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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.

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. Plain Language Summary Landslides exhibit a wide range of behaviors around the world. Some landslides travel slowly downslope (velocities <1 m/yr) over a period of years or even decades. In contrast, other landslides fail catastrophically and travel downslope at high rates (>1 m/s), claiming lives and causing major damages. In some cases, these catastrophic landslides also display a precursory period of slow movement which presents an opportunity to detect them before they rapidly collapse. Here we investigated the movement of the Rolling Hills Estates (RHE) landslide, Los Angeles, California, before it moved many meters and damaged more than 12 houses on 8 July 2023. This landslide occurred after one of the wettest years on record in California. We found that the RHE landslide started sliding in 2023 in response to relatively high precipitation without obvious signs of movement in the three years preceding this event. This behavior differed starkly from the persistent motion of nearby slow‐moving landslides that experienced nearly identical rainfall conditions. Our findings show that the RHE can be defined as a progressive failure and its failure time can be estimated approximately through analyzing its movement before the occurrence of the failure. Key Points We used satellite remote sensing to characterize the precursory motion of a catastrophic landslide Progressive failures and slow‐moving landslides can be distinguished by analyzing their time‐dependent motion Special attention should be given to landslides where no apparent historical movement has occurred

Slow-moving landslides move downslope at velocities that range from mm year−1 to m year−1. Such deformations can be measured using satellite-based synthetic aperture radar interferometry (InSAR). We developed a new method to systematically detect and quantify accelerations and decelerations of slowly deforming areas using InSAR displacement time series. The displacement time series are filtered using an outlier detector and subsequently piecewise linear functions are fitted to identify changes in the displacement rate (i.e., accelerations or decelerations). Grouped accelerations and decelerations are inventoried as indicators of potential unstable areas. We tested and refined our new method using a high-quality dataset from the Mud Creek landslide, CA, USA. Our method detects accelerations and decelerations that coincide with those previously detected by manual examination. Second, we tested our method in the region around the Mazar dam and reservoir in Southeast Ecuador, where the time series data were of considerably lower quality. We detected accelerations and decelerations occurring during the entire study period near and upslope of the reservoir. Application of our method results in a wealth of information on the dynamics of the surface displacement of hillslopes and provides an objective way to identify changes in displacement rates. The displacement rates, their spatial variation, and the timing of accelerations and decelerations can be used to study the physical behavior of a slow-moving slope or for regional hazard assessment by linking the timing of changes in displacement rates to landslide causal and triggering factors.

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. Plain Language Summary Landslides can be triggered by several factors including heavy rain, snowmelt, and saturated soils, making prediction difficult, especially across large regions. To improve understanding and prediction, we combined a weather‐driven hydrologic model with machine learning to track how water moves through the landscape and contributes to landslides. We applied this approach to California's extreme winter storms from December 2022 to January 2023, when nine consecutive ARs brought heavy rains and flooding, and triggered over 600 landslides. Our analysis found that 89% of these landslides occurred where rainfall overwhelmed the land's ability to absorb, store, or drain water. Key triggers included intense rainfall, rain falling on already saturated soils, and melting snow or ice. These findings highlight the need to consider multiple weather and water‐related factors in landslide prediction efforts. Key Points Meteorologically informed hydrologic modeling and K‐means clustering improve understanding of widespread landslide triggers A regional‐scale water balance analysis from process‐based modeling offers explanatory power for widespread landslide events K‐means clustering of modeled hydrometeorological variables can be used to identify distinct drivers that increase landslide potential

Rock glaciers are a prominent component of many alpine landscapes and constitute a significant water resource in some arid mountain environments. Here, we employ satellite-based interferometric synthetic aperture radar (InSAR) between 2016 and 2019 to identify and monitor active and transitional rock glaciers in the Uinta Mountains (Utah, USA), an area of ∼3000 km2. We used mean velocity maps to generate an inventory for the Uinta Mountains containing 205 active and transitional rock glaciers. These rock glaciers are 11.9 ha in area on average and located at a mean elevation of 3308 m, where mean annual air temperature is −0.25 ∘C. The mean downslope velocity for the inventory is 1.94 cm yr−1, but individual rock glaciers have velocities ranging from 0.35 to 6.04 cm yr−1. To search for relationships with climatic drivers, we investigated the time-dependent motion of three rock glaciers. We found that rock glacier motion has a significant seasonal component, with rates that are more than 5 times faster during the late summer compared to the rest of the year. Rock glacier velocities also appear to be correlated with the snow water equivalent of the previous winter's snowpack. Our results demonstrate the ability to use satellite InSAR to monitor rock glaciers over large areas and provide insight into the environmental factors that control their kinematics.

