Simulating landslide-induced tsunamis in the Yangtze River at the Three Gorges in China

Landslide-induced tsunamis may cause fatalities, damages and financial losses. In the Three Gorges Reservoir Area of China, several large landslides are still unstable and persistently creeping toward the Yangtze River. In this paper, we investigate the impacts of landslide-induced tsunamis in the Three Gorges Reservoir by using a hybrid numerical approach. One of the largest unstable mass in this area, the Huangtupo landslide, is chosen as the study object. First, the landslide deformation and initiating velocities are obtained by using the finite-discrete element method. The landslide-induced tsunamis and their impacts on shipping on the Yangtze River are then investigated through smooth particle hydrodynamics modelling. Our results reveal that an approximately 80% reduction in shear strength of the tip in the landslide will lead to catastrophic failure of the landslide, with sliding velocities of up to 8 m/s. Subsequently, such a collapse may initiate a river tsunami, propagating up to 9 m on the nearby reservoir banks within 3 km. The impacts on surrounding floating objects, such as surges and sways, heaves and rolls, are up to 110 m, 8 m and 6°, respectively. The simulations indicate that although the likelihood of a catastrophic failure of the whole landslide is low, the partial sliding still poses severe threat to the nearby reservoir banks and shipping on the Yangtze River. Thus, we recommend continuous monitoring as well as landslide early warning systems at this and also other hazardous sites in this area

A quantitative enhanced assessment for ancient landslide reactivation risk considering cross-time scale joint response mechanism

Ancient landslide has strong concealment and disturbance sensitivity due to its special geotechnical mechanical characteristics, and it is the potential hazard that cannot be ignored in human activities and major engineering planning. The quantitative assessment of ancient landslide reactivation risk has become more necessary for pre-disaster scientific warning. However, because the mechanisms of deformation and damage during the evolution of ancient landslides are quite complex, traditional landslide risk assessment methods only select the single-time scale and relatively stable environmental factors for analysis, lacking consideration of dynamic triggering factors such as rainfall. Focusing on the complexity, a quantitative enhanced assessment for ancient landslide reactivation risk considering cross-time scale joint response mechanism is proposed. First, on the basis of systematic analysis of the implicit genesis mechanism and explicit characterization, an evaluation system of the cross-time scale joint characteristics of ancient landslide reactivation is constructed. Then, XGBoost algorithm and SBAS-InSAR are used to establish the long-time scale developmental evolution mechanism model and the short-time scale dynamical trigger model, respectively. Subsequently, we propose a cross-time scale joint response mechanism. The information entropy weight method is applied to calculate the contribution degree of long-short time scale assessment models for ancient landslide reactivation based on the constraints of quantitative interval thresholds, and the assessment processes of different time scales are dynamically and quantitatively correlated. Finally, the updated optimization of the assessment of ancient landslide reactivation risk is achieved. In this research, experimental analysis was carried out for ancient landslide groups in a geological hazard-prone area in Fengjie County, Chongqing, a typical mountainous region of China. The results of the comparative analysis validate the superiority of the method in this paper. It helps to accurately assess the ancient landslide potential hazard in advance, providing scientific basis and technical support for the risk assessment of mountainous watershed geological hazards and major engineering projects

Rapid characterisation of the extremely large landslide threatening the Rules Reservoir (Southern Spain)

When an active landslide is first identified in an artificial reservoir, a comprehensive study has to be quickly conducted to analyse the possible hazard that it may represent to such a critical infrastructure. This paper presents the case of the El Arrecife Landslide, located in a slope of the Rules Reservoir (Southern Spain), as an example of geological and motion data integration for elaborating a preliminary hazard assessment. For this purpose, a field survey was carried out to define the kinematics of the landslide: translational in favour of a specific foliation set, and rotational at the foot of the landslide. A possible failure surface has been proposed, as well as an estimation of the volume of the landslide: 14.7 million m3. At the same time, remote sensing and geophysical techniques were applied to obtain historical displacement rates. A mean subsidence rate of the landslide around 2 cm/year was obtained by means of synthetic aperture radar interferometry (InSAR) and ground-penetrating radar (GPR) data, during the last 5 and 22 years, respectively. The structure-from-motion (SfM) technique provided a rate up to 26 cm/year during the last 14 years of a slag heap located within the foot of the landslide, due to compaction of the anthropical deposits. All of this collected information will be valuable to optimise the planning of future monitoring surveys (i.e. differential global positioning systems, inclinometers, ground drilling, and InSAR) that should be applied in order to prevent further damage on the reservoir and related infrastructures.This work was mainly supported by the European Regional Development Fund (ERDF) through the project “RISKCOAST” (SOE3/P4/E0868) of the Interreg SUDOE Programme. The work of J.P.G., M.M-S., P.R. and J.M.A. was also supported by the “Ramón y Cajal” Programme (RYC-2017–23335) of the Spanish Ministry of Science, the project “MORPHOMED”—PID2019-107138RB-I00 / SRA (State Research Agency / https://doi.org/10.13039/501100011033) and the project “RADANDALUS” (P18-RT-3632) and B-RNM-305-UGR1818 of the FEDER / Junta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento y Universidades

