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Background: Earthquakes and landslides pose significant threats to human safety and property, necessitating early warning systems. However, the high construction costs of earthquake early warning systems present a challenge. Purpose: Landslide warnings are more prevalent, so linking them to earthquake warnings could address cost concerns. Hence, it is crucial to validate the feasibility of utilizing GNSS landslide monitoring as assistance for earthquake early warning systems. Methods: This paper analyzes acceleration anomaly data from 31 GNSS landslide monitoring points near the epicenter of the May 2, 2023, MW = 5.2 Baoshan earthquake in Yunnan. The response time was determined as the time difference between an earthquake's occurrence and GNSS's acceleration anomalies. This calculation helps measure the time delay and sensitivity between these two events. Data were obtained from the geological disaster monitoring and early warning management system. Results: GNSS landslide monitoring showed high sensitivity to nearby earthquakes. The fastest response time among the 31 data points was 8 seconds, while the slowest was 56 seconds, all falling within the one-minute mark. A linear correlation was found between acceleration anomaly response time and distance from the epicenter, indicating the feasibility of GNSS landslide monitoring-assisted earthquake monitoring. Conclusion: A proposal is made for a GNSS landslide monitoring cluster to establish a multi-dimensional landslideearthquake disaster warning system. This approach offers new methods for combining earthquake and landslide early warning systems, leveraging existing infrastructure for cost-effectiveness and enhancing disaster preparedness. [ABSTRACT FROM AUTHOR]

The aim of this paper is to report on fatal landslides resulting from two extraordinary storm events in January and February 2023, in Auckland, New Zealand, including the characteristics of the terrain, the site geology, and the storms. The January 27 Auckland Anniversary storm was from an "atmospheric river," dumping Summer's worth of rain (265 mm) in one day. This was the highest 24-h total on record, estimated a 1 in 200-year event, with a peak 2-min rainfall of 4.2 mm, coinciding with Friday's evening "rush hour." This led to widespread landsliding throughout Auckland, including a fatal landslide in Parnell, central Auckland. Notably, this occurred only meters from a similarly destructive landslide in 1997. With January 2023 Auckland's wettest January on record, and with the region still in a state of emergency, Tropical Cyclone Gabrielle crossed the region on February 13–14. This also caused widespread landsliding, including a fatal landslide at Muriwai on the west Auckland coast. Fatal landslides from the same escarpment had occurred in 1965, close to the February 13, 2023, fatal landslide. Taken together, both storm events caused > 140,000 landslides across the North Island, and flooding, making hundreds of people homeless, with damage estimates of > US$8.6 billion (3.4% of NZ's GDP). For future disaster risk reduction, attention should be paid to revising legislation and local planning, which currently allows people to legally build (and re-build) houses in landslide-prone areas of New Zealand. [ABSTRACT FROM AUTHOR]

In the present paper, an active landslide located to the southeast of the urban area of Alcoy (Alicante, Spain) is analysed. This landslide affects the N-340 road, as well as an area in which some industrial buildings dated from the fifteenth century and catalogued as Places of Cultural Interest are placed. This region is characterised by the existence of a steep relief composed of materials from the Miocene to the present, on which many gravitational movements are developed. The displacements have been analysed using high-resolution spotlight mode SAR images captured by the Spanish SAR satellite PAZ (which operates at X-band), which were processed by advanced differential SAR Interferometry (A-DInSAR). The behaviour of the instability has been determined using InSAR-derived time series displacement maps. Additionally, the study has been completed with a detailed geomorphological mapping at 1:2000 scale and an analysis of structural damage carried out in situ. The landslide was modelled in 3D, from a deterministic and probabilistic point of view, confirming the global instability of the area and the relationship of the landslide with rainfall and the construction of a new road. The integration of all this information obtained and its subsequent analysis has made possible to precisely define the extension of the instability, its kinematics, and to identify the main triggering factors. This information will be crucial for the proper management of this landslide by the local authorities. [ABSTRACT FROM AUTHOR]

Landslide monitoring is a mandatory step in landslide risk assessment. It requires collecting data on landslide conditions (e.g., areal extent, landslide kinematics, surface topography, hydrogeometeorological parameters, and failure surfaces) from different time periods and at different scales, from site-specific to local, regional, and national, to assess landslide activity. In this analysis, we collected information on landslide monitoring techniques from 17 members of the Earth Observation and Geohazards Expert Group (from EuroGeoSurveys) deployed between 2005 and 2021. We examined the types of the 75 recorded landslides, the landslide techniques, spatial resolution, temporal resolution, status of the technique (operational, non-operational), time of using (before the event, during the event, after the event), and the applicability of the technique in early warning systems. The research does not indicate the accuracy of each technique but, rather, the extent to which Geological Surveys conduct landslide monitoring and the predominant techniques used. Among the types of landslides, earth slides predominate and are mostly monitored by geological and engineering geological mapping. The results showed that Geological Surveys mostly utilized more traditional monitoring techniques since they have a broad mandate to collect geological data. In addition, this paper provides new insights into the role of the Geological Surveys on landslide monitoring in Europe and contributes to landslide risk reduction initiatives and commitments (e.g., the Kyoto Landslide Commitment 2020). [ABSTRACT FROM AUTHOR]

A destructive landslide occurred in spring 2019, in Hosein Abad Kalpush Village, Northeast of Iran, after an unusually rainy period. Significant damages were observed in buildings and roads located on the slope failure zone. This paper represents a preliminary reconnaissance report prepared through the site visit done by the authors, a few days following the landslide occurrence. Then, the geotechnical and geophysical properties of the sliding area are reported using the site investigation tests. Subsequently, the rainfall data are incorporated for numerical slope stability analyses. This study shows the variation of the failure mechanism of the slope before and after rainfall. The results confirm that the factor of safety decreases 1.78 times after a 3-month period of rain with rainfall intensity about 6 times the long-term average. Finally, the performance of two slope stability improvement strategies is evaluated to mitigate the landslide hazard. The results of the study can provide a perspective on the climate change impacts in Iran for future geosystem designs, geohazard prediction, and the selection of rainfall-induced landslide mitigation strategies. [ABSTRACT FROM AUTHOR]

