Estimating weakening on hillslopes caused by strong earthquakes

The 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

Dynamic rainfall-induced landslide susceptibility: A step towards a unified forecasting system

The initial inception of the landslide susceptibility concept defined it as a static property of the landscape, explaining the proneness of certain locations to generate slope failures. Since the spread of data-driven proba- bilistic solutions though, the original susceptibility definition has been challenged to incorporate dynamic ele- ments that would lead the occurrence probability to change both in space and in time. This is the starting point of this work, which combines the traditional strengths of the susceptibility framework together with the strengths typical of landslide early warning systems. Specifically, we model landslide occurrences in the norther sector of Vietnam, using a multi-temporal landslide inventory recently released by NASA. A set of static (terrain) and dynamic (cumulated rainfall) covariates are selected to explain the landslide presence/absence distribution via a Bayesian version of a binomial Generalized Additive Models (GAM). Thanks to the large spatiotemporal domain under consideration, we include a large suite of cross-validation routines, testing the landslide prediction through random sampling, as well as through stratified spatial and temporal sampling. We even extend the model test towards regions far away from the study site, to be used as external validation datasets. The overall per- formance appears to be quite high, with Area Under the Curve (AUC) values in the range of excellent model results, and very few localized exceptions. This model structure may serve as the basis for a new generation of early warning systems. However, the use of The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) for the rainfall component limits the model ability in terms of future prediction. Therefore, we envision subsequent development to take this direction and move towards a unified dynamic landslide forecast. Ultimately, as a proof-of-concept, we have also implemented a potential early warning system in Google Earth Engine

Landslide size matters: a new data-driven, spatial prototype

The standard definition of landslide hazard requires the estimation of where, when (or how frequently) and how large a given landslide event may be. The geoscientific community involved in statistical models has addressed the component pertaining to how large a landslide event may be by introducing the concept of landslide-event magnitude scale. This scale, which depends on the planimetric area of the given population of landslides, in analogy to the earthquake magnitude, has been expressed with a single value per landslide event. As a result, the geographic or spatially-distributed estimation of how large a population of landslide may be when considered at the slope scale, has been disregarded in statistically-based landslide hazard studies. Conversely, the estimation of the landslide extent has been commonly part of physically-based applications, though their implementation is often limited to very small regions. In this work, we initially present a review of methods developed for landslide hazard assessment since its first conception decades ago. Subsequently, we introduce for the first time a statistically-based model able to estimate the planimetric area of landslides aggregated per slope units. More specifically, we implemented a Bayesian version of a Generalized Additive Model where the maximum landslide size per slope unit and the sum of all landslide sizes per slope unit are predicted via a Log-Gaussian model. These “max” and “sum” models capture the spatial distribution of (aggregated) landslide sizes. We tested these models on a global dataset expressing the distribution of co-seismic landslides due to 24 earthquakes across the globe. The two models we present are both evaluated on a suite of performance diagnostics that suggest our models suitably predict the aggregated landslide extent per slope unit. In addition to a complex procedure involving variable selection and a spatial uncertainty estimation, we built our model over slopes where landslides triggered in response to seismic shaking, and simulated the expected failing surface over slopes where the landslides did not occur in the past. What we achieved is the first statistically-based model in the literature able to provide information about the extent of the failed surface across a given landscape. This information is vital in landslide hazard studies and should be combined with the estimation of landslide occurrence locations. This could ensure that governmental and territorial agencies have a complete probabilistic overview of how a population of landslides could behave in response to a specific trigger. The predictive models we present are currently valid only for the 25 cases we tested. Statistically estimating landslide extents is still at its infancy stage. Many more applications should be successfully validated before considering such models in an operational way. For instance, the validity of our models should still be verified at the regional or catchment scale, as much as it needs to be tested for different landslide types and triggers. However, we envision that this new spatial predictive paradigm could be a breakthrough in the literature and, in time, could even become part of official landslide risk assessment protocols

Standing on the shoulder of a giant landslide:A six-year long InSAR look at a slow-moving hillslope in the western Karakoram

In this work, we investigate a slow-moving, large landslide (∼20 km2) in the Chitral district in Northern Pakistan, near several villages. The slow-moving landslide was reported more than four decades ago but has never been examined afterward. Interferometric Synthetic Aperture Radar (InSAR) analyses, using Sentinel-1 data that span a period of six years, allowed us to retrieve the spatio-temporal pattern of hillslope deformation. We combined both ascending and descending orbits to identify vertical and horizontal deformations. Our results showed that the crown is moving relatively fast in comparison to the nearby regions; 30 mm/year and 40 mm/year in downward and eastward directions, respectively. Also, step-like deformations observed over the crown reflect a deep-seated landslide. At the footslope, on the other hand, we captured relatively high deformations but in an upward direction; specifically 30 mm/year and 30 mm/year in upward and eastward directions, respectively. We have discussed the possible roles of meteorologic and anthropogenic factors causing hillslope deformation occurred during the six-year period under consideration. We observed a seasonal deformation patterns that might be mainly interpreted to be governed by the influence of snowmelt due to increasing temperatures during the start of spring. Overall, the same mechanism might be present in many other hillslopes across the whole Hindukush-Himalayan-Karakoram range, where seasonal snowmelt is an active agent. In this context, this research provides a case study shedding a light on the hillslope deformation mechanism at the western edge of the Himalayan range.</p

On the estimation of landslide intensity, hazard and density via data-driven models

Maps that attempt to predict landslide occurrences have essentially stayed the same since 1972. In fact, most of the geo-scientific efforts have been dedicated to improve the landslide prediction ability with models that have largely increased their complexity but still have addressed the same binary classification task. In other words, even though the tools have certainly changed and improved in 50 years, the geomorphological community addressed and still mostly addresses landslide prediction via data-driven solutions by estimating whether a given slope is potentially stable or unstable. This concept corresponds to the landslide susceptibility, a paradigm that neglects how many landslides may trigger within a given slope, how large these landslides may be and what proportion of the given slope they may disrupt. The landslide intensity concept summarized how threatening a landslide or a population of landslide in a study area may be. Recently, landslide intensity has been spatially modeled as a function of how many landslides may occur per mapping unit, something, which has later been shown to closely correlate to the planimetric extent of landslides per mapping unit. In this work, we take this observation a step further, as we use the relation between landslide count and planimetric extent to generate maps that predict the aggregated size of landslides per slope, and the proportion of the slope they may affect. Our findings suggest that it may be time for the geoscientific community as a whole, to expand the research efforts beyond the use of susceptibility assessment, in favor of more informative analytical schemes. In fact, our results show that landslide susceptibility can be also reliably estimated (AUC of 0.92 and 0.91 for the goodness-of-fit and prediction skill, respectively) as part of a Log-Gaussian Cox Process model, from which the intensity expressed as count per unit (Pearson correlation coefficient of 0.91 and 0.90 for the goodness-of-fit and prediction skill, respectively) can also be derived and then converted into how large a landslide or several coalescing ones may become, once they trigger and propagate downhill. This chain of landslide intensity, hazard and density may lead to substantially improve decision-making processes related to landslide risk