A multi-sensor remote sensing approach for understanding slow-moving landslide reactivation: a case study from North Central Iran following reservoir impoundment and extreme precipitations
Authors: Vassileva Magdalena , Motagh Mahdi , Roessner Sigrid , Xia Zhuge 
Published: March 2024

Anthropogenic activities, including the operation of reservoirs and infrastructure expansion, coupled with extreme climatic events are increasing landslide hazards worldwide, but information on the detailed impact of these factors on slope stability is often lacking. In-situ monitoring systems in these potential landslide-prone areas are often unavailable, challenging landslide hazard assessment. This study comprises a multi-scale and multi-sensor satellite remote sensing approach in combination with advanced statistical methods to investigate the life cycle of the catastrophic Hoseynabad-e Kalpush landslide failure that occurred in March-April 2019 in Semnan province of North Central Iran. The landslide occurred on the adjacent slope of a nearby reservoir built in early 2013 following an exceptional precipitation period in the spring of 2019. The failure resulted in the damage of more than 300 houses, of which 163 had to be evacuated due to the severity of the destruction.In our remote sensing approach, we first derived the spatiotemporal evolution of the pre-, co- and post-failure landslide kinematic fields using Digital Image Correlation based on PlanetScope 3-m resolution data (November 2018 and May 2019) and Multi-temporal InSAR using ascending and descending orbits Envisat ASAR (July 2003 to September 2010) and Sentinel-1 (October 2014 to December 2021) acquisitions. Remote sensing results are then integrated with advanced statistical and clustering approaches to derive trends and seasonality in the time series of the analyzed remote sensing data before correlating the results with external triggering factors. Long-term monthly cumulative precipitation observations (2000-2022) were obtained from The Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). The reservoir water level was derived by a GIS-based approach using Landsat-8 (April 2013 to August 2016), PlanetScope (August 2016 to December 2021) data and the Shuttle Radar Topography Mission (SRTM) 1 arc-second global digital elevation model.Our results suggest that the impoundment of the recently built reservoir reactivated the previously relict landslide and triggered a retrogressive destabilization mechanism. During the pre-failure creeping, the landslide stability conditions permanently degraded. The combination of exceptional precipitation of 2019 and the sudden increment of pore-water pressure, was the final trigger of the landslide main failure in March of that same year in what is a typical deep-seated failure mechanism. In the aftermath, the landslide was still active, with trends in displacement rate comparable to the pre-failure phase, which decreased until its final stabilization in the second half of 2021. The outcomes of this study reveal the complex interactions between reservoir water level changes and extreme precipitation events in influencing landslide kinematics and elevating the hazard of landslide reactivation and failure. Thus, the investigation of the Hoseynabad-e Kalpush landslide case is also relevant for other settings where artificial reservoirs have been built adjacent to relict landslide-prone slopes and where no or only limited in-situ monitoring data are available.