NH3.1

The slope stability analyses using limit equilibrium method (LEM) and finite element method (FEM) are mostly concerned about the factor of safety (FS) value of the slope. LEM cannot predict the soil behaviour after failure, while FEM can only be used to measure the material deformation before failure. Currently the Smoothed Particle Hydrodynamics (SPH) method has begun to be used as an alternative to overcome excess distortion of the mesh in FEM analysis due to post-failure large deformations in slope stability analysis. In this study, the behaviour of soil materials will be modelled as particles using the SPH method with reference to the previous research. The Bingham fluid model is used as a viscoplastic model of the soil material, and the Drucker-Prager soil constitutive model is used to describe the elastic-plastic behaviour of the soil. This modelling algorithm uses the equivalent viscosity of the Bingham fluid model as the initial stress between particles, and it uses the Drucker-Prager criterion with the associated flow rule to describe particle displacement due to slope failure. The soil particles are modelled as cohesive soil with a slope angle to the horizontal axis so that they can be compared with previous studies. The failure pattern is expected to be able to show areas of particles that are not deformed and particles that have collapsed. The FS value of the slope is obtained by the strength reduction method which seeks a non-convergent solution of each reduction in soil strength parameters.

Keywords: Smoothed Particle Hydrodynamics (SPH); Slope Stability; Bingham Fluid Model; Drucker-Prager Model; Strength Reduction Method

How to cite: Andreatama, B., Prakoso, W. A., Bahsan, E., Marthanty, R. R. D. R., and Sjah, J.: Modeling Large Deformation Slope Failure Using The Smoothed Particle Hydrodynamics (SPH) Method, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8579, https://doi.org/10.5194/egusphere-egu21-8579, 2021.

Lahars represent one of the world destructive natural phenomena as number of casualties (Manville et al., 2013). Lahars originate as mixtures of water and volcanic deposits frequently by heavy rainfalls; they are erosive floods capable of increase in volume along its path to more than 10 times their initial size, moving up to 100 km/h in steeply sloping as far as an extreme distance of hundreds of kilometers.

Beside tools of early warning, security measures have been adopted in volcanic territory, by constructing retaining dams and embankments in key positions for containing and deviating possible lahars (Leung et al., 2003). This solution could involve a strong environmental impact both for the works and the continuous accumulation of volcanic deposits, such that equilibrium conditions could lack far, triggering more disastrous events.

The growing frequency of lahars in the Vascún Valley area, Tungurahua Volcano Ecuador, maybe for the climatic change, has recently produced smaller (shorter accumulation periods) and therefore less dangerous events.

Momentary ponds form along rivers in volcanic areas, when they become usually blocked by landslides of volcanic deposits, which are originated by pyroclastic flows and lahars. The most frequent cause of a breakout of such natural ponds is the overflow of water across the newly formed dam and subsequent erosion and rapid downcutting into the loose rock debris.

Dam collapse can occur by sliding of the volcanic deposit or by its overturning. By eroding the blockage and flowing out river channel downstream, the initial surge of water will incorporate a dangerous volume of sediments. This produces lahars with possible devastating effects for settlements in their path (Leung et al., 2003).

The use of simulation tools (from the cellular automata model LLUNPIY) and field data (including necessary subsoil survey) permit to individuate points, where dams by backfills, easy to collapse, can produce momentary ponds.

Small temporary dams with similar (but controlled) behavior of above mentioned dams can be designed and built at low cost by local backfills in order to allow the outflow of streams produced by regular rainfall events. This result is achieved by properly dimensioning a discharge channel at the dam base (Lupiano et al., 2020).

So small lahars can be triggered for minor rainfall events, lahar detachments can be anticipated for major events, avoiding simultaneous confluence with other lahars (Lupiano et al., 2020).

REFERENCES

Leung, MF, Santos, JR, Haimes, YY (2003). Risk modeling, assessment, and management of lahar flow threat. Risk Analysis, 23(6), 1323-1335.

Lupiano, V., Chidichimo, F., Machado, G., Catelan, P., Molina, L., Calidonna, C.R., Straface, S., Crisci, G. M., And Di Gregorio, S. (2020) - From examination of natural events to a proposal for risk mitigation of lahars by a cellular-automata methodology: a case study for Vascún valley, Ecuador. Nat. Hazards Earth Syst. Sci., 20, 1–20, 2020.

