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Updated: May 21, 2025

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Published on: May 1, 2018
Detection and updation of landslide inventory before and during impoundment in the Baihetan reservoir area using
Jiawei Dun1,2,3, Wenkai Feng4,5, Xiaoyu Yi4,5
1State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, 610059, China. djw@stu.cdut.edu.cn.
Abstract:
The Baihetan Hydropower Station, the second largest in the world, is currently impounded, posing significant challenges to reservoir slope stability and nearby community's safety. Thus, continuous monitoring as well as updation of landslide inventory is pressing requirements. InSAR technology, with its mm-scale precision and round-the-year usability, will be highly effective in this region. However, single-source SAR data are limited for long-term detection, and traditional atmospheric models in InSAR grapple with attenuating the external atmospheric disturbances caused by impoundment, affecting InSAR accuracy. Therefore, we used multi-source SAR data (ALOS PALSAR and Sentinel-1A/B), and used time-series InSAR with the GACOS atmospheric correction model to accurately detect and update landslide inventory before and during impoundment. The results show that a total of 52 landslides were detected, including 31 newly detected during impoundment. Among them, 22 landslides have toe slopes in direct contact with water. Comparing landslides before and during impoundment, deformations exhibit three behaviors: new deformation emergence, weakening of existing deformation, and continuous increase. These landslides mainly develop in landforms with an inclination angle of 30° to 40°, trending northeast and northwest, and an altitude of 800 to 1200 m. Most landslides reside in non-massive rock strata and are modulated by fault zones, and their frequency diminishes with increasing distance from the reservoir boundary. Moreover, the deformation time series results show that intense summer rainfall and rapid reservoir water level rise are key factors accelerating deformation in active landslides and reactivating unstable slopes. Thus, this research can be directly used for landslide prevention and mitigation in the Baihetan reservoir area, providing an important reference for detecting similar reservoir landslides in atmospherically influenced areas.
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