With the current climate change, the attention is more and more focused on “adaptation strategies”, that take place by multi-stakeholder actions (research, businesses, society and government) towards resilient communities. With these aims, the PNRR Innovation Ecosystem “Tech4You - Technologies for climate change adaptation and quality of life improvement” has been proposed by a partnership including public (University of Calabria, University “Magna Graecia” of Catanzaro, University “Mediterranea” of Reggio Calabria, University of Basilicata, Consiglio Nazionale delle Ricerche, etc.) and some private partners. Tech4You has been funded by means of the Next Generation EU Program, through the Italian Ministry of University and Research and lasts three years. Within the cited R&I Project, the Spoke 1 “ANTARES - CirculAr techNologies to miTigate geo-hydrologicAl and foRest firE riskS” includes the Goal “Technologies and innovative multi-scale tools for landslide risk prevention” articulated in two Pilot Projects (PPs). PPs aim to: a) make multi-scale and interdisciplinary on-site laboratories as demonstration systems and knowledge generators and as decision support for the management of landslide risk (adaptation, mitigation/reduction) and b) propose methods and tools for quantitative modelling of diffuse and local landslides, training the planning, the scheduling and the designing of landslide risk adaptation, mitigation/reduction activities.

The first year of activity has been addressed to the collection of databases, from various sources, regarding climatic, geological, geotechnical, morphological, etc. and models and methods for landslide triggering and evolution, already implemented and tested in other study areas. In this way, a first draft of the Catalogues and Libraries is being created, to be continuously upgraded, during the project and after its end. The proposed approach is circular and multi-scale based, spanning from regional scale (Calabria and Basilicata) to basins and detailed scale, finalized to in-situ laboratories.  Results, at the end of the Project, will be friendly used and updated, with a circularity and multidisciplinary approach, by various stakeholders, such as national, regional and territorial administrations, freelancers, enterprises, communities, etc. For that, the come up with a general platform and dedicated platforms will be realized by companies or Consortium of companies, selected by means of Public Tender (called “Cascade Calls”).         

This work was funded by the Next Generation EU - Italian NRRP, Mission 4, Component 2, Investment 1.5, call for the creation and strengthening of 'Innovation Ecosystems', building 'Territorial R&D Leaders' (Directorial Decree n. 2021/3277) - project Tech4You - Technologies for climate change adaptation and quality of life improvement, n. ECS0000009. This work reflects only the authors’ views and opinions, neither the Ministry for University and Research nor the European Commission can be considered responsible for them.

How to cite: Coscarelli, R., Moraci, N., Gullà, G., and Moramarco, T.: Technologies and innovative multi-scale tools for landslide risk prevention: the activities of the first year within the Innovation Ecosystem “Tech4You”., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8781, https://doi.org/10.5194/egusphere-egu24-8781, 2024.

Liguria is an Italian region bordered on the North by the Alps and on the South by the Thyrrenian Sea. This geographical location and its topography lead to the occurrence of numerous weather scenarios. The rainfall pattern and the steep topography give rise to frequent landslide events in the region.

This work aims to investigate the relationship between the occurrence of landslides and different weather scenarios.

A catalogue with detailed spatial and temporal information on 475 rainfall-induced landslides that occurred in Liguria region in 2019 and 2020 is available and is used to perform the analysis.

Forecasts of local atmospheric conditions for the Liguria region are calculated daily by the Regional Agency for the Environmental Protection of Liguria region (ARPAL), and are used to issue regional weather vigilance bulletins to be used by the civil protection authority to give geo-hydrological alerts. The atmospheric conditions are classified in 7 weather scenarios and 8 sub-scenarios by ARPA. The classification is based on both the synoptic circulation and the types of precipitation and antecedent conditions. We observe that the most frequent scenario associated with landslide occurrences in the region is the “West-Southern weather pattern”, whereas the most frequent sub-scenario is the “Intense rainfall and rainstorms”.

Temporal analyses are carried out to assess variations in the monthly distribution of the weather scenarios. In addition, the characteristics of the rainfall conditions responsible for the failures are evaluated to search for peculiarities related to different weather scenarios.

This work was supported by the 2021-2023 Cooperation Agreement between CNR-IRPI and the Regional Agency for the Environmental Protection of Liguria region (ARPAL), Italy, and the PRIN-ITALERT project (PRIN2022 call - grant number: 202248MN7N, CUP: B53D23006720006) funded by NextGenerationEU.