Remote Sensing Approaches and Related Techniques to Map and Study Landslides

Landslide is one of the costliest and fatal geological hazards, threatening and influencing the socioeconomic conditions in many countries globally. Remote sensing approaches are widely used in landslide studies. Landslide threats can also be investigated through slope stability model, susceptibility mapping, hazard assessment, risk analysis, and other methods. Although it is possible to conduct landslide studies using in-situ observation, it is time-consuming, expensive, and sometimes challenging to collect data at inaccessible terrains. Remote sensing data can be used in landslide monitoring, mapping, hazard prediction and assessment, and other investigations. The primary goal of this chapter is to review the existing remote sensing approaches and techniques used to study landslides and explore the possibilities of potential remote sensing tools that can effectively be used in landslide studies in the future. This chapter also provides critical and comprehensive reviews of landslide studies focus¬ing on the role played by remote sensing data and approaches in landslide hazard assessment. Further, the reviews discuss the application of remotely sensed products for landslide detection, mapping, prediction, and evaluation around the world. This systematic review may contribute to better understanding the extensive use of remotely sensed data and spatial analysis techniques to conduct landslide studies at a range of scales

An improved algorithm for identifying shallow and deep-seated landslides in dense tropical forest from airborne laser scanning data

© 2018 Landslides are natural disasters that cause environmental and infrastructure damage worldwide. They are difficult to be recognized, particularly in densely vegetated regions of the tropical forest areas. Consequently, an accurate inventory map is required to analyze landslides susceptibility, hazard, and risk. Several studies were done to differentiate between different types of landslide (i.e. shallow and deep-seated); however, none of them utilized any feature selection techniques. Thus, in this study, three feature selection techniques were used (i.e. correlation-based feature selection (CFS), random forest (RF), and ant colony optimization (ACO)). A fuzzy-based segmentation parameter (FbSP optimizer) was used to optimize the segmentation parameters. Random forest (RF) was used to evaluate the performance of each feature selection algorithms. The overall accuracies of the RF classifier revealed that CFS algorithm exhibited higher ranks in differentiation landslide types. Moreover, the results of the transferability showed that this method is easy, accurate, and highly suitable for differentiating between types of landslides (shallow and deep-seated). In summary, the study recommends that the outlined approaches are significant to improve in distinguishing between shallow and deep-seated landslide in the tropical areas, such as; Malaysia

Spatial Estimations of Soil Properties for Physically-based Soil Erosion Modelling in the Three Gorges Reservoir Area, Central China

Soils present a central medium for processes between the environmental spheres, and therefore play a key role in the functioning of terrestrial ecosystems. However, soil erosion as a natural force of landscape evolution adversely affects the capacity of soils to support ecosystem services. Moreover, inadequate agricultural practices, deforestation, and construction activities amplify natural soil loss rates and transform soil erosion to a major threat for managed ecosystems worldwide. Particularly, the Three Gorges Reservoir Area in China is highly susceptible to soil erosion by water. This is attributable to unfavorable environmental conditions, such as rainfall events of high intensity and steep slope inclinations in areas of extensive, but small-scale crop cultivation. Moreover, in the course of the impoundment of the Yangtze River in the area of the Three Gorges, resettlements and accompanied deforestation reinforced the risk of hazardous soil erosion, which attenuates soil productivity and threatens the functioning of the reservoir. Therefore, conservation measures to stabilize steep sloping surfaces have been implemented to mitigate the hazardous effects of soil erosion. However, to assess the conservation measures an efficient tool is required to identify spatial soil erosion patterns in small, mountainous, and data scarce catchments within the Three Gorges Reservoir Area. The present thesis aims to provide an efficient modelling framework that facilitates a detailed quantification of sediment reallocations due to erosive rainfall-runoff events. Therefore, Digital Soil Mapping techniques based on Latin Hypercube Sampling and Random Forest regression were applied to derive spatially distributed data on soil properties and to furnish a physically- and event-based soil erosion model. The soil sampling design was optimized to address the difficult terrain, an integrative use of legacy soil samples, and a reduced sample set size. Furthermore, the present thesis introduces a spatial uncertainty measure, which was used to identify areas for additional sampling to further refine initially processed soil property maps. In addition, continuous data on rainfall, runoff, and sediment yields were obtained to identify erosive rainfall-runoff events and to calibrate the physically-based soil erosion model EROSION 3D. Evaluation of the hypercube sampling design was conducted by comparing it to a simulated Latin Hypercube design without constraints in terms of operability and efficiency adjustments. Using the optimized sample set size of n = 30, the proposed sample design adequately reproduced the variation of terrain parameters, which served as proxies on the target soil properties of coarse, medium, and fine topsoil sand contents. Furthermore, the validity of the approach was assessed by estimating the spatial distribution of the target soil properties and validating the results independently. The results show convincing accuracies with R²-values between 0.59 and 0.71. The adequacy of the uncertainty-guided sampling for refining initial mapping approaches was evaluated by comparing the refined maps of topsoil silt and clay contents to the initial and further mapping approaches that exclusively used random samples from the entire study area. For the comparative analysis, the quality of the approaches was assessed by independent, bootstrap-, and cross-validation. The refined mapping approach performs best, showing a reduced spatial uncertainty of 31% for topsoil silt and 27% for topsoil clay compared to the initial approaches. Using independent validation, the accuracy increases by similar proportions, showing an accuracy of R² = 0.59 for silt and R² = 0.56 for clay. The EROSION 3D model runs were evaluated using the measured sediment yields. The model performs well for large events (sediment yield > 1 Mg) with an average individual model error of 7.5%, while small events show an average error of 36.2%. The focus of analysis was led on the large events to evaluate reallocation patterns. Soil losses occur on approximately 11.1% of the study area with an average soil loss rate of 49.9 Mg ha-1. Soil loss mainly occurs on crop rotation areas with a spatial proportion of 69.2% for ‘corn-rapeseed’ and 69.1% for ‘potato-cabbage’. Deposition occurs on 11% of the study area. Forested areas (9.7%), infrastructure (41%), cropland (corn-rapeseed: 13.6%, potato-cabbage: 11.3%), and grassland (18.4%) are affected by deposition. Since the vast majority of annual sediment yields (80.3%) were associated to a few large erosive events, the modelling framework can be recommended to identify sediment reallocations and to assess conservation measures in small catchments in the Three Gorges Reservoir Area

