@article{desai_handwerger_daniels_2024, title={Evaluating Landslide Susceptibility on the Big Sur Coast, California, USA using Complex Network Theory}, url={https://doi.org/10.5194/egusphere-egu24-14020}, DOI={10.5194/egusphere-egu24-14020}, abstractNote={As a result of extreme weather conditions such as heavy precipitation, natural slopes can fail dramatically. While the pre-failure deformation is sometimes apparent in retrospect, it remains challenging to predict the sudden transition from gradual deformation to runaway acceleration. Recent advancements in remote sensing techniques, like satellite radar interferometry (InSAR), enable high spatial and temporal resolution measurements of deformation and topographic information, providing valuable insights into landslide detection and activity. Landslides are common on the Big Sur coast, Central California, USA due to active tectonics, mechanically weak rocks, and high seasonal precipitation. We use satellite InSAR data from Copernicus Sentinel-1A/B to identify 23 active landslides within our 175 km2 study site; one is Mud Creek, a slow-moving, deep-seated landslide that catastrophically failed in May 2017 and another is Paul’s Slide, which has experienced nearly constant motion for decades. We use multilayer networks to investigate the spatiotemporal patterns of slow deformation on the 23 active landslides. In our analysis, we transform observations of the study site — ground surface displacement (InSAR) and topographic slope (digital elevation model) — into a spatially-embedded multilayer network in which each layer represents a sequential data acquisition period. We use community detection, which identifies strongly-correlated clusters of nodes, to identify patterns of instability. We have previously shown [Desai et al., Physical Review E, 2023] that using high-quality data containing information about the fluidity (via velocity as a proxy) and susceptibility (slope) of the area successfully forecasts the transition of the Mud Creek landslide — the only formally slow-moving landslide in this collection to have catastrophically collapsed — from stable to unstable. Using multivariate analysis, we compare the traits of the active landslides, such as precipitation, vegetation, deformation, topography, NDVI, and radar coherence, against the results of the community detection. A strong indicator of instability is a combination of poor InSAR coherence and high displacement. Combined with community detection, we are able to differentiate between creeping landslides that are stable and landslides that display concerning trends that may warn of catastrophic failure.}, author={Desai, Vrinda D. and Handwerger, Alexander L. and Daniels, Karen E.}, year={2024}, month={Mar} }
 @article{desai_fazelpour_handwerger_daniels_2023, title={Forecasting landslides using community detection on geophysical satellite data}, volume={108}, ISSN={2470-0045 2470-0053}, url={http://dx.doi.org/10.1103/PhysRevE.108.014901}, DOI={10.1103/PhysRevE.108.014901}, abstractNote={As a result of extreme weather conditions, such as heavy precipitation, natural hillslopes can fail dramatically; these slope failures can occur on a dry day, due to time lags between rainfall and pore-water pressure change at depth, or even after days to years of slow motion. While the prefailure deformation is sometimes apparent in retrospect, it remains challenging to predict the sudden transition from gradual deformation (creep) to runaway failure. We use a network science method-multilayer modularity optimization-to investigate the spatiotemporal patterns of deformation in a region near the 2017 Mud Creek, California landslide. We transform satellite radar data from the study site into a spatially embedded network in which the nodes are patches of ground and the edges connect the nearest neighbors, with a series of layers representing consecutive transits of the satellite. Each edge is weighted by the product of the local slope (susceptibility to failure) measured from a digital elevation model and ground surface deformation (current rheological state) from interferometric synthetic aperture radar (InSAR). We use multilayer modularity optimization to identify strongly connected clusters of nodes (communities) and are able to identify both the location of Mud Creek and nearby creeping landslides which have not yet failed. We develop a metric, i.e., community persistence, to quantify patterns of ground deformation leading up to failure, and find that this metric increased from a baseline value in the weeks leading up to Mud Creek's failure. These methods hold promise as a technique for highlighting regions at risk of catastrophic failure.}, number={1}, journal={Physical Review E}, publisher={American Physical Society (APS)}, author={Desai, Vrinda D. and Fazelpour, Farnaz and Handwerger, Alexander L. and Daniels, Karen E.}, year={2023}, month={Jul} }