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Landslide dams are extremely dangerous because dammed rivers can inundate upstream areas with rising water levels and flood downstream areas after dam breaching. The longevity of landslide dams, which is uncertain, is of great significance for dam failure prevention and mitigation since it determines the time available to take mitigation measures. In this study, the full longevity of landslide dams is divided into three stages (infilling, overflowing and breaching) for better estimation. The influences of dam characteristic parameters (triggers, dam materials and geometric/hydrological parameters) on the full longevity of landslide dams (the period from landslide dam formation to the end of dam failure) as well as on each of the three stages are analysed based on the database. Based on eight dimensionless variables, regression models for estimating the full longevity of landslide dams are developed with a R2 value of 0.781, and regression models for the three-stage longevity (the longevity as the sum of the periods of the three stages) by considering infilling, overflowing and breaching are established with a R2 value of 0.938. It is found that the landslide dam longevity cannot be predicted by one or two influencing factors since it is affected by multiple factors. The relative importance of each control variable is evaluated based on sensitivity analysis: the trigger is the most significant variable in the breaching stage since it affects the size of dam particles, the water content and the inflow rate (e.g. the rainfall trigger results in a larger inflow rate); the lake volume coefficient is more significant in the overflowing stage because it indicates the potential volume of water eroding the dam; and the average annual discharge coefficient is the most important factor in the infilling stage because it controls the time to impound water. The longevity predicted by different models are compared. The models developed in this paper show better accuracy due to the consideration of more parameters based on more cases. In particular, the three-stage longevity regression model shows better accuracy than that of other models because it considers the particular influencing factors for each stage. Three case studies (the “10·10” Baige, Hsiaolin and Tangjiashan landslide dams) are presented to show the application of the regression models developed in this paper. The dam longevity can be predicted more precisely if the timely inflow rate can be estimated by site monitoring or multi-temporal remote sensing images and pre-event digital elevation model (DEM).

Landslide dams, especially stable landslide dams, have been recognised as important contributors to regional geomorphological evolution. The eastern edge of the Tibetan Plateau provides good conditions for the formation of stable landslide dams. To identify stable landslide dams on the eastern margin of the Tibetan Plateau, the Google Earth Engine (GEE) was first used to map water surfaces in the study area. Then, stable landslide dams were identified using high-precision remote sensing images provided by Google Earth. A field investigation and a sampling of typical stable landslide dams were also adopted to characterise the landslide dams. The results show that 101 stable landslide dams are present in the study area, covering an area of 27.75 × 104 km2. There are four types of stable landslide dams, as follows: (1) landslides, (2) rock avalanches, (3) moraines, and (4) debris flows. The morphological parameters of a dam, which include dam height, dam width, dam volume, and catchment area, can be fitted with different relationship curves, with respect to the number of landslide dams. The source areas of landslide dams are generally located in the upper-middle and upper sections of adjacent mountains. The stability of a landslide dam is mainly controlled by the structure of the dam and the relationship between the dam volume and catchment area. Structurally, large rocks with large particle sizes are difficult to activate using river water and the large gaps between the rocks provide sufficient channels for the flow of river water. In regard to the relationship between the dam volume and catchment area, a river with a small catchment area in the study area is commonly blocked by a large dam volume. This study provides a unique opportunity to study the spatial distribution and clarify the factors influencing the stability of stable landslide dams.

A rapid and accurate prediction of a landslide dam's life span is of significant importance for emergency geological treatment. However, current prediction models for the state of a landslide dam are based solely on geomorphological indexes, and do not take into consideration attribute properties such as landslide types, trigger factors, and dam types. This study investigates the relationships between a landslide dam's geometry and the capacity of the barrier lake and proposes fitting models, which supplement the current landslide dam database. Subsequently, six predictive models for landslide dam life span are established, utilizing machine learning algorithms such as logistic regression, k-nearest neighbors, support vector machine, Naïve Bayes, decision tree, and random forest, which consider five factors, including geometry parameters and attribute properties. The performances of these six models are analyzed and compared to a typical prediction model, the dimensionless blockage index (DBI). The results suggest that the models established in this study not only have a consistent absolute accuracy as the DBI model, but also overcome the disadvantage that a large number of cases cannot be judged by the DBI model. Among the formulated machine learning models, the random forest model exhibits the highest absolute accuracy (89%), lowest error rate (7%), lowest false alarm rate (15%), and no uncertainty rate. Additionally, three renowned landslide dams, namely the Costantino, Hsiaolin, and Baige landslide dams, are analyzed to illustrate the applicability of the established machine learning models. The study results provide essential guidance for the predictions and emergency geological treatments of landslide dam disasters.

