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Ensuring the safety of personnel and providing practical escape routes for potential victims play essential roles in reducing the human vulnerability to site-specific landslides. However, the escape guidelines adopted by government departments and public welfare organizations are vague and unfocused because they ignore the features of individual people and spatial differences among hazard zones. In this paper, a scenario simulation model for quantitatively evaluating the escape behavior and human vulnerability was proposed using a Python platform based on the landslide runout process and hazard zone characteristics. A database that includes detailed information for the hazard zone's demographic structure and behavioral characteristics was established based on sampling surveys and field investigations. The probability values of scenario input parameters such as the escape route and speed were calculated and quantified by classic probability theory. The registered population over 65 years old accounted for 13.46% of the total, and most residents had no more than a primary school education background. Older adults were inclined to escape a moving landslide by running parallel to the sliding direction, although the public considers this direction to be the most dangerous. The exposure time in the hazard zone is 930 to 980 h per hectare during a corn planting year; the model simulation revealed that the cumulative mortality can be significantly reduced by promoting disaster prevention awareness and improving the advance warning time. This research and the developed quantitative human vulnerability framework provide user-friendly disaster prevention rehearsal guidelines and a reference for human disaster mitigation. [ABSTRACT FROM AUTHOR]

Geohazard recognition and inventory mapping are absolutely the keys to the establishment of reliable susceptibility and hazard maps. However, it has been challenging to implement geohazards recognition and inventory mapping in mountainous areas with complex topography and vegetation cover. Progress in the light detection and ranging (LiDAR) technology provides a new possibility for geohazard recognition in such areas. Specifically, this study aims to evaluate the performances of the LiDAR technology in recognizing geohazard in the mountainous areas of Southwest China through visually analyzing airborne LiDAR DEM derivatives. Quasi-3D relief image maps are generated based on the sky-view factor (SVF), which makes it feasible to interpret precisely the features of geohazard. A total of 146 geohazards are remotely mapped in the entire 135 km2 study area in Danba County, Southwest China, and classified as landslide, rock fall, debris flow based on morphologic characteristics interpreted from SVF visualization maps. Field validation indicate the success rate of LiDAR-derived DEM in recognition and mapping geohazard with higher precision and accuracy. These mapped geohazards lie along both sides of the river, and their spatial distributions are related highly to human engineering activities, such as road excavation and slope cutting. The minimum geohazard that can be recognized in the 0.5 m resolution DEM is about 900 m2. Meanwhile, the SVF visualization method is demonstrated to be a great alternative to the classical hillshaded DEM method when it comes to the determination of geomorphological properties of geohazard. Results of this study highlight the importance of LiDAR data for creating complete and accurate geohazard inventories, which can then be used for the production of reliable susceptibility and hazard maps and thus contribute to a better understanding of the movement processes and reducing related losses. [ABSTRACT FROM AUTHOR]

Landslide disasters are associated with severe losses on the Loess Plateau of China. Although early warning systems and susceptibility mapping have mitigated this issue to some extent, most methods are qualitative or semi-quantitative in the site-specific range. In this paper, a quantitative spatial distribution model is presented for site-specific loess landslide hazard assessment. Coupled with multi-temporal remote sensing images and high-precision UAV cloud point data, a total of 98 loess landslides that have occurred since 2004 on the Heifangtai terrace were collected to establish a landslide volume-date and retreating distance database. Eleven loess landslides are selected to construct a numerical model for parameter back analysis, and the accuracy of the simulation results is quantitatively evaluated by the centroid distance and overlapping area. Different volumes and receding distance rates of landslides are fitted to determine the relationship between cracks and potential volume, and different volumes and parameters are combined to simulate the spatial distribution of potential loess landslides. The results of this study reveal that landslide volumes mainly range between 1 × 103 and 5 × 105 m3, and the historical occurrence probability reaches 0.551. The optimal parameters are estimated by the maximum likelihood method to obtain a uniform distribution parameter value probability model, and the results show that the error of the estimated length within a range of 0.05 from the optimal parameter does not exceed 15%. In the selected slope slide case, farmland near the toe of the slope primarily includes exposed hazards with probabilities greater than 0.7. This work provides a useful reference for local disaster reduction and a theoretical methodology for hazard assessments. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Three groundwater domes are found in the center and eastern part of the Heitai terrace. • The hydrological processes are characterized by multi-temporal 2D ERT and sensors. • Landslide deposits block the seepage and result in local groundwater level rising. • Landslide type and magnitude are affected by groundwater seepage and movement. Abstract In the Heifangtai terrace, NW China, flooding irrigation is common for the cropland production and has increased the groundwater table by 20–30 m over the past five decades. It is believed that excessive irrigation has induced a large number of loess landslides in this area. However, these loess landslides responses to hydrological changes have still not been well understood. Therefore, 43 boreholes and 51 2D Electrical resistivity tomography (ERT) profiles with a total length of 40 km have been investigated to analyze the hydrological characteristics for the whole terrace and characterize the effects of hydrological changes on the occurrence of different types of loess landslides. Our main findings are as follows: (1) Three groundwater domes are found in the center and eastern part of the Heitai terrace and the groundwater table is gradually rising in the whole terrace based on the ERT results. (2) The rising rate of groundwater table at the center of the terrace is twice greater than that at the margin of the terrace and it takes approximately 4 months for irrigation water to penetrate from the top surface to the saturated layer. (3) Landslide deposits could block drainage channels of groundwater, increase the localized hydraulic gradient and result in rising of local groundwater table at the edge of the terrace. (4) The resultant hydraulic gradient impels the groundwater from the center to seep out on the eastern and southern margins and new landslides are prone to occur in the main scarp of landslides with rising groundwater table. The type and magnitude of landslides are affected by groundwater seepage and movement. In order to fundamentally control the occurrence of landslides, effective drainage system and the restrained amount of irrigation water are needed. [ABSTRACT FROM AUTHOR]

