Until now, a full numerical description of the spatio-temporal dynamics of a landslide could be achieved only via physics-based models. The part of the  geoscientific community  developing data-driven model has instead focused on predicting where landslides may occur via susceptibility models. Moreover, they have estimated when landslides may occur via models that belong to the early-warning-system or to the rainfall-threshold themes. In this context, few published researches have explored a joint spatio-temporal model structure. Furthermore, the third element completing the hazard definition, i.e., the landslide size (i.e., areas or volumes), has hardly ever been modeled over space and time. However,  technological advancements in data-driven models have reached a level of maturity that allows to model all three components (Where, When and Size). This work takes this direction and proposes for the first time a solution to the assessment of landslide hazard in a given area by jointly modeling landslide occurrences and their associated areal density per mapping unit, in space and time. To achieve this, we used a spatio-temporal landslide database generated for the Nepalese region affected by the Gorkha earthquake. The model relies on a deep-learning architecture trained using an Ensemble Neural Network, where the landslide occurrences and densities are aggregated over a squared mapping unit of 1x1 km and classified/regressed against a nested 30~m lattice. At the nested level, we have expressed predisposing and triggering factors. As for the temporal units, we have used an approximately 6-month resolution. The results are promising as our model performs satisfactorily both in the susceptibility (AUC = 0.93) and density prediction (Pearson r = 0.93) tasks. This model takes a significant distance from the common susceptibility literature, proposing an integrated framework for hazard modeling in a data-driven context.

To promote reproducibility and repeatability of the analyses in this work, we share data and codes in a GitHub repository accessible from this link: https://github.com/ashokdahal/LandslideHazard. 

How to cite: Dahal, A., Tanyas, H., Van Westen, C., Van der Meijde, M., Mai, P. M., Huser, R., and Lombardo, L.: Space-time modelling of co-seismic and post-seismic landslide hazard via Ensemble Neural Networks., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3496, https://doi.org/10.5194/egusphere-egu23-3496, 2023.

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 shallow landslides at regional scales, in which efforts have been taken to separately investigate the spatial component (i.e., landslide susceptibility) and temporally-varying conditions (e.g., rainfall thresholds). However, the joint assessment of shallow landslides in space and time using data-driven methods remains challenging.

In the present work, we aim to predict the occurrence of precipitation-induced shallow landslides in space and time (i.e., the where and the when) within the Italian province of South Tyrol (7,400 km²). In this context, we test the added value of describing the precipitation leading to landslide occurrence as a functional predictor, in contrast to traditional approaches where precipitation is taken as a scalar predictor. We built upon hourly precipitation data from the Integrated Nowcasting through Comprehensive Analysis system (INCA, provided by Geosphere Austria) and past landslide occurrences from 2000 to 2021, which systematically relate to damage-causing landslide events. The methodical framework comprised filtering the landslide inventory, sampling landslide absences in space and time (i.e., balanced across years and months), extracting static and dynamic environmental factors (e.g., topography, lithology, land cover, and hourly precipitation), and removing trivial areas and time periods. We implemented a Functional Generalized Additive Model (FGAM) to derive statistical relationships between the different static factors as scalar predictors, the hourly precipitation preceding a potential landslide event as a functional predictor, and the occurrence in space and time of shallow landslides. The resulting predictions were assessed using cross-validation and transferred into space for different precipitation measures in order to hindcast landslide events.

The results from this novel approach are expected to integrate landslide predictions in space and time for large areas by accounting for static and dynamic (i.e., hourly precipitation grids) landslide controls, seasonal effects, and the underlying data limitations (e.g., inventory incompleteness). The findings associated with this research are framed within the PROSLIDE project, which has received funding from the research program Research Südtirol/Alto Adige 2019 of the Autonomous Province of Bozen/Bolzano – Südtirol/Alto Adige.

How to cite: Moreno, M., Steger, S., Lombardo, L., Opitz, T., Crespi, A., Marra, F., de Vugt, L., Zieher, T., Rutzinger, M., Mair, V., Pittore, M., and van Westen, C.: Functional regression for space-time prediction of precipitation-induced shallow landslides in South Tyrol, Italy, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9538, https://doi.org/10.5194/egusphere-egu23-9538, 2023.