Manville, V., Major, J.J. and Fagents, S.A. (2013). Modeling lahar behavior and hazards. in Fagents, SA, Gregg, TKP, and Lopes, RMC (eds.) Modeling Volcanic Processes: The Physics and Mathematics of Volcanism. Cambridge: Cambridge University Press, pp. 300–330.

How to cite: Lupiano, V., Calidonna, C., Catelan, P., Chidichimo, F., Crisci, G. M., Di Gregorio, S., and Straface, S.: Learning from nature: favoring small lahars formation for hazard mitigation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2412, https://doi.org/10.5194/egusphere-egu21-2412, 2021.

Several powerful physics-based computational landslide run-out models have been developed and validated throughout the last years. The geohazards community applies these forward models in simulation tools to predict potential landslide run-out outcomes including their uncertainties, and uses inverse approaches to conduct reanalyses and to infer on model parameters for calibration purposes. Yet it remains challenging to turn these computational frameworks into robust, transparent and transferrable simulation-based decision support tools for geohazard mitigation. In particular, the landscape of uncertainties – such as those resulting from the idealised model description itself, input data (e.g., material parameters or topographic data), and numerical scheme related hyperparameters – is still not systematically managed when conducting landslide simulations. Probabilistic hazard maps that take these uncertainties into account imply a large number of model evaluations, which constitutes a computational bottle neck. This issue can be overcome by using High Performance Computing (HPC) resources along with the existing software and resources. Alternatively, physics-informed machine learning strategies use simulation results of the original process model, i.e., the simulator, to train a statistically valid representation, the so-called emulator. Once being trained, the emulator significantly reduces computational costs, while at the same time it grants access to an estimation of the introduced error. A software framework has recently been set up to integrate Gaussian process emulation and the landslide run-out model r.avaflow, an open-source mass flow simulation tool. Emulation-based sensitivity analysis was of comparable quality to conventional studies, and the computational costs were cut significantly. The emulator allowed for the first time to conduct a global sensitivity analyses at every location simultaneously for a complete landslide impact area. A joint effort across different institutes in Europe has been made in this contribution to test the potential and limitation of the emulation techniques by revisiting a number of published case studies. Selection of test cases has been made according to data availability, failure type and computational demand. Preliminary findings suggest that the emulator is capable of reducing the computational effort of modelling various flow-like landslides substantially. Future work will focus on curating a well-defined database of test scenarios across multiple institutes with cases ranging from small to medium-sized debris flows to large rock avalanches.

How to cite: Yildiz, A., Baselt, I., Edrich, A.-K., Fischer, J.-T., Mergili, M., Zhao, H., and Kowalski, J.: Emulation techniques for rapid flow-like geohazards: a case study-based performance analysis, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2453, https://doi.org/10.5194/egusphere-egu21-2453, 2021.

The scaling of events in geomorphology relates the magnitude of an event to its frequency. The size-frequency distributions of landslides have been found to follow a power-law scaling. However, the scaling of lakes formed by the deposition of landslides in the river bed received less attention. In this study, we simulate landslide occurrence, their runouts and the resulting lakes and observe that landslide-dammed lakes also follow a power-law scaling, although the scaling relationship of the landslides does not predict the scaling of the landslide-dammed lakes. A rollover is present in both distribution, and its location depends on the resolution of the topographic input data.

We find that cumulative density plots are the most appropriate to highlight the influence of glacial imprint on landslide scaling, and that fluvial landscapes present results following more closely the power-law scaling. However, since lake volume is influenced by valley shape, and can be inferred from drainage area as well as landslide size, its scaling cannot be directly explained by glacial imprint and landslide scaling.

Thus, among the 8 mountain ranges investigated, the Southern Alps of New Zealand and the Tibetan Plateau of Wenchuan present similar distributions with a high amount of big lakes (> 10⁸ m³), while the Central Mountain Range of Taiwan exhibit a similar pattern to the Canadian Rockies and European Alps. The Japanese Alps, Mendoza Andes and Cordillera de Talamanca present much smaller lakes.

How to cite: Argentin, A.-L., Prasicek, G., Robl, J., Hergarten, S., Hölbling, D., Abad, L., and Dabiri, Z.: Size-frequency distribution of landslide-dammed lakes from a simulation approach, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9862, https://doi.org/10.5194/egusphere-egu21-9862, 2021.