Landslides which occur at unexpected locations and times, and which have unrecognizable risk areas, often cause mass casualties. Despite there being few preceding landslides in the same season, the catastrophic Qijiaba landslide occurred in the new government-selected settlement of Wufu village, Sichuan Province, China, on September 5, 2021, killing three people and destroying 13 houses. In this paper, the landslide features and causes are discussed based on field investigations, remote sensing interpretations, and laboratory-based soil experiments. The results showed that it was a typical soil landslide within a geomorphic hollow of the slope with a volume of 20,230 m3, recurrence interval of at least 69 years, and runout distance of 137.5 m. This type of landslide is difficult to predict. The soil strength in the sliding surface decreased suddenly as the water content increased, and the soil's rich clay mineral and high clay particle contents determined its high mobility (Heim's ratio of 0.44). The occurrence time of the Qijiaba landslide lagged 1.3 days behind the peak rainfall, and the 2-day cumulative rainfall was a crucial factor in the landslide occurrence. Finally, the large water confluence area behind the main scarp was determined to be 3.9 times the area of the landslide itself, thereby providing abundant water conditions to induce failure. The factors underlying the casualties included inappropriate site selection, insufficient awareness of potential landslides, and failure to evacuate in time. The results of this study provide a basis for future prevention, mitigation, and early warning of similar landslides. [ABSTRACT FROM AUTHOR]

Previous studies about probabilistic back analysis for shear strength parameters of landslides generally adopted a fixed slip surface. This setting may lead to unreliable results due to the uncertainty of slip surface location speculated by limited observations. Based on Bayes' theorem, this paper proposes a probabilistic framework for the back analysis of landslides considering slip surface uncertainty. The posterior distributions of shear strength parameters in Bayesian inference are solved by Markov chain Monte Carlo simulation method. To improve computational efficiency, a response surface function based on extreme learning machine is constructed to approximate the relationship between shear strength parameters and the corresponding factor of safety and critical slip surface. A synthetic slope, for which the actual shear strength parameters and slip surface are known, is used to compare the proposed and traditional methods. The effects of measurement error of slip surface and prior distribution of shear strength parameters on probabilistic back analysis results are also investigated. Results show that the shear strength parameters obtained from traditional probabilistic back analyses neglecting slip surface uncertainty significantly deviate from actual values, and are greatly affected by prior mean of shear strength parameters. The proposed method performs better than traditional method and is less affected by the prior distributions of shear strength parameters, and the smaller the measurement error of slip surface, the higher the Bayesian back analysis accuracy. A practical landslide is applied to further verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Understanding the kinematics and triggers of a landslide is central to assessing the risk of a landslide. This understanding can be achieved through a robust instrumentation plan that addresses key factors such as meteorological conditions, groundwater conditions, and deformation trends and velocity. High-frequency landslide displacement measurements at multiple locations across a sliding mass allow for the evaluation of the impact of meteorological events in the short and long terms. Installation of a monitoring system that provides reliable and accurate high-frequency displacement data from multiple locations can be expensive, and thus, this approach is not commonly used. Single-frequency differential GNSS (dGNSS) provides a relatively low-cost alternative to achieve long-term monitoring goals at multiple locations within a landslide. Networks of single-frequency Geocube™ dGNSS units were installed at test sites at two slow-moving Canadian landslides: the Ten-mile landslide near Lillooet, BC, and the Chin Coulee landslide near Taber, AB. The Geocube system monitored surficial deformation every 60 s for the duration of the monitoring program (> 500 day) and achieved a precision in direct measurements characterized by a standard deviation of 10.5 mm or less. The quality of the collected data was compared to data provided by a high-end multi-frequency GMX 910 Leica GPS, installed at the Ripley landslide near Ashcroft, BC. The data collected from the Geocube systems revealed that displacement orientations and rates varied within each of the sliding masses. The application of multiple monitoring points provided a relatively low-cost method to evaluate the shape of the sliding shear surface and the causal factors driving movement at each site. A practical evaluation of cost-effective dGNSS systems that provide reliable information as required for enhanced understanding of landslide kinematics and the development of early warning systems is presented in this paper. [ABSTRACT FROM AUTHOR]

An accurate landslide displacement prediction is an important part of landslide warning system. Aiming at the dynamic characteristics of landslide evolution and the shortcomings of traditional static prediction models, this paper proposes a dynamic prediction model of landslide displacement based on singular spectrum analysis (SSA) and stack long short-term memory (SLSTM) network. The SSA is used to decompose the landslide accumulated displacement time series data into trend term and periodic term displacement subsequences. A cubic polynomial function is used to predict the trend term displacement subsequence, and the SLSTM neural network is used to predict the periodic term displacement subsequence. At the same time, the Bayesian optimization algorithm is used to determine that the SLSTM network input sequence length is 12 and the number of hidden layer nodes is 18. The SLSTM network is updated by adding predicted values to the training set to achieve dynamic displacement prediction. Finally, the accumulated landslide displacement is obtained by superimposing the predicted value of each displacement subsequence. The proposed model was verified on the Xintan landslide in Hubei Province, China. The results show that when predicting the displacement of the periodic term, the SLSTM network has higher prediction accuracy than the support vector machine (SVM) and auto regressive integrated moving average (ARIMA). The mean relative error (MRE) is reduced by 4.099% and 3.548% respectively, while the root mean square error (RMSE) is reduced by 5.830 mm and 3.854 mm respectively. It is concluded that the SLSTM network model can better simulate the dynamic characteristics of landslides. [ABSTRACT FROM AUTHOR]