The Hindu Kush-Himalaya (HKH) is one of the greatest geologically fragile young mountain systems in the world and are highly vulnerable to landslides. Extreme rainfall, seismic activity and human interventions result in landslides and related hazards that cause severe economic losses each year and can cause hundreds of fatalities annually. Effective response, mitigation and planning for landslide impacts is often challenging due to limited information on historical landslide behavior, land surface characteristics, impacts, and triggering processes. High resolution and publicly available satellite data, Earth system models, and machine learning approaches can provide enhanced understanding of where and when landslides impact  the HKH and importantly how these patterns may change in the future. Several efforts led by NASA, including the High Mountain Asia program and the SERVIR program have enabled new datasets, models, and capabilities to support both scientific advancement and capacity building activities within this region in terms of cascading hazards and their impacts. This work leverages a global and regional modeling approach called the Landslide Hazard Assessment for Situational Awareness (LHASA) as well as a machine-learning driven algorithm for identifying landslides called the Semi-Automatic Landslide Detection (SALaD) to bridge spatial and temporal scales for improved situational awareness of landslide hazards. Building upon several downscaled, regionally focused near real-time and forecasted precipitation information, this work also presents an initial assessment of changing patterns of potential landslide hazard across this region considering the past several decades and looking to the end of the 21^st century. Through harnessing open source tools and data products available for HKH, this work demonstrates the potential for improving situational awareness and characterization of landslide hazards within the regional context at daily to decadal scales. Working closely with regional stakeholders, these capabilities will inform emergency response and planning on the ground as well as provide context for possible future mitigation needs.

How to cite: Kirschbaum, D., Stanley, T., and Amatya, P.: Harnessing new tools and satellite products to support landslide forecasting and capacity building over High Mountain Asia, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3629, https://doi.org/10.5194/egusphere-egu23-3629, 2023.

Understanding the process of landslide failure is crucial for predicting and minimizing the consequences of landslides. Landslide failure can be caused by a variety of factors, including geology, topography, and soil conditions, while environmental triggers such as precipitation and earthquakes initiate the movement. We can better understand the risks associated with landslides and apply appropriate steps to decrease those risks by disclosing the precise mechanisms that contribute to landslides in a specific location. To reveal these mechanisms, we use an advanced mathematical model called the Topological Data Analyses (TDA) that decodes the landslide's shapes and configurations as it includes factors such as the slope of the failures, the presence of cliffs or other steep terrain features, and kinematic propagation of the failures. Then we use these features to categorize the different landslide failure mechanisms such as slides, flows, falls, and complex landslides. Our study paves the way to classify existing and past inventories that miss these failure type information. This information will help the landslide predictive community in general and in the different stages of the landslide risk cycle as pertinent information of failure mechanisms are important for effective forecasting, susceptibility, hazard, and risk modelling.

How to cite: Bhuyan, K., Rana, K., Ferrer, J., and Nava, L.: Predicting landslide failure mechanisms using advanced mathematical models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5911, https://doi.org/10.5194/egusphere-egu23-5911, 2023.

The identification of areas susceptible to landslides is critical for planners, managers, and decision makers in developing functional mitigation strategies. Recent applications of machine learning and data mining methods have demonstrated their effectiveness in large-scale assessments of landslide susceptibility. At Climate X, we utilise a range of big Earth remote sensing data alongside machine learning techniques to evaluate the spatial susceptibility landslides at continental scale. We compile several conditioning factors— including topographic, subsurface, and land use data—and combine them with continental scale landslide inventories to generate landslide susceptibility maps for Europe and North America. Climate model projections for different emissions scenarios are then used to understand how climate change could modify the spatial occurrence of landslide events with a focus on landslides triggered by rainfall within steeper terrain. Our results demonstrate how the combined application of big Earth data and machine learning can provide time sensitive assessments of landslide hazard over large spatial scales.

How to cite: Reveley, G., Mitchell, H., Burke, C., Brennan, J., Woodhouse, S., and Ramsamy, L.: Continental Scale Landslide Susceptibility Mapping Using Machine Learning Techniques, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16218, https://doi.org/10.5194/egusphere-egu23-16218, 2023.