Rainfall-induced shallow landslides are dangerous natural hazards, mainly due to their high temporal frequency, which causes fatalities and high economic damage worldwide. Early Warning Systems (EWS), generally based on definition of rainfall thresholds needed for landslides triggering, are useful tools for risks mitigation. Thresholds generally do not take into account soil hydrological conditions, which play an important role both in landslide triggering. Rainfall measures are also uncertain due to the limited spatial representativeness of ground sensors and the low density of currently available measuring networks. Moreover, in the last years, soil moisture data have become available over large areas (basin and regional scales), thanks to their measurement through satellite sensors.

The aim of this research is to develop a new integrated model to predict shallow landslides, based on a multidisciplinary approach involving physical models, data-driven methods and the implementation of satellite soil moisture and rainfall. The model is developing in Oltrepò Pavese (Northern Italy, Southern Lombardy), affected during the last 11 years by numerous events triggered by intense and frequent rainfalls, causing human fatalities, damaging/blocking roads and bridges, destructing cultivations (mainly vineyards).

To define satellite soil moisture (and rainfall) products, different remote sensing platform are investigating. A very new soil moisture product provided by Sentinel-1 images by ESA (European Space Agency) allows a fine spatial resolution (1 km) and a revisit time of ~7 days. Coarse resolution soil moisture products (~20 km) characterized by a daily temporal resolution and higher accuracy (e.g., SMAP–Soil Moisture Active and Passive, SMOS–Soil Moisture Ocean Salinity, ASCAT–Advanced SCATterometer) is used. These are validated through two hydrological monitoring stations already installed in two representative basins.

The prediction of shallow landslides are carried on by means of a model able to integrate spatial probability of occurrence and temporal occurrence, considering also satellite soil moisture and rainfall products. Empirical and physically-based thresholds considering different initial soil hydrological conditions on soil moisture, which seem the best indicators for shallow landslide triggering, are developing.

Predicition model is tested and validated with real cases, assessing its reliability, to build a prototypal Early Warning System for shallow landslide prediction, that will constitute a valuable tool for Civil Protection in attempt to mitigate the risk in the Oltrepò Pavese area. This work was made in the frame of the project ANDROMEDA, funded by Fondazione Cariplo.

How to cite: Bordoni, M., Vivaldi, V., Brocca, L., Ciabatta, L., and Meisina, C.: An integrated model for prediction of shallow landslides at regional scale with the integration of satellite hydrological data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14912, https://doi.org/10.5194/egusphere-egu21-14912, 2021.

Rainfall-induced landslides are notoriously dangerous phenomena which can cause a notable death toll as well as major economic losses globally. Usually, shallow landslides are triggered by prolonged or severe rainfalls and frequently may evolve into potentially catastrophic flow-like movements. Shallow failures are typical in hilly and mountainous areas due to the combination of several predisposing factors such as slope morphology, geological and structural setting, mechanical properties of soils, hydrological and hydrogeological conditions, land-use changes and wildfires. Because of the ability of these phenomena to travel long distances, buildings and infrastructures located in areas improperly deemed safe can be affected.

Spatial and temporal hazard posed by flow-like movements is due to both source characteristics (e.g., location and volume) and the successive runout dynamics (e.g., travelled paths and distances). Hence, the assessment of shallow landslide susceptibility has to take into account not only the recognition of the most probable landslide source areas, but also  landslide runout (i.e., travel distance). In recent years, a meaningful improvement in landslide detachment susceptibility evaluation has been gained through robust scientific advances, especially by using statistical approaches. Furthermore, various techniques are available for landslide runout susceptibility assessment in quantitative terms. The combination of landslide detachment and runout dynamics has been admitted by many researchers as a suitable and complete procedure for landslide susceptibility evaluation. However, despite its significance, runout assessment is not as widespread in literature as landslide detachment assessment and still remains a challenge for researchers. Currently, only a few studies focus on the assement of both landslide detachment susceptibility (LDS) and landslide runout susceptibility (LRS).