How to cite: Brunetti, M. T., Gariano, S. L., Solimano, M., Melillo, M., Peruccacci, S., De Stefanis, P. G., and Cicoria, M.: Weather scenarios associated with rainfall-induced landslides in the Liguria Region, Italy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7684, https://doi.org/10.5194/egusphere-egu24-7684, 2024.

In 2023, Slovenia experienced two major natural disasters caused by prolonged and intense rainfall event. The first occurred in May 2023 with over 2,000 shallow landslides triggered by prolonged heavy rainfall that lasted for about three weeks (from May 5 to May 23). This event mainly affected the north-east of Slovenia. The landslides caused major material damage to agricultural land and infrastructure, and at least 10 houses were evacuated.

The most notable event occurred in August 2023 and was estimated to be one of the largest in the history of independent Slovenia. In the period between August 3 and 6, 2023, precipitation with heavy storms and intense rainfall covered almost all of Slovenia. The extreme rainfall led to widespread flooding and triggered numerous landslides.

We estimate that there were around 10,000 landslides across Slovenia, with a particularly high density in some areas. Due to hydro-meteorological conditions (increased water flow and rising groundwater levels), a large portion of the landslides turned into mud or debris flows and were deposited a few to several hundred meters away from the source area. The main reason for the extreme landslide disasters was the heavy rainfall and high soil moisture as a result of the rainfall in July.

Due to the large scale of the landslide disaster in August, a detailed damage assessment is still being carried out. Preliminary estimates by the Geological Survey of Slovenia (GeoZS) indicate that around 10,000 landslides occurred (with an area of 1,000 to over 75,000 m2) and caused damage of more than €3 billion, of which around 40% had a direct impact on the built environment.

In both cases, the GeoZS emergency service provided residents and the relevant authorities with landslide forecasts and warnings using the existing national MASPREM system (Slovenian Landslide Forecast and Warning System). Although the Slovenian LEWS is operational, the performance of the forecasts has shown that about 15 to 25 % of the warnings were false, which we attribute to the short period of antecedent percipitations (the current calculation takes into account 3 days of antecedent percipitations)  and lack of soil and hydrological related parameters (e.g. effective percipitations, soil moisture, etc.).

Fundings:

This research was funded by the Slovenian Research And Innovation Agency through research projects J6-4628 and programme P1-0419. Additional financial support was provided by the Ministry of Natural Resources and Spatial Planning, Ministry of Defence (through project MASPREM) and project “Development of research infrastructure for the international competitiveness of the Slovenian RRI space – RI-SI-EPOS” (co-financed by the Republic of Slovenia, Ministry of Education, Science and Sport and the European Union from the European Regional Development Fund).

How to cite: Peternel, T., Jemec Auflič, M., Šegina, E., Turk, D., Jež, J., and Bavec, M.: May and August 2023: the extreme landslide events in Slovenia, triggered by extreme rainfall, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16859, https://doi.org/10.5194/egusphere-egu24-16859, 2024.

With the increasing frequency of high intensity rainfall events, landslides on natural slopes have become a critical concern from a disaster management perspective. Rainfall-induced landslides are caused by the reduction in the soil shear strength due to the increased pore water pressure induced by rainfall and/or rapid snowmelt. It is important to understand the mechanism of failure for employing reliable early warning and effective risk reduction strategies. Geotechnical slope stability analysis can be carried out easily on a slope scale, however, extending this at a regional scale is demanding due to the spatial variability of hydrological and geotechnical properties. Physics-based landslide susceptibility models are designed with the explicit goal of using hydrological mechanisms for the identification of possible landslide source areas, primarily computing factor of safety (FS) values on a grid.  However, given that the majority of these models operate independently, integrating them into a fully automated Landslide Early Warning Systems (LEWS) remains a significant technical challenge. This work proposes a methodology that leverages meteorological forecasts sourced from the MET Weather Application Programming Interface (API), in conjunction with topographical and soil properties, to project Factor of Safety (FS) values on an hourly basis. A case study from Norway has been used as a pilot for the demonstration of the method proposed. The forecasted FS values are dynamically visualized in real-time within the data platform of the Norwegian Geotechnical Institute, NGI Live, which can also be used as a map overlay for other infrastructure projects in the study area. The proposed method holds the promise of providing physics-based decision support for disaster risk reduction and critical infrastructure management efforts.