Strong ground motion intensity measures, for example Peak Ground Acceleration (PGA) or Peak Ground Velocity (PGV), are important dynamic features, or predictive variables, in most regional earthquake induced landslide susceptibility models. Despite global reliance on these ground motion intensity measures, little work has been done to evaluate how dynamic feature selection, and underlying ground motion models, influence the predictive performance of landslide susceptibility models. Here, we conduct a feature sensitivity analysis, training a suite of 131 comparative logistic regression models on the distribution of landslides from the 2016 Mw 7.8 Kaikōura earthquake on the South Island of New Zealand. This analysis uses a combination of common susceptibility features (e.g., slope, curvature), distance to a surface fault rupture (both a susceptibility and dynamic feature), and 9 ground motion intensity measures (PGA, PGV, Arias Intensity, PSA - Peak Spectral Acceleration at 0.3, 1.0, 3.0, and 10.0 seconds, MMI - Modified Mercalli Intensity, and Duration of Shaking) derived from 4 published ground motion models for the Kaikōura earthquake. Ground motion is highly correlated with distance to a surface fault rupture (a Pearson R² as high as 0.86). Models trained using both distance to surface fault rupture and a ground motion intensity measure produce high model performance but are overfit to the Kaikōura landslide distribution with negative model coefficients for most ground motion intensity measures. Excluding distance to a surface fault rupture still produces high model performance (less than a 0.04 drop in Model AUC) when including the most predictive ground motion intensity estimates (typically MMI, PSA at a period of 0.3 seconds, PGA, or PGV from the USGS ShakeMap) and results in more explainable, and likely more applicable, model coefficients. Although MMI and PSA at a period of 0.3 seconds (3.3 Hz) appear to be good predictors of the landslide distribution from the Kaikōura earthquake, MMI can be influenced by the availability of felt reports and the frequency of shaking can vary in different earthquakes. PGA and PGV provide acceptable model performance for the Kaikōura landslide distribution and are likely more applicable to other events. Highly variable performance is observed when applying the same ground motion intensity measures from different published ground motion models. The choice of ground motion model may, therefore, introduce a high degree of uncertainty into the landslide susceptibility analysis that remains relatively underappreciated in most studies. Additional recorded strong motion data will likely be required to further improve ground motion models, and thereby landslide susceptibility models, for future events.

How to cite: Bloom, C., Stahl, T., Massey, C., Howell, A., and Singeisen, C.: The influence of strong ground motion intensity measures on earthquake induced landslide susceptibility estimates, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9463, https://doi.org/10.5194/egusphere-egu23-9463, 2023.

Most shallow landslides are triggered by prolonged or short intense precipitation events. In dynamic physically-based model approaches for landslide susceptibility assessment, the input precipitation data is often derived from a single or a small number of rain gauges. However, precipitation patterns show a high variance in their spatial distribution that is insufficiently captured by standard rain gauge networks, particularly if inter-station distances are large. Spatially distributed weather radar-derived rainfall products have been used as input for physically-based landslide models to overcome the shortcomings of interpolated station measurements. However, the use of weather radar precipitation in physically-based modelling is not straightforward, since it represents an indirect measurement and thus requires pre-processing steps. With this in regard, the Integrated Nowcasting through Comprehensive Analysis (INCA) system (publicly released by GeoSphere Austria) provides historical (from 2011) hourly precipitation data at a 1 x 1 km resolution that combines weather radar data, station data and elevation data for the inclusion of elevation effects. The result is a pre-processed dataset that integrates the quantitative accuracy of station data with the spatial information provided by the radar data.

In this study, we investigate whether the use of INCA precipitation data leads to improved model performance of TRIGRS compared to a conventional set-up using station data. We model slope stability in a 53 km² sub-catchment located in South Tyrol (Italy) for an event that occurred in August 2016 with the INCA data and with precipitation data derived from a single station. The study compares the performances of the two model set-ups and their required parameter calibrations. First tests indicate that the model set-up using INCA data outperforms the station data set-up, as the spatial trend present in the INCA dataset of the modelled storm event follows the spatial trend present in the landslide inventory. In earlier studies and in a preliminary comparison with station data from South Tyrol, the historical INCA data was also shown to underestimate higher precipitation intensities, indicating that the two model set-ups require separate parameter calibrations. In future research, the calibrated model using the historical INCA dataset could be used with the nowcasting datasets from INCA to investigate if and how the INCA dataset can be used for landslide early warning systems.