In this study, the adoption of a combined approach allowed to estimate shallow landslide susceptibility to both detachment and potential runout. Such procedure is based on the integration between LDS assessment via Machine Learning techniques (applying the Ensemble approach) and LRS assessment through GIS-based tools (using the “reach angle” method). This methodology has been applied to the Cinque Terre National Park (Liguria, north-west Italy), where risk posed by flow-like movements is very high. Nine predisposing factors were chosen, while a database of about 300 rainfall-induced shallow landslides was used as input. In particular, the obtained map may be useful for urban and regional planning, as well as for decision-makers and stakeholders, to predict areas that may be affected by rainfall-induced shallow landslides  in the future and to identify areas where risk mitigation measures are needed.

How to cite: Di Napoli, M., Di Martire, D., Calcaterra, D., Firpo, M., Pepe, G., and Cevasco, A.: A combined procedure to assess rainfall-induced shallow landslide detachment, transit and runout susceptibility using Machine Learning and GIS techniques, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3415, https://doi.org/10.5194/egusphere-egu21-3415, 2021.

Numerical weather models are used in a variety of applications, including a growing body of landslide hazard assessment models. Heretofore, these applications have not included global landslide forecasts but this remains an important gap in better understanding the future spatiotemporal impact that landslides can have on populations and infrastructure. We explore the feasibility of using a precipitation forecast within the Landslide Hazard Assessment for Situational Awareness (LHASA) v2.0 model, which is designed to provide estimates of potential landslide hazard for rainfall triggers. Data on precipitation, soil moisture, and snow mass is available from NASA’s Goddard Earth Observing System Forward Processing product (GEOS-FP), which provides global scale products in both forecast and assimilation modes. These variables are incorporated into the LHASA Forecast model by replacing satellite rainfall estimates from the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) with forecasted rainfall from GEOS-FP. The LHASA Forecast model also uses soil moisture and snow mass estimates from GEOS-FP rather than soil moisture and snow mass data from the Soil Moisture Active-Passive (SMAP) level 4 product. The LHASA Forecast model was run retrospectively at a daily scale with forecasted precipitation with up to a 3 day lead time. Results are compared with the LHASA v2.0 model that uses SMAP and IMERG data. Analysis of the LHASA Forecast system was conducted in several different ways. First, performance was assessed with categorical and continuous statics to determine how closely the forecasted probabilities match that of the LHASA v2.0 nowcast landslide probabilities. The outputs of LHASA v2.0 and LHASA Forecast are also compared for several high impact rainfall events that triggered landslides to determine the skill in identifying the potential high hazard areas. Preliminary results suggest that for large precipitation events (e.g. tropical storms), the same general hazard areas are identified; however, this can vary largely by geography and precipitation regime, owing to differences in spatial resolution and phase errors of the forecasted precipitation. This presentation outlines the preliminary work to address forecasted landslide hazard globally and discusses next steps towards improving landslide forecast skill.

How to cite: Khan, S., Kirschbaum, D. B., Stanley, T., Amatya, P., and Emberson, R.: Towards A Global Landslide Forecast, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8989, https://doi.org/10.5194/egusphere-egu21-8989, 2021.

A better detection of landslide occurrence is critical for disaster prevention and mitigation, and a standing pursuit owing to increasing and widespread impact of slope failures on human activities and natural environment in a changing world. However, the detection of rainfall-induced landslide is limited in some areas by data scarcity and method applicability. In this study, we proposed distributed rainfall thresholds within homogeneous slope units, by considering the interaction of landslide-influencing geo-environmental conditions and landslide-triggering rainfall variables. Homogeneous slope units are extracted based on detailed terrain analysis. Various landforms are identified and used to obtain slope units with homogeneous slope traits. The concept behind the distributed rainfall threshold models is that rainfall threshold for landslide occurrence varies with geo-environmental conditions such as slope gradient. Thus, a link can be established between landslide-influencing geo-environmental conditions and landslide-triggering rainfall variables. We used elevation, slope, plan and profile curvature, mean annual precipitation and temperature, soil texture and land cover as independent variables. Rainfall duration and cumulated rainfall of landslide-triggering rainfall events are automatically calculated and used, the former as one of independent variables, and the latter as the dependent variable. A support vector regression (SVR) and a multiple linear regression (MLR) method are used. The error and correlation coefficient measurement indicate a better performance of SVR method. Compared with grid units, the model scores high accuracy for slope units. The models are implemented at a regional scale (Guangdong, China). The SVR model in slope units ran with error of 0.16 mm and correlation coefficient of 0.93.

How to cite: Jia, G., Gariano, S. L., and Tang, Q.: Advancing rainfall-induced landslide detection using homogeneous slope units and distributed rainfall thresholds, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6145, https://doi.org/10.5194/egusphere-egu21-6145, 2021.