Catastrophic landslides cause billions of dollars in damages and claim thousands of lives annually, whereas slow-moving landslides with negligible inertia dominate sediment transport on many weathered hillslopes. Surprisingly, both failure modes are displayed by nearby landslides (and individual landslides in different years) subjected to almost identical environmental conditions. Such observations have motivated the search for mechanisms that can cause slow-moving landslides to transition via runaway acceleration to catastrophic failure. A similarly diverse range of sliding behavior, including earthquakes and slow-slip events, occurs along tectonic faults. Our understanding of these phenomena has benefitted from mechanical treatments that rely upon key ingredients that are notably absent from previous landslide descriptions. Here, we describe landslide motion using a rate- and state-dependent frictional model that incorporates a nonlocal stress balance to account for the elastic response to gradients in slip. Our idealized, one-dimensional model reproduces both the displacement patterns observed in slow-moving landslides and the acceleration toward failure exhibited by catastrophic events. Catastrophic failure occurs only when the slip surface is characterized by rate-weakening friction and its lateral dimensions exceed a critical nucleation length h* that is shorter for higher effective stresses. However, landslides that are extensive enough to fall within this regime can nevertheless slide slowly for months or years before catastrophic failure. Our results suggest that the diversity of slip behavior observed during landslides can be described with a single model adapted from standard fault mechanics treatments.

Rapid detection of landslides is critical for emergency response, disaster mitigation, and improving our understanding of landslide dynamics. Satellite-based synthetic aperture radar (SAR) can be used to detect landslides, often within days of a triggering event, because it penetrates clouds, operates day and night, and is regularly acquired worldwide. Here we present a SAR backscatter change approach in the cloud-based Google Earth Engine (GEE) that uses multi-temporal stacks of freely available data from the Copernicus Sentinel-1 satellites to generate landslide density heatmaps for rapid detection. We test our GEE-based approach on multiple recent rainfall- and earthquake-triggered landslide events. Our ability to detect surface change from landslides generally improves with the total number of SAR images acquired before and after a landslide event, by combining data from both ascending and descending satellite acquisition geometries and applying topographic masks to remove flat areas unlikely to experience landslides. Importantly, our GEE approach does not require downloading a large volume of data to a local system or specialized processing software, which allows the broader hazard and landslide community to utilize and advance these state-of-the-art remote sensing data for improved situational awareness of landslide hazards.

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.

On August 14, 2021, a Mw 7.2 earthquake struck the Tiburon Peninsula of western Haiti triggering thousands of landslides. Three days after the earthquake on August 17, 2021, Tropical Storm Grace crossed shallow waters offshore of southern Haiti triggering more landslides worsening the situation. In the aftermath of these events, several organizations with disaster response capabilities or programs activated to provide information on the location of landslides to first responders on the ground. Utilizing remote sensing to support rapid response, one organization manually mapped initiation point of landslides and three automatically detected landslides. The 2021 Haiti event also provided a unique opportunity to test different automated landslide detection methods that utilized both SAR and optical data in a rapid response scenario where rapid situational awareness was critical. As the methods used are highly replicable, the main goal of this study is to summarize the landslide rapid response products released by the organizations, detection methods, quantify accuracy and provide guidelines on how some of the shortcomings encountered in this effort might be addressed in the future. To support this validation, a manually mapped polygon-based landslide inventory covering the entire affected area was created and is also released through this effort.

A rapidly growing population across mountain regions is pressuring expansion onto steeper slopes, leading to increased exposure of people and their assets to slow‐moving landslides. These moving hillslopes can inflict damage to buildings and infrastructure, accelerate with urban alterations, and catastrophically fail with climatic and weather extremes. Yet, systematic estimates of slow‐moving landslide exposure and their drivers have been elusive. Here, we present a new global database of 7,764 large (A ≥ 0.1 km2) slow‐moving landslides across nine IPCC regions. Using high‐resolution human settlement footprint data, we identify 563 inhabited landslides. We estimate that 9% of reported slow‐moving landslides are inhabited, in a given basin, and have 12% of their areas occupied by human settlements, on average. We find the density of settlements on unstable slopes decreases in basins more affected by slow‐moving landslides, but varies across regions with greater flood exposure. Across most regions, urbanization can be a relevant driver of slow‐moving landslide exposure, while steepness and flood exposure have regionally varying influences. In East Asia, slow‐moving landslide exposure increases with urbanization, gentler slopes, and less flood exposure. Our findings quantify how disparate knowledge creates uncertainty that undermines an assessment of the drivers of slow‐moving landslide exposure in mountain regions, facing a future of rising risk, such as Central Asia, Northeast Africa, and the Tibetan Plateau. Plain Language Summary Slow‐moving landslides can damage buildings and infrastructure, while potentially leading to thousands of fatalities with a sudden collapse. As populations expand in mountain regions, more communities settling into steeper terrain could be exposed to landslide‐prone areas. Yet, our estimates of populations exposed to landslides excludes slow‐moving landslides. We address this by identifying unstable slopes, inhabited by human settlements, from a new global database of 7,764 reported large slow‐moving landslides located in nine IPCC mountain‐risk regions. Across most regions, we find that landslide exposure increases with sprawling urbanized areas, though clearly not with steeper terrain. We show regional contrasts in how exposure to floods may drive people to settle on unstable slopes. East Asia stands out in how landslide exposure increases in more urbanized basins with gentler slopes and less flood exposure. Our results indicate that communities in mountain regions, facing increasing future landslide and flood risk, have the least certain insight on slow‐moving landslide exposure and their drivers. Key Points We present a database of 7,764 reported large (A ≥ 0.1 km2) slow‐moving landslides in nine IPCC regions and find 563 are inhabited We learn more about landslide exposure from regional responses to flood exposure than from an abundance of slow‐moving landslides Urbanization in basins can be a relevant driver of landslide exposure, while steepness and flood exposure have varying regional influences