This study is related to the PROSLIDE project that received funding from the research program Research Südtirol/Alto Adige 2019 of the Autonomous Province of Bozen/Bolzano (Südtirol/Alto Adige). In addition, the study also made use of the High-Performance Computing systems at the University of Innsbruck.

How to cite: de Vugt,  ., Zieher, T., Schneider-Muntau, B., Moreno, M., Steger, S., and Rutzinger, M.: Improving the performance of a dynamic slope stability model (TRIGRS) with integrated spatio-temporal precipitation data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7845, https://doi.org/10.5194/egusphere-egu23-7845, 2023.

Coseismic landslides can result in significant economic loss and casualties. In Taiwan, combined effects of high seismicity, geology and steep topographic relief cause the high susceptibility of landslides associated with earthquakes. In this study, we use Newmark analysis, decision tree (DT) and multivariate decision tree (MDT) algorithm to perform the nowcasting and delivery susceptibility map on website. The strong-motion records with local magnitude larger than 6.0 from 1990 to 2020 are collected and the 175 potential planar failure slopes with similar lithology are selected as the target slopes (TS). We first found the representative station (RS) satisfied the specific thresholds of peak ground acceleration (> 196 gal) and Newmark displacement (> 10 cm), and then hillslopes around the TS associated with the RS with potential failures caused by earthquakes were carefully mapped by satellite images. The classification labels of failure and non-failure are used for the classification and regression trees (CART), C5.0 and multivariate regression trees (MRT). Overall, the accuracy (ACC) and false-negative rates (FNR) of C5.0 model for entire Taiwan were 83.3% and 10.7%, respectively. In advanced, the ACC can reach 95.8% in central Taiwan with merely 5.6% FNR. We use 2022 Hualien Yuli earthquake and 2022 Chishang earthquake to validate the DT model. The ACC is 83.3% with FNR = 0% in Hualien Yuli earthquake and the ACC is 76.9% with FNR = 0% in Chishang earthquake for entire Taiwan C5.0 model, indicates the model has reliable prediction outcomes. However, these two earthquakes didn’t cause the coseismic landslide case associate with 175 TS to validate the true positive portion. Additional TS, which are the coseismic landslide caused by 2022 earthquakes, should be added in our training data. Finally, the results in this study have been displayed on the web-based for rapid coseismic landslide susceptibility assessment providing the distribution of risk slopes with traffic lights for emergency response and disaster mitigation.

How to cite: Lai, T.-S., Chao, W.-A., Yang, C., Wu, Y.-M., and Chang, J.-M.: Testing and Validation of Multiple Decision Trees Models for Rapid Coseismic Landslide Susceptibility Assessment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16358, https://doi.org/10.5194/egusphere-egu23-16358, 2023.

Rainfall-induced landslides represent an important hazard in mountainous regions worldwide. Landslides commonly impact the functioning of infrastructure assets such as roads and railways and occasionally damage buildings or result in fatalities. In the Nordic region, rainfall-induced landslides constitute a significant hazard, accounting for a considerable amount of Norway's national landslide database entries.

Because of climate change, the frequency of rainfall and soil moisture conditions that usually trigger landslides will become more variable. This leads to weaker predictions for the location and frequency of future landslide events from current models. Understanding how the landslide hazard will change can help plan mitigation along linear infrastructure and reduce the risk to the population.

Here, we report the findings from the NordicLink project, financed by Nordforsk, where a methodology to characterise landslide hazard at a global scale has been adopted to develop Nordic hazard maps.

The methodology to characterise the landslide hazard at a global scale has been developed within the activities of the "Global Infrastructure Resilience Index" (GIRI) project, funded by the Coalition for Disaster Resilient Infrastructure (CDRI). The method combines landslide susceptibility and rainfall to compute landslide probability at a global scale. The susceptibility map classifies terrains into five susceptibility classes by combining slope, vegetation, lithology, and soil moisture information from global datasets. Rainfall information has been obtained from the W5E5 dataset for the period 1979-2016 and the IPSL-CM6A-LR climate model from ISIMIP3b dataset SSP126 and SSP585 scenarios for the period 2061-2100. To characterise the rainfall triggering potential, the 24 h rainfall intensities have been used to distinguish between five rainfall hazard classes. Finally, a hazard matrix has been employed to combine landslide susceptibility and rainfall. The output is a probabilistic hazard map covering the world with a resolution of three arc seconds (approximately 90 m at the equator).

