Overview

Seasonal Slow-Moving landslide in Aizawl, India

Aizawl (population ~300,000), the capital city of Mizoram, India is prone to shallow landslides. Weak and porous folded shale and sandstone bedrock, high levels of precipitation during the monsoon season, steep hillslopes, and rapid development contribute to this problem. At least 7 rapid shallow landslide events occurred in the past 30 years. Here we apply Interferometric Synthetic Radar (InSAR) with multi-temporal baseline approach to monitor surface deformation in Aizawl. We use the ALOS and COSMO-SkyMed satellites to generate time series during 2007 – 2010 and 2012 – 2015 time periods, respectively. Our results identify two, active, slow-moving landslide areas: Ramhlun Sports Complex and Ramthar Veng. These landslides to the east and south of the Aizawl town agree with field observations in 2013. Our time series analysis shows that the mean creep rate of the landslide areas is ~25 mm/yr, and the creep rate is significantly higher in the wet seasons (~50 mm/yr) than in the dry seasons (~19 mm/yr). We also find that the creep rates correlates with surface slope. To explore the link between hydrology and slow-moving landslides in Aizawl, we use the Tropical Rainfall Measuring Mission model to estimate daily precipitation in Aizawl. We find that the creep rate and daily precipitation (or rainfall intensity) are correlated (~ 0.4) with ~11 and ~7 days time shift at the Ramhlun Sports Complex and the Ramthar Veng sites, respectively. Assuming infiltration through a 10 m thick landslide, this lag implies a hydraulic diffusivity of ~10-4 m2/s broadly consistent with the hydraulic diffusivity of clays. These time shifts coupled with topography and field observations of the landslide geology allow us to probe the mechanism for seasonal creep. We use scaling and finite-difference approach to capture changes in pore pressure and water table to fit the observed creep. Our work demonstrates the potential of using InSAR to characterize seasonal slow-moving landslides. This method is especially important for populated cities on steep hillslope that lack dense, ground-based, long-term monitoring networks (e.g. GPS). 

A shift from drought to extreme rainfall drives a stable landslide to catastrophic failure (Handwerger et al., 2019) 

The addition of water on or below the earth’s surface generates changes in stress that can trigger both stable and unstable sliding of landslides and faults. While these sliding behaviours are well-described by commonly used mechanical models developed from laboratory testing (e.g., critical-state soil mechanics and rate-and-state friction), less is known about the field-scale environmental conditions or kinematic behaviours that occur during the transition from stable to unstable sliding. Here we use radar interferometry (InSAR) and a simple 1D hydrological model to characterize 8 years of stable sliding of the Mud Creek landslide, California, UsA, prior to its rapid acceleration and catastrophic failure on May 20, 2017. Our results suggest a large increase in pore-fluid pressure occurred during a shift from historic drought to record rainfall that triggered a large increase in velocity and drove slip localization, overcoming the stabilizing mechanisms that had previously inhibited landslide acceleration. Given the predicted increase in precipitation extremes with a warming climate, we expect it to become more common for landslides to transition from stable to unstable motion, and therefore a better assessment of this destabilization process is required to prevent loss of life and infrastructure. 

Rapid landslide detection using satellite imagery and cloud computation (Handwerger et al., 2022)