Landslides have taken thousands of lives worldwide in the last decades, especially in developing countries. In the Colombian Andes, tropical rainfall conditions and steep terrains are the most common triggering factors of landslides. According to DESINVENTAR in Colombia between 1921-2020, 10.438 landslides have been registered and left almost 7.313 fatalities and destructive outcomes to the economic system. Rainfall thresholds have been used to forecast the occurrence of landslides. Physically-based rainfall thresholds take into account the effects of rainfall coupling hydrological and geotechnical models providing a wide understanding of the physical behavior of the rainfall throw the hillslope and infiltration processes. On the other hand, Machine Learning methods have been implemented to evaluate the correlation between the spatial distribution of the landslide hazard and the morphometric parameters of the basin (e.g. average slope, area, and Melton ratio).

This work was performed implementing the physically-based model TRIGRS to analyze the distribution of the safety factor under different combinations of intensity and duration from gauge-based IDF curves. And, morphometric parameters were calculated to 14 basins distributed along the Colombian Andes; all them were processed by machine learning methods to correlate the influence of each parameter with the rainfall threshold.  The results of coupling physically-based models and machine learning methods could provide criteria that allow setting up a procedure that defines a condition of instability based on the distribution of the safety factor in a basin.

Keywords: Rainfall thresholds, Shallow Landslides, Morphometric Parameters, IDF Curves, TRIGRS

How to cite: Jaramillo-González, R., Aristizábal, E., and García-Aristizábal, E. F.: Rainfall Thresholds for Shallow Landslides by coupled Physically-Based Models and Machine Learning methods in Colombian Andes Basins, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9123, https://doi.org/10.5194/egusphere-egu21-9123, 2021.

Rainfall is one of the most significant triggering factors for shallow landslides. The early warning for such phenomena requires the definition of a threshold based on a critical rainfall condition that may lead to diffuse landsliding. The developing of these thresholds is frequently done through empirical or statistical approaches that aim at identifying thresholds between rainfall events that triggered or non-triggered landslides. Such approaches present several problems related to the identification of the exact amount of rainfall that triggered landslides, the local geo-environmental conditions at the landslide site, and the minimum rainfall amount used to define the non-triggering events. Furthermore, these thresholds lead to misclassifications (false negative or false positive) that always induce costs for the society. The aim of this research is to address these limitations, accounting for classification costs in order to select the optimal thresholds for landslide risk management.

Starting from a database of shallow landslides occurred during five regional-scale rainfall events in the Italian Central Alps, we extracted the triggering rainfall intensities by adjusting rain gouge data with weather radar data. This adjustment significantly improved the information regarding the rainfall intensity at the landslide site and, although an uncertainty related to the exact timing of occurrence has still remained. Therefore, we identified the rainfall thresholds through the Receiver Operating Characteristic (ROC) approach, by identifying the optimal rainfall intensity that separates triggering and non-triggering events. To evaluate the effect related to the application of different minimum rainfall for non-triggering events, we have adopted three different values obtaining similar results, thus demonstrating that the ROC approach is not sensitive to the choice of the minimum rainfall threshold. In order to include the effect of misclassification costs we have developed cost-sensitive rainfall threshold curves by using cost-curve approach (Drummond and Holte 2000). As far as we know, this is the first attempt to build a cost-sensitive rainfall threshold for landslides that allows to explicitly account for misclassification costs. For the development of the cost-sensitive threshold curve, we had to define a reference cost scenario in which we have quantified several cost items for both missed alarms and false alarms. By using this scenario, the cost-sensitive rainfall threshold results to be lower than the ROC threshold to minimize the missed alarms, the costs of which are seven times greater than the false alarm costs. Since the misclassification costs could vary according to different socio-economic contexts and emergency organization, we developed different extreme scenarios to evaluate the sensitivity of misclassification costs on the rainfall thresholds. In the scenario with maximum false-alarm cost and minimum missed-alarm cost, the rainfall threshold increases in order to minimize the false alarms. Conversely, the rainfall thresholds decreases in the scenario with minimum false-alarm cost and maximum missed-alarm costs. We found that the range of variation between the curves of these extreme scenarios is as much as half an order of magnitude.

