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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

Slope instability due to tectonic, hydrological and anthropogenic activities cause severe landslides in Himalaya. Joshimath, a densely populated Himalayan town witnessed a catastrophic landslide event during December 2022 and January 2023 causing damages to ∼700 buildings. We use Interferometric synthetic aperture radar, Global Positioning System and rainfall measurements to probe the kinematics of the Joshimath landslide. We separate the seasonal and episodic deformation components using singular spectrum analysis. While the low amplitude annual landslide motions are modulated by seasonal precipitation, acceleration phases are triggered by extreme rain events. Our analysis revealed episodes of cascading motions triggered by extreme rain events resulting an overall increase in landslide velocity from −22 mm/yr during 2004–2010 to −325 mm/yr during 2022–2023. We estimate the landslide depth (∼30 m) and hydraulic diffusivity (∼3 × 10−5 m2/s) using a 1‐D pore‐water pressure diffusion model. Our study reveals the importance of systematic monitoring of ground deformation and weather parameters for landslide hazard mitigation. Plain Language Summary The Himalayas, world's largest and youngest mountain range formed as a result of the India‐Eurasia continental collision hosts thousands of landslides each year related to tectonic and anthropogenic activities. It is important to characterize the landslide kinematics, especially the slow moving landslides at the fast‐developing Himalayan urban centers for hazard assessment. A recent catastrophic landslide event in the Joshimath town in NW Himalaya occurred between December 2022 and January 2023 caused damages to ∼700 buildings leading to mass evacuation. We map the spatio‐temporal evolution of land deformation at Joshimath using about two decades of geodetic measurements and singular spectrum analysis technique. While the decadal‐scale landslide motions match well with the overall increase in rainfall intensity, the episodic accelerations are triggered by extreme rain events. The extreme rainfall event on 19 October 2021, triggered the latest episode of landslide acceleration, which eventually led to the tragic disaster. The present study, for the first time, provides constraints on the landslide depth and hydraulic diffusivity. Our study also emphasis on the systematic monitoring of ground deformation and weather parameters at the Himalayas for an effective landslide hazard preparedness and mitigation. Key Points Joshimath landslide displays episodic and seasonal kinematic changes; episodic accelerations are triggered by extreme rain events The 19 October 2021 extreme rain event triggered the latest episode of landslide acceleration culminating to the 2022 disaster Depth and hydraulic diffusivity of the landslide is constrained using 1‐D pore‐water pressure diffusion model

Many slow‐moving landslides in the Three Gorges Reservoir (TGR) area exhibit episodic movement patterns in response to variations in the reservoir water level and seasonal rainfall. These landslides are typically characterized by pre‐existing shear zones composed of thick, clay‐rich layers that are sensitive to hydrological forcings. The residual shear strength of these shear zones is believed to control the dynamic movements of these landslides. In this article, we review the recent advances in the investigation of a large slow‐moving landslide, the Huangtupo landslide, in the TGR area. This landslide has been chosen as a benchmark for studying similar landslides in this area. This review focuses mainly on the research carried out by the authors and their collaborators over the last 5 years on topics including the landslide mechanisms and forcings, rheological behaviors and constitutive models of the landslide materials, the numerical simulation of clastic shear‐zone soils, and the effects landslide‐induced tsunamis would have on the ships in the reservoir. In addition, new perspectives and challenges for future research on slow‐moving landslides are provided.

This paper presents recommended methodologies for the quantitative analysis of landslide hazard, vulnerability and risk at different spatial scales (site-specific, local, regional and national), as well as for the verification and validation of the results. The methodologies described focus on the evaluation of the probabilities of occurrence of different landslide types with certain characteristics. Methods used to determine the spatial distribution of landslide intensity, the characterisation of