型中得到了反映，但在稳定模型中却没有反映出来。相对于动态模型，稳定模型低估了不稳定性或不稳定位置的数量。这项研究表明，模型之间土壤-基岩界面的地形偏好不同。动态模型在检测土深较深、坡度较大的滑坡时表现出地形偏好，而稳态模型更适合检测贡献面积较大的滑坡。阐明滑坡类型的地形偏好对于比较动态模型和稳定模型的性能至关重要。
Used to reduce damage from rainfall-induced landslides and establish disaster-warning systems, physically based stability models are efficient tools for evaluating spatial or temporal patterns of susceptibility to shallow landslides. Such comprehensive models can be classified as dynamic and steady hydrological models, which combine the simulation of dynamic or steady-state subsurface saturation generation with slope stability analysis. Although numerous dynamic and steady models have been proposed and validated against actual landslides, comprehensive comparisons of the performance and detection preferences for topographic characteristics (hereafter, topographic preferences) between the dynamic and steady models are lacking. Based on detailed observational data on topography at the soil–bedrock interface, this study compared numerical predictions from dynamic and steady models to a case of rainfall-induced shallow landslides in a small catchment. The dynamic model was based on time-varying generation of subsurface saturation using the three-dimensional Richards equation, and the steady model was based on the steady-state distribution of subsurface saturation. The results showed that both the dynamic and steady models could generally detect cells at risk of failure in the landslide areas. However, the precision of the dynamic model was approximately double that of the steady model. Rainfall patterns had significant impacts on the timing and locations of landslides, and the delayed rainfall peak pattern was most prone to landslides, which was reflected by the dynamic model but not the steady model. The steady model underestimated instability or the number of unstable locations relative to the dynamic model. This study revealed that the topographic preference at the soil–bedrock interface differs between the models. The dynamic model exhibited topographic preference in detecting landslides with deep soil depth and steep gradient, whereas the steady model was better for detecting landslides with large contribution areas. The elucidation of such topographic preferences for landslide types is crucial for comparing the performance of dynamic and steady models.