Revealing the time lag between slope stability and reservoir water fluctuation from InSAR observations and wavelet tools— a case study in Maoergai Reservoir (China)

Reservoir water fluctuation in supply and storage cycle have strong triggering effects on landslides on both sides of reservoir banks. Early identification of reservoir landslides and revealing the relationship between slope stability and the triggering factors including reservoir level and rainfall, are of great significance in further protecting nearby residents’ lives and properties. In this paper, based on the small baseline subset time series method (SBAS-InSAR), the potential landslides with active displacements in the river bank of Maoergai hydropower station in Heishui County from 2018 to 2020 were monitored with Sentinel-1 data. As a result, a total of 20 unstable slopes were detected. Subsequently, it was found through a gray correlation analysis that the fluctuation of the reservoir water level is the main triggering factor for the displacement on unstable slopes. This paper applied wavelet tools to quantify the time lag between slope stability and reservoir water fluctuation, revealing that the displacement exhibits a seasonal trend, whose high-frequency signal displacement has an interannual period (1 year). Based on the Cross Wavelet Transform (XWT) analysis, under the interannual scale of one year, the reservoir water fluctuation and nonlinear displacement show a clear common power in wavelet. Additionally, a time lag of 65–120 days between slope stability and reservoir water fluctuations has been found, indicating that the non-linear displacements were behind the water level changes. Among the factors affecting the time lag, the elevation of the points and their distance to the bank shore show Pearson’s correlation coefficients of 0.69 and 0.70, respectively. The observed time lag and correlations could be related to the gradual saturation/drainage processes of the slope and the drainage path. This paper demonstrates the technical support to quantitatively reveal the time lag between slope stability and reservoir water fluctuation by InSAR and wavelet tools, providing strong support for the analysis of the mechanisms of landslides in Maoergai reservoir area.The work was supported by the National Natural Science Foundation of China (Grant No. 41801391), ESA-MOST China DRAGON-5 project (ref. 59339) and the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project (SKLGP2020Z012) and Sichuan Science Foundation for Outstanding Youth (23NSFJQ0167)

Review of Monitoring and Early Warning Technologies for Cover-Collapse Sinkholes

Sinkhole collapse has become a major geohazard in many karst areas. The development of monitoring and early warning technologies is essential to investigate mechanisms of sinkhole formation. This paper reviews latest research on monitoring and early warning technologies surrounding cover-collapse sinkholes. Monitoring the hydrodynamic conditions in areas susceptible of sinkhole collapse has proven to be useful to help understand the relationship of rainfall, surface water, and groundwater in karst areas. Monitoring hydrodynamic conditions of karst groundwater includes rainfall monitoring, groundwater level monitoring, air pressure monitoring, and groundwater quality monitoring. Observations from the monitoring system and known sinkhole collapses could be used to simulate and predict hydrogeologic, geologic, and atmospheric conditions favorable to sinkhole formation. Monitoring technologies of deformation for the overburden soil include Ground Penetrating Radar (GPR), Time Domain Reflectometry (TDR), and Brillouin Optical Time Domain Reflectometer (BOTDR). GPR is more suitable to investigate relatively flat terrain with dry soil cover. TDR and BOTDR were more suitable for linearly distributed monitoring sites because of the cohesion between soil mass and optical fiber

Deformation Monitoring in an Alpine Mining Area in the Tianshan Mountains Based on SBAS-InSAR Technology

The fragile habitat of alpine mining areas can be greatly affected by surface disturbances caused by mining activities, particularly open-pit mining activities, which greatly affect the periglacial environment. SBAS-InSAR technology enables the processing of SAR images to obtain highly accurate surface deformation information. This paper applied SBAS-InSAR technology to obtain three years of surface subsidence information based on the 89-scene Sentinel-1A SLC products, covering a mining area (tailings and active areas) in the Tianshan Mountains and its surroundings from 25th December 2017 to 2nd January 2021. The data were adopted to analyze the characteristics of deformation in the study region and the mining areas, and the subsidence accumulation was compared with field GNSS observation results to verify its accuracy. The results showed that the study area settled significantly, with a maximum settlement rate of −44.80 mm/a and a maximum uplift rate of 28.04 mm/a. The maximum settlement and accumulation of the whole study area over the three-year period were −129.39 mm and 60.49 mm, respectively. The mining area had a settlement value of over 80 mm over the three years. Significantly, the settlement rates of the tailings and active areas were −35 mm/a and −40 mm/a, respectively. Debris accumulation in the eastern portion of the tailings and active areas near the mountain was serious, with accumulation rates of 25 mm/a and 20 mm/a, respectively, and both had accumulation amounts of around 70 mm. For mine tailing pile areas with river flows, the pile locations and environmental restoration should be appropriately adjusted at a later stage. For gravel pile areas, regular cleaning should be carried out, especially around the mining site and at the tunnel entrances and exits, and long-term deformation monitoring of these areas should be carried out to ensure safe operation of the mining site. The SBAS-InSAR measurements were able to yield deformations with high accuracies over a wide area and cost less human and financial resources than the GNSS measurement method. Furthermore, the measurement results were more macroscopic, with great application value for surface subsidence monitoring in alpine areas

Dynamic landslides susceptibility evaluation in Baihetan Dam area during extensive impoundment by integrating geological model and InSAR observations

On April 6, 2021, the Baihetan dam launched impoundment, and the reservoir water surface elevation dramatically increased from 660 m to 812 m until October 2021, which may induce large-scale landslides in reservoir bank. Accurate landslides susceptibility evaluation during impoundment is crucial for controlling the possible disasters and taking early evacuation or disaster prevention measures. Although many traditional geological models can accurately evaluate the landslides susceptibility of the reservoir area, they are inadequate to make the prompt response to the quick condition changes of reservoir bank induced by impoundment, while InSAR technology can provide a dynamic observation to monitor the small displacement occurring in the reservoir bank. Thus, this study proposes a new approach to dynamically evaluate the landslides susceptibility on reservoir banks during impoundment integrating geological evaluation model and InSAR observation. The approach combined stability coefficient of the reservoir area calculated from Scoops 3D model and geotechnical parameters, with slope displacement from InSAR technology to evaluate the susceptibility of landslides. The comparison between before and during impoundment shows that when the reservoir water elevation increased by 150 m, the high risk area of geohazards increases by 14.26 km2. The field validation confirms that the approach provides an effective and accurate dynamic landslide susceptibility evaluation, which forms a timely response to the geoenvironment changes in reservoir bank caused by the 150-m water level increment during impoundment