The landslide mechanism and failure mode of the gently sloping Piedmont region are analyzed in Fangshan and Zhutoushan hills in the Nanjing area of Jiangsu Province, China. These hills are underlain by volcanic rocks and have a distinctive stratigraphic structure composed of fill or clay at the surface, coarse-grained soil or fully weathered rock in the middle, and highly or moderately weathered rock at the bottom. Landslides occurred during or after heavy rainfall, and the monitored displacements of the slope surface indicated that sliding had not stopped after a long period of continuous rainfall. Through monitoring of groundwater and simulation, we found that the low-permeability highly weathered rock can restrict the activity of the lower aquifer, resulting in a rapid rise of pore pressure and formation of confined water in response to rainfall, which can lead to slope failure. We discuss the mechanism and avulsed–drifted failure mode of the landslides considering seepage deformation and pressure balance. Finally, we propose appropriate control measures for these landslides. The results of this paper will contribute to prevention of similar landslide geological hazards. [ABSTRACT FROM AUTHOR]

Early Warning Systems (EWS) are efficient tools for preventing and mitigating the risks associated to landslides occurrence. In this paper, an integrated methodology for landslides' analysis is presented and described. Such methodology is aimed at the creation of early warning systems and is based on the integration between a modern monitoring technique, such as the Ground-Based Interferometric Synthetic Aperture Radar (GBInSAR), along with advanced numerical modelling. The paper also shows the application of the proposed methodology to the case study of a rockslide in central Italy. The integration between monitoring data, thanks to a GBInSAR survey and advanced numerical simulations with the combined Finite-Discrete Elements Method (FDEM), allowed for the definition of a set of surface velocity thresholds to be adopted for the long-term monitoring of the landslide and for the creation of an effective EWS. [ABSTRACT FROM AUTHOR]

Owing to the heavy rainfall, a landslide occurred at the Anqian Iron mine, at 18:00(UTC + 8) on November 24, 2019, in China. The landslide was about 3.0 × 104 m3 and caused damage to the road of transporting waste materials. Failure characteristics and the mechanism of this landslide were analyzed in this study. The landslide area was divided into three parts: the rear tension cracking area, the middle sliding deformation area, and the front colluvium area. A contact-free measuring technique using the new ShapeMetrix3D system was applied and 204 joints were analyzed based on equal-angle stereographic projection. Thus, a conceptual model of the mechanism of the landslide was constructed and the formation process of the landslide was divided into three stages: the first shearing and dislocation stage; the second sliding, front bulging, and rear tractive cracking stage; and the third local rock mass collapse and colluvium depositing stage. Numerical modeling was performed to discover the landslide mechanism by progressively reducing the shear strength of rock mass. The results showed that the original slope was stable, whereas heavy rainfall triggered the landslide, and the predicted failure surface matched closely the field investigations. The factor of safety obtained by real three-dimensional analyses was slightly higher than that obtained by plane problem analyses, and the difference was attributed to the three-dimensional effect of the landslide. This paper also presents the results obtained from the parametric analysis in order to understand the impact of shear strength parameters on the overall stability of the slope. [ABSTRACT FROM AUTHOR]

In a strong earthquake, not only a large number of coseismic landslides are triggered, but relaxation and cracks in the rocks and soils are induced, which make these rocks and soils vulnerable to instability during subsequent rainfall; thus, strong earthquakes always have long-term effects on landslides. The geo-hazards along the Zhaotong-Qiaojia road in the 2014 Ludian earthquake (Ms 6.5) of Yunnan Province, China, were investigated, and the Ganjiazhai giant landslide was chosen as a case study. First, using the limit equilibrium analysis and Newmark method, the critical seismic intensity of the landslide before the earthquake was evaluated. Secondly, the dynamic failure process of the landslide under the measured ground motion was simulated with the discontinuous deformation analysis method. Lastly, based on stress–seepage coupling analysis and precipitation data from Ludian meteorological station, the stability of the landslide during subsequent rainfall after the earthquake was predicted. The results show that the critical seismic intensity was within degrees VIII–IX, which is consistent with the results of the earthquake damage investigation. The dynamic failure process can be divided into four stages, and four scarps formed; the potential sliding zones during the subsequent rainfall were at the first scarp and the fourth scarp, and their critical rainfall amounts were 35–40 mm and 55–60 mm, respectively. In this paper, failure process simulation and stability prediction of the landslide before, during, and after the strong earthquake are presented, which provide analysis methods for the dynamic stability of landslides in the meizoseismal area. [ABSTRACT FROM AUTHOR]

Landslide warning models are important for mitigating landslide risks. The rainfall threshold model is the most widely used early warning model for predicting rainfall-triggered landslides. Recently, the rainfall threshold model has been coupled with the landslide susceptibility (LS) model to improve the accuracy of early warnings in the spatial domain. Existing coupled models, designed based on a matrix including predefined rainfall thresholds and susceptibility levels, have been used to determine the warning level. These predefined classifications inevitably have subjective rainfall thresholds and susceptibility levels, thus affecting the probability distribution information and eventually influencing the reliability of the produced early warning. In this paper, we propose a novel landslide warning model in which the temporal and spatial probabilities of landslides are coupled without predefining the classified levels. The temporal probability of landslides is obtained from the probability distribution of rainfall intensities that triggered historical landslides. The spatial probability of landslides is then obtained from the susceptibility probability distribution. A case study shows that the proposed probability-coupled model can successfully provide hourly warning results before the occurrence of a landslide. Although all three models successfully predicted the landslide, the probability-coupled model produced a warning zone comprising the fewest grid cells. Quantitatively, the probability-coupled model produced only 39 grid cells in the warning zone, while the rainfall threshold model and the matrix-coupled model produced warning zones including 81 and 49 grid cells, respectively. The proposed model is also applicable to other regions affected by rainfall-induced landslides and is thus expected to be useful for practical landslide risk management. [ABSTRACT FROM AUTHOR]