Background The study of past landslide dams and their consequences has gained a considerable significance for forecasting induced hydraulic risk on people and property. Landslide dams are rather frequent in Italy, where a broad climatic, geological and morphological variability characterize different part of the peninsula, and have already been studied in literature, focusing different geographical regions with different levels of detail. In order to develop specific tools to assess the landslide dam formation and stability, the first step is to realize a large data archive including a big number of data, collected with a consistent methodology to standardize the quality. Description For this reason, this paper reports the results of an extensive bibliographic work and geomorphologic investigation on landslide dams that lead to the development of the wider systematic inventory in Italy. Through the revision and the update of scientific works and historical reports, three hundreds of landslide dams from the Alps to the Southern Apennine and Sicily were identified. During investigations and through cartographic and aerial photos interpretation, several geomorphic parameters of the landslide, the dam body, the valley and the lake, if any, have been determined, or estimated using historical and bibliographical documents analysis. Conclusions The collected data were resumed in a database, formed by 57 information fields easy to collect and measure to privilege intuitive usability and future implementation. In order to describe the characteristics of landslide dams in Italy some specific analysis on the different types of landslide movements and their volume, the dam longevity, the main triggers and their geographical distribution were carried out.

Under strong earthquakes and extreme rainfall conditions, landslide clusters near rivers may block incoming water flows, forming cascade landslide dams. When subjected to extreme hydrological conditions, these cascade landslide dams can experience overtopping, which erodes and entrains material from the dam surface, compromising dam stability, which can potentially lead to outburst flooding. Although existing research provides valuable insights into landslide dam failures, it falls short in addressing the complexities of cascading failures in dam groups. The influence of various factors on this intricate process is still only partially understood, highlighting the need for a comprehensive understanding of dam surface evolution and flood flow dynamics specifically within the context of cascading failures in landslide dams. In this study, we conduct physical model experiments to categorize the stages of cascade landslide dam failure, identify typical failure modes, and investigate the mechanisms affecting breach morphology evolution and flood flow development during cascading failures. Our findings reveal that cascade landslide dams can amplify floods during cascading breaches, with peak flow rates increasing progressively from upstream to downstream. In cascade systems, downstream dams experience accelerated breach processes, where layer erosion becomes predominant. Significant fluctuations in river water levels between cascades can lead to slope failures in both upstream and downstream dams. Specifically, downstream dams exhibit more rapid breach widening, fewer instances of collapse at the breach slope compared to upstream dams, and earlier peak flow timing, making cascade landslide dam breaches more challenging for emergency response. These observations highlight the notable differences between cascade landslide dam failures and single dam failures. Based on these findings, we provide insights into developing mathematical models for cascade landslide dam failures to improve physical-based flood predictions, which will assist the development of early warning strategies for cascade landslide dam breach floods.

Landslides involving solid–fluid interaction such as submarine landslides and landslide dams occur frequently around the world, which may bring severe damage to human lives and properties. Investigation of such landslides is thus of significance to hazard prevention and mitigation. To conduct the analysis, there are three key points to be addressed: (a) the landslide failure process, (b) the free surface flow, and (c) the solid–fluid interaction process. Discontinuous deformation analysis (DDA) method is suitable for analyzing discontinuous blocky systems and has outstanding advantages in simulating the landslide failure process. Meanwhile, smoothed particle hydrodynamics (SPH) method is well-suited for modeling the free surface flow. However, the consideration of solid–fluid interaction in these two methods is seldom, which somehow restricts their applications. With the aim to take advantages of these two methods, a coupled DDA–SPH method in two-dimensional case is proposed, in which the solid–fluid interaction is forced using a penalty approach. The SPH formulations are implemented into DDA code. Several numerical examples are presented to check the validity of the proposed method. A dam-break test is first investigated to show the success of implementing SPH into DDA code for modeling the fluid flow in later simulations of fluid–solid systems. Subsequently, the performance of the coupled DDA–SPH method is validated through a submarine rigid landslide, and the simulation results are in good agreement with the experimental data. Further, an extension study on the submarine deformable landslide is performed, in which the landslide mass consists of multiple blocks and a sensitivity analysis on the interface friction angle between blocks is conducted. Finally, a designed landslide dam is simulated to show the applicability and feasibility of the coupled DDA–SPH method.

