NH3.7 | Towards innovative Landslide monitoring, modelling, and Early Warning Systems
Towards innovative Landslide monitoring, modelling, and Early Warning Systems
Convener: Luca Piciullo | Co-conveners: Stefano Luigi Gariano, Neelima Satyam, Samuele Segoni, Tina Peternel
| Thu, 18 Apr, 08:30–10:15 (CEST)
Room 1.15/16
Posters on site
| Attendance Thu, 18 Apr, 10:45–12:30 (CEST) | Display Thu, 18 Apr, 08:30–12:30
Hall X4
Orals |
Thu, 08:30
Thu, 10:45
Landslide early warning systems (LEWS) are cost effective non-structural mitigation measures for landslide risk reduction. For this reason, the design, application and management of LEWS are gaining consensus not only in the scientific literature but also among public administrations and private companies.
LEWS can be applied at different spatial scales of analysis, reliable implementations and prototypal LEWS have been proposed and applied from slope to regional scales.
The structure of LEWS can be schematized as an interrelation of four main components: monitoring, modelling, warning, response. However, tools, instruments, methods employed in the components can vary considerably with the scale of analysis, as well as the characteristics and the aim of the warnings/alerts issued. For instance, at local scale instrumental devices are mostly used to monitor deformations and hydrogeological variables with the aim of setting alert thresholds for evacuation or interruption of services. At regional scale rainfall thresholds are widely used to prepare a timely response of civil protection and first responders. For such systems, hydro-meteorological thresholds built combining different variables represent one of the most promising and recent advancement. Concerning the modeling techniques, analyses on small areas generally allow for the use of physically based models, while statistical models are widely used for larger areas.
This session focuses on LEWS at all scales and stages of maturity (i.e., from prototype to active and dismissed ones). Test cases describing operational application of consolidated approaches are welcome, as well as works dealing with promising recent innovations, even if still at an experimental stage. The session is not focused only on technical scientific aspects, and submissions concerning practical and social aspects are also welcome.

Contributions addressing the following topics will be considered positively:
- conventional and innovative slope-scale monitoring systems for early warning purposes
- conventional and innovative regional prediction tools for warning purposes
- innovative on-site instruments and/or remote sensing devices implemented in LEWS
- warning models for warning/alert issuing
- operational applications and performance analyses of LEWS
- communication strategies
- emergency phase management

Orals: Thu, 18 Apr | Room 1.15/16

Chairpersons: Luca Piciullo, Stefano Luigi Gariano, Neelima Satyam
On-site presentation
Liyana Hayatun Syamila Ramlee, Khamarrul Azahari Razak, Zamri Ramli, and Zakaria Mohamed