Landslides not only cause property losses, but also kill and injure large numbers of people every year in the mountainous areas. These losses and casualties may be avoided to some extent by early warning systems for landslides. In this paper, a realtime monitoring network and a computer-aided automatic early warning system (EWS) are presented with details of their design and an example of application in the Longjingwan landslide, Kaiyang County, Guizhou Province. Then, according to principle simple method of landslide prediction, the setting of alarm levels and the design of appropriate counter-measures are presented. A four-level early warning system (Zero, Outlook, Attention and Warning) has been adopted, and the velocity threshold was selected as the main warning threshold for the landslide occurrence, but expert judgment is included in the EWS to avoid false alarms. A case study shows the applicability and reliability for landslide risk management, and recommendations are presented for other similar projects. [ABSTRACT FROM AUTHOR]

Landslides in the Himalayan region are primarily controlled by natural parameters, including rainfall, seismic activity, and anthropogenic parameters, such as the construction of large-scale projects like road development, tunneling and hydroelectric power projects and climate change. The parameters which are more crucial among these are a matter of scientific study and analysis. This research, taking Solan district, Himachal Pradesh, India, as the study area, aims to assess the impact of anthropogenic activities on landslide susceptibility at a regional scale. Landslide distribution was characterized into two groups, namely Rainfall-Induced Landslide (RIL) and Human-Induced Landslide (HIL) based on triggering factors. Multiple data such as slope angle, aspect, profile curvature, distance to drainage, distance to lineament, lithology, distance to road, normalized difference vegetation index (NDVI) and land use land cover (LULC) have been considered for delineating the landslide susceptibility zonation (LSZ) map. The effect of anthropogenic activities on landslide occurrences has been examined through the distribution of landslides along national highways and land use land cover changes (LULCC). Two sets of LSZ maps with a LULC of time interval covering five years (2017 & 2021) were prepared to compare the temporal progression of LULC and landslide susceptibility during the five years. The results indicated the significant impact of anthropogenic activities on the landslide susceptibility. LSZ map of the year 2021 shows that 23% area falls into high and very high susceptible classes and 48% area falls into very low and low susceptibility classes. Compared to LSZ map of 2017, high and very high susceptible classes have been increased by 15%, whereas very low and low susceptible classes have been reduced by 7%. The present case study will help to understand the potential driving parameters responsible for HIL and also suggest the inclusion of LULC in landslide susceptibility analysis. The study will demonstrate new opportunities for research that could help decision-makers prepare for future disasters, both in the Indian Himalayan region and other areas. [ABSTRACT FROM AUTHOR]

The 2005 Kashmir earthquake has triggered widespread landslides in the Himalayan mountains in northern Pakistan and surrounding areas, some of which are active and are still posing a significant risk. Landslides triggered by the 2005 Kashmir earthquake are extensively studied; nevertheless, spatio-temporal landslide susceptibility assessment is lacking. This can be partially attributed to the limited availability of high temporal resolution remote sensing data. We present a semi-automated technique to use the Sentinel-2 MSI data for co-seismic landslide detection, landslide activities monitoring, spatio-temporal change detection, and spatio-temporal susceptibility mapping. Time series Sentinel-2 MSI images for the period of 2016–2021 and ALOS PALSAR DEM are used for semi-automated landslide inventory map development and temporal change analysis. Spectral information combined with topographical, contextual, textural, and morphological characteristics of the landslide in Sentinel-2 images is applied for landslide detection. Subsequently, spatio-temporal landslide susceptibility maps are developed utilizing the weight of evidence statistical modeling with seven causative factors, i.e., elevation, slope, geology, aspect, distance to fault, distance to roads, and distance to streams. The results reveal that landslide occurrence increased from 2016 to 2021 and that the coverage of areas of relatively high susceptibility has increased in the study area. [ABSTRACT FROM AUTHOR]

In early December 2020, an atmospheric river (AR) and rain-on-snow (ROS) event impacted the Haines, Alaska area, resulting in record-breaking rainfall and snowmelt that caused flooding and dozens of mass movement events. We consider the AR—a one-in-500-year event—as the trigger for the devastating Beach Road Landslide (BRLS), which destroyed or damaged four residences and took the lives of two people. The BRLS started as a debris avalanche and transitioned into a debris flow, with a total approximate landslide volume of 187,100 m3. Geomorphic analysis using lidar data identified evidence of paleo-landslides and displaced masses of rock, one of which served as the source area for the BRLS. Significant structural features in the weak ultramafic bedrock defined the head scarp area and formed the failure plane. This study illustrates the importance of identifying pre-existing landslide features and source areas likely to produce future landslides. As an increase in ROS events is projected for Southeast Alaska with warmer and wetter winters, we recommend the development of an AR scale coupled with geological information for the region, to enhance warnings to residents in landslide-prone areas. [ABSTRACT FROM AUTHOR]

A landslide-triggering rainfall threshold for the southern part of Thailand was introduced. The new threshold explicitly integrates the variables of event rainfall and cumulative rainfall from precipitation data corresponding to 92 landslide events in the study area. To determine event rainfall, a suitable inter-event criterion (IEC) had to be defined that separated two consecutive rainfalls. A rainfall intensity no greater than 2 mm/day lasting at least 1 day (IEC2,1) was the criterion established to separate two consecutive rainfalls in the study area. Using quantile regression, an event rainfall depth-duration (ED) threshold was drawn at a probability level of 5%. A 20-day cumulative event rainfall depth (CR20) was the most suitable duration of cumulative rainfall depth. A CR20 of 100 mm was the third rainfall variable to give a threshold that integrated cumulative rainfall with event rainfall depth-duration (CED). This threshold is proposed for the study area depicted in three-dimensional space. The CED threshold is implemented by an assessment of CR20 followed by an assessment of ED. The hit rate (HR) of the CED threshold is a little lower than the HR of the ED threshold, but the CED threshold is far better than the ED threshold in terms of false alarm rate (FAR) since the CR20 of 100 mm filters out many non-triggering rainfalls prior to the assessment of the event rainfall. [ABSTRACT FROM AUTHOR]