In the NordicLink project, higher-quality Nordic-scale data and landslide inventories are used as input to the above-mentioned procedure to obtain probabilistic hazard maps covering Norway, Sweden, and Finland. The study concludes with a comparison between the NordicLink hazard maps and the (global) GIRI model. As expected, landslide hazard is higher in western Norway and decreases towards the East. Finland is the country with the lowest landslide hazard.

How to cite: Palau, R. M., Nadim, F., Gisnås, K., Vicari, H., Dijkstra, J., Gilbert, G., and Solheim, A.: Landslide hazard assessment for climate change adaptation of linear infrastructure: From the global scale to the Nordic scale, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14345, https://doi.org/10.5194/egusphere-egu23-14345, 2023.

While the triggering process of landslides remains are multiple, the importance of seismic waves is well established. The leading approach to study coseismic landslides is through statistical studies or simple models such as the Newmark method. While providing useful information, these approaches fall short at predicting landslide triggering especially in complex environments such as submarine conditions. Here we study the possibility to establish a simple physically-based model to fulfill this purpose. Assuming strain is localized in a thin weak layer at the base of the landslide, we model the landslide as slip on a planar sloping surface. By analogy to tectonic faults, we adopt the rate-and-state friction law on this surface, a phenomenological law widely used to describe slow sliding on faults during earthquakes. This approach produces a range of landslide behaviors ranging from stable and unstable conditions. With a one-dimensional mathematical and numerical model, representing a wave incidence normal to the landslide interface, we identify the main triggering factors of slow and fast sliding and characterize the non-linear evolution of the slip instability. In particular, we map the range of slip behaviors as a function of non-dimensional numbers, such as the ratio of incident wave frequency to seismic resonance frequency of the layer. The incident wave amplitude also play an important role in the model: the slip velocity during acceleration depends exponentially on the ratio of the incident stress wave amplitude to the ambient confining stress. This basic model is a starting point that can be extended to include other relevant processes like the coupling between pore pressure and slip.

How to cite: Lestrelin, H., Ampuero, J.-P., Mercerat, D., and Courboulex, F.: Modelling the onset of earthquake-induced landslides as triggered slip under rate-and-state friction law, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6257, https://doi.org/10.5194/egusphere-egu23-6257, 2023.

Machine learning algorithms are commonly used for landslide susceptibility mapping; however, their application for spatiotemporal landslides prediction remains widely unexplored. Only static predisposing factors are needed for susceptibility assessment, which indicates where landslides are more likely to occur in the future. Therefore, dynamic parameters, such as critical or antecedent rainfall, which are mainly related to the temporal occurrence of landslides, remain unused.

This work provides a contribution to fix this gap by proposing an innovative methodology for the application of the Random Forest (RF) algorithm for spatio-temporal landslides prediction, landing to a more complete hazard assessment. This dynamic approach is based on the method of identification of non-landslide events in comparison with the reporting day and location of the landslide events; conceived to include both static and dynamic parameters as model input variables. Among other advantages, RF allows the calculation of the Out-of-Bag Error (OOBE) and depicts Partial Dependence Plots (PDPs), two useful indices of the influence of each input variable in determining the triggering of landslides. In this work, these indicators were used to verify the applicability of RF with the proposed methodology, investigating if the model outcomes are consistent with the triggering mechanism observed in the inventoried landslides.

The study area is the Metropolitan City of Florence (MCF), Central Italy, for which a detailed and dated landslide inventory is available, mainly composed of shallow landslides and debris flows. As first dynamic variable it was chosen to use the cumulative rainfall at various time steps, which allows to consider both short and long-term rainfall. The month of observation of the events is used as second dynamic input parameter, as a categorical type, to represent the seasonal variability. In addition, a static index related to the predisposition of the area to landslides (i.e., a classical susceptibility map) was inserted, to directly compare the influence of static and dynamic parameters on spatiotemporal prediction of landslides.

The goals of this research are: i) to understand how to populate training and test datasets with observations sampled over space and time, ii) to assess which rainfall variables are statistically more influential on landslides triggering, and iii) to verify the applicability of the proposed dynamic approach for landslides probability assessment.