How to cite: Frattini, P., Sala, G., Lanfranconi, C., Rusconi, G., and Crosta, G.: How to account for misclassification costs in shallow-landslide rainfall thresholds?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10742, https://doi.org/10.5194/egusphere-egu21-10742, 2021.

Landslides triggered by rainfall or seismic activity are a significant source of loss of life and property damage in mountainous regions. In these settings, it is critical to plan development and infrastructure to avoid impact from landslides. To do so, it is necessary to have a clear understanding of the topographic characteristics of areas both where landslides are initially triggered but also the down-slope areas where debris and bedrock fragments are deposited. Recent research has investigated the characteristics of landslide locations triggered by seismic motion, providing guidelines about the most hazardous parts of a given landscape. In this contribution, we report on a set of analyses conducted on a large compilation of landslide inventories associated with major rainfall events around the world. This compilation includes a number of previously published inventories together with 6 newly mapped inventories of landslides created using high-resolution imagery and machine learning techniques. To our knowledge, together these form the most comprehensive compilation of rainfall triggered landslide inventories gathered to date.

We analyse a number of topographic characteristics associated with these landslides using the 30m resolution SRTM DEM, including local slope, average upstream slope, relief, topographic roughness, wetness index, and topographic position index. We analyse these parameters for both the scar of the landslide as well as the area of deposition. While there is significant dispersion across inventories for several of these parameters, there are consistent relationships between landslide likelihood and roughness, slope, and wetness index. Although the relationships identified with slope and roughness are consistent with prior work, the relationship between wetness index and landslide likelihood suggests that the calculation of wetness index from topography alone may not effectively represent the saturation state of the hillslopes. We anticipate that these findings could be useful for other regional and global landslide modelling studies and local calibration of landslide susceptibility assessment.

How to cite: Emberson, R., Kirschbaum, D., Amatya, P., Tanyas, H., and Marc, O.: Topographic characteristics of rainfall triggered landslides from a newly compiled set of inventories, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12335, https://doi.org/10.5194/egusphere-egu21-12335, 2021.

Landslide triggering thresholds provide the rainfall conditions that are likely to trigger landslides, therefore their derivation is key for prediction purposes. Different variables can be considered for the identification of thresholds, which commonly are in the form of a power-law relationship linking rainfall event duration and intensity or cumulated event rainfall. The assessment of such rainfall thresholds generally neglects initial soil moisture conditions at each rainfall event, which are indeed a predisposing factor that can be crucial for the proper definition of the triggering scenario. Thus, more studies are needed to understand whether and the extent to which the integration of the initial soil moisture conditions with rainfall thresholds could improve the conventional precipitation-based approach. Although soil moisture data availability has hindered such type of studies, yet now this information is increasingly becoming available at the large scale, for instance as an output of meteorological reanalysis initiatives. In particular, in this study, we focus on the use of the ERA5-Land reanalysis soil moisture dataset. Climate reanalysis combines past observations with models in order to generate consistent time series and the ERA5-Land data actually provides the volume of water in soil layer at different depths and at global scale. Era5-Land project is, indeed, a global dataset at 9 km horizontal resolution in which atmospheric data are at an hourly scale from 1981 to present. Volumetric soil water data are available at four depths ranging from the surface level to 289 cm, namely 0-7 cm, 7-28 cm, 28-100 cm, and 100-289 cm. After collecting the rainfall and soil moisture data at the desired spatio-temporal resolution, together with the target data discriminating landslide and no-landslide events, we develop automatic triggering/non-triggering classifiers and test their performances via confusion matrix statistics. In particular, we compare the performances associated with the following set of precursors: a) event rainfall duration and depth (traditional approach), b) initial soil moisture at several soil depths, and c) event rainfall duration and depth and initial soil moisture at different depths. The approach is applied to the Oltrepò Pavese region (northern Italy), for which the historical observed landslides have been provided by the IFFI project (Italian landslides inventory). Results show that soil moisture may allow an improvement in the performances of the classifier, but that the quality of the landslide inventory is crucial.

How to cite: Palazzolo, N., Peres, D. J., Creaco, E., and Cancelliere, A.: Exploring the potential of soil moisture reanalysis data for improving the identification of regional landslide triggering thresholds, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2243, https://doi.org/10.5194/egusphere-egu21-2243, 2021.