The majority of landslides in the Indian Himalayas are triggered by rainfall and earthquake. The geology and geomorphology also play an important role and constitute the major predisposing factor controlling landslides. Additionally, active tectonics modify the landscape, impacting the occurrence and distribution of landslides. The objective of this study is to investigate the role of some of the major predisposing factors to assess landslide patterns in the Yamuna River Basin as it is prone to recurrent landslide hazards. The approach mainly deals with the application of geographical information system-based statistical analysis and quantifying frequency-area distribution of rainfall-triggered landslides for the years 2015 to 2017. The analysis shows that the frequency of landslides is highest in the slope range 40°–70° and the mean landslide size is higher in the slope range 0°–20° and 30°–40°, remaining constant on further increase in slope angle. The study shows that some critical slope range controls both the frequency and size of landslides. Landslides were mostly restricted to south facing slopes. About 91% of the landslide occur in the drainage density range of 1.2 to 2.1 km/km2 while landslides show very low occurrence in either lower (<1.2) or higher (>2.1) drainage density. Frequency-size distribution analysis of landslides depicts a distinct cutoff point but an indistinct rollover point. Under-sampling of small landslides can be a reasonable cause for this. Like other studies, the present data fits very well for medium and large landslides following an inverse power law with a scaling exponent of −2.16. Around 80% of landslides are smaller than the cutoff size and 20% larger than this value. The geology and tectonics of Lesser Himalayas and Sivaliks offer favorable conditions for frequent landslides. Areas such as Dehradun, Uttarkashi, Sirmaur, Shimla, and Solan are very prone to landslide activity owing to the presence of poorly consolidated sandstone, conglomerate, quartzites, phyllites, etc. Moreover, the majority of larger landslides are in the Dehradun district where precipitation, drainage density, and lithology are highly favorable to trigger large events. The present study has identified the critical range of predisposing factors responsible for landslides and presents the frequency-size distribution of landslides in the Yamuna River Basin. These are the key inputs in landslide susceptibility mapping and risk assessment. Therefore, the findings from the present analysis can be very helpful in landslide risk reduction and landslide hazard zonation and probably plan critical locations for installation of early warning signals. [ABSTRACT FROM AUTHOR]

With the increasing of extreme rainfall frequency, landslides accompanied by mudslides often lead to serious casualties and property damage. On 3rd July 2021, a debris flow occurred in Izu Mountain area, Shizuoka County, Japan, and then resulted in 26 deaths and 131 houses destroyed, where houses were mainly built along the banks of the creek. In order to analyse the landslide state and distribution, a two-dimensional debris flow dynamic model (Massflow) was selected to simulate the process of the landslide-generated debris flow. When the model results are considered together with remote sensing images, the volume distribution of the unstable landslide is also able to be determined. The results show that (1) the affected areas are mainly concentrated at the outfall of the gully and on both sides of the streets. (2) The pore pressure ratio is an important factor affecting the damage range of this debris flow. (3) The increase of the pore pressure ratio in the landslide make the movement distance of debris flow increase significantly. [ABSTRACT FROM AUTHOR]

Landslides are a significant hazard in many parts of the world and exhibit a high, and often underestimated, damage potential. Deploying landslide early warning systems is one risk management strategy that, amongst others, can be used to protect local communities. In geotechnical applications, slope stability models play an important role in predicting slope behaviour as a result of external influences; however, they are only rarely incorporated into landslide early warning systems. In this study, the physically based slope stability model CHASM (Combined Hydrology and Stability Model) was initially applied to a reactivated landslide in the Swabian Alb to assess stability conditions and was subsequently integrated into a prototype of a semi-automated landslide early warning system. The results of the CHASM application demonstrate that for several potential shear surfaces the Factor of Safety is relatively low, and subsequent rainfall events could cause instability. To integrate and automate CHASM within an early warning system, international geospatial standards were employed to ensure the interoperability of system components and the transferability of the implemented system as a whole. The CHASM algorithm is automatically run as a web processing service, utilising fixed, predetermined input data, and variable input data including hydrological monitoring data and quantitative rainfall forecasts. Once pre-defined modelling or monitoring thresholds are exceeded, a web notification service distributes SMS and email messages to relevant experts, who then determine whether to issue an early warning to local and regional stakeholders, as well as providing appropriate action advice. This study successfully demonstrated the potential of this new approach to landslide early warning. To move from demonstration to active issuance of early warnings demands the future acquisition of high-quality data on mechanical properties and distributed pore water pressure regimes. [ABSTRACT FROM AUTHOR]

Evaluating the hazards of debris flows is necessary for risk assessment. The May 12, 2008 earthquake in Wenchuan County, China caused many landslides and loose slopes. Under continuous or heavy rainfall, the landslides and loose slopes are easily destabilized and provide huge amounts of material source for debris flows, significantly increasing the hazard level of associated disasters. Moreover, the hazard of debris flows is not always constant, but variable over time because of the changes in influencing factors (e.g., geomorphology, rainfall conditions, and earthquakes). This study proposes a coupled model based on the TRIGRS (Transient Rainfall Infiltration and Grid based Regional Slope-stability Model) and Flow-R (Flow path assessment of gravitational hazards at a Regional scale) models for a dynamic hazard assessment of debris flows applied in a small watershed, Bayi Gully. This coupled model considers the impact of the actual rainfall process, potential debris sources on slope surfaces, and existing loose sources in the debris flow channel. The results indicate that (1) the hazard assessment by the coupled model is superior to that by the static Flow-R model; (2) the spatial distribution of landslides, debris flow sources, debris flow hazard areas, hazard probability, and debris flow energy vary with rainfall duration; and (3) the debris flow hazard area is strongly affected by topographic and geomorphic factors. Thus, the coupled model provides a powerful and robust tool for the hazard assessment and disaster prevention and mitigation of debris flows. [ABSTRACT FROM AUTHOR]