The RF model employed through the proposed methodology showed encouraging results, consistent with the actual knowledge of the physical mechanism of the triggering of shallow landslides and debris flows (mainly influenced by short and intense rainfall). Some benchmark configurations have been identified which represent a promising starting point for future applications of machine learning models for landslide probability mapping.

How to cite: Nocentini, N., Rosi, A., Segoni, S., and Fanti, R.: Analysis of the influence of rainfall in the triggering of landslides through machine learning: an innovative approach in the perspective of spatiotemporal landslide forecasting, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5708, https://doi.org/10.5194/egusphere-egu23-5708, 2023.

Slope units are terrain partitions bounded by drainage and divide lines, which have been shown to overcome many of the weaknesses of the traditional grid mapping units in landslide susceptibility models. Namely, they better capture the geometry of the terrain, mitigate the need to use multiple raster resolutions when the size and shape of landslides in the region are highly variable, provide a solution for incorporating landslide data in different formats (i.e., point and vector), and are more amenable to landslide repositories with less accurate landslide locations. However, the use of slope units in landslide susceptibility studies remains limited due, in part, to challenges with current delineation methods, including prohibitive computational costs, time-intensive manual processing, or indeterminate parameterizations. We introduce a computationally efficient algorithm for the parameter-free delineation of slope units. Our method determines the scaling of the watersheds at the threshold between fluvial and hillslope processes. It then subdivides these watersheds according to their longest flow paths. Our algorithm can run in parallel, effectively delineate slope units orders of magnitude faster than other parameter-free methods, and requires no significant pre- or post-processing to use. Here we explore the implementation of our algorithm and demonstrate some of the advantages of slope units over the grid-cell mapping unit for evaluating landslide susceptibility.

How to cite: Woodard, J., Mirus, B., Leshchinsky, B., and Crawford, M.: An efficient and parameter-free algorithm to delineate slope units for landslide susceptibility, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9240, https://doi.org/10.5194/egusphere-egu23-9240, 2023.

Rainfall-induced shallow landslides, which mobilize the first few meters of soil cover (usually <2m) following significant rainfall events, can severely impact human life. They most frequently damage human activities as they often dam rivers, invade roads, destroy crops and occasionally cause the loss of human lives.

Such landslides can develop in vineyards, as they are commonly grown on hillslopes, causing farmers to lose revenue. However, not all vineyards are managed the same way: standard management techniques include (1) Tillage and Total Tillage (T/TT), which is the tillage of the soil between rows up to 6 times a year; (2) Permanent Grass Cover (PGC), in which grass is allowed to grow between rows and (3) ALTernating tillage-grass (ALT), the practice of tilling every other row.

Since land use has been proven to impact landslide susceptibility, the present work aims to investigate how landslide susceptibility would be affected by vineyard management changes.

To do so, a probabilistic version of the physically based landscape evolution model Lapsus-LS was adopted.

Created as a physically based model, LAPSUS simulates soil movement downslope by calculating the critical rainfall needed for triggering landsliding. After calibrating the critical rainfall threshold, the model calculates a slide trajectory and accumulation lobe with a double multiple flow routine.

The model requires as inputs the Digital Terrain Model (DTM) of the area, range values for geotechnical parameters, and a land use map of the site. Associated with the latter are values of root cohesion, which vary among different vineyard management practices: root cohesion is lower in T and TT vineyards and is higher in PGC and ALT vineyards.

In its probabilistic version, the model selects each input from a range of acceptable values and runs its course 100 times to compile a map illustrating which cells are more commonly predicted as unstable. Cells calculated as unstable in more than 50% of the iterations are classified as such.

The model was applied in the basin of Rio Vergomberra (municipality of Canneto Pavese, PV), a hilly area of 0.54 km², in the Oltrepò Pavese (located in the southern-west sector of the region of Lombardy, in Italy) where shallow landslides triggered by rainfall are expected. Vineyards in the area are managed through T and TT techniques in the southeast sector, where most of the landslides have occurred, and through PGC and ALT in the northwest sector, where no landslides have occurred.

It was therefore evaluated how the predicted landslide susceptibility would be affected if vineyards currently cultivated with T and TT management techniques were to be managed through PGC.

