Luigi Lombardo

University of Twente | UT · Department of Applied Earth Science

Understanding the dynamics between public disaster assistance, disaster damages, and social vulnerability at county-level is crucial for designing effective disaster mitigation strategies. This study utilized the Local Bivariate Moran Index (LBMI) and geographically weighted regression (GWR) models to examine spatial patterns and relationships betw...

Mountainous landscapes affected by strong earthquakes typically exhibit higher landslide susceptibility in post-seismic periods compared to pre-seismic conditions. This concept is referred to as the earthquake legacy effect, which needs to be better understood to develop an accurate post-seismic landslide hazard assessment. The earthquake legacy ef...

Landslides can occur in different periods under different predisposing factors that are changed over time or no longer exist. We aim to address how discarding this important rule or assuming conditions to be intact can be detrimental in fast-evolving regions as it can easily mislead susceptibility models. Instead, this research prompts researchers...

This work pinpoints a discernible paradox in Landslide Hazard Assessment for Situational Awareness (LHASA): 1) simplicity in avoiding the intrinsic uncertainties stored in each part cascade through the entire computational process and diminish the value of a more inclusive and integrated analysis, and 2) including enough accessible and achievable c...

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...

Delineating spatiotemporal variations in landslide susceptibility patterns is crucial for landslide prevention and management. In this study, we present a space–time modeling approach to predict the annual landslide susceptibility of the main island of Taiwan from 2004 to 2018. Specifically, we use a Bayesian version of the binomial generalized add...

Shallow landslides represent potentially damaging processes in mountain areas worldwide. These geomorphic processes are usually caused by a combination of predisposing, preparatory, and triggering environmental factors. At regional scales, data-driven methods have been used to model shallow landslides by addressing the spatial and temporal componen...

This study aims to derive and evaluate new empirical rainfall thresholds as the basis for landslide early warning in Progo Catchment, Indonesia, using high-resolution rainfall datasets. Although attempts have been made to determine such thresholds for regions in Indonesia, they used coarse-resolution data and fixed rainfall duration that might not...

To accurately quantify landslide hazard in a region of Turkey, we develop new marked point-process models within a Bayesian hierarchical framework for the joint prediction of landslide counts and sizes. We leverage mark distributions justified by extreme-value theory, and specifically propose ‘sub-asymptotic’ distributions to flexibly model landsli...

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...

Recent wildfires in Australia have led to considerable economic loss and property destruction, and there is increasing concern that climate change may exacerbate their intensity, duration, and frequency. hazard quantification for extreme wildfires is an important component of wildfire management, as it facilitates efficient resource distribution, a...

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 probabilistic solutions though, the original susceptibility definition has been challenged to incorporate dynamic elements that woul...

Classifying a given landscape on the basis of its susceptibility to surface processes is a standard procedure in low to mid-latitudes. Conversely, these procedures have hardly been explored in periglacial regions. However, global warming is radically changing this situation and will change it even more in the future. For this reason, understanding...

Hydro-morphological processes (HMP, any natural phenomenon contained within the spectrum defined between debris flows and flash floods) pose a relevant threat to infrastructure, urban and rural settlements and to lives in general. This has been widely observed in recent years and will likely become worse as climate change will influence the spatio-...

The literature on landslide susceptibility is rich with examples that span a large number of topics. However, the component that pertains to the extension of the susceptibility framework toward space-time modeling is largely unexplored. This statement is even more valid when looking at the landslide risk context, where hardly any scientific contrib...

This study set out to derive empirical rainfall thresholds for landslides in the Progo Catchment, Indonesia, using high-resolution satellite-based precipitation products (SPPs) and rain gauge data. The SPPs are the gauge-adjusted version of the Global Satellite Mapping of Precipitation (GSMaP-GNRT) and the bias-corrected version of the Climate Pred...

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...

We develop a slope-unit based landslide susceptibility model using the benchmark dataset proposed in the session, located in Central Italy. As a result, we produce two susceptibility maps based on the two different landslide presence attribute fields included in the dataset. The proposed dataset is a subset of a much larger one, recently used to ob...

A R T I C L E I N F O Edited by Jing M. Chen Keywords: Hillslope deformation InSAR Prediction Line-of-sight velocity Multivariate regression Sentinel-1 Spatio-temporal model A B S T R A C T Spatiotemporal patterns of earth surface deformation are influenced by a combination of the geologic, topo-graphic, seismic, anthropogenic, meteorological and c...

Classifying a given landscape on the basis of its susceptibility to surface processes is a standard procedure in low to mid-latitudes. Conversely, these procedures have hardly been explored in periglacial regions, primarily because of the limited presence of human settlements and, therefore, the little need for risk assessment. However, global warm...

Strong earthquakes not only induce co-seismic mass wasting but also exacerbates the shear strength of hillslope materials and cause higher landslide susceptibility in the subsequent years following the earthquake. Previous studies have mainly investigated post-seismic landslide activity mainly by using landslide inventories. However, landslide inve...

This manuscript presents an analytical protocol based on explainable AI where the susceptibility to hydro-morphological processes is estimated per catchment at the continental scale. In doing so, we highlight the strength of this approach, for each covariate contribution can be queried and understood at the single mapping unit level. To further e...

The use of Artificial Neural Network (ANN) approaches has gained a significant role over the last decade in the field of predicting the distribution of effects triggered by natural forcing, this being particularly relevant for the development of adequate risk mitigation strategies. Among the most critical features of these approaches, there are the...

The last three decades have witnessed substantial developments in data-driven models for landslide prediction. However, this improvement has been mostly devoted to models that estimate locations where landslides may occur, i.e., landslide susceptibility. Although susceptibility is crucial when assessing landslide hazard, another equally important p...

