资源环境科技发展态势分析平台

Long-term landslide deformation is disruptive and costly in urbanized environments. We rely on TerraSAR-X satellite images (2009-2014) and an improved data processing algorithm (SqueeSAR (TM)) to produce an exceptionally dense Interferometric Synthetic Aperture Radar ground deformation time series for the San Francisco East Bay Hills. Independent and principal component analyses of the time series reveal four distinct spatial and temporal surface deformation patterns in the area around Blakemont landslide, which we relate to different geomechanical processes. Two components of time-dependent landslide deformation isolate continuous motion and motion driven by precipitation-modulated pore pressure changes controlled by annual seasonal cycles and multiyear drought conditions. Two components capturing more widespread seasonal deformation separate precipitation-modulated soil swelling from annual cycles that may be related to groundwater level changes and thermal expansion of buildings. High-resolution characterization of landslide response to precipitation is a first step toward improved hazard forecasting.

Plain Language Summary In an ever-expanding urban environment, we opt to live with the risk of catastrophic natural hazards through a perceived safety net of building codes and engineering solutions. Unfortunately, our concern for these hazards is often focused on their immediate impact to our everyday lives and does not account for imperceptible processes that may become significant over decades in time. Notoriously, some landslides slowly and continuously deform, ultimately causing costly unpredicted damage to homes, lifelines, and other infrastructure. Recent advances in satellite technology allow us to accurately measure these long-term movements, tracking where and when they occur. We show that the duration and amount of seasonal precipitation and associated water pressure changes determine how fast the landslides move and how recent drought conditions have slowed their advance. The satellite data allow us to differentiate the landslide deformation from normal seasonal changes in unaffected areas, giving us greater predictability of this hazard.