Malaysia is committed to accelerate the achievement of UN Sendai Framework for Disaster Risk Reduction 2015-2030, and support a newly launched agenda, “The Early Warning for All initiative, 2023-2027”. While investing into landslide risk reduction strategies through the National Slope Master Plan 2009-2023, landslides remained the major contributor to the highest number of human losses in Malaysia, and even so with new, emerging risk and compounding disaster as a result of local climate change impact. So far, landslide and debris flow occurred more than 25 times with 442 casualties in the last three decade. Amongst are the geological disaster debris flow in Jerai Geopark (Yan, Kedah) recorded on 18 August 2021 resulted in six fatalities, with more than RM75 million direct economic losses reported, and indirect cascading impact to local socio-economic activity and food security system. This study advances the people-center, end-to-end early warning system for debris flow in the tourism-dominated region in Kedah. It is worth mentioning that this Japanese-designated EWS is the first-ever system locally built in Malaysia, which was co-designed, co-developed and co-implemented driven by the local communities and multi-stakeholders in a tropical environment. Several unmanned assisted vehicles-based LiDAR missions were jointly conducted to quantitatively understand the possible remaining systemic risk for future debris flow in the upstream of the study area located in the vicinity of the Mount of Jerai. A complete system consists of wire-cable detection, vibration sensor, and siren system coupling with historical inventory analysis, hazard mapping, exposure assessment and systemic risk evaluation. The EWS development was carried out across sector, and carefully installed based on the detailed geological survey, geohazard mapping, vulnerability analysis, and risk assessment over several water catchments in the areas. A science-based knowledge coupled with the Local, Traditional, and Indigenous Knowledge (LTIK) was collectively explored and translated into series of Community-led Disaster Risk Reduction (CLDRR), an extended version of traditional Community-based Disaster Risk Management (CBDRM) that widely conducted at various implementation scales. The early warning system was later integrated with the public warning system to expand its dissemination scales and acceptance level. Interestingly, a local landslide risk reduction model was co-developed with several partnership modality (public-private-academia-NGO), namely as YAN DRR Model, to support the build-back-better agenda and rejuvenate the multi-scale eco-tourism and food security industry. An integrated EWS system was tested and demonstrated in the last two- commemoration years. Several innovations for improving local risk communication system are intelligently explored and strategically documented. As a conclusion, the study provides a new insight into locally-led and nationally-supported landslide disaster risk reduction strategy, by empowering an impact-based early warning system for debris flow and landslides, integrating with the innovated LTIK approach and strengthening local champions in the vulnerable regions. Remarkably, this study demonstrates regional benchmarking, national commitment, and local wisdom to reduce the number of human- and economic losses through an impact-based early warning system, led by vulnerable community and powered by humanizing technology for building societal resilience in a changing climate.

Keywords: Landslide Disaster, Debris Flow, Disaster Risk Management, People-centered EWS

How to cite: Ramlee, L. H. S., Razak, K. A., Ramli, Z., and Mohamed, Z.: Impact-based Early Warning System for Debris flow in Malaysia: A Science-based and Localization Approach for Strengthening Disaster Resilience, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19871, https://doi.org/10.5194/egusphere-egu24-19871, 2024.

On-site presentation
Emma Hudson-Doyle and Saskia de Vilder

Much of Aotearoa NZ is hilly or mountainous and experiences high rainfall and frequent earthquakes. Consequently, landslides are an existing or potential hazard in many parts of the country, with the risk from landslides likely to increase with climate change. Cyclone Gabrielle (February 2023) highlighted the devasting impact of such landslides on people, property, and infrastructure networks. To increase the resilience of Aotearoa NZ to landslide-induced disasters, we need robust and consistent information that creates an evidence base to inform effective decision-making in the management of landslide risk nationwide, considering planners, policy-makers, emergency managers and government officials, lifeline and infrastructure managers, as well as other technical experts (e.g., engineers). This decision-relevant information needs to include when and where landslides occur, who and what they may impact, and how people, businesses, and communities perceive, mitigate, and respond to this hazard.

In this presentation we will detail how the new Hōretireti Whenua / Sliding Lands five-year Endeavour programme will integrate social science into the development of new probabilistic and scenario-based, nationally applicable, landslide hazard and risk models that can incorporate climate change scenarios. We will first introduce the vision for the integrated landslide risk models which will include: a) probabilistic models of landslide susceptibility considering earthquakes and rain, b) landslide runout models considering diverse landslide types, c) the multi-hazard risk modelling tool (RiskScape) to integrate hazard and vulnerability model components into forecast models, and d) the MERIT Tool to quantify the socio-economic impact of landslide hazards.

We will then present initial results from the first phase of associated social-science research that has mapped the range of decision maker needs for susceptibility, impact, and risk information, considering different decision sectors, demands, and timescales. Developed from this, a set of user personas and decision-scenarios will inform the effective communication of landslide risk across stakeholder groups, as well as inform effective application of the landslide risk models into decision-making for short-term risk management and long-term resilience. We will also present plans to investigate how individuals and organisations conceptualise landslide phenomena, models, and vulnerabilities using research techniques such as mental models and influence diagrams; with a view to integrating findings to not just improve communication of model outputs, but to also enhance decision-makers understanding of the national risk models. Through this we aim to increase effective uptake of these risk models into stakeholder decision-making across diverse organisations and sectors.

