Unified landslide hazard assessment using hurdle models: a case study in the Island of Dominica

Abstract

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 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 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 Matérn covariance function based on the stochastic partial differential equation (SPDE) 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.

A data-driven framework for landslide size space-time modelling

“…This initial hypothesis is confirmed in the ReliefVar-SD plot, where this parameter positively contributes to the increase of landslide sizes. The slope unit area shows a negative effect on very small SUs, while the contribution appears to positive on larger SUs, which is associated with previous study (Bryce et al, 2022). For lithology, the class F (Mudstone intercalated with allochthonous material) has the highest positive effect on landslide size.…”

“…However, their model lacked the ability to inform whether any given slope will be unstable. For this reason, Aguilera et al (2022) and Bryce et al (2022) extended this framework by building a joint landslide susceptibility and area prediction model. But even in these cases, one main issue still persisted, for they produced temporally stationary estimates of landslide extents.…”

Modeling the size of co-seismic landslides via data-driven models: the Kaikōura's example

“…Moreover, it needs to undergo several tests both in case of seismic-and rainfall-induced landslides. To this purpose, the two remaining contributions have tried to replicate a similar experimental setting at regional scales, one focusing on co-seismic landslides (Aguilera et al, 2022) and one on rainfall-triggered (Bryce et al, 2022) ones. However, the academic process that the modeling of landslide susceptibility has undergone before becoming a standard across the geoscientific community has required thousands of contributions.…”