Agricultural irrigation has increased the groundwater level in the Heitai terrace (part of the Heifangtai terrace) by 20 m over nearly five decades, which causes 3–5 landslides each year at the edge of the terrace. The Heitai terrace is of great interest in the study of loess-related landslides; but there is no unanimous agreement on the types of either the landslides in this study site or the loess-related slope failures in general. On the basis of aerial images (res. 5 cm), Digital Elevation Model (res. 10 cm), and field investigations, we analyzed the distribution and failure mode of the landslides in Heitai. The geological structure and characteristics of 69 landslides (vol. 5 × 10 3 –6 × 10 6 m 3 ) are studied. The preliminary results of groundwater recharge in the terrace and formation of the apparent spring lines on the slope surface are analyzed to better understand the failure modes. We divided the landslides in Heitai into two groups based on the location of the failure surface, i.e. loess landslide and loess-bedrock landslide, of which the development is governed by the angle between the principle direction of slope deformation and the dip of bedrock bedding. We further analyzed the failure mode of each type observed in Heitai, defined as follows: loess-bedrock planar slide, loess-bedrock irregular slide, loess flowslide, loess slide, and loess flow. The proposed types of loess-related landslide are to be incorporated in the Varnes classification (Varnes, 1978) in consideration of the engineering properties of loess, and to provide backward compatibility for Heitai and potentially other regions in the Loess Plateau of China. [ABSTRACT FROM AUTHOR]

Real-time monitoring and intelligent early warning system are crucial and significant to take mitigation measures and reduce casualties and property losses related to landslides. It is difficult to obtain entire monitoring data in the accelerated deformation phase in a landslide event, and hard to issue early warning information using a traditional monitoring approach with fixed and low sampling frequency. Displacement increments of loess landslides induced by agriculture irrigation on the Heifangtai terrace could be sudden and extremely rapid. Typical landslide types include loess flowslides and loess falls. It is of practical significance to develop a self-adaptive data acquisition monitoring technique and establish a real-time landslide early warning system (LEWS) to meet the needs for risk mitigation of rapid sliding slopes on the Heifangtai terrace. The monitoring technique can wirelessly transmit displacement data and the LEWS was devised using the new artificial intelligence. The LEWS could automatically release the warning information in advance of the event once the early warning parameters exceed default thresholds. In this study, the early warning procedures, real-time monitoring approach, intelligent LEWS, a multiple criteria warning model, warning release and emergency mitigation measures, and performance are introduced in detail. Six loess landslides at Heifangtai and eight landslides in other regions of China have been successfully warned since its implementation in 2012. This study proposed an effective and practical solution for the early warning of loess landslides at Heifangtai. Two typical loess landslides that had successful early warnings at Heifangtai were presented. The successful implementation could serve as a reference for global rapid slope failure cases, considering the complex nature of landslide behaviors and failure mechanisms. • A self-adaptive data acquisition technique was successfully developed to monitor sudden failure of loess landslides. • 3D WebGIS-based platform developed by SKLGP becomes more intelligent and accurate. • Multiple alert criteria model including deformation rate, the rate increment, and tangential angle, is established. • The LEWS with multiple alert criteria model has successfully forecasted six loess landslides since its implementation in 2017. [ABSTRACT FROM AUTHOR]

The Heifangtai terrace located in Yongjing County of Gansu Province in northwest China is characterized by a large number of landslides with frequent failure events related to agricultural irrigation activities. Thus, it is a task of great importance to identify and monitor the active loess slopes and sort out the impact factors. In this study, we employed the time series InSAR method to map the active slopes in the Heifangtai terrace with two TerraSAR-X data stacks covering the year of 2016 and two Sentinel-1 data stacks from 2016 to 2018. The detected active spots are mainly distributed along the edges of the terrace. Time series displacements derived from TerraSAR-X and Sentinel-1 observations were validated by comparisons against GPS and crack gauge measurements. Good agreement between InSAR and GPS was achieved at slow moving locations, while unwrapping errors induced by sudden accelerations make InSAR underestimate the pre-failure displacements with respect to GPS measurements. The trends of InSAR-measured displacement and crack gauge measurements also match well with correlation higher than 0.75. InSAR-measured displacements at the Jiaojia landslide group were highly correlated with the bury of groundwater record, which suggested that the rising of groundwater level may seriously decrease the stability of loess slopes. • Unstable slopes in the Heifangtai terrace are mapped with TerraSAR-X and Sentinel-1 InSAR analyses from 2016 to 2018. • InSAR results are validated by comparisons with GPS and crack gauge measurements. • Displacement patterns of typical landslides within the study area are analyzed. • Intensive irrigation on the terrace significantly affects the stability of loess slopes. [ABSTRACT FROM AUTHOR]