How to cite: Hudson-Doyle, E. and de Vilder, S.: Introducing Hōretireti Whenua / Sliding Lands:  integrating social science into nationally applicable landslide risk models for Aotearoa NZ, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1437, https://doi.org/10.5194/egusphere-egu24-1437, 2024.

On-site presentation
Mauro Rossi, Ivan Marchesini, Maria Teresa Brunetti, Silvia Peruccacci, Vinicio Balducci, and Fausto Guzzetti

As prioritized by the Sendai Framework, enhancing disaster preparedness is fundamental for the effective response, for taking actions in anticipation of events, and to ensure that the appropriate capacities are in place for effective response and recovery at all levels. Under this view early warning systems can be seen as irreplaceable tools to supporting the Civil Protection authorities in the preparedness and response phases. This is particularly relevant for the case of rainfall-induced slope failures that occur worldwide every year, claiming lives and causing severe economic disruption. Implementing early warning systems to forecast the occurrence of such geo-hydrological phenomena is difficult and challenging both from the scientific and technological side. Here we present a framework developed in Italy for the operational forecasting of rainfall induced landslides over large areas, which includes (i) models tools and technological supports for landslide prediction; (ii) algorithms for nowcasts and forecasts production using diversified inputs; (iii) operational early warning system procedures and technological supports for landslide forecasting; (iv) interfaces for the query and analysis of the early warning system outputs; (v) criteria, tools and technological supports for the validation of the early warning system outputs. The main lessons learned in the last two decades during the implementation of such framework are presented and discussed, highlighting the possible future challenges.

How to cite: Rossi, M., Marchesini, I., Brunetti, M. T., Peruccacci, S., Balducci, V., and Guzzetti, F.: A framework for the territorial landslide early warning system implementation: applications, lessons learnt and future challenges, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15220, https://doi.org/10.5194/egusphere-egu24-15220, 2024.

On-site presentation
Brian D. Collins, Skye C. Corbett, and Dianne L. Brien

Precipitation-triggered landslides resulting from prolonged and/or intense storms threaten lives and damage infrastructure throughout the world each year.  In the United States, population centers along the West Coast (e.g., Seattle, Washington; Portland, Oregon; San Francisco, California) have particularly high risk from landslides due to the intersection of typical cool season (October through May) Pacific cyclone storm tracks with intense urbanization located on and near steep hillslopes.  Past efforts on landslide early warning in California dating to the late 1970s and running through the mid-1990s were initially focused on the development of rainfall intensity-duration thresholds coupled with a recognition that little landsliding tended to occur prior to an antecedent rainfall condition being reached – essentially a proxy for soil saturation.  However, available technology at the time did not allow for economic and logistically viable subsurface monitoring of in situ hydrologic conditions in steep, landslide prone terrain.  In 2009, the U.S. Geological Survey (USGS) began development of a regional subsurface hydrologic monitoring network in the San Francisco Bay area to assist with keeping emergency managers informed about periods of elevated hazard from rainfall-induced shallow landslides.  The network currently consists of four stations, each located within hillslopes susceptible to shallow landslide initiation and representative of those that have failed in the past.  The stations are spatially arranged to capture the meteorological variability of the approximately 18,000 km2 region.  Each station consists of two monitoring nests with soil moisture and piezometric level sensors placed at variable depths within the typically 0.5 to 1-m-deep soil profiles.  The goal of the monitoring network is to identify the time frame(s) during which soil saturation may be reached during storms and thus enable generation of positive pore water pressures that can cause shallow landslide initiation.