Simulation of the Initiation and Motion of Seismically Induced Aso-Ohashi Landslide During the 2016 Kumamoto Earthquake

To verify the mechanism of Aso-Ohashi landslide which is the seismically induced slope failure triggered by 2016 Kumamoto earthquake, field investigations and numerical simulations with terrain data measured by airborne laser scanners are carried out. A finite difference method (FDM) model is set up by the true terrain data. The stability of landslide region under shear strength reduction method and earthquake acceleration wave input were discussed. The effect of foreshock which caused strength reduction indicated that the critical value of strength was c = 16 kPa and ϕ = 33.87 degree. The earthquake wave made only steeply dipping region unstable and moved, but landslide hardly occurred in gently dipping region. In order to verify that only the steeply dipping region was moved due to earthquake, a GIS (Geographic Information System)-based depth average Bingham model which estimates the movement of slide mass is presented

Remote Sensing of Natural Hazards

Each year, natural hazards such as earthquakes, cyclones, flooding, landslides, wildfires, avalanches, volcanic eruption, extreme temperatures, storm surges, drought, etc., result in widespread loss of life, livelihood, and critical infrastructure globally. With the unprecedented growth of the human population, largescale development activities, and changes to the natural environment, the frequency and intensity of extreme natural events and consequent impacts are expected to increase in the future.Technological interventions provide essential provisions for the prevention and mitigation of natural hazards. The data obtained through remote sensing systems with varied spatial, spectral, and temporal resolutions particularly provide prospects for furthering knowledge on spatiotemporal patterns and forecasting of natural hazards. The collection of data using earth observation systems has been valuable for alleviating the adverse effects of natural hazards, especially with their near real-time capabilities for tracking extreme natural events. Remote sensing systems from different platforms also serve as an important decision-support tool for devising response strategies, coordinating rescue operations, and making damage and loss estimations.With these in mind, this book seeks original contributions to the advanced applications of remote sensing and geographic information systems (GIS) techniques in understanding various dimensions of natural hazards through new theory, data products, and robust approaches

Multi-Temporal X-Band Radar Interferometry Using Corner Reflectors: Application and Validation at the Corvara Landslide (Dolomites, Italy)

From the wide range of methods available to landslide researchers and practitioners for monitoring ground displacements, remote sensing techniques have increased in popularity. Radar interferometry methods with their ability to record movements in the order of millimeters have been more frequently applied in recent years. Multi-temporal interferometry can assist in monitoring landslides on the regional and slope scale and thereby assist in assessing related hazards and risks. Our study focuses on the Corvara landslides in the Italian Alps, a complex earthflow with spatially varying displacement patterns. We used radar imagery provided by the COSMO-SkyMed constellation and carried out a validation of the derived time-series data with differential GPS data. Movement rates were assessed using the Permanent Scatterers based Multi-Temporal Interferometry applied to 16 artificial Corner Reflectors installed on the source, track and accumulation zones of the landslide. The overall movement trends were well covered by Permanent Scatterers based Multi-Temporal Interferometry, however, fast acceleration phases and movements along the satellite track could not be assessed with adequate accuracy due to intrinsic limitations of the technique. Overall, despite the intrinsic limitations, Multi-Temporal Interferometry proved to be a promising method to monitor landslides characterized by a linear and relatively slow movement rates