Landslide Scenarios Research Articles

Large-scale flood risk assessment in data-scarce areas: an application to Central Asia

Abstract. The countries of Kazakhstan, Kyrgyz Republic, Tajikistan, Turkmenistan, and Uzbekistan in Central Asia are highly prone to natural hazards, particularly floods, earthquakes, and landslides. The European Union, in collaboration with the World Bank and the Global Facility for Disaster Reduction and Recovery (GFDRR), created the programme Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia (SFRARR) to advance disaster and climate resilience in the region. As part of the SFRARR project, the “Regionally consistent risk assessment for earthquakes and floods and selective landslide scenario analysis for strengthening financial resilience and accelerating risk reduction in Central Asia” was developed to achieve the project's objectives. This article presents the data, model, methodology, and results for the five Central Asian countries of the flood risk assessment, which represents the first high-resolution regional-scale transboundary risk assessment study in the area aiming to provide tools for decision-making. The output information will inform and enable the World Bank to initiate a policy dialogue. A fully probabilistic risk assessment for fluvial floods has been carried out for these countries to support regional and national risk financing and insurance applications, including potential indemnity and/or parametric risk financing solutions for a regional programme. A homogenised risk assessment methodology for the five countries and across multiple hazards (floods and earthquakes) and asset types has been adopted to obtain strategic financial solutions consistent across geographical areas and economic sectors. The largest relative (to the total exposed value) expected annual damages are found in Kazakhstan and Tajikistan, with values above 6 ‰. In the five considered countries, the largest relative expected annual damages by sector are found for the transport and agricultural sectors. Climate change is expected to have contrasting impacts, with increases in risk for some regions (the most severe increase is found in the Mangistauskaya region in Kazakhstan) and decreases for other regions (Lebap, Turkmenistan; Khatlon, Tajikistan; Samarkand, Uzbekistan; and Batken, Kyrgyz Republic).

Deep learning-based landslide tsunami run-up prediction from synthetic gage data

The present study proposes a deep learning model based on Long-Short Term Memory (LSTM) that uses gage measurements for prediction of landslide-driven maximum tsunami run-up. In an attempt to overcome the limitation of insufficient real-world data in the field, our methodology refers to analytical models to create a comprehensive dataset employing a time series recorded from an offshore gage as input and its corresponding maximum run-up at the shoreline as output, for different landslide scenarios with pre-determined parameters. The LSTM-based model is then trained using this dataset in order to predict the maximum run-up. The results, with mean values of 0.211 m, 0.149 m, 1.745% and 0.9988 for RMSE, MAE, MAPE and R2, respectively, indicate that our model is both accurate and precise. As the data-driven models such as the one proposed here are often utilized to identify relationships that may not be immediately apparent from the physical models alone, our interdisciplinary approach has the potential to foster the development of innovative solutions and methodologies for addressing complex natural hazards by enhancing early warning systems, preparedness and response to tsunamis.

Tsunami hazard induced by a submarine landslide in the Tagus delta off Lisbon (Portugal)

AbstractSubmarine landslides can be a major source of tsunamis, with waves highly dependent of the properties of both the landslide and the marine domain. This study investigates the tsunamigenesis of a shallow-water submarine landslide, recently mapped at the mouth of the Tagus delta River, off Lisbon, Portugal. The Tagus delta landslide is estimated to be 8 ky old, with a depositional maximum thickness of ~ 20 m and a volume of approximately 0.27 km3, extending over more than 9 km in length and 3 km in width. We assess the tsunamigenic potential and hazard of this possible early Holocene landslide using numerical simulations over high resolution reconstructed paleo-bathymetry, and project a similar future landslide scenario under the present-day conditions. Results show that both paleo- and future landslide scenarios are tsunamigenic, posing a significant hazard to the nearest coasts of Lisbon and surrounding areas. At the source region, the modelled tsunami reached heights of 0.8 m (paleo-scenario) and 1.45 m (future-scenario), increasing to approximately 2 m (both scenarios) as it propagated towards Lisbon’s coastline. This study is a first attempt to address the tsunamigenic potential of a submarine landslide occurring in a shallow water environment of the Portuguese margin domain. The results reinforce the need to include these small, but frequent, events and their induced tsunamis in marine geo-hazard assessments of the region.