A 3D litho-structural model synthetizes a geological setting by defining 3D geometries of lithological layers considering stratigraphic relationships, weathering and tectonics. It combines quantitative and qualitative data from different dimensions and acquisition types (field measures and observations, geophysics, boreholes, DEM) into a single structured database. This aesthetic 3D representation enables to work on the same object, despite different sources of datasets, making it a highly useful integrative tool for various ways to monitor and analyze landslides prone areas.

This type of model is used on site scale for large phenomena, for a better understanding of their internal structure and to extract information to be included for failure numerical modelling. However, there are a very few examples of 3D geological models used for large areas subject to spatially limited events. Indeed, the transition from 2D to 3D information remains difficult, especially in case of sparse input data, reinforcing 3D interpretation uncertainties and decreasing the robustness of the model. Thus, most of regional scale geological 3D models used for landslides analyses are simplified and the different lithological layers used for susceptibility and hazard assessments suffer from uncertainties difficult to quantify.

The aim of this contribution is to show how two local scale 3D geological models can contribute and improve the robustness of a regional 3D geological model for the purpose of landslide susceptibility and hazard assessment. The local and regional 3D geological models integrate different data types of uneven quality by successive iterations, to interpret structural and lithological layers geometries with GeoModeller. This software is based on cokriging calculation method of orientation and location of geological interfaces and faults. The regional model will be compared to the local 3D models results, as references to assess regional model uncertainties. This iterative process enables to improve each 3D model with different data sets from one scale to another. Still, models results must be confirmed by field validation to reduce uncertainties as much as possible.

This study focuses on the 40 km long French Basque coast in the southwest of France, which presents complex faulting and geological heterogeneities inherited from the Pyrenean orogeny – these are relatively well mapped along the shore. Both of the local sites are different and characteristic of regional coastal geomorphological types and of specific lithological formations. These are made of flyschs, limestones and marls, the top of which are more or less weathered and capped by Quaternary detritic formations of variable thickness. This coast is subject to various types of shallow and moderately deep instabilities (slides, rockfalls and flows). By defining the geometry of lithology and faults, the 3D models results will enable to:

• Characterize how lithology and structures, as predisposition factors, influence landslides susceptibility to specific landslide types,
• Integrate lithological layers and structural discontinuities to physical-based models to assess landslide susceptibility and hazard on regional (1 : 25,000) and on local (1 : 2,500) scales,
• Improve the geological knowledge of the French Basque coast.

How to cite: Guillen, L., Caritg, S., Bourbon, P., Dewez, T., Lévy, C., Cuccurullo, A., Garnier, C., Gallipoli, D., and Thiery, Y.: From local to regional 3D litho-structural modelling: a methodology towards multi-scale landslide susceptibility and hazard assessment, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14076, https://doi.org/10.5194/egusphere-egu21-14076, 2021.

Landslides are one of the most relevant natural threat in mountainous regions, resulting each year in billion of direct and indirect losses incurred worldwide. Furthermore, it is widely acknowledged that these processes are both strongly dependent on local geomorphological, geological and environmental features and highly sensitive to weather- and climate-related events such as intense precipitation or snowmelt. However, most regional landslides hazard and risk models to date struggle capturing this complex interplay of quasi-static and dynamic drivers and triggering factors, hence severely hampering their operational use for implementing timely risk mitigation and adaptation measures.

We aim to introduce a sound and relatively straightforward geostatistical approach to landslides hazard and risk modelling based on heterogeneous spatiotemporal point processes, which has potential for the assimilation of empirical observations from different sources (including, e.g., remote sensing) for iterative calibration and free from thresholds of continuous monitoring parameters. Such approach could be efficiently used to obtain large-area, near-real time stochastic simulation of landslide processes as input to further risk analysis and management activities by civil protection authorities and policy planners. Perspectives and limitations of the proposed approach stemming from a preliminary exemplification in a case study in Central Asia will be outlined and discussed.

How to cite: Pittore, M., Oezturk, U., and Steger, S.: Towards Large-area Dynamic Modeling of Landslides Hazard and Risk with Spatiotemporal Point Processes, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16029, https://doi.org/10.5194/egusphere-egu21-16029, 2021.

How to cite: Luti, T., Segoni, S., Munafò, M., and Casagli, N.: National scale soil sealing monitoring data as a new explanatory variable for landslide susceptibility models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-861, https://doi.org/10.5194/egusphere-egu21-861, 2021.