Using the more than 10-year record of soil moisture time series data available from the network stations, combined with piezometric records indicating times of positive pore water pressure and observations of triggered landslides, we developed soil moisture thresholds that are now used for situational awareness to alert for the potential for widespread shallow landsliding and debris flows ahead of incoming storms.  Messaging indicating that hillslope soils are nearing or are at saturation is provided to the U.S. National Weather Service (NWS) several days ahead of storms to provide for sufficient time for communication with emergency managers so that they may identify and prepare appropriate resources for response should landslides occur.  The monitoring network, combined with established USGS-NWS communication protocols, has been successfully used to alert for expected landslide conditions in numerous storm events over the past 10 years, including the precipitation-record-setting 2022-2023 winter season.  Ongoing research is aimed at updating thresholds with recent data and developing semi-automatic routines for monitoring and alerting.

How to cite: Collins, B. D., Corbett, S. C., and Brien, D. L.: Development and implementation of subsurface hydrologic thresholds for identifying widespread shallow landslide and debris flow occurrence in the San Francisco Bay area (California, USA), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7260, https://doi.org/10.5194/egusphere-egu24-7260, 2024.

On-site presentation
Guido Rianna, Gennaro Sequino, Gaetano Pecoraro, Alfredo Reder, and Michele Calvello

The municipalities of the Amalfi and Sorrento Coasts (Campania Region, southern Italy) have historically been affected by weather-induced slope instability phenomena causing casualties and heavy economic damages, often associated with transport interruptions limiting the crucial tourist activities in the area. Due to the considerable geomorphological complexity of the area, even the simple cataloguing of the events is often challenging. Furthermore, the significant differences between the dynamics affecting the slopes (rockslides, debris flows, flowslides in pyroclastic covers) result in substantial differences in the atmospheric patterns able to trigger such events: from short-duration (up to sub-hourly scale) heavy precipitation events up to long-lasting rainfalls anticipated by particularly wet periods. Despite the significant differences among the dynamics, the warning system current operational in the Region considers three reference durations for cumulative precipitation (24, 48, and 72 hours) and it is based on three alert levels simply associated with the return time of potentially triggering precipitation (2, 5 and 10 years).

This study wants to fill this knowledge gap by investigating the main recent weather-induced slope instability phenomena in the area in relation to the recorded characteristics of the associated weather events. The investigation aims at multiple objectives.

  • Comparing, over a common period, the records of different landslide catalogues available over the area - FraneItalia  (https://franeitalia.wordpress.com/), ITALICA (doi.org/10.5194/essd-15-2863-2023), Franceschini et al., 2022  (doi.org/10.1007/s10346-021-01799-y); Extreme Severe Weather Database (https://eswd.eu), Italian hydrological-geological Portal (https://idrogeo.isprambiente.it) - with the goal to verify consistency and to analyse the reasons leading to any discrepancies.
  • Verifying the performance of the currently operational warning system for a set of events assumed as reliable, as they are included in more than one catalogue, taking into account the seasonality of events and the triggering precipitation patterns.
  • Evaluating the capabilities of atmospheric reanalysis (ERA5land 10.24381/cds.e2161bac and CERRA 10.24381/cds.a7f3cd0be) made available by the Copernicus Climate Change Service to reconstruct the precipitation patterns that triggered the events (back-analysis). The topic is of great interest due to the high temporal resolution (1 hour) and spatial resolution (9 km for ERA5 land, 5.5 km for CERRA), which could therefore adequately cover areas where current sensor networks may be lacking or temporarily non-operational.
  • Assessing if and how the soil moisture content data returned by atmospheric reanalysis can support the back-analysis and/or the forecasting of landslide events (ERA5 is updated with a delay of only 5 days with respect of present time). Given the spatial resolution of the reanalysis, they are not expected to actually reproduce local in-situ conditions, but they rather should act as proxies to evaluate the average wetness status of the slopes and then the presence of conditions predisposing to landslide triggering.

The results discussed regarding the case study of the Amalfi and Sorrento Coasts can be readily extended to other geographical and geomorphological contexts, at national and continental scale.

