Predicting shallow landslide size and location across a natural landscape: Application of a spectral clustering search algorithm
Predicting shallow landslide size and location across a natural landscape: Application of a spectral clustering search algorithm
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DOI：
10.1002/2015jf003520
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发表时间：
2015-12
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影响因子：
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通讯作者：
D. Bellugi;D. Milledge;W. Dietrich;J. Perron;J. McKean
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作者：
D. Bellugi;D. Milledge;W. Dietrich;J. Perron;J. McKean
预测浅层滑坡的大小和位置对于了解景观的形式和演变以及危险识别非常重要。我们测试了一个最近开发的模型，该模型将搜索算法与 3D 边坡稳定性分析相结合，该分析可以在经过深入研究的景观和 10 年滑坡清单中预测这两个关键属性。我们使用基于过程的子模型来估计一系列引发滑坡的暴雨的土壤深度、根部强度和孔隙压力。我们通过独立于边坡稳定性模型的现场测量参数化子模型，无需根据观测值校准预测。该模型通常再现观测到的滑坡大小和位置分布，与观测到的滑坡的 65% 重叠，并且其中 55% 和 28% 的情况下预测滑坡大小的系数分别在 2 和 1.5 倍以内。预测有 5% 的景观不稳定，而记录的滑坡面积只有 2%。错过的滑坡不是由于搜索算法造成的，而是由于边坡稳定性模型的制定和参数化以及观测到的滑坡地图的不准确性造成的。相对于无限坡度方法，我们的模型没有改进位置预测，但可以预测滑坡大小，改进过程表示，并减少对有效参数的依赖。降雨强度或根部粘聚力的增加通常会增加滑坡的规模并使位置沿中空轴移动，而粘聚力的增加会将不稳定的位置限制在土壤最深的区域。我们的研究结果表明，浅层滑坡的丰度、位置和规模最终是由共同变化的地形、物质和水文特性控制的。估计整个景观的根系强度、孔隙压力和土壤深度的时空模式可能是剩下的最大挑战。
Predicting shallow landslide size and location across landscapes is important for understanding landscape form and evolution and for hazard identification. We test a recently developed model that couples a search algorithm with 3‐D slope stability analysis that predicts these two key attributes in an intensively studied landscape with a 10 year landslide inventory. We use process‐based submodels to estimate soil depth, root strength, and pore pressure for a sequence of landslide‐triggering rainstorms. We parameterize submodels with field measurements independently of the slope stability model, without calibrating predictions to observations. The model generally reproduces observed landslide size and location distributions, overlaps 65% of observed landslides, and of these predicts size to within factors of 2 and 1.5 in 55% and 28% of cases, respectively. Five percent of the landscape is predicted unstable, compared to 2% recorded landslide area. Missed landslides are not due to the search algorithm but to the formulation and parameterization of the slope stability model and inaccuracy of observed landslide maps. Our model does not improve location prediction relative to infinite‐slope methods but predicts landslide size, improves process representation, and reduces reliance on effective parameters. Increasing rainfall intensity or root cohesion generally increases landslide size and shifts locations down hollow axes, while increasing cohesion restricts unstable locations to areas with deepest soils. Our findings suggest that shallow landslide abundance, location, and size are ultimately controlled by covarying topographic, material, and hydrologic properties. Estimating the spatiotemporal patterns of root strength, pore pressure, and soil depth across a landscape may be the greatest remaining challenge.