Landslide is a global environmental geological hazard caused by natural or human activities. Since the impoundment of the Three Gorges reservoir area, the geological disasters such as collapses, landslides and other kinds of geological disasters increased obviously due to the periodic fluctuation of the water level in the Yangtze River. Wanzhou District is located in the center of the Three Gorges Reservoir Area, which plays an important role in the prevention and control of geological hazards in the whole Three Gorges Reservoir Area. This is because a large number of deep bedrocks old landslides are distributed among this region, such as Taibaiyan ancient landslide, Caojiezi ancient landslide, Anlesi ancient landslide, Pipaping ancient landslide, and Diaoyanping ancient landslide. In this study, Quasi-Persistent Scatterers InSAR (QPS-InSAR) time-series method is proposed to identify and monitor the ancient landslides in Wanzhou. In this method, the High-coherent test is applied to Quasi-Persistent Scatterers (PSC) selection, and PSC and persistent scatterer are combined to improve the density of measurement points in vegetation area. The QPS-InSAR method is also characterized by the appropriate combination of differential interferograms produced by a Minimum Spanning Tree and the employment of the phase triangulation algorithm to estimate the optimal phase. This technique was performed on 8 scenes of L-band ALOS PALSAR ascending data acquired during 2015–2018, then deformation rate maps and time series for ancient landslide were generated, which were applied to retrieve time series displacement for the large-scale landslide in Wanzhou District. The experiment results show that there are obvious landslide deformation patterns detected in this region with displacement velocity larger than − 21 mm/yr during the observation period. Finally, the influencing factors such as geological conditions, distribution of rainfall and reservoir water level change in the Three Gorges Reservoir area, and deformation mechanism of Wanzhou landslide are analyzed. The monitoring results will help the local government to carry out regular landslide inspection and strengthen landslide disaster early warning. [ABSTRACT FROM AUTHOR]

Zhouqu County in Gansu Province, Northwest China, is typically highly prone to landslides. On July 12, 2018, a landslide blocked the Bailong River near Zhouqu County, posing a serious threat to the life and property of local residents and the safety of infrastructure. Small baseline subset interferometry synthetic aperture radar technology (SBAS-InSAR) was adopted to identify the potential active landslides in the surrounding area of Zhouqu County, using ascending and descending orbit Sentinel-1 satellite images taken from October 2017 to December 2018. The surface deformation areas detected by SBAS-InSAR were verified by optical remote sensing image interpretation and field investigation, and a total of 23 active landslides were identified finally. The deformation characteristics of four typical landslides are analysed in detail using deformation velocity and rainfall data. It is found that the deformation velocity of landslides in this area is mainly affected by rainfall and there is a lag effect. The results can provide a reference for the prevention and control of landslide risk in Zhouqu County. [ABSTRACT FROM AUTHOR]

On 22 March 2014, a massive, catastrophic landslide occurred near Oso, Washington, USA, sweeping more than 1 km across the adjacent valley flats and killing 43 people. For the following 5 weeks, hundreds of workers engaged in an exhaustive search, rescue, and recovery effort directly in the landslide runout path. These workers could not avoid the risks posed by additional large-scale slope collapses. In an effort to ensure worker safety, multiple agencies cooperated to swiftly deploy a monitoring and alerting system consisting of sensors, automated data processing and web-based display, along with defined communication protocols and clear calls to action for emergency management and search personnel. Guided by the principle that an accelerating landslide poses a greater threat than a steadily moving or stationary mass, the system was designed to detect ground motion and vibration using complementary monitoring techniques. Near real-time information was provided by continuous GPS, seismometers/geophones, and extensometers. This information was augmented by repeat-assessment techniques such as terrestrial and aerial laser scanning and time-lapse photography. Fortunately, no major additional landsliding occurred. However, we did detect small headscarp failures as well as slow movement of the remaining landslide mass with the monitoring system. This was an exceptional response situation and the lessons learned are applicable to other landslide disaster crises. They underscore the need for cogent landslide expertise and ready-to-deploy monitoring equipment, the value of using redundant monitoring techniques with distinct goals, the benefit of clearly defined communication protocols, and the importance of continued research into forecasting landslide behavior to allow timely warning. [ABSTRACT FROM AUTHOR]

Landslides are downslope movements of rock and soil materials under the influence of gravity, and often these are triggered by various causes, and the factors that govern the landslide occurrence in Brunei Darussalam, Borneo, have not been investigated in any detail, although landslide hazards are the second most damaging hazards in the country. The equatorial tropical monsoon conditions contribute to the cause of landslides in the sultanate, and our mapping of landslide sites in the Brunei-Muara region has revealed a number of key factors that initiate and influence the formation and nucleation of landslides. And these are mainly related to extent of urbanisation, amount and intensity of rainfall, loss of vegetation cover, slope and geological factors. These factors may or may not work together for a particular landslide event, but the occurrence of landslides has been linked to these factors. Gravitational active faults were also mapped at three landslide sites and related to creep. Google, and 30 m shuttle radar topography satellite images, followed by detailed fieldwork with the aid of unmanned aerial vehicle tool, were employed to map the topographic, structural, bedrock, and landslide details and to differentiate the types of landslides. The landslide and rainfall data were obtained from the National Disaster Management Centre and Geotechnical and Geological Section of the Public Works Department and used in combination with the bedrock and slope maps. Our work should be used by local administrative departments to plan any future anthropogenic activity in the region. [ABSTRACT FROM AUTHOR]

On August 10, 2019, due to the effect of a rainstorm caused by Super Typhoon Lekima, a landslide occurred in Shanzao Village, China. It blocked the Shanzao stream, forming a barrier lake, and then the barrier lake burst. This is a rare natural disaster chain of typhoon-rainstorm-landslide-barrier lake-flooding. This study was built on field surveys, satellite image interpretation, the digital elevation model (DEM), engineering geological analysis and empirical regression. The purpose was to reveal the characteristics and causes of the landslide, the features and formation process of the barrier lake and the dam break flooding discharge. The results show that the volume of the landslide deposit is approximately 2.4 × 105 m3. The burst mode of the landslide dam is overtopping, which took only 22 minutes from the formation of the landslide dam to its overtopping. The dam-break peak flow was 1353 m3/s, and the average velocity was 2.8–3.0 m/s. This study shows that the strongly weathered rock and soil slope has low strength and high permeability under the condition of heavy rainfall, which reminds us the high risk of landslides and the importance of accurate early warning of landslides under heavy rainfalls in densely populated areas of Southeast China, as well as the severity of the disaster chain of typhoon-rainstorm-landslide-barrier lake-flooding. [ABSTRACT FROM AUTHOR]