A mixed total Lagrangian-updated Lagrangian Smoothed Particles Hydrodynamics method for geomechanics simulations with discontinuities

This study presents a novel approach for simulating geotechnical problems including the initiation and post-failure behavior of discontinuities. The developed method is constituted by a mixed total Lagrangian--updated Lagrangian Smoothed Particle Hydrodynamics (SPH) method, which the main characteristic is to distinguish between internal forces within a body and contact forces from interactions with other bodies as internal stress and collision stress, respectively. Internal stress effects are calculated using total Lagrangian SPH interpolations, while collision stress effects are computed with updated Lagrangian. Fractures are simulated by employing plastic deformation as a damage measure, with fully damaged particles detached from their original body and treated as a separate particulate material, interacting via collision stress. Numerical tests confirm the method's capability in accurately modeling elastic collisions, friction forces and tension and shear fractures, validated against experimental data. Finally, we show the applicability of the proposed by simulating a real-scale landslide scenario, the Selborne experiment. This final numerical test demonstrates the capability of the method to simulate a landslide detached from the main soil mass as observed in the experiment.

Source characterization of the 1996 Biak tsunami based on earthquake and landslide scenarios

Source characterization of the 1996 Biak tsunami based on earthquake and landslide scenarios

Dynamic prediction model of landslide displacement based on (SSA-VMD)-(CNN-BiLSTM-attention): a case study

Accurately predicting landslide displacement is essential for reducing and managing associated risks. To address the challenges of both under-decomposition and over-decomposition in landslide displacement analysis, as well as the low predictive accuracy of individual models, this paper proposes a novel prediction model based on time series theory. This model integrates a Convolutional Neural Network (CNN) with a Bidirectional Long-Short Term Memory network (BiLSTM) and an attention mechanism to form a comprehensive CNN-BiLSTM-Attention model. It harnesses the feature extraction capabilities of CNN, the bidirectional data mining strength of BiLSTM, and the focus-enhancing properties of the attention mechanism to enhance landslide displacement predictions. Furthermore, this paper proposes utilizing the Variational Mode Decomposition (VMD) method to decompose both landslide displacement and its influencing factors. The VMD algorithm’s parameters are optimized through the Sparrow Search Algorithm (SSA), which effectively minimizes the influence of subjective bias while maintaining the integrity of the decomposition. A fusion of the Maximal Information Coefficient (MIC) and Grey Relational Analysis (GRA) is then employed to identify the critical influencing factors. The selected sequence of factors that conforms to the criteria is used as the input variable for displacement prediction via the CNN-BiLSTM-Attention model. The cumulative displacement prediction is derived by aggregating the results from each sequence. The study reveals that the SSA-VMD-CNN-BiLSTM-Attention model introduced herein achieves superior predictive accuracy for both periodic and random term displacements than individual models. This advancement provides a dependable benchmark for forecasting displacement in similar landslide scenarios.

Improving Landslide Prediction: Innovative Modeling and Evaluation of Landslide Scenario with Knowledge Graph Embedding

