Abstract
The region of Tajikistan where the Rogun Hydropower Project is currently under construction has experienced large and catastrophic slope failures in the past, often triggered by earthquakes. Co-seismic slope failures are thus common and pose a high hazard potential; however, to date, no specific analysis of slope activity in this area has been presented in international journals. Here, we present an inventory of active landslides identified through satellite imagery analysis and in particular by exploiting space-borne differential radar interferometry. Surface displacements provide the basis for the detection of active slope instabilities, which are then further classified by using geomorphological indicators visible in optical satellite imagery. Additionally, the proximity of active landslides to tectonic lineaments, as well as regional seismicity, is analysed to investigate potential relationships and to provide an integrated river damming hazard potential. The results show that approximately 31% of all detected landslides would have a high damming hazard potential upon catastrophic failure, highlighting the importance of such phenomena for efficient long-term land use planning and management of hydropower plants.
Highlights
The stability and lifetime of hydropower construction projects in mountain areas are strongly dependent on local slope activity (Nadim et al 2006; Strom and Abdrakhmatov 2018; Valagussa et al 2019; Dini et al 2020)
This study explores the use of standard DInSAR to map slope instabilities and evaluate their damming hazard potential in a region in Tajikistan, Central Asia
The results indicate that 31% of the analysed detected slope instabilities present a high damming hazard to the Rogun Hydropower Project (HPP), 53% a medium hazard and 16% a low hazard
Summary
The stability and lifetime of hydropower construction projects in mountain areas are strongly dependent on local slope activity (Nadim et al 2006; Strom and Abdrakhmatov 2018; Valagussa et al 2019; Dini et al 2020). Mean velocities are high enough to lead to temporal phase aliasing in the majority of detected features in both satellites, highlighting the dependence on the satellite radar wavelength and a major limiting factor when using DInSAR (cf section 2.5) (Wasowski and Bovenga 2014; Manconi 2021) For both satellites, the observed displacement falls within the slow (1.6 m/year) to very slow (16 mm/year) velocity classes defined by Hungr et al (2014), where features detected only in ALOS PALSAR-2 differential interferograms typically show lower velocities compared to Sentinel-1 data, not enabling the expected enhanced detection of fast slope movements. Less conservative estimations include those derived by Guzzetti et al (2008)
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