How to cite: Abraham, M. T., Piciullo, L., Liu, Z., Robinson, H., Paulsen, E. S., and Blomberg, A. E. A.: Regional-scale landslide forecasting using physics-based slope stability models , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9720, https://doi.org/10.5194/egusphere-egu24-9720, 2024.

Italy faces significant vulnerability to landslides, necessitating reliable forecasting models for effective property and population protection. These models must not only guarantee high accuracy but also facilitate easy integration into early warning systems for civil protection.

Physically based landslide forecasting models meticulously replicate the triggering mechanism of shallow landslides. These models employ numerous input parameters interconnected through complex mathematical relationships to assess the probability of landslide occurrences. Despite their precision, these techniques encounter challenges in spatializing geotechnical and hydrogeological parameters across extensive areas, restricting their application to slope-scale assessments. Additionally, the output of these models, presented as probability maps, lacks immediate utility for civil protection purposes, where a risk definition would be more operationally advantageous.

This study aims to address this gap by analyzing the optimal criterion for spatializing input data of physical models for regional-scale application. The goal is to develop a procedure that transforms model outcomes into readily usable risk scenarios. The study focuses on the Metropolitan City of Florence, leveraging a richly populated database of geotechnical and hydrogeological parameters. The selected model, HIRESSS (High-Resolution Slope Stability Simulator), simulates events occurring from January to March 2016, encompassing eight reported landslide events.

Through p-value analysis derived from statistical hypothesis testing, the study explores two criteria for parameterizing geotechnical and hydrological variables: a lithological criterion and one based on pedological-landscape units. This dual approach aims to consider both the lithological origin of soils and the impact of surface erosive processes on the spatial variability of input parameters. The study employs an innovative GIS-based procedure, integrating field surveys and morphometric parameters, to connect landslide probability maps with vulnerability and elements at risk, ultimately determining a risk scenario for the catchment area of the Cesto stream (southeast of Florence).

The analysis highlights the mixed criterion as the most supported spatialization approach, incorporating lithological factors for cohesion and friction angle and pedological-landscape criteria for hydraulic conductivity, soil unit weight, and porosity. Back-analysis validation reaffirms the model's high predictive capability with the adopted mixed-criterial parametrization. The results align with our understanding of landslide triggering mechanisms, particularly sensitive to cohesion and slope gradient.

The study concludes with a GIS-based risk analysis, providing impact scenarios for identified exposed elements. This final product proves instrumental for both prevention and emergency management. Once calibrated, the developed procedure holds potential for automation and replication in other study areas, offering a scalable solution for landslide risk assessment and mitigation.

How to cite: Morreale, G., Nocentini, N., Masi, E. B., Rosi, A., Segoni, S., and Tofani, V.: Optimizing operational efficiency in physically based landslide forecasting models: a multi-criterial parameterization approach in evaluating slope stability risk scenarios - a case study in Florence, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13149, https://doi.org/10.5194/egusphere-egu24-13149, 2024.

A common and largely unresolved problem of national-scale landslide early warning systems is their independent evaluation. In this work, we evaluated the performance of a recently proposed deep-learning-based system for short-term forecasting of rainfall-induced shallow landslides in Italy. For our evaluation, we used hourly rainfall measurements from the same rain gauge network used to construct the forecasting system, and different and independent information on the timing and location of 163 rainfall-induced landslides that occurred in Italy in a period non considered in the construction of the forecasting system, obtained from the FraneItalia catalogue (https://zenodo.org/records/7923683). The independent evaluation confirmed the good predictive performance of the forecasting system and revealed no geographical or temporal bias in the forecasts. The analysis also revealed that the forecasting system was more effective at predicting multiple landslides in the same general area than single landslides. This was a good result, as multiple landslides are potentially more dangerous than single failures. Analysis of the few misclassified landslide cases showed that approximately one-third of the landslides were rockfalls, and for approximately another third there was uncertainty about when or where the landslides occurred. We conclude that, despite the inevitable misclassifications inherent in any probabilistically based national-scale landslide forecasting system, the deep-learning-based system analysed is well suited for short-term operational forecasting of rainfall-induced shallow landslides in Italy.

