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Landslides, ranging from slips to catastrophic failures, pose significant challenges for prediction. This study employs a physically inspired framework to assess landslide hazard at a regional scale (Big Sur Coast, California). Our approach integrates techniques from the study of complex systems with multivariate statistical analysis to identify areas prone to landslide hazards. We successfully apply a technique originally developed on the 2017 Mud Creek landslide and refine our statistical metrics to characterize landslide hazard within a larger geographical area. Our method is compared against factors such as landslide location, slope, displacement, precipitation, and InSAR coherence using multivariate statistical analysis. Our network analyses, which incorporates spatiotemporal dynamics, perform better as a monitoring technique than traditional methods. This approach has potential for real-time monitoring and evaluating landslide hazard across multiple sites 

Abstract Landslides pose a significant hazard worldwide. Despite advances in landslide monitoring, predicting their size, timing, and location remains a major challenge. We revisit the 2017 Mud Creek landslide in California using radar interferometry, pixel tracking, and elevation change measurements from satellite and airborne radar, lidar, and optical data. Our analysis shows that pixel tracking of optical imagery captured the transition from slow motion to runaway acceleration starting ~ 1 month before catastrophic failure—an acceleration undetected by satellite InSAR alone. Strain rate maps revealed a new slip surface formed within the landslide body during acceleration, likely a key weakening mechanism. Failure forecast analysis indicates the acceleration followed a hyperbolic trend, suggesting failure time could have been predicted at least 6 days in advance. We also inverted for the landslide thickness during the slow-moving phase and found variations from < 1 to 36 m. While thickness inversions provide important first-order information on landslide size, more work is needed to better understand how landslide subsurface properties and deforming volumes may evolve during the transition from slow-to-fast motion. Our findings underscore the need for integrated remote sensing techniques to improve landslide monitoring and forecasting. Future advancements in operational monitoring systems and big data analysis will be critical for tracking slope instability and improving regional-scale failure predictions. 

Abstract During December 2022–January 2023, nine atmospheric rivers (ARs) struck California consecutively, causing catastrophic flooding and 600+ landslides. The extensive footprints of landslide‐triggering storms and their diverse hydrometeorological forcings highlight the urgent need to incorporate regional‐scale hydrometeorology into landslide research. Here, using a meteorologically‐informed hydrologic model, we simulate the time‐evolving water budget during the nine‐AR event and identify hydrometeorological conditions that contributed to widespread landslide occurrences across California. Our analysis reveals that 89% of observed landslides occurred under excessively wet conditions, driven by precipitation exceeding the capacities of infiltration, storage, evapotranspiration, and soil drainage. Using K‐means clustering, we identify three distinct hydrometeorological pathways that increased landslide potential: intense precipitation‐induced runoff (∼32% of reported landslides), rain on pre‐wetted soils (∼53%), and snowmelt and soil ice thawing (∼15%). Our findings highlight the importance of constraining the compounding factors that influence slope stability over spatial scales consistent with landslide‐triggering weather systems. 

Rock glaciers are common landforms in mountainous areas of the western US. The motion of active rock glaciers is a key indicator of ice content, offering connections to climate and hydrologic systems. Here, we quantified the movement of six rock glaciers in the La Sal and Uinta Mountains of Utah through repeat differential GPS surveying. Networks of 10–41 points on each rock glacier were surveyed in September 2021; July 2022; September 2022; and July 2023. We found that all features are moving with average annual rates of motion from 1.5 ± 0.8 to 18.5 ± 7.5 cm/yr. Rock glaciers move up to 3× faster in the summer than in the winter, and rates of motion were greater in 2023 after a winter with above-average snowfall, emphasizing the role of liquid water availability. Velocities of individual points in the winter of 2021–22 are positively correlated with velocities during the winter of 2022–23, suggesting that spatial variability of motion is not stochastic, but rather reflects internal properties of each rock glacier. Bottom temperature of snow measurements during winter, and the temperature of springs discharging water in summer, suggest that these rock glaciers contain modern permafrost. Radiocarbon data document advance of one rock glacier during the Little Ice Age. Our GPS dataset reveals complicated patterns of rock glacier movement, and the network of survey points we established will be a valuable baseline for detecting future cryosphere change in these mountains. 