In the context of circular high steep slopes within a 500-m aperture spherical radio telescope (FAST) site in Pingtang, Guizhou Province in southwest China, a pertinent small-area karst landslide risk assessment is carried out based on geographic information system and fuzzy analytical hierarchy process. Results show the following: (a) The AUC value of hazard assessment is 0.826, which means a good adaptability and a relatively higher accuracy, and the AUC value of risk assessment is 0.803, which means the accuracy of landslide risk assessment is within acceptable level. (b) The slopes of FAST area are generally at the medium or low-hazard level, with high-hazard region accounting for only 3.17% of the total area. High-hazard regions are primarily found in two locations, one is located in nearby the Guangmingding slope where the dangerous rock masses were basically cleared away, the other is located in nearby feed tower 5H where potentially unstable rock masses were reinforced to improve the stability of slope. (c) High-vulnerability area of FAST accounts for approximately 43.62%. As a direct result, the high-risk area of FAST site accounts for 34.36% based on the overlap of vulnerability and hazard. The high risk is concentrated in the surrounding areas such as the feed tower, support pillar, and telescope mirror, and the high-risk area overlaps most of the fault fracture zone in the study area. Our findings provide meaningful references for landslide prevention and monitoring of FAST areas and are instructive in theory and practice. [ABSTRACT FROM AUTHOR]

On May 12, 2019, a landslide occurred near a construction site of a reservoir dam along the left bank of the Wulong River, Qinghai Province, China. Video footage showed that the entire failure process of the landslide lasted for 15 min. Although an excavator that parked on the foundation pit was buried and the Wulong River was blocked, this landslide did not cause any casualties. Based on field investigations, 3D laser scanning, unmanned aerial vehicle observations, and borehole surveys, this study aims to reveal the cause and initiation mechanisms of the landslide. The landslide developed on a typical dip slope with thick-bedded conglomerates intercalated with a few mudstone layers. The slide mass mainly consisted of conglomerates with a dip angle varying from 20 to 40°, failing along bedding plane contacts with the underlying muddy intercalation. Numerous tension cracks had developed in the upper part of the slope due to stress relaxation during the process of bank slope evolution associated with rapid river incision. These cracks damaged the integrity of the rock mass and promoted rapid rainfall infiltration. The water infiltrated into the slope and deteriorated the mechanical properties of the rock mass, which resulted in long-term deterioration of the slope stability conditions. In addition, the temperature of Tongren County was below zero for 5 months in a year, hence the freeze–thaw cycle of the water filled in the cracks would have reduced the shear strength further. There were 10 days of continuous rainfall before the landslide occurred, and the cumulative precipitation in 2018 and 2019 was also significantly more than that of previous years. Three primary factors are involved in the landslide event: (1) thick-bedded conglomerates intercalated with a few mudstone layers with contrasts in strength, (2) development of numerous relaxation cracks, and (3) long-term rainfall accumulation. [ABSTRACT FROM AUTHOR]

This study aimed to illustrate the seismic response and instability process of a double-row anti-slide pile-reinforced bridge foundation and gravel landslide. Selecting the gravel slope of Jiuzhai Valley along the under-construction Chengdu-Lanzhou high-speed railway as the site prototype, large-scale shaking table tests were first conducted at a similitude ratio of 1:70, with sine waves and El Centro waves as the seismic wave inputs. The amplitudes of the input seismic waves were increased, while the acceleration, dynamic earth pressure, and strain distribution were monitored in the gravel landslide. The dynamic response patterns of anti-slide pile-reinforced bridge foundations in gravel landslides were illustrated. The front-row and back-row anti-slide piles should be a reasonable distance from the bridge foundation. The response acceleration manifested an elevation amplification effect with an increasing elevation in the slope behind the anti-slide piles. Back-row anti-slide piles reinforcing the bridge foundation can reduce the effect of landslide thrust on the bridge foundation and maintain a uniform distribution of earth pressure behind the bridge foundation, mitigating seismic effects. The dynamic earth pressure peaked at the top of the bridge foundation and then decreased along the depth. The back-row anti-slide piles displayed greater resonance coupling and unloading effects before and after reaching the load-carrying limit, respectively. In seismic strengthening design involving bridge foundations and gravel landslides, when the earthquake-induced resonance coupling effect on inclined, loosely packed land masses is fully considered, pre-reinforcement measures (e.g., high-pressure grouting and anchor spraying) should be carried out on the gravel slopes. [ABSTRACT FROM AUTHOR]

Regional empirical-statistical thresholds indicating the precipitation conditions initiating landslides are of crucial importance for landslide early warning system development. The objectives of this research were to use landslide and precipitation data in an empirical-statistical approach to (1) identify precipitation-related variables with the highest explanatory power for landslide occurrence and (2) define both trigger and trigger-cause based thresholds for landslides in Rwanda, Central-East Africa. Receiver operating characteristics (ROC) and area under the curve (AUC) metrics were used to test the suitability of a suite of precipitation-related explanatory variables. A Bayesian probabilistic approach, maximum true skill statistics and the minimum radial distance were used to determine the most informative threshold levels above which landslide are high likely to occur. The results indicated that the event precipitation volumes E, cumulative 1-day rainfall (RD1) that coincide with the day of landslide occurrence and 10-day antecedent precipitation are variables with the highest discriminatory power to distinguish landslide from no landslide conditions. The highest landslide prediction capability in terms of true positive alarms was obtained from single rainfall variables based on trigger-based thresholds. However, that predictive capability was constrained by the high rate of false positive alarms and thus the elevated probability to neglect the contribution of additional causal factors that lead to the occurrence of landslides and which can partly be accounted for by the antecedent precipitation indices. Further combination of different variables into trigger-cause pairs and the use of suitable thresholds in bilinear format improved the prediction capacity of the real trigger-based thresholds. [ABSTRACT FROM AUTHOR]

