Unified landslide hazard assessment using hurdle models: a case study in the Island of Dominica
Unified landslide hazard assessment using hurdle models: a case study in the Island of Dominica
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DOI：
10.1007/s00477-022-02239-6
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发表时间：
2022-06-02
影响因子：
4.2
通讯作者：
Castro-Camilo, Daniela
中科院分区：
文献类型：
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作者：
Bryce, Erin;Lombardo, Luigi;Castro-Camilo, Daniela
气候引起的自然灾害是对社区的威胁。它们可能造成生命损失和对基础设施的严重破坏，而且由于气候变化，它们变得越来越频繁。在热带地区尤其如此，在近代历史上，大型飓风一直出现在那里。这些事件由于风速高而造成损害，而且它们所排放的降雨强度大/持续时间长，还可能以广泛的泥石流/泥石流的形式引发一连串水文地貌危害。科学界为减轻这些水文形态威胁的潜在损害而制定的准备措施包括评估它们可能在特定景观中出现的地方。这个概念被称为敏感性，它通常是通过实现二进制分类器来估计滑坡发生的概率。然而，预测哪里可能发生山体滑坡可能不是足够的信息，因为它无法传达山体滑坡可能有多大。这项工作提出了使用灵活的伯努利对数高斯栅栏模型，同时模拟滑坡的发生和每个面积单位的大小。协变量和空间信息的引入使用一个广义的加性建模框架。为了科普高空间分辨率的数据，我们的模型使用马尔可夫表示的Matern协方差函数的基础上的随机偏微分方程的方法。假设高斯先验，我们的模型可以集成到类的潜在高斯模型，推理方便地进行集成嵌套拉普拉斯近似方法的基础上。我们在多米尼加使用我们的建模方法，飓风玛丽亚（2017年9月）在岛上引发了数千次类似浅流的山体滑坡。我们的研究结果表明，我们不仅可以估计滑坡可能发生的地方，他们可能有多大，但我们也可以联合收割机在一个统一的滑坡灾害模型，这是第一次。
Climatically-induced natural hazards are a threat to communities. They can cause life losses and heavy damage to infrastructure, and due to climate change, they have become increasingly frequent. This is especially the case in tropical regions, where major hurricanes have consistently appeared in recent history. Such events induce damage due to the high wind speed they carry, and the high intensity/duration of rainfall they discharge can further induce a chain of hydro-morphological hazards in the form of widespread debris slides/flows. The way the scientific community has developed preparatory steps to mitigate the potential damage of these hydro-morphological threats includes assessing where they are likely to manifest across a given landscape. This concept is referred to as susceptibility, and it is commonly achieved by implementing binary classifiers to estimate probabilities of landslide occurrences. However, predicting where landslides can occur may not be sufficient information, for it fails to convey how large landslides may be. This work proposes using a flexible Bernoulli-log-Gaussian hurdle model to simultaneously model landslide occurrence and size per areal unit. Covariate and spatial information are introduced using a generalised additive modelling framework. To cope with the high spatial resolution of the data, our model uses a Markovian representation of the Matern covariance function based on the stochastic partial differential equation approach. Assuming Gaussian priors, our model can be integrated into the class of latent Gaussian models, for which inference is conveniently performed based on the integrated nested Laplace approximation method. We use our modelling approach in Dominica, where hurricane Maria (September 2017) induced thousands of shallow flow-like landslides passing over the island. Our results show that we can not only estimate where landslides may occur and how large they may be, but we can also combine this information in a unified landslide hazard model, which is the first of its kind.