The increasing frequency and magnitude of landslides underscore the growing importance of landslide prediction in light of factors like climate change. Traditional methods, including physics-based methods and empirical methods, are beset by high costs and a reliance on expert knowledge. With the advancement of remote sensing and machine learning, data-driven methods have emerged as the mainstream in landslide prediction. Despite their strong generalization capabilities and efficiency, data-driven methods suffer from the loss of semantic information during training due to their reliance on a ‘sequence’ modeling method for landslide scenarios, which impacts their predictive accuracy. An innovative method for landslide prediction is proposed in this paper. In this paper, we propose an innovative landslide prediction method. This method designs the NADE ontology as the schema layer and constructs the data layer of the knowledge graph, utilizing tile lists, landslide inventory, and environmental data to enhance the representation of complex landslide scenarios. Furthermore, the transformation of the landslide prediction task into a link prediction task is carried out, and a knowledge graph embedding model is trained to achieve landslide predictions. Experimental results demonstrate that the method improves the F1 score by 5% in scenarios with complete datasets and 17% in scenarios with sparse datasets compared to data-driven methods. Additionally, the application of the knowledge graph embedding model is utilized to generate susceptibility maps, and an analysis of the effectiveness of entity embeddings is conducted, highlighting the potential of knowledge graph embeddings in disaster management.

Rapid evaluation of earthquake-induced landslides by PGA and Arias intensity model: insights from the Luding Ms6.8 earthquake, Tibetan Plateau

On September 5, 2022, a magnitude 6.8 earthquake occurred along the Xianshuihe Fault Zone in Luding County, Tibetan Plateau, China, leading to a significant outbreak of landslides. The urgent need for a swift and accurate evaluation of earthquake-induced landslides distribution in the affected area prompted this study. This research delves into regional geological data, scrutinizes post-earthquake Peak Ground Acceleration (PGA) and Arias Intensity (Ia) associated with the Luding earthquake, and conducts earthquake-induced landslides risk assessments within the Luding earthquake zone using the Newmark model. Validation of the earthquake-induced landslides risk assessment outcomes rooted in PGA and Ia relies on an earthquake-induced landslides database, revealing Area Under the Curve (AUC) values of 0.73 and 0.84 in respective ROC (Receiver Operating Characteristic) curves. These results unequivocally affirm the exceptional accuracy of earthquake-induced landslides evaluation using Ia calculations, emphasizing its suitability for the swift prediction and evaluation of earthquake-induced landslides. The earthquake-induced landslides risk assessment based on Ia computation reveals the area with extremely high-risk and high-risk of earthquake-induced landslides encompass 0.71% of the entire study area. Notably, these areas are predominantly clustered within seismic intensity VII zones and primarily trace the Moxi fault zone, extending from the southern portion of the middle east along the Dadu River and the Moxi fault, with reach up to Dewei Township in the north and Caoke Township in the south. Hazard-prone regions predominantly align with slopes featuring gradients of 30°–45° and bear a strong correlation with fault activity. Furthermore, the results of this evaluation are harmonious with the findings from remote sensing interpretation and on-site field investigations pertaining to the earthquake-induced landslides. This body of knowledge can serve as a crucial reference for expedited assessment, emergency response and subsequent supplementation of earthquake-induced landslide databases when confronting similar earthquake-induced landslide scenarios.

LANDSLIDE TSUNAMI HAZARD ASSESSMENT: A NUMERICAL MODEL FOR THE SIMULATION OF MULTIPLE LANDSLIDE-INDUCED TSUNAMIS SCENARIOS IN A MONTE CARLO FRAMEWORK

Submarine landslides can pose serious tsunami hazard to coastal communities. However, performing a comprehensive landslide tsunami hazard assessment for a given area is in general difficult in view of the large uncertainties associated with tsunamigenic source parameters. These are often known only approximately, on the basis of estimates of the landslide geometry, slide material properties, and kinematics. Here, we present an efficient model to perform such hazard analyses, based on solving the linear mildslope equation with a time-dependent source term representing the seafloor motion as detailed in Iorio et al. (2021). This approach allows carrying out many computations, for a large number of landslide scenarios, in a Monte Carlo (MC) approach framework, at a reduced computational cost compared to other available methods, while still providing physically accurate simulations of most landslide tsunami generation and propagation.