Review of the literature on the reconstruction of the rainfall responsible for slope failures reveals that criteria for the identification of rainfall events are lacking or somewhat subjective. To overcome this problem, we developed an algorithm for the objective and reproducible reconstruction of rainfall events and of rainfall conditions responsible for landslides. The algorithm consists of three distinct modules for (i) the reconstruction of distinct rainfall events, in terms of duration ( D, in h) and cumulated event rainfall ( E, in mm), (ii) the identification of multiple ED rainfall conditions responsible for the documented landslides, and (iii) the definition of critical rainfall thresholds for possible landslide occurrences. The algorithm uses pre-defined parameters to account for different seasonal and climatic settings. We applied the algorithm in Sicily, southern Italy, using rainfall measurements obtained from a network of 169 rain gauges, and information on 229 rainfall-induced landslides occurred between July 2002 and December 2012. The algorithm identified 29,270 rainfall events and reconstructed 472 ED rainfall conditions as possible triggers of the observed landslides. The algorithm exploited the multiple rainfall conditions to define objective and reproducible empirical rainfall thresholds for the possible initiation of landslide in Sicily. The calculated thresholds may be implemented in an operational early warning system for shallow landslide forecasting. [ABSTRACT FROM AUTHOR]

In Italy, rainfall-induced landslides are recurrent phenomena that cause societal and economic damage. Thus, assessing the rainfall conditions responsible for landslides is important and may contribute to reducing risk. The prediction of rainfall-induced landslides relies primarily on empirical rainfall thresholds. However, the thresholds are affected by uncertainties that limit their use in operational warning systems. A source of uncertainty lies in the characterization of the rainfall events responsible for landslides. Objective criteria for the definition of rainfall events are lacking. To overcome the problem, we propose an algorithm that reconstructs the rainfall events, identifies the rainfall conditions that have resulted in landslides, and measures the duration and the cumulated rainfall for the events. The algorithm is independent from the local settings and uses a reduced set of parameters to account for different physical settings and operational conditions. We tested the algorithm in Sicily, Italy, with rainfall and landslide information between January 2002 and December 2012. The rainfall conditions responsible for landslides identified by the algorithm were compared against results obtained manually. The algorithm was proven capable of accurately reconstructing most (87.7 %) of the rainfall events. For each landslide, the algorithm identified a variable number of rainfall conditions responsible for the failures, which are equally likely triggers of the landslide. This opens the possibility of evaluating the uncertainty introduced by different criteria to determine the rainfall events responsible for landslides. Use of the algorithm shall contribute to reducing the uncertainty in the definition of landslide-triggering rainfall events, to compiling large catalogues of rainfall events with landslides and to determining reliable rainfall thresholds for possible landslide occurrence. [ABSTRACT FROM AUTHOR]

Since 2003, a series of over eighty sensors has been installed at Turtle Mountain, site of the 1903 Frank Slide. The purpose of these instruments is to both characterize and provide warning for a second large rock avalanche from the eastern face of the mountain, where various unstable masses have been identified. Although studies continue on the mountain to better understand the deformation patterns and interpretations of the slope kinematics, significant effort has been expended to develop a structure for the warning and emergency response that clearly outlines not only responsibilities and communications protocols during an emergency, but also day-to-day operational responses and procedures to ensure that the system remains operational. From a day-to-day operational perspective, a systematic and repeatable set of procedures is required in order to ensure that not only are data trends reviewed and reported on, but scheduled checks of system functionality are undertaken. An internal Roles and Responsibilities Manual has been developed to clearly outline responsibilities for geoengineering, information technology (IT), and management staff to ensure that system checks are completed and that support is in place on a 24/7 basis should components of the system cease to operate properly or should unacceptable deformations require review. In addition to that, a clear and concise troubleshooting manual has been developed. This document provides simple diagnoses of problems within the system and a clear roadmap of how to fix each component. From a warning and emergency response perspective, a series of color-coded alert conditions has been developed should unacceptable deformations be observed. At each alert level, clear responsibilities for actions and communications have been identified for geoengineering staff, provincial emergency management authorities, municipal officials, and first responders. This has been documented in the emergency response protocol. All documents described here are 'living' documents that are updated on a regular basis as changes to the system are made. An annual mock warning exercise has been developed and run in order to test responses to a hypothetical emergency and generate updates to the system documentation. [ABSTRACT FROM AUTHOR]

The objective of analyzing hazard and risk in an area is to utilize the result in selecting appropriate landslide risk reduction strategies. However, this does not happen always, and most often results of the hazard and risk analysis remain at an academic level. The under or non-utilization of results in pre-disaster planning could be due to several reasons, including difficulties in understanding the scientific content/meaning of the models, and lack of information on the practical significance and utility of the models. In this study, an attempt is made to highlight the uses of hazard and risk information in different landslide risk reduction strategies along a transportation corridor in Nilgiri, India. At first, a quantitative analysis of landslide hazard and risk was made. The obtained information was then incorporated in risk reduction options such as land use zoning, engineering solutions, and emergency preparedness. For emergency preparedness, the perception of the local Nilgiri communities toward landslide risk was evaluated and simplified maps were generated for the benefit and understanding of end users. A rainfall threshold-based early warning system was presented, which could be used in risk awareness programs involving public participation. The use of quantitative risk information in the cost-benefit analysis for the planning of structural measures to protect the road and railway alignments was also highlighted, and examples were shown how the transport organizations could implement these measures. Finally, the study provided examples of the utility of hazard and risk information for spatial planning and zoning, indicating areas where the landslide hazard is too high for planning future developments. [ABSTRACT FROM AUTHOR]