How to cite: Rianna, G., Sequino, G., Pecoraro, G., Reder, A., and Calvello, M.: Investigating recent weather-induced landslides and the related weather characteristics in a test area in Campania Region (Italy) for warning purposes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12607, https://doi.org/10.5194/egusphere-egu24-12607, 2024.

On-site presentation
Jana Lim, Giorgio Santinelli, Ashok Dahal, Anton Vrieling, and Luigi Lombardo

Globally, there is an urgent need for accurate and effective Landslide Early Warning Systems (LEWS). Most LEWS are currently based on a single aggregated measure of rainfall derived from either in-situ measurements or radar estimates. Relying on a summary metric of precipitation may not capture the intricacies of rainfall dynamics that could improve landslide prediction. Here, we present a proof-of-concept for constructing a LEWS that is based on an integrated spatio-temporal modelling framework. Our proposed protocol builds upon a recent approach that uses the entirety of the rainfall time series instead of the traditional cumulated scalar approximation. Specifically, we use a Gated Recurrent Unit to process the whole rainfall signal and combine the output features with a second neural network dedicated to incorporating terrain characteristics. We benchmark this approach against a baseline run that relies on terrain and a cumulative rainfall metric. Our protocol leads to better performance in the context of hindcasting landslides which uses past rainfall estimates from CHIRPS. This provides a stronger case to repeat the same experiment using weather forecasts. If analogous results are produced in the forecasting context, this could justify adopting such models for operational purposes.  

How to cite: Lim, J., Santinelli, G., Dahal, A., Vrieling, A., and Lombardo, L.: Dynamic Susceptibility of Rainfall-Induced Landslides: A Gated Recurrent Unit Approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3185, https://doi.org/10.5194/egusphere-egu24-3185, 2024.

On-site presentation
Nicola Nocentini, Ascanio Rosi, Luca Piciullo, Zhongqiang Liu, and Samuele Segoni

The literature is rich with applications of machine learning techniques for assessing landslide susceptibility maps, which are limited to spatial prediction only. However, aspects related to extending the application framework to space-time landslides forecasting remain largely unexplored.

To fill this gap, this study introduces an innovative dynamic (i.e., time-dependent) application of the Random Forest (RF) algorithm. RF, among its advantages, allows the calculation of the Out-of-Bag Error (OOBE, which measures the error that would be committed if a given input variable is excluded from the RF classifier) and to visualize the Partial Dependence Plots (PDPs, depicting the relationship between each class of an input variable and the model outcome). These indices were discussed in this study to explore the algorithm's logic and verify its reliability.

The dynamic methodology proposed in this study involves using a spatially and temporally explicit landslide inventory as well as identifying non-landslide events over space and time. This procedure allows the inclusion of dynamic variables such as cumulative rainfall, snowmelt, and their seasonal variability, as model input. It also allows the inclusion of traditional static parameters such as lithology and geomorphologic attributes. Another key contribution of this study is that the RF model, once trained and tested using landslide and non-landslide events identified over space and time, produced a predictor that was subsequently applied to the entire study area before, during, and after specific landslide events. For each selected day, a specific and time-dependent landslide probability map was generated, simulating a real-time application in a warning system.

A case study in Kvam, Norway, was selected because of the availability of a comprehensive rainfall-induced landslide inventory, and the two major landslide events that occurred in June 2011 and May 2013 were selected for the simulations. Various model configurations involving the augmentation of non-landslide events were investigated to assess the model's sensitivity. The resulting pixel-based probability maps were validated using the Double Threshold Validation Tool (DTVT), a promising validation method based on the aggregation of pixels into catchment areas.

The reliability of the model was verified, and several benchmark configurations for the dynamic application of the RF model were provided. The generated landslide probability maps exhibit the ability to distinguish ordinary situations (low probability values where no critical rainfall was recorded, and no landslides occurred) from high-risk events (high probability values where highly intense rainfall triggered several landslides). The validation tool employed demonstrates the model's good performance and defines a criticality level suitable for early warning purposes. This study represents a step forward in comparison to traditional landslide susceptibility assessments and demonstrates the applicability of a novel method for spatiotemporal landslide probability mapping through machine learning, with perspectives of application to early warning systems.