Enhancing Risk Analysis toward a Landscape Digital Twin Framework: A Multi-Hazard Approach in the Context of a Socio-Economic Perspective

In the last decades, climate and environmental changes have highlighted the fragility and vulnerability of the landscape, especially in mountain areas where the effects are most severe. This study promotes the methodological setup of a landscape digital twin to establish a multi-disciplinary and multi-scalar hazard overview according to a matrix framework implementable over time and space. The original contribution to the research addresses a holistic vision that combines meaningfully qualitative with quantitative approaches within a multi-hazard framework from the socio-economic perspective. This contribution presents road network risk analysis by exploiting flooding and landslide scenarios. The critical road segments or nodes most vulnerable or impacted by network performance and accessibility can be identified with minimal preprocessing from credible open-source sources. Service maps are used to show the spatial distribution of risk scores for different typologies of points of interest and hazards. Origin-destination matrix graphs display changes in travel time between facilities under various scenarios. Using a risk scores formula to generate risk maps has made it possible to effectively represent the interconnectedness among natural hazards, infrastructure, and socio-economic factors, fostering more resilient decision-making processes. The method’s applicability is tested through a case study in northern Italy’s Piedmont Region.

Probabilistic landslide tsunami modeling of the 2018 Palu Bay event

On September 28, 2018, a Mw 7.5 earthquake triggered near Central Sulawesi generated a highly destructive tsunami within Paul Bay (Indonesia). Field surveys and various studies conducted after the event showed that, as a result of the earthquake, several large submarine landslides were triggered along the shores of the bay, that significantly contributed to tsunami generation. The estimated geometry and other parameters for these slides, however, were affected by a large uncertainty. Here, we present a probabilistic tsunami hazard analysis of this event, based on Monte Carlo simulations using a linear Mild Slope Equation (MSE) model combined with a Green’s function approach, that allow efficiently simulating a large number of stochastic landslide tsunami generation and propagation scenarios within Palu Bay, for each of the identified landslides. In the MSE model, a space and time-dependent source term is used to represent the seafloor motion associated with each landslide scenario. In the Green’s function approach, a large database of elementary solutions and their tsunami elevation at a large number of coastal save points is pre-computed for tsunamis generated by a unit seafloor acceleration specified over a small area. Then, given an actual submarine landslide scenario, with a specific acceleration function, the tsunami elevation at the save points is simply and efficiently computed as a weighed linear superposition of the elementary solutions. Tsunami runup is finally obtained using a semi-empirical method, based on results computed at save points for each landslide scenario. The model is applied to the 2018 Palu Bay tsunami event, allowing to investigate how the uncertainty in landslide parameters affects tsunami hazard. A comparison with observed runups is made, which shows that these fall within the range of uncertainty of the simulated probabilistic runups.

Tsunami Wave Characteristics from the 1674 Ambon Earthquake Event Based on Landslide Scenarios

This study focuses on understanding the historical tsunami events in Eastern Indonesia, specifically the Banda Sea region, by extracting information from the limited and challenging-to-interpret historical records. The oldest detailed account of a tsunami in Indonesia dates back to 1674, documented in the book Waerachtigh Verhael Van de Schlickelijcke Aerdbebinge by Rumphius. The study aims to comprehend the primary source of the tsunami and analyze its characteristics to facilitate future tsunami risk reduction. The methodology includes collecting topography and bathymetry data, conducting landslide scenario analysis, employing a two-layer wave propagation model, and performing spectral analysis. The study utilizes comprehensive datasets, investigates potential landslide scenarios, simulates tsunami propagation, and analyzes frequency characteristics using the fast Fourier transform. The 1674 event yielded a runup height of approximately 50–100 m, whereas this study underestimated the actual runup. To illustrate the tsunami wave along the bay’s coastline, a Hovmöller diagram was employed. By analyzing the Hovmöller diagram, the power spectral density was computed, revealing five prominent period bands: 6.96, 5.16, 4.1, 3.75, and 3.36 min. The integration of these components provides a rigorous approach to understanding tsunami dynamics and enhancing risk assessment and mitigation in the study area.