Shallow landslides are frequently occurring hazards in mountainous landscapes all over the world. These processes are caused by a combination of static (i.e., predisposing factors: topography, material properties) and dynamic controls (i.e., preparatory and triggering factors: heavy rainfall, snow-melt). Data-driven methods have been used to model...

Several studies on empirical rainfall thresholds for landslide occurrence depend on the measurements of nearest rain gauges to the landslides, without taking in consideration the morphological and hydrological settings of the areas. Therefore, we introduce the DEWS (Distance, Elevation, Watershed, and Slope unit) QGIS software tool, for selecting r...

The Svalbard Archipelago represents the northernmost place on Earth where cryospheric hazards, such as thaw slumps (TSs) and thermo-erosion gullies (TEGs) could take place and rapidly develop under the influence of climatic variations. Svalbard permafrost is specifically sensitive to rapidly occurring warming, and therefore, a deeper understanding...

High spatiotemporal resolution satellite data have been available to provide rainfall estimates with global coverage and relatively short latency. On the other hand, a rain gauge measures the actual rain that falls to the surface, but its network density is commonly sparse, particularly those that record at sub-daily records. These datasets are ext...

Ground motion simulations solve wave equations in space and time, thus producing detailed estimates of the shaking time series. This is essentially uncharted territory for geomorphologists, for we have yet to understand which ground motion (synthetic or not) parameter, or combination of parameters, is more suitable to explain the coseismic landslid...

Mountainous landscapes affected by strong earthquakes exhibit relatively higher landslide susceptibility in post-seismic periods compared to pre-seismic conditions. This concept is referred to as the earthquake legacy effect and is mainly examined by monitoring either rapid landslide occurrences or slow-moving landslides over time. To provide a mor...

The current status of technological advancement does not allow to generate complete flood simulations in real-time for large geographic areas. This hinders warning-systems, interactive planning tools and detailed forecasts and as a consequence the population cannot be quickly or reliably informed of where large masses of water will flow. Our novel...

Co-seismic landslides are triggered by strong ground shaking in mountainous areas, resulting in threats to human activity and infrastructure. Methods for physically-based modelling of co-seismic landslide triggering play an important role in disaster prevention and mitigation. Current approaches, however, focus on direct and full failure of sloping...

The gully erosion susceptibility literature is largely dominated by contributions focused on model comparison. This has led to prioritize certain aspects and leave others underdeveloped as compared to other natural hazard applications. For instance, in gully erosion data-driven modeling most studies use different platforms when it comes to data man...

Hydro-morphological processes (HMP, any natural phenomenon contained within the spectrum defined between debris flows and flash floods) pose a relevant threat to infrastructure, urban and rural settlements and to lives in general. This has been widely observed in recent years and will likely become worse as climate change will influence the spatio-...

Landslide susceptibility assessment using data-driven models has predominantly focused on predicting where landslides may occur and not on how large they might be. The spatio-temporal evaluation of landslide susceptibility has only recently been addressed, as a basis for predicting where and when landslides might occur. The present study combines t...

Rapid and accurate identification of landslides is an essential part of landslide hazard assessment, and in particular it is useful for land use planning, disaster prevention, and risk control. Recent alternatives to manual landslide mapping are moving in the direction of artificial intelligence—aided recognition of these surface processes. However...

Landslides of the slide-type movement represent a potential threat to people and infrastructure in mountain areas all over the world. At regional scales, data-driven models are typically used to assess landslide susceptibility, i.e., to map where landslides are more or less likely to occur. Such assessments frequently serve as basic input for lands...

Knowledge of geomorphological processes, their dynamics, and resulting landforms shaped decades of this geoscientific field. The recent advances in technologies for the acquisition of spatial data have brought fundamental changes in increasing the accuracy and frequency of evaluating the rate of geomorphological processes. Digitization, miniaturiza...

Portraying spatiotemporal variations in landslide susceptibility patterns is crucial for landslide prevention and management. In this study, we implement a space-time modeling approach to predict the landslide susceptibility on a yearly basis across the main island of Taiwan, from 2004 to 2018. We use a Bayesian version of a binomial generalized ad...

Statistical models for landslide hazard enable mapping of risk factors and landslide occurrence intensity by using geomorphological covariates available at high spatial resolution. However, the spatial distribution of the triggering event (e.g., precipitation or earthquakes) is often not directly observed. In this paper we develop Bayesian spatial...

Landslide susceptibility corresponds to the probability of landslide occurrence across a given geographic space. This probability is usually estimated by using a binary classifier which is informed of landslide presence/absence data and associated landscape characteristics. Here, we consider the Italian national landslide inventory to prepare slope...

The Svalbard Archipelago represents the northernmost place on Earth where cryospheric hazards, such as thaw slumps (TS) and thermo-erosion gullies (TEG) could take place and rapidly develop under the influence of climatic 15 variations. Svalbard permafrost is specifically sensitive to rapidly occurring warming and therefore, a deeper understanding...

Understanding the effects of snowmelt in terms of large slope deformation in high mountainous areas could come from the use of Interferometric Synthetic Aperture Radar (InSAR) techniques. In this work, we investigate a slow moving, extremely large landslide (~20 km2) in the Chitral region in Northern Pakistan, which threatens several villages. Our...

For decades, a full numerical description of the spatio-temporal dynamics of a landslide could be achieved only via physics-based models. The part of the geomorphology community focusing on data-driven model has instead focused on predicting where landslides may occur via susceptibility models. Moreover, they have estimated when landslides may occu...