Work supported by PRIN-ITALERT project (PRIN2022 call - grant number: 202248MN7N) funded by NextGenerationEU

How to cite: Nocentini, N., Rosi, A., Piciullo, L., Liu, Z., and Segoni, S.: Spatiotemporal landslide forecasting through machine learning and perspectives of applications for early warning: a case study in Kvam, Norway, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15046, https://doi.org/10.5194/egusphere-egu24-15046, 2024.

On-site presentation
Nunziarita Palazzolo, David J. Peres, Pierpaolo Distefano, Luca Piciullo, Pietro Scandura, and Antonino Cancelliere

Landslide prediction is crucial for the design of early warning systems, and the integration of soil moisture information aims to enhance the accuracy of such predictions. This study focuses on the development of artificial neural networks (ANNs) designed to recognize conditions that trigger landslides, incorporating soil moisture data alongside precipitation. Specifically, using ANNs, we investigate the advantage of deriving thresholds without a specific parametric equation, and, due to their flexibility to incorporate multiple input variables, they allow for a comprehensive analysis of landslides. Specifically, the research utilizes observed precipitation and ERA5-Land reanalysis soil moisture data at four different depth layers. To assess the effectiveness of the proposed approach under diverse climatic and geomorphological conditions, two distinct case studies are considered, namely Sicily Island (Italy) and a group of catchments in the Bergen area of Norway. The proposed methodology involves three main steps: i) the acquisition of rainfall and landslide data; ii) the creation of a database of triggering (TE) and non-triggering (NTE) events; iii) the development of ANNs predicting when a landslide is triggered from input precipitation and soil moisture data. A measure of the prediction uncertainty of the developed ANN models, related to the fact that a limited sample of triggering events may be available, is also carried out. Overall, the developed ANN classifiers, incorporating soil moisture information in addition to precipitation, prove to have better predictive performance than those relying solely on precipitation data. In our study, we also carry out comparisons to traditional power law thresholds, derived by optimizing the true skill statistic (TSS) based on cumulative precipitation and duration (E-D). While the power law E-D thresholds reach a TSS equal to 0.50 for both study areas, the inclusion of soil moisture information can lead to significant performance improvements, yielding TSS values up to about 0.90. These results corroborate the potentialities of the use of soil moisture information and machine learning techniques in improving landslide prediction. 

How to cite: Palazzolo, N., Peres, D. J., Distefano, P., Piciullo, L., Scandura, P., and Cancelliere, A.: Improving landslide triggering thresholds using artificial neural networks and reanalysis multi-layer soil moisture information , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9805, https://doi.org/10.5194/egusphere-egu24-9805, 2024.

On-site presentation
Pasquale Marino, Daniel Camilo Roman Quintero, Giovanni Francesco Santonastaso, and Roberto Greco

Rainfall-induced landslides are widespread geohazards, often characterized by shallow and fast movements. Their occurrence is not easily predictable. Particularly, southern Apennines of Campania (Italy), widely covered by loose pyroclastic deposits laying upon limestone bedrock, are often subjected to massive shallow landslides after intense and long precipitation. The operational early warning systems for rainfall-induced landslides (LEWS) usually rely on empirical thresholds based only on the precipitation information (e.g., intensity and duration of rainfall event), which give rise to false and missed alarms. The reliability of landslide prediction would benefit from the inclusion of hydrological information about the state of the slope prior to rainfall events. In fact, in the last decade, novel hydrometeorological thresholds that mix hydrologic predisposing factors and the features of rainfall events have been developed for landslide forecasting. Specifically, adding information linked to major hydrological processes occurring in the slope improves the performance of LEWS.