Case study of an active landslide at the flank of a water reservoir and its response during earthquakes

Slow-moving active landslides are a widespread phenomenon and often cause major damage to infrastructure. Normally, such slow slides do not cause fatalities, but this can change during extreme events such as a heavy rain or a strong earthquake. In particular their response to earthquakes is only partially understood, while documented field observation of such cases are rare. Considering their mobile pre-seismic state, one would expect active slides to be highly susceptible to an acceleration during earthquakes. When such a landslide affects a flank of a water reservoir, the consequences of its collapse can be devastating due to subsequent tsunami waves overtopping the dam. In this article, a case study of a deep-seated slow-moving landslide with a volume of about 5 million m3 at the flank of a water reservoir in the Swiss Alps is presented. Extensive field investigations and monitoring allow a profound understanding of the kinematics of the landslides and its dependency on the hydrological conditions. Together with laboratory ring shear tests, this forms the basis for quantifying rate effects in the shear zone over a very wide velocity range and allows formulating and calibrating an accurate mechanical landslide model. The material point method is used to investigate potential landslide scenarios during different earthquakes. The simulations show rather small co-seismic displacements and even for pessimistic scenarios, a threat from tsunami waves seems unlikely. This implies that slow-moving active landslides can be less susceptible to co-seismic acceleration than stable slopes due to their ductile behaviour.

Multi-Level Data Analyses for Characterizing Rainfall-Induced Landslide Scenarios: The Example of Catanzaro Municipality (South Italy)

This paper presents a GIS-based approach to create a multilevel data system for detailed knowledge of landslide occurrences in small territorial units such as municipalities. The main aim is to collect all the available data (geological, geomorphological, and climatic data, as well as landslide inventory maps and catalogues) in a structured data management system and perform further analyses to identify the typical landslide scenarios of the study area that can be useful in landslide risk management. We demonstrated the use of the methodology analyzing landslide risk in the municipality of Catanzaro (southern Italy), having a surface of 111.7 km2, 20.5% of which was affected by landslides. The spatial and temporal distribution of landslides highlighted that in several cases, they are reactivations of pre-existing phenomena. In fact, in the municipality, approximately 17% of the buildings fall within landslides-affected areas, 7.9% of which are in areas where landslides are classified as active. Furthermore, active landslides involve 8.1% and 9.5% of the roads and railways, respectively. In the 1934–2020 study period, 53% of activations occurred between October and December and were triggered by daily rain which in the highest percentage of cases (49%) showed values between 50 and 100 mm. The proposed GIS platform can be easily updated in order to preserve the landslide history of the area and can be enriched with further thematic layers (i.e., layers concerning flood events, which often occur simultaneously with major landslide events). The case study demonstrates how the platform can support landslide risk management in terms of monitoring, planning remedial works, and the realization/updating of civil protection plans.

Landslide Induced Tsunami Hazard at Volcanoes: the Case of Santorini

The destructive tsunami on 22 December 2018 due to the flank collapse of the Anak Krakatau volcano was a bitter reminder of large tsunami risks and of the shortcomings of the existing tsunami warning systems for atypical sources (tsunamis generated by non-seismic and complex sources). In the Mediterranean, several tsunamis were generated by landslides associated with volcanic systems in the past.The volcanic unrest experienced in 2011–2012 on the Santorini volcanic island in the Southern Aegean Sea pointed out the need to identify and quantify tsunami hazard and risk due to possible flank instability which may be triggered as a result of volcanic unrest or nearby seismotectonic activities. Inspired from this need, in this study we examined three possible landslide scenarios in Santorini Island with tsunamigenic potential. The results show that the scenarios considered in our study are able to generate significant local tsunamis impacting Santorini and the nearby islands, as well as producing significant impact along the coasts of the Southern Aegean Sea. While maximum tsunami amplitudes/arrival time ranges are 1.2 m/30-90 min for locations in the Greek-Turkish coasts in the far field, they are in the order of ≈60 m/1-2 min for some locations at the Santorini Island. The extreme tsunami amplitudes and short arrival times for locations inside the Santorini Island is a major challenge in terms of tsunami hazard warning and mitigation. As an effort to address this challenge, a discussion on the requirements for local tsunami warning system addressing atypical sources in the context of multi-hazard disaster risk reduction is also provided.