The result was a lowering of the predicted susceptibility in previously unstable T and TT vineyards, despite the steep slope angles.

The result is also supported by the generally lower number of landslides in PGC vineyards compared to T and TT vineyards in the Oltrepò Pavese. In the presented study area alone, all five past landslides that occurred in vineyards were located in tilled vineyards. 

How to cite: Giarola, A., Meisina, C., Tarolli, P., Schoorl, J. M., Baartman, J. E. M., Zucca, F., and Bordoni, M.: Predicting the impact of vineyard management changes on landslide susceptibility by incorporating probabilistic parameterization into the landscape evolution model LAPSUS, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8551, https://doi.org/10.5194/egusphere-egu23-8551, 2023.

Catastrophic events, such as Val Pola 1987, Sarno 1998, Casamicciola Terme 2009 and 2022, have showed the fragility of the Italian territory towards geo-hydrological hazards, that represent a serious threat to buildings, infrastructures and, of course, for human beings. Given local geological and morphological factors (predictor factors), and following the climate crisis, the connection between flash floods and landslides is becoming stronger and stronger. For this reason, both the scientific community and stakeholders, such as the owners/ managers of the electro-energetic system (EES), are moving their interest in this field especially for risk planning purposes. According to national and European policies, in fact, they are called to increase the resilience of power network against natural hazards, particularly those related to climate change, trying to predict their temporal and spatial occurrence.

Rockfalls, slides and debris flow represent the most rapid processes of slopes evolution and they are conditioned by the local morphology, geology and hydrology. For this study, three methods for determining a reasonable susceptibility mapping to these phenomena were evaluated, moving from the most subjective up to the most physically based. In the first one, a simple reclassification of the territory using the slope and the spatial frequency of landslides was adopted. For the second method, a linear model was implemented considering three different predictors of superficial landslide susceptibility i.e., slope, geology, and use of soil. This model has been compared with the reference landslide catalog obtaining a good “visual” accordance but with R² coefficient = 0.4, not so satisfactorily. The third method discriminates areas prone to rockfall, debris and slides using an elaborated General Linear Model-GLM that considers several predictors directly taken from spatial data of morphology (Digital Terrain Model), geology, hydrology and use of soil. This method was validated using the Relative Operating Characteristic-ROC error scores obtaining fairly good performance (Area Under the Curve-AUC = 0.65).

Even though there are several open problems regarding the most appropriate scale for studying geo-hydrological processes, the estimation made by the third method can be considered a suitable methodology to map landslide susceptibility. Italian EES is rather dense and covers the whole national territory, including large parts of mountain areas. Since it is necessary to predict the most vulnerable components of electrical networks, a well-built susceptibility map can increase the territorial information highlighting those areas where more investigations are needed due to possible hazardous situations that may occur in the future with a particular kinematics (rockfalls, slides or debris), because of the activation/reactivation of landslides.

This study provides information to government or private company to assure the protection of the infrastructure and to prepare quick reply during the early stages of emergency.

How to cite: Bernardo, N. and Abbate, A.: Resilience of the Italian power network against natural hazards: a methodology for the spatial susceptibility mapping of landslides, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13023, https://doi.org/10.5194/egusphere-egu23-13023, 2023.

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 peri-glacial regions, mostly because of the limited presence of human settlements and thus of the need for risk assessment. However, global warming is radically changing this situation and will change it even more in the years to come. For this reason, understanding the spatial and spatio-temporal dynamics of gemorphological processes in peri-arctic environments can be crucial to make informed decision in such unstable environments but also to shed light on what changes may follow at lower latitudes. For this reason, here we explored the use of artificially intelligent models capable of recognizing locations prone to develop retrogressive thaw slumps (RTS). These are cryospheric hazards induced by permafrost degradation and their development can negatively affect human settlements or infrastructure, change the sediment budget dynamics and release greenhouse gases. Specifically, we test a binomial Generalized Additive Modeling structure to estimate probability of RTS occurrences/development in the North sector of the Alaskan territory. The results we obtain show that our binary classifier is able to accurately recognize locations prone to RTS, in a number of goodness-of-fit and cross-validation routines. Overall, our analytical protocol has been implemented with the idea in mind of building an open source tool scripted in Python. 