The study refers to landslide-prone areas nearby the town of Cervinara, on the slopes of Partenio Massif, representative of a geomorphological setting typical of wide areas of Campania (Italy). Firstly, to obtain a significant data series for statistical analyses, a 1000-year hourly synthetic dataset, mimicking the hydrological response of the slope to meteorological forcing, was generated. Specifically, a stochastic NSRP rainfall model was coupled with a physically based model of the unsaturated flow through the soil cover, hydraulically connected to a linear reservoir simulating a perched aquifer which develops in the uppermost part of the bedrock during the wet season. Both the models had been previously calibrated and validated based on field monitoring data. The synthetic dataset of the slope cover response to precipitation is obtained in terms of soil suction and water content, and perched aquifer water level. The stability of the slopes is assessed under the infinite slope hypothesis, allowing the identification of the occurrence of landslides. The results highlight how novel approaches in the definition of thresholds, considering the 3D hydrometeorological space (i.e., root zone soil moisture, aquifer water level and rainfall event depth), can significantly improve their predictive performance, compared to the common bidimensional thresholds based either on meteorological or hydrometeorological variables.

Moreover, in real practical applications for landslide forecasting, it is not always possible to implicitly assume a perfect knowledge of the variables to be measured for defining the thresholds, especially for a wide area. In fact, both the hydrological and meteorological variables are affected by significant uncertainty, mainly owing to spatial variability. Similarly, the calculated factor of safety, based on the simulated soil moisture and pressure and the assumed soil physical parameters, can be affected by uncertainty, as slope morphological characteristics and soil hydraulic and geotechnical properties are also variable in space. Thus, in this respect, the effects of the uncertainty of slope geomorphological characteristics, as well as of soil hydraulic and geotechnical properties, embedded as probabilistic variables, have been investigated on the obtained 3D hydrometeorological thresholds and on the corresponding predictive performance.

How to cite: Marino, P., Roman Quintero, D. C., Santonastaso, G. F., and Greco, R.: Uncertainty analysis of 3D hydrometeorological thresholds for rainfall-induced landslides forecasting , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9587, https://doi.org/10.5194/egusphere-egu24-9587, 2024.

Virtual presentation
Anamika Sekar and Srikrishnan Siva Subramanian

The Idukki district of Kerala experienced its worst disaster since 1924 in 2018 due to the excessive rain during the monsoon season from June to August. There were devastating landslides throughout the district accounting for the highest among the state which included debris flow, soil slides and rockfalls,  ever since making the area more susceptible to landslides. Thus, forging the impending need of an early warning system is crucial. Here, the study attempts to create a rainfall threshold to predict landslides through a satellite based precipitation product - GPM (Global Precipitation Mission Integrated Multi-satellitE Retrievals). The global coverage and the improved resolution of the data makes it more effective in mitigating landslide disaster risk.  Hourly precipitation data was obtained from GPM for the 23 grids within the district where there was an occurrence of debris flow. As the largest number of landslides happened during a second spell of intense rainfall that lasted from July 11 to August 19, 2018, the rainfall during this time was analyzed. A total of 4 days in these two months were identified with high amounts of rainfall with August 15th showing continuous rainfall for 24 hours. Using the rainfall and the landslide data for 2018 the overall regional threshold over the district was estimated to be I= 5.42D-0.16, where I is the rainfall intensity in mm/hr and D is the event duration. A more precise threshold could be obtained by considering grid-wise rainfall thresholds in a more detailed analysis. 

How to cite: Sekar, A. and Siva Subramanian, S.: Satellite derived Intensity- Duration (ID) thresholds for landslide forecast in Idukki, India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15655, https://doi.org/10.5194/egusphere-egu24-15655, 2024.

Posters on site: Thu, 18 Apr, 10:45–12:30 | Hall X4

Display time: Thu, 18 Apr 08:30–Thu, 18 Apr 12:30
Chairpersons: Samuele Segoni, Tina Peternel
Roberto Coscarelli, Nicola Moraci, Giovanni Gullà, and Tommaso Moramarco

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.