A Scenario-Based Case Study: Using AI to Analyze Casualties from Landslides in Chittagong Metropolitan Area, Bangladesh

Understanding the complex dynamics of landslides is crucial for disaster planners to make timely and effective decisions that save lives and reduce the economic impact on society. Using the landslide inventory of the Chittagong Metropolitan Area (CMA), we have created a new artificial intelligence (AI)-based insight system for the town planners and senior disaster recovery strategists of Chittagong, Bangladesh. Our system generates dynamic AI-based insights for a range of complex scenarios created from 7 different landslide feature attributes. The users of our system can select a particular kind of scenario out of the exhaustive list of 1.054 × 1041 possible scenario sets, and our AI-based system will immediately predict how many casualties are likely to occur based on the selected kind of scenario. Moreover, an AI-based system shows how landslide attributes (e.g., rainfall, area of mass, elevation, etc.) correlate with landslide casualty by drawing detailed trend lines by performing both linear and logistic regressions. According to the literature and the best of our knowledge, our CMA scenario-based AI insight system is the first of its kind, providing the most comprehensive understanding of landslide scenarios and associated deaths and damages in the CMA. The system was deployed on a wide range of platforms including Android, iOS, and Windows systems so that it could be easily adapted for strategic disaster planners. The deployed solutions were handed down to 12 landslide strategists and disaster planners for evaluations, whereby 91.67% of users found the solution easy to use, effective, and self-explanatory while using it via mobile.

Lake volume and potential hazards of moraine-dammed glacial lakes – a case study of Bienong Co, southeastern Tibetan Plateau

Abstract. The existence of glacial lakes in the southeastern Tibetan Plateau (SETP) is a potential hazard to downstream regions, as the outburst of such lakes has the potential to result in disastrous glacial lake outburst floods (GLOFs). In the present study, we conducted a comprehensive investigation of Bienong Co, a moraine-dammed glacial lake in the SETP. First, the lake basin morphology was determined, and the lake volume was estimated, showing that the maximum lake depth is ∼181 m and the lake volume is ∼102.3×106 m3. These scenarios included the possibility of GLOFs being triggered by ice avalanches (Scenarios A1–3) from the mother glacier or by landslides from the lateral moraines (Scenarios B1–3 and C1–3). Avalanche volumes of the nine trigger scenarios were obtained from the Rapid Mass Movement Simulation (RAMMS) modeling results. Next, the Basic Simulation Environment for Computation of Environmental Flow and Natural Hazard Simulation (BASEMENT) model was used to simulate the generation and propagation of the avalanche-induced displacement waves in the lake. With the model, the overtopping flows and erosion on the moraine dam and the subsequent downstream floods were also simulated. The results indicate that the ice avalanche scenario may cause the largest mass volume entering the lake, resulting in a displacement wave up to 25.2 m in amplitude (Scenario A3) near the moraine dam. Landslide scenarios with smaller volumes entering the lake result in smaller displacement waves. Scenarios A1, A2, and A3 result in released water volumes from the lake of 24.1×106, 25.3×106, and 26.4×106 m3, respectively. Corresponding peak discharges at the moraine dam are 4996, 7817, and 13 078 m3 s−1, respectively. These high discharges cause erosion of the moraine dam, resulting in breach widths of 295, 339, and 368 m, respectively, with the generally similar breach depth of approximately 19 m. In landslide scenarios, only overtopping flows generated by Scenarios B3 and C3 cause erosion on the moraine dam, with breach depths of 6.5 and 7.9 m and breach widths of 153 and 169 m, respectively. According to our simulations, GLOFs generated by Scenarios A1–3 all flow through 18 settlements downstream in 20 h, threatening more than half of them. Both Scenarios B3 and C3 produce GLOFs that flow through the first eight settlements downstream in 20 h and have a relatively small impact on them. Comparisons of the area, depth, and volume of glacial lakes for which the bathymetry data are available show that Bienong Co is the deepest known glacial lake with the same surface area on the Tibetan Plateau. This study could provide a new insight into moraine-dammed glacial lakes in the SETP and be a valuable reference for GLOF disaster prevention for the local government.

