43 research outputs found
Co-seismic hillslope weakening
Get PDFReduction in shear strength (RSS) of hillslope materials due to earthquakes have been rarely discussed numerically in regional scale analyses. Despite the limited literature, an empirical relationship between peak ground acceleration (PGA) and RSS was proposed based on Newmark's permanent-deformation analysis coupled with static limit equilibrium approach. However, the empirical relationship is solely based on co- and post-seismic landslide inventories associated with the 2008 Wenchuan earthquake and transferability of the approach is yet to be tested. To address this issue, we apply the same method to areas affected by the 2015 Gorkha and 2018 Palu earthquakes. Our results showed a good agreement in variation of the RSS with respect to PGA. This suggests that the approach is transferable for the estimation of co-seismic hillslope weakening in other geographies. We also analyzed the RSS in sedimentary, metamorphic and igneous rocks. Our results showed that igneous rocks feature the highest RSS in response to given ground shaking and it is followed by metamorphic and sedimentary rocks. Ultimately, we also discussed the RSS likely caused by precipitation events. Our findings imply that the RSS caused by 0.1 g of ground shaking may be 14 times larger than RSS due to precipitation. This argument needs further analyses but overall, our findings provide new insights into hillslope weakening in relation to both earthquake and precipitation.</p
Distribution-agnostic landslide hazard modelling via Graph Transformers
Get PDFIn statistical applications, choosing a suitable data distribution or likelihood that matches the nature of the response variable is required. To spatially predict the planimetric area of a landslide population, the most tested likelihood corresponds to the Log-Gaussian case. This causes a limitation that hinders the ability to accurately model both very small and very large landslides, with the latter potentially leading to a dangerous underestimation of the hazard. Here, we test a distribution-agnostic solution via a Graph Transformer Neural Network (GTNN) and implement a loss function capable of forcing the model to capture both the bulk and the right tail of the landslide area distribution. An additional problem with this type of data-driven hazard assessment is that one often excludes slopes with landslide areas equal to zero from the regression procedure, as this may bias the prediction towards small values. Due to the nature of GTNNs, we present a solution where all the landslide area information is passed to the model, as one would expect for architectures built for image analysis. The results are promising, with the landslide area distribution generated by the Wenchuan earthquake being suitably estimated, including both zeros, the bulk and the extremely large cases. We consider this a step forward in the landslide hazard modelling literature, with implications for what the scientific community could achieve in light of a future space–time and/or risk assessment extension of the current protocol.</p
Distinct susceptibility patterns of active and relict landslides reveal distinct triggers:A case in northwestern Turkey
Get PDFEstimating near-surface reduction in shear-strength on hillslopes caused by strong ground shaking
Get PDFThe weakening of hillslopes during strong earthquakes increases landsliding rates in post-seismic periods. However, very few studies have addressed the amount of coseismic reduction in shear strength of hillslope materials. This makes estimation of post-seismic landslide susceptibility challenging. Here we propose a method to quantify the maximum shear-strength reduction expected on seismically disturbed hillslopes. We focus on a subset of the area affected by the 2008 Mw 7.9 Wenchuan, China earthquake. We combine physical and data-driven modeling approaches. First, we back-analyze shear-strength reduction at locations where post-seismic landslides occurred. Second, we regress the estimated shear-strength reduction against peak ground acceleration, local relief, and topographic position index to extrapolate the shear-strength reduction over the entire study area. Our results show a maximum of 60%-75% reduction in near-surface shear strength over a peak ground acceleration range of 0.5-0.9 g. Reduction percentages can be generalized using a data-driven model
Assessing landslide risk on a Pan-European scale
Get PDFAssessing landslide risk is a fundamental step in planning prevention and mitigation actions in mountainous landscapes. To date, most landslide risk analyses address this topic at the scale of a slope or catchment. Whenever the scale involves regions, nations, or continents, the landslide risk analysis is hardly implemented. To test this theoretical framework, we present a practical case study, represented by the European landscape. In this contribution, we take the main Pan-European mountain ranges and provide an example of risk assessment at a continental scale. We consider challenges like cross-national variations landslide mapping and digital data storage. A two-stepped protocol is developed to identify areas more prone to failure. With this initial information, we then model the possible economic consequences, particularly in terms of human settlements and agricultural areas, as well as the exposed population. The analytical protocol firstly results in an unbiased landslide susceptibility map, which is combined with economic and population data. The landslide risk is presented in both the spatial distribution of possible economic losses and the identification of risk hotspots. The latters are defined through a bivariate classification scheme by combining the landslide susceptibility and exposure of human settlements. Ultimately, the exposed population is represented during the two sub-daily cycles across the study area
Estimating weakening on hillslopes caused by strong earthquakes
Get PDFThe weakening of hillslopes during strong earthquakes increases landsliding rates in post-seismic periods. However, very few studies have addressed the amount of coseismic reduction in shear strength of hillslope materials. This makes estimation of post-seismic landslide susceptibility challenging. Here we propose a method to quantify the maximum shear-strength reduction expected on seismically disturbed hillslopes. We focus on a subset of the area affected by the 2008 Mw 7.9 Wenchuan, China earthquake. We combine physical and data-driven modeling approaches. First, we back-analyze shear-strength reduction at locations where post-seismic landslides occurred. Second, we regress the estimated shear-strength reduction against peak ground acceleration, local relief, and topographic position index to extrapolate the shear-strength reduction over the entire study area. Our results show a maximum of 60%–75% reduction in near-surface shear strength over a peak ground acceleration range of 0.5–0.9 g. Reduction percentages can be generalized using a data-driven model.</p
Space-time modeling of cascading hazards:Chaining wildfires, rainfall and landslide events through machine learning
Get PDFThe current study sets out to explore yearly landslide susceptibility dynamics on slopes regularly affected by fires. To do so, two yearly inventories have been generated, one for the landslides and one for the wildfires, for an area of approximately 2 km2 and for a period of 24 years. It is important to stress that space–time data-driven models employed for susceptibility assessment are relatively new, and their application so far has mostly linked landslide occurrences to the precipitation trigger and the standard morphometric characteristics of the landscape at hand. Here we also consider an additional element of disturbance to the slope equilibrium, in the form of burnt areas tested from one up to three years priors to the reference landslide occurrence time. The relevance of the wildfire spatiotemporal signal is tested as part of a multi-variate modeling procedure. The results highlight at least a 10 % performance increase when these wildfire-related predictors are featured (from an average AUC of 0.75 to 0.85 in a random forest modeling framework). The associated yearly variations in the landslide occurrence probability are translated into individual maps, stressing the extent to which the standard and static definition of susceptibility does not hold especially in the context of multiple hazards and in an urban setting such as the Camaldoli hills (Naples, Italy) we chose for our test site.</p