Samuele Segoni, Nicola Nocentini, Camilla Medici, Francesco Barbadori, Alessio Gatto, Rachele Franceschini, Matteo Del Soldato, and Ascanio Rosi

A regional-scale landslide forecasting model based on rainfall thresholds was optimized for operational early warning. In particular, we addressed two main issues that usually hinder the operational implementation of this kind of models: (i) the excessive number of false alarms, resulting in civil protection system activation without any real need, and (ii) the validation procedure, usually performed over periods too short to guarantee model reliability.

To overcome these limitations, several techniques for reducing the number of false alarms were applied in this study, and a multiple validation phase was conducted using data from different sources. An intensity-duration threshold system for each of the five alert zones composing the Liguria region (Italy) was identified using a semiautomatic procedure called MaCumBA, considering three levels of criticality: low, moderate, and high. The thresholds were developed using a landslide inventory collected from online newspapers by a data mining technique called SECaGN. This method was chosen to account for only those events that echo on the Internet and therefore impact society, ignoring landslides occurred in remote areas, not of interest for civil protection intervention and resulting in false alarms. A calibration phase was performed to minimize the impact of false alarms, allowing at least one false alarm per year over the moderate criticality level. A novel datset containing only very severe disasters that required national-level emergencies was used to calibrate the high criticality threshold. In addition, we applied an innovative approach to include an antecedent rainfall indicator as third variable. The threshold is thus not represented by a traditional line in a 2D spce, but by a plane in a 3D space.

This approach allowed for a consistent reduction in false alarms. The results were validated through an independent landslide inventory and were compared with (i) the alert issued by the regional civil protection agency to observe the improvements achieved with the proposed model and to evaluate to what extent the proposed model is consistent with the assessments of the civil protection and (ii) a dataset of the national states of emergency to verify the suitability of the developed thresholds for alerting citizens. The 3D thresholds showed high predictive capabilities, confirming their suitability for implementation in an operational landslide early warning system.

How to cite: Segoni, S., Nocentini, N., Medici, C., Barbadori, F., Gatto, A., Franceschini, R., Del Soldato, M., and Rosi, A.: Optimized 3D rainfall thresholds: false alarms reduction and multi-source validation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11530, https://doi.org/10.5194/egusphere-egu24-11530, 2024.

Maria Teresa Brunetti, Stefano Luigi Gariano, Monica Solimano, Massimo Melillo, Silvia Peruccacci, Pietro Gabriele De Stefanis, and Michele Cicoria

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.

Tina Peternel, Mateja Jemec Auflič, Ela Šegina, Domen Turk, Jernej Jež, and Miloš Bavec

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.).


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.

Minu Treesa Abraham, Luca Piciullo, Zhongqiang Liu, Haakon Robinson, Erling Singstad Paulsen, and Ann Elisabeth Albright Blomberg

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.

Greta Morreale, Nicola Nocentini, Elena Benedetta Masi, Ascanio Rosi, Samuele Segoni, and Veronica Tofani

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.

Massimo Melillo, Fausto Guzzetti, Michele Calvello, Gaetano Pecoraro, and Alessandro C. Mondini

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.

Liangxuan Yan, Kunlong Yin, and Lixia Chen

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.

Use of Ground-Based Interferometric Radars (GB-InRa) as remote, real-time landslide early-warning systems
(withdrawn after no-show)
Alessandro Pettinari
Dong Haoyu

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.

Rosa Menichini, Gaetano Pecoraro, and Michele Calvello

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.

Tianxin Lu and Peng Han

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.

Xiao Ye, Hong-Hu Zhu, Bin Shi, and Filippo Catani

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.

Ashok Anand and Alok Bhardwaj

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.

Juliette Dubois, Sébastien Imperiale, Anne Mangeney, and Jacques Sainte-Marie

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.