Multi-hazard performance evaluation of hillside buildings under earthquakes and landslides

Earthquake-induced landslide, a cascading hazard in hilly regions, can substantially increase the damaging effects of an earthquake. This necessitates performance evaluation of buildings situated in hilly regions under the effects of landslide-only and landslides following earthquakes. In this study, the performance of two commonly observed hillside building configurations is assessed—namely, step-back and split foundation—subjected to the above-mentioned multi-hazard scenarios using an uncoupled approach. The uncertainty in the soil properties that influence the landslide-induced forces is considered based on laboratory tests of soil samples collected from active landslide sites and an existing database of landslide events. Three-dimensional hillside buildings are analysed for uphill-side landslide scenarios by performing force-controlled non-linear static analyses. Further, a suite of earthquake ground motions is scaled to a predefined intensity level, and then non-linear time history analyses are performed to obtain the damaged state of the buildings prior to assessing the landslide response. It is observed that the earthquake loading history and its direction of excitation significantly affect the response of hillside buildings to a landslide following an earthquake. Further, the step-back building is observed to be more vulnerable to landslide damage when compared to its split foundation counterpart for a given uphill-side landslide scenario.

Potential Landslide Origin of the Seram Island Tsunami in Eastern Indonesia on 16 June 2021 Following an Mw 5.9 Earthquake

ABSTRACT A 51 cm tsunami amplitude was observed in Tehoru, Seram Island (Indonesia), following an Mw 5.9 earthquake. Such a relatively large tsunami is highly unexpected from this size earthquake. Our analyses showed that the tsunami was 15 times larger in Tehoru tide gauge station than that recorded on two other stations located nearby. These observations imply that the tsunami was most likely generated by a secondary source such as a submarine landslide that potentially occurred near Tehoru. Local people reported landslide activities around Tehoru following the earthquake. We conducted numerical modeling of the tsunami by considering the tectonic source and found that the resulting tsunami was only a few centimeters in Tehoru. Therefore, it is very likely that the earthquake was not responsible for the tsunami observed in Tehoru. By assuming that a submarine landslide was responsible for the tsunami, we applied spectral analysis and tsunami backward raytracing to gain information about the potential size and location of the landslide. Backward raytracing was also applied to identify the earthquake source of the tsunami. Numerical modeling of eight candidate landslide scenarios showed that a landslide with a length and a thickness of approximately 4 km and 50 m, respectively, was potentially responsible for the tsunami. We note that our results serve only as the first and preliminary estimates. Bathymetric surveys and high-resolution bathymetry data are essential to provide more detailed information about the landslide.

A Numerical Study of SMART Cables Potential in Marine Hazard Early Warning for the Sumatra and Java Regions

We present results from a series of exploratory numerical experiments based on ocean bottom pressure and seismic data from a simulated linear array of SMART cable stations off the trench in the Sumatra-Java region. We use six rupture scenarios to calculate tsunami propagation using hydrodynamic simulations. Through these experiments we show that such an addition would result in up to several hours of improvement in the detection of earthquakes and tsunamis compared to the existing (minimal) DART systems in the northern and southern Indian Ocean. By simulating tsunamis from 58 submarine landslide scenarios in the region, we show that the SMART system can provide invaluable information in early warning against landslide tsunamis. We also calculate seismic phase arrival times from six source scenarios at existing seismic stations and our proposed SMART cables. Statistical analysis of our results shows that inclusion of such a SMART array can improve the important network parameters for the detection, evaluation and locating of seismic events.