This work presents the ideal combination of space-borne and ground-based (GB) Interferometric Synthetic Aperture Radar (InSAR) applications. In the absence of early investigation reporting and specialized monitoring, the Zhongbao landslide unexpectedly occurred on 25 July 2020, forming a barrier lake that caused an emergency. As an emergency measure, the GB-InSAR system was installed 1.8 km opposite the landslide to assess real-time cumulative deformation with a monitoring frequency of 3 min. A zone of strong deformation was detected, with 178 mm deformation accumulated within 15 h, and then a successful emergency warning was issued to evacuate on-site personnel. Post-event InSAR analysis of 19 images acquired by the ESA Sentinel-1 from December 2019 to August 2020 revealed that the landslide started in March 2020. However, the deformation time series obtained from satellite InSAR did not show any signs that the landslide had occurred. The results suggest that satellite InSAR is effective for mapping unstable areas but is not qualified for rapid landslide monitoring and timely warning. The GB-InSAR system performs well in monitoring and providing early warning, even with dense vegetation on the landslide. The results show the shortcomings of satellite InSAR and GB-InSAR and a clearer understanding of the necessity of combining multiple monitoring methods.

It is crucial to predict landslide displacement accurately for establishing a reliable early warning system. Such a requirement is more urgent for landslides in the reservoir area. The main reason is that an inaccurate prediction can lead to riverine disasters and secondary surge disasters. Machine learning (ML) methods have been developed and commonly applied in landslide displacement prediction because of their powerful nonlinear processing ability. Recently, deep ML methods have become popular, as they can deal with more complicated problems than conventional ML methods. However, it is usually not easy to obtain a well-trained deep ML model, as many hyperparameters need to be trained. In this paper, a deep ML method—the gated recurrent unit (GRU)—with the advantages of a powerful prediction ability and fewer hyperparameters, was applied to forecast landslide displacement in the dam reservoir. The accumulated displacement was firstly decomposed into a trend term, a periodic term, and a stochastic term by complementary ensemble empirical mode decomposition (CEEMD). A univariate GRU model and a multivariable GRU model were employed to forecast trend and stochastic displacements, respectively. A multivariable GRU model was applied to predict periodic displacement, and another two popular ML methods—long short-term memory neural networks (LSTM) and random forest (RF)—were used for comparison. Precipitation, reservoir level, and previous displacement were considered to be candidate-triggering factors for inputs of the models. The Baijiabao landslide, located in the Three Gorges Reservoir Area (TGRA), was taken as a case study to test the prediction ability of the model. The results demonstrated that the GRU algorithm provided the most encouraging results. Such a satisfactory prediction accuracy of the GRU algorithm depends on its ability to fully use the historical information while having fewer hyperparameters to train. It is concluded that the proposed model can be a valuable tool for predicting the displacements of landslides in the TGRA and other dam reservoirs.

The stability analysis of damaged landslides and unstable debris is important for rescue work and emergency operations. This paper investigates a predisposed geological emergence, inducing the factors and deformation processes of the Zhongbao landslide, which happened on July 25, 2020. The stability of the landslide debris was evaluated by an integrated monitoring system consisting of ground-based radar, unmanned aerial vehicles, airborne Lidar, thermal infrared temperature monitoring, GNSS displacement monitoring, deep displacement monitoring, and rainfall monitoring. The strata and weak layer controlled the landslide failure, and topography defined the boundary of the failed rock mass. A continually intensive rainfall caused the deformation and accelerated failure of the landslide. The shallow and steep deposit (Part I) firstly slid at a high velocity, and then pushed the rear part of the landslide (Part II) to deform, forming numerous cracks, which accelerated the rainfall infiltrating into the rock mass. The moisture content increase could decrease the strength of the shale rock within the bedding planes. Finally, with the rock and soil mass sliding along the weak layer, a barrier dam and a barrier lake were formed. The monitoring and numerical simulation results showed that after the landslide failure, there was still local collapse and deformation occurrences which threatened rescue work and barrier lake excavation, and the stability of the accumulation area gradually decreased as the rainfall increased. Therefore, the barrier dam was not excavated until the accumulation rate gradually stabilized on July 28. Moreover, most of the reactivated deposits still accumulated in the transportation and source areas. Thus, in August, the displacement of the landslide debris gradually accelerated in a stepwise manner, and responded strongly to rainfall, especially in the accumulation area, so that it was inferred that the damaged landslide could slide again and cause a more threatening and severe failure. The analysis results of the study area can provide references for the failure mechanism of a rainfall-induced landslide and the stability evaluation of a damaged landslide.

The mechanical behavior of loess is highly predicated on variation in its moisture content. While the impacts of the water content on the shearing behavior and collapsibility of loess have been extensively studied, its effect on tensile characteristics has received relatively little attention. In this study, a series of tensile tests were conducted on remolded specimens of a clayey loess that were collected from Ili Valley in China. Two sets of loess specimens with varying water contents were prepared separately using wetting and drying methods. The influence of the water content on the tensile stress–strain response, failure mode and tensile strength was investigated by combining the tensile test results and particle image velocimetry (PIV) analysis. On this basis, a nuclear magnetic resonance (NMR) test and scanning electron microscopy (SEM) observations were implemented in order to assist with the interpretation of the underlying mechanism. The test results indicate that the tensile failure process and the variation of tensile strength with varying water contents differ for specimens that are prepared with wetting and drying methods; a finding which arises from the differences in the soil microstructure, clay–water interaction and the distribution of capillary and adsorbed water. This research has shown that the tensile strength of clayey loess is essentially dominated by the clay’s hydration/cementation and the development of capillary and adsorption suction as well as the microstructural evolution that occurs with the change in the water content. Based on the experimental observations, a conceptual model is proposed in order to interpret the effect of water content on loess’ tensile behavior.