How to cite: Melillo, M., Guzzetti, F., Calvello, M., Pecoraro, G., and Mondini, A. C.: Demonstration of a deep-learning-based system for rainfall induced shallow landslides forecasting in Italy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7505, https://doi.org/10.5194/egusphere-egu24-7505, 2024.

To carry out refined early warning for shallow landslides in typhoon rainstorm area, it is necessary to propose hourly precipitation warning criteria. The Slope Warning Model (SWM) of analyzing the correlation between landslides and rainfall process proposes short-term early warning criteria and rainfall duration. Individual slope warning framework is established based on regional average rainfall threshold and adjustment rainfall concerning specific geotechnical factors. The regional landslide rainfall threshold, counted by I-D model, is proposed as the regional average threshold. The geological environment of individual slope is investigated as geographical figures, geological structure, composition of soils, hydrological conditions and slope cutting. In terms of the simulation of infinite slope model, slope stability varies as geological factors changes due to rainfall infiltration, which may determine threshold adjustment values. Wenzhou City, Zhejiang Province of China, located in eastern coast area where is frequently impacted by typhoon, was taken as a study area. Quantitative threshold calculation formula has been proposed. Early warning criteria of different duration is proposed according to the geological factors of individual slope. This study proposed a short-term quantitative landslide meteorological warning model for individual slope. It may provide new ideas and references for "each-slope, each-threshold" of landslide early warning and risk management.

 Keywords: shallow landslide, early warning, typhoon rainstorm, individual slope, “each-slope. each-threshold”

How to cite: Yan, L., Yin, K., and Chen, L.: Shallow landslide early warning in typhoon rainstorm area based on Slope Warning Model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19430, https://doi.org/10.5194/egusphere-egu24-19430, 2024.

Understanding the unstable evolution of railway slopes is the premise for preventing slope failure and ensuring the safe operation of trains. However, as two major factors affecting the stability of railway slopes, few scholars have explored the unstable evolution of railway slopes under the joint action of rainfall-vibration. Based on the model test of sandy soil slope, the unstable evolution process of slope under train vibration, rainfall, and rainfall-vibration joint action conditions is simulated in this paper. By comparing and analyzing the variation trends of soil pressure and water content of slope under these conditions, the change laws of soil pressure and water content under the influence of rainfall-vibration joint action are explored. The main control factors affecting the stability of slope structure under the joint action conditions are further defined. Combined with the slope failure phenomena under these three conditions, the causes of slope instability resulting from each leading factor are clarified. Finally, according to the above conclusions, the unstable evolution of the slope under the rainfall-vibration joint action is determined. The test results show that the unstable evolution process of sandy soil slope, under the rainfall-vibration joint action, can be divided into: rainfall erosion cracking, vibration promotion penetrating, and slope instability sliding three stages. If it is in a short period of time when the vibration starts or ends, the slope will also generate structural changes in vibration densification (vibration loosening). In the process of slope unstable evolution, rainfall and vibration play the roles of inducing and promoting slide respectively. In addition, the deep cracks, which are the premise for the formation of the sliding surface, and the violent irregular fluctuation of soil pressure, which reflects the near penetration of the sliding surface, constitute the instability characteristics of the railway slope together. This paper reveals the unstable evolution of sandy soil slopes under the joint action of rainfall-vibration, hoping to provide the theoretical basis for the early warning and prevention technology of railway slopes.

How to cite: Haoyu, D.: Unstable evolution of railways slope under the rainfall-vibration joint action, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-137, https://doi.org/10.5194/egusphere-egu24-137, 2024.

Shallow rainfall-induced landslides are triggered by intense or prolonged rainfall. Warning models employed within territorial landslides early warning systems (Te-LEWS) are typically based on rainfall thresholds expressed in terms of cumulative rainfall or average intensity with respect to the duration of the rainfall event, completely neglecting antecedent conditions. However, recent studies demonstrated that introducing, directly or by means of models, the effects of antecedent soil moisture content in empirical thresholds can improve the performance of the warning models.

This preliminary study focuses on the definition of a pilot monitoring site that produces rainfall and soil moisture data measured by an Internet of Things (IoT) monitoring network and by the use of analogous reanalysis products (i.e., ERA5-Land dataset). The activities are being developed in the context of the Horizon Europe project “The HuT: The Human-Tech Nexus - Building a Safe Haven to cope with Climate Extremes”. The final aim is to use IoT monitoring of rain and soil moisture, combined with reanalysis data, to improve, at municipal level, the territorial warning procedures already existing and operational at regional level.

