Landslides located beside reservoirs tend to be unstable or are characterized by large deformation during the drawdown process. This has been accepted by many experts. In this paper, we use Qiaotou Landslide, which is located beside the Three Gorges Reservoir (TGR), as a typical case study to investigate and predict the deformation mechanism during the drawdown process of TGR in detail. According to field investigation, the landslide mass is mainly composed of thick, loose silt and clay mixed with fragments of rock. Bedrock is mainly composed of silty sandstone. Field and laboratory tests indicate that the landslide mass has a high permeability coefficient. If the water level declines fast, intense seepage force may result. Based on these data, we establish a three-dimensional geological model of Qiaotou Landslide by FLAC 3D and perform a numerical simulation using the saturatedunsaturated fluid-solid coupling theory. For the simulation, we assume that the drawdown from 175 to 145 m takes place with a speed of 25 cm/day, which is based on the extreme water level regulation program of TGR. The simulation shows that this causes a significant deformation in the landslide mass and that the maximum displacement within the landslide is 24.2 cm. During the drawdown process, the maximum displacement zone is shifting from the upper part of the landslide where bedrock surface is steeper and thickness of loose deposits is less to the middle part of the landslide where bedrock surface is less steep and thickness of loose deposits is higher. The deformation mechanism indicates that in the early stage of the drawdown the deformation of the landslide mass is mainly caused by seepage and in the later stage mainly by consolidation.

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In 2003, the Three Gorges Project (TGP, China), currently the world's largest hydroelectric power plant by total capacity, went into operation. Due to large-scale impoundment of the Yangtze River and its tributaries and also due to resettlement, extensive environmental impacts like land use change and increase of geohazards are associated with the TGP. Within the Yangtze Project,we investigate these effects for the Xiangxi (香溪) catchment which is part of the Three Gorges Reservoir. The aim of this study is to evaluate the susceptibility for mass movement within the Xiangxi River backwater area using geographic information system (GIS). We used existing mass movements and the conditioning factors (geology, elevation, slope, curvature, land use, and land use change) for analyzing mass movement susceptibility. Mass movements and geology were mapped in the field to establish a mass movement inventory and a geological map. Land use and digital elevation model (DEM) were obtained from remote-sensing data. We determined the relation between mass movements and the conditioning factors by using the frequency ratio method and found strong relation between mass movements and both natural and human-influenced conditioning factors.

A landslide susceptibility map is very important and necessary to efficiently prevent and mitigate the losses brought by natural hazard for a large area. For the purpose of landslide susceptibility analysis for the whole Xiangxi catchment (3,209 km 2 ), Artificial Neural Network (ANN) analysis was applied as the main method. The whole catchment was divided into two parts: the training area and the implementation area. The backwater area (559 km 2 ) of Xiangxi catchment was used as the training area for the ANN method. In the training area the correlations between the landslide distribution and its causative factors, which includes lithology, slope angle, slope curvature and river network, have been analyzed based on the geological map and digital elevation model (DEM). The back-propagation training algorithm in ANN was selected to train the sample data from the training area, which were composed of input data (causative factors) and target output data (landslide occurrence), in order to find the correlations between them. Based on these correlations and input data in the implementation area (causative factors), the network output data were obtained for the implementation area. In the end, a map of landslide susceptibility, which was established by network output data, was presented for Xiangxi catchment. ArcGIS was applied to extract and quantify input information from a DEM for susceptibility analysis and also to present the result visually. As a result, a landslide susceptibility map, in which 70 % of all landslides are rightly classified in the training area (backwater area), was created for Xiangxi catchment.