How to cite: Elia, L., Castellaro, S., and Lombardo, L.: Spatial modeling of cryospheric hazards: predicting retrogressive thaw slumps in Alaska, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6467, https://doi.org/10.5194/egusphere-egu23-6467, 2023.

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 these new developments by proposing a data-driven model capable of estimating how large landslides may be, for the Taiwanese territory in a fourteen year time window. To solve this task, our model assumes that landslide sizes follow a Log-Gaussian probability distribution in space and time. Spatially the area is subdivided into 46074 slope units, with 14 annual timesteps from 2004 to 2018. Based on this subdivision, the model we implemented regressed landslide sizes against a covariate set that includes temporally static and dynamic properties. In the validation of our model, we nested a wide range of cross-validation (CV) procedures, such as a randomized 10fold-CV, a spatially constrained CV, a temporal leave-one-year-out CV, and a spatio-temporal CV. The final performance was described both numerically as well as in map form.

Overall, our space-time model achieves interpretable and satisfying results. With the availability of more complete landslide inventories, both temporally and spatially, we envision that spatio-temporal landslide size prediction will become the next challenge for geomorphologists to finally address a fundamental component of the landslide hazard definition. And, because of it’s spatio-temporal nature, we also envision that it may lead to landslide simulation studies for varying climate scenarios.

How to cite: Lombardo, L., Fang, Z., Wang, Y., and van Westen, C.: Space-time landslide size modelling in Taiwan, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6999, https://doi.org/10.5194/egusphere-egu23-6999, 2023.

Rainfall-induced shallow landslides are among the most common gravitational mass movements on natural and artificial slopes. In addition, these events are also responsible for severe consequences on ecosystem services provided by forests and rural landscapes, and on human lives, activities and infrastructures. Considering that the frequency of critical rainfall events is expected to increase in the future due to climate change, the development and application of physically-based models for assessing hydrogeological instability are necessary to monitor the potential occurrence of such landslide events and to suggest possible adaptive forest management. SlideforMAP, a software developed by the ecorisQ international association (ecorisq.org), is a physically-based model that quantifies the slope failure probability and tree roots' contribution to preventing soil mass movement. In this study, SlideforMAP was applied in the Mt. Nerone district (central Apennines, Italy) to asses the local landslide susceptibility. According to the national landslide inventories, significant landslides occurred in this area in the past. However, recent land-use changes that promoted forest recolonization on abandoned fields and grasslands, have substantially reduced the frequency of these critical events. This process enhanced the contribution of root reinforcement to landslide occurrence prevention. In fact, the historical landslides (covering about 14% of the entire Mt. Nerone area) are currently located on new forests previously used as agro-pastoral lands like in most of the study district. The SlideforMAP analysis detected potentially susceptible areas using factors such as morphology and related effects on water flow directions, soil type, and forest cover. We reconstructed some scenarios based on different rainfall return periods and forest cover, allowing for a pre-assessment of the potential hazard and risk levels in the investigated area. We found that the urban settlements and infrastructures are exposed to significant damage and that forested areas could play a primary protection role against shallow landslides. In detail, 17% and 32% of the total forest area in Mt. Nerone can potentially assume a primary function of direct protection of structures and infrastructures, respectively. The forest types more involved in this role are hop hornbeam-manna ash, turkey and downy oak, and beech forests, whereas 18% of the surface area subjected to risk of infrastructure damage is on pasture lands. Moreover, we were able to detect the forest areas with a substantial mitigation role and those where functional improvement is recommended. Finally, we were able to determine the mitigation effect of the forest expansion on the reduction of landslide frequency and to assess the current landslide susceptibility of the Mt. Nerone district. This study confirms the relevance of physically-based models in supporting land and forest management decision-making, aiming to increase the provisioning of ecosystem services and guarantee the safety of local communities, preserving the integrity of related cultural heritages and landscapes.

How to cite: Vitali, A., Murgia, I., Malandra, F., Prosdocimi, M., Tonelli, E., Baglioni, L., Giadrossich, F., Cohen, D., Schwarz, M., and Urbinati, C.: Susceptibility assessment of shallow landslides occurrence in the Mt. Nerone district (central Apennines, Italy), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9501, https://doi.org/10.5194/egusphere-egu23-9501, 2023.