The test site has been installed within the Campus of the University of Salerno in Fisciano, Campania region (Italy) since February 2023. The pilot site has been instrumented with sensors monitoring soil moisture from different providers and with a weather station; the sensors have been installed at different depths and with different procedures. The collected data were analyzed and processed using various data analysis algorithms, with the aim of: i) establishing correlations between the local weather conditions and the hydrologic soil response; ii) make a comparison between the data collected from different providers and in different local conditions.

Establishing these relationships allowed to evaluate the peculiarities and reliability of the different sensors and to identify the best configuration for future in-situ installations. More generally, this study highlights the importance of developing a monitoring network based on diffuse low-cost sensors and a proper real-time data transmission, analysis and processing, in order to provide further knowledge to system managers of territorial warning systems in the analysis of the monitoring data, and thus support for their decisions before and during extreme weather conditions.

How to cite: Menichini, R., Pecoraro, G., and Calvello, M.: IoT monitoring and reanalysis data of soil moisture and rainfall for landslide warning: a test case, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10745, https://doi.org/10.5194/egusphere-egu24-10745, 2024.

Landslide monitoring is an important means to prevent the landslide disaster. Among all elements of landslide monitoring, slope surface deformation is a piece of direct evidence to judge whether slope slips, which makes it indispensable in qualitative and quantitative analysis of slope stability. Current mainstream surface monitoring methods using GNSS are difficult to lay out densely on a large scale in a deformation region due to the high cost of equipment, leading to few surface points available for detection. With the rapid development of camera resolution and image processing, photogrammetry based on computer vision has great prospects in the application of slope real-time monitoring.

We introduce a low-cost landslide visual monitoring system using close-range terrestrial photogrammetry that deploys fixed cameras to capture the slope surface periodically and calculating the displacement of feature points from sequential slope images to generate the slope surface deformation network. A new machine learning framework is proposed to achieve image recognition, camera calibration and distance mapping altogether. We conduct indoor landslide experiments which verify the high precision, accuracy, and stability of our system.

How to cite: Lu, T. and Han, P.: Slope surface deformation monitoring by close-range terrestrial photogrammetry, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16641, https://doi.org/10.5194/egusphere-egu24-16641, 2024.

Monitoring evolution process of subsurface geo-interfaces in active slow-moving landslides can help understand landslide thermo-hydro-mechanical dynamics and predict potential landslide hazards. However, characterizing the behavior of these geo-interfaces and revealing their interactions remain challenging due to the general lack of high-resolution subsurface observations. To this end, we propose a novel fiber optic nerve sensing (FONS) system based on ultra-weak fiber Bragg grating (UWFBG) to sense the temperature, moisture and strain of geomaterials along a borehole in nearly real-time. The system is able to accurately locate and identify multiple potential slip surfaces and other critical geo-interface behaviors that may be relevant to landslide instability. The measurements confirm the foremost contribution of short-duration high-intensity extreme rainfall to accelerating landslide movement. We also attempted to employ machine learning algorithms based on classification principles to predict what hydrometeorological regimes would drive an accelerated deformation event. These subsurface data will allow us to investigate the multi-physical characteristics of geo-interfaces from daily to annual and even multi-annual scales and link cyclic thermo-hydro-mechanical external conditions to progressive failure. This work highlights the increasing impact of extreme weather events on landslide geohazards and the importance of multidisciplinary approaches for accurate prediction and early warning. Integrating FONS with remote sensing and ground-based technologies can create a comprehensive space-sky-ground-subsurface monitoring framework for landslides.

How to cite: Ye, X., Zhu, H.-H., Shi, B., and Catani, F.: Monitoring and forecasting subsurface geo-interfaces behavior of active slow-moving landslides using fiber optic nerve sensing system, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18498, https://doi.org/10.5194/egusphere-egu24-18498, 2024.

The Himalayan belt includes the geologically unstable mountainous terrain of upper Uttarakhand, which is distributed throughout Sonprayag, Sitapur, Rampur, Barasu, Kalimath, Madhyamaheshwar, Chamoli, Birahi, Byasi, and Atali.

