Large slope deformations detection and monitoring along shores of the Potrerillos dam reservoir, Argentina, based on a small-baseline InSAR approach

Abstract

Large slope deformations detection and monitoring along shores of the Potrerillos dam reservoir, Argentina, based on a small-baseline InSAR approach Abstract The Argentina National Road 7 that crosses the Andes Cordillera within the Mendoza province to connect Santiago de Chile and Buenos Aires is particularly affected by natural hazards requiring risk management. Integrated in a research plan that intends to produce landslide susceptibility maps, we aimed in this study to detect large slope movements by applying a satellite radar interferometric analysis using Envisat data, acquired between 2005 and 2010. We were finally able to identify two large slope deformations in sandstone and clay deposits along gentle shores of the Potrerillos dam reservoir, with cumulated displacements higher than 25mm in 5years and towards the reservoir. There is also a body of evidences that these large slope deformations are actually influenced by the seasonal reservoir level variations. This study shows that very detailed information, such as surface displacements and above all water level variation, can be extracted from spaceborne remote sensing techniques; nevertheless, the limitations of InSAR for the present dataset are discussed here. Such analysis can then lead to further field investigations to understand more precisely the destabilising processes acting on these slope deformations.

Landslide Identification and Monitoring along the Jinsha River Catchment (Wudongde Reservoir Area), China, Using the InSAR Method

“…Therefore, a landslide in the Wudongde reservoir area along Jinsha River catchment will potentially threaten the safety of the hydropower station and the people who live in the lower reaches. Landslide inventory mapping at the catchment scale is the prerequisite for landslide stability analysis, hazard evaluation and risk assessment [3], which refers to the spatial locations of landslides for hazard assessments [4,5]. In particular, the detection of large and active instable bodies is much more important since these creep bodies can change to sudden movements such as rock falls, landslides or earth flows [6].…”

Simultaneous state–parameter estimation of rainfall-induced landslide displacement using data assimilation

“…In China alone, land-slide hazards account for about 72.6 % of the total geological disasters from 2005 to 2014 (Xue et al, 2016). Therefore, landslides are important to study, lending themselves for prevention studies like early warning systems and deformation predictions (Liu et al, 2014;Jiang et al, 2016;Michoud et al, 2016).…”

“…A particle filter (PF) is a typical sequential data assimilation algorithm which was initially put forward by . Because the PF is nonlinear filtering based on Bayesian estimation, it can solve nonlinear and non-Gaussian problems (Moradkhani and Weihermüller, 2011). Landslides and parameters that describe landslides are typically nonlinear (Leeuwen, 2010), so we choose PF as the algorithm to integrate multisource data with the model.…”

Simultaneous state-parameter estimation of rainfall-induced landslide displacement using data assimilation

“…In China, the landslide hazard accounts for about 72.6% of the total geological disasters from 2005 to 2014 (Xue et al, 2016). Therefore, landslide research is a hot topic studied by people, and it is necessary to do some prevention 25 study like the early warning and deformation prediction (Liu et al, 2014;Jiang et al, 2016;Michoud et al, 2016).…”