Abstract Catastrophic landslides are often preceded by slow, progressive, accelerating deformation that differs from the persistent motion of slow‐moving landslides. Here, we investigate the motion of a landslide that damaged 12 homes in Rolling Hills Estates (RHE), Los Angeles, California on 8 July 2023, using satellite‐based synthetic aperture radar interferometry (InSAR) and pixel tracking of satellite‐based optical images. To better understand the precursory motion of the RHE landslide, we compared its behavior with local precipitation and with several slow‐moving landslides nearby. Unlike the slow‐moving landslides, we found that RHE was a first‐time progressive failure that failed after one of the wettest years on record. We then applied a progressive failure model to interpret the failure mechanisms and further predict the failure time from the pre‐failure movement of RHE. Our work highlights the importance of monitoring incipient slow motion of landslides, particularly where no discernible historical displacement has been observed. 

Abstract Rock glaciers are common in alpine landscapes, but their evolution over time and their significance as agents of debris transport are not well‐understood. Here, we assess the movement of an ice‐cemented rock glacier over a range of timescales using GPS surveying, satellite‐based radar, and cosmogenic¹⁰Be surface‐exposure dating. GPS and InSAR measurements indicate that the rock glacier moved at an average rate of ∼10 cm yr⁻¹in recent years. Sampled boulders on the rock glacier have cosmogenic surface‐exposure ages from 1.2 to 10 ka, indicating that they have been exposed since the beginning of the Holocene. Exposure ages increase linearly with distance downslope, suggesting a slower long‐term mean surface velocity of 3 ± 0.3 cm yr⁻¹. Our findings suggest that the behavior of this rock glacier may be dominated by episodes of dormancy punctuated by intervals of relatively rapid movement over both short and long timescales. Our findings also show that the volume of the rock glacier corresponds to ∼10 m of material stripped from the headwall during the Holocene. These are the first cosmogenic surface‐exposure ages to constrain movement of a North American rock glacier, and together with the GPS and satellite radar measurements, they reveal that rock glaciers are effective geomorphic agents with dynamic multi‐millennial histories. 

Rock glaciers are common geomorphic features in alpine landscapes and comprise a potentially significant but poorly quantified water resource. This project focused on three complementary questions germane to rock glacier hydrology: 1) Does the composition of rock glacier meltwater vary from year to year? 2) How dependent is the composition of rock glacier meltwater on lithology? And 3) How does the presence of rock glaciers in a catchment change stream water chemistry? To address these questions, we deployed automated samplers to collect water from late June through mid-October 2022 in two rock-glacierized mountain ranges in Utah, United States characterized by different lithologies. In the Uinta Mountains of northern Utah, where bedrock is predominantly quartzite, water was collected at springs discharging from two rock glaciers previously shown to release water in late summer sourced from internal ice. In the La Sal Mountains of southeastern Utah, where trachyte bedrock is widespread, water was collected at a rock glacier spring, along the main stream in a watershed containing multiple rock glaciers, and from a stream in a watershed where rock glaciers are absent. Precipitation was also collected, and data loggers for water temperature and electric conductivity were deployed. Water samples were analyzed for stable isotopes with cavity ring-down spectroscopy and hydrochemistry with ICP-MS. Our data show that water discharging from rock glaciers in the Uinta Mountains exhibits a shift from a snowmelt source to an internal ice source over the course of the melt season that is consistent from year to year. We also found that the chemistry of rock glacier water in the two study areas is notably different in ways that can be linked back to their contrasting bedrock types. Finally, in the La Sal Mountains, the properties of water along the main stream in a rock-glacierized basin resemble the properties of water discharging from rock glaciers, and strongly contrast with the water in a catchment lacking rock glaciers. Collectively these results underscore the role of rock glaciers as an agent influencing the hydrochemistry of water in high-elevation stream systems.