Landslide dam failures have devastating capability to cause significant hazards to human lives and infrastructure along their flooding paths. Recent studies have explored such hazards based on environmental seismology to analyze the process of evolution using the time-frequency characteristics of seismic signals. However, most research has focused on reconstructing individual processes (e.g., debris flows, landslides, and floods). Previous studies on a comprehensive analysis of the entire hazard chain process have been limited. Challenges lie in the integration of seismic signals with different energy levels during different hazard stages. To address this issue, post-hazard surveys, remote sensing, and seismic signals were obtained in this study to comprehensively analyze the evolution process of the Danba landslide dam failure hazard chain of June 17, 2020, in China. We developed weak-signal processing using a band-pass filter, empirical mode decomposition, fast Fourier transform, and short-time Fourier transform to process and analyze the seismic data and accurately extract the signals for the debris flow, landslide, flood, and noise attenuation in the hazard chain. The evolution process of each stage in the hazard chain was then interpreted. The debris flow, landslide, barrier lake bursting, and flood routing were analyzed to determine their evolution modes. In addition, the reactivation of ancient landslides downstream caused by flood erosion was also identified, with stage explanations. This method provides new ideas for interpreting the hazard chain processes and evolution modes of landslide dam failure hazard chains and theoretical guidance for future hazard early warning and mitigation.

Global warming in high mountain areas has led to visible environmental changes as glacial retreat, formation and evolution of moraine dammed lakes, slope instability, and major mass movements. Landslide dams and moraine dams are rather common in the Cordillera Blanca Mountains Range, Peru, and have caused large damages and fatalities over time. The environmental changes are influencing the rivers’ and dams’ equilibrium, and the potential induced consequences, like catastrophic debris flows or outburst floods resulting from dam failures, can be major hazards in the region. The studies of past landslide dam cases are essential in forecasting induced risks, and specific works on this topic were not developed in the study region. Reflecting this research gap, a database of 51 cases and an evolution study of landslide dams in the Cordillera Blanca Mountains is presented. The main morphometric parameters and information of the landslide, the dam body, the valley, and the lake, if any, have been determined through direct and indirect survey techniques. Low variability in some of the main morphometric parameter distributions (valley width and landslide volume) has been shown, most likely due to an environmental control connected to the regional tectonic and glacial history. In order to analyze present and future landslide dam evolution, a morphological analysis was carried out using two recently developed geomorphological indexes employed on the Italian territory. The results of the Cordillera Blanca analysis have been compared with a large Italian landslide dam inventory, highlighting as much the differences as the similarities between the two datasets. The long-term geomorphological evolution changes are evaluated. Many of the stable dams are in disequilibrium with their surrounding environment and their classification result is of “uncertain determination.”

Landslide dams, typically composed of newly deposited, loose, and heterogeneous materials, are highly susceptible to failure induced by overtopping and seepage, particularly under extreme hydrological conditions. Accurate prediction of such breaching processes is essential for flood risk management and emergency response, yet existing models generally consider only a single failure mechanism. This study develops a mathematical model to simulate landslide dam breaching under the coupled action of overtopping and seepage erosion. The model integrates surface erosion and internal erosion processes within a unified framework and employs a stable time-stepping numerical scheme. Application to three real-world landslide dam cases demonstrates that the model successfully reproduces key breaching characteristics across overtopping-only, seepage-only, and coupled erosion scenarios. The simulated breach hydrographs, reservoir water levels, and breach geometries show good agreement with field observations, with peak outflow and breach timing predicted with errors generally within approximately 5%. Sensitivity analysis further indicates that the model is robust to geometric uncertainties, as variations in breach outcomes remain smaller than the imposed parameter perturbations. These results confirm that explicitly accounting for the coupled interaction between overtopping and seepage significantly improves the representation of complex breaching processes. The proposed model therefore provides a reliable computational tool for analyzing landslide dam failures and supports more accurate hazard assessment under multi-mechanism erosion conditions.

The 2018 Hokkaido Eastern Iburi Earthquake struck the eastern Iburi region (epicenter: 42.691°N, 142.007°E, depth: 37.0 km) of Hokkaido, Japan, at 3:07.59 JST, September 6, 2018 (18:07.59, September 5, 2018 UTC). Many shallow landslides were triggered by this Mw 6.6 (Mj 6.7) earthquake. The basement complex in the affected area (sedimentary rocks) is covered with thick pyroclastic fall deposits derived from the Tarumae Volcano, etc., and the strong seismic shocks triggered shallow landsliding of them. Shallow landslides moving along valley type topography traveled greater distances than those moving along planar slope topography. Some shallow landslides occurred on relatively gentle slopes (< 30°). The earthquake also induced several large-scale deep-seated landslides, including one that has formed a landslide dam in the Hidaka-horonai River. Landslides were densely distributed over hilly regions (elevation: 200–400 m) within an area of approximately 400 km2 in Atsuma (landslides caused 36 deaths), Abira, and Mukawa, and the number of landslides and the total area of the landslides were the largest in Japan ever since the Meiji Era (1868–1912). The catchments where shallow landslides were concentrated were severely devastated.