There are many more mountain ranges in the region. The rains that fall during the monsoon season are the most common cause of landslides in this mountain chain, whereas earthquakes and aftershocks are the least common cause. Hill slopes are becoming unstable as a result of human involvement in nature,

which includes activities such as cutting roads without following scientific principles, dumping garbage along roadsides, using landslide , deformation, and illegal mining operations, among other things. Once the prone regions have been identified and hazard zonation maps have been prepared, it will be possible to protect the different entitlements that are at risk due to the landslip threat.

The current investigation is an inventory-based technique that makes use of satellite data in order to determine the grey regions that exist within the region. The topo sheets of India, the geological maps that are already in existence, the data from remote sensing, the historical landslip data from 2010 to 2023, and the field inspection were all done. It is possible to construct primary topographic data with the use of a Digital Elevation Model (DEM), which includes aspects, slopes, curvatures, hill shades, mean curvatures, plan curvatures, relief, and drainage density respectively.  Afterwards, a Landslip Hazard Zonation (LHZ) Map is created by superimposing several theme layers. This is done in order to facilitate the process of making logical decisions and to facilitate the implementation of mitigation measures in advance of an occurrence. The observed landslip is mostly composed of rock and boulder falls as well as debris flow. Within this complex network of mountain ranges, there are significant dynamic sites that need the attention of scientists for further investigation. The findings will be disseminated to catastrophe authorities and governmental organizations in order to facilitate the development of contingency plans for dealing with future occurrences.

How to cite: Anand, A. and Bhardwaj, A.: Unmanned Aerial Vehicles and Terrestrial Laser Scanning are used to Monitor Kshetrapal landslides in the Chamoli Hazard Areaof Upper Himalayan Region in Uttarakhand, India., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11755, https://doi.org/10.5194/egusphere-egu24-11755, 2024.

We simulate the generation of acoustic and tsunami waves generated by submarine landslides using the linear model developed in [1]. The model is able to reproduce both acoustic and surface gravity waves generated by a moving source (e.g. earthquake, landslide) in a vertically stratified ocean.

There are only a few studies that focus on the generation of acoustic waves by submarine landslides. In [2], the combined analysis of field data and simulations underline the presence of an interference pattern in the acoustic waves' spectrogram. The interference pattern has a time-varying bandwidth, which is a signature of the submarine landslide dynamics. In a previous work [3], we used the model developed in [1] to reproduce the interference pattern for a static source.

Here we use the same model to simulate a submarine landslide in the 2D case. The simulations reproduce the time-varying bandwidth. They are then used to study the influence of two parameters on the acoustic spectrograms, namely landslide velocity and topography. Different velocity profiles available in the literature [4] are tested. For the topography, we use as reference the 2D case simulated in [1]. We also provide illustrations for the tsunami generation by the landslide.

[1] Dubois J, Imperiale S, Mangeney A, Bouchut F, Sainte-Marie J. Acoustic and gravity waves in the ocean: a new derivation of a linear model from the compressible Euler equation. Journal of Fluid Mechanics. 2023;970:A28. doi:10.1017/jfm.2023.595

[2] Caplan-Auerbach, J., Dziak, R. P., Bohnenstiehl, D. R., Chadwick, W. W., and Lau, T.-K. (2014), Hydroacoustic investigation of submarine landslides at West Mata volcano, Lau Basin, Geophys. Res. Lett.,  41,  5927– 5934, doi:10.1002/2014GL060964.

[3] Dubois, J., Imperiale, S., Mangeney, A., and Sainte-Marie, J.: Simulation of the hydro-acoustic and gravity waves generated by a landslide, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-15937, https://doi.org/10.5194/egusphere-egu23-15937, 2023.

[4] Farin, M., Mangeney, A., de Rosny, J., Toussaint, R., & Trinh, P.-T. (2019). Relations between the characteristics of granular column collapses and resultant high-frequency seismic signals. Journal of Geophysical Research: Earth Surface, 124, 2987–3021. https://doi.org/10.1029/2019JF005258

How to cite: Dubois, J., Imperiale, S., Mangeney, A., and Sainte-Marie, J.: Submarine landslides, tsunami and hydroacoustic waves: simulation and sensitivity analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11561, https://doi.org/10.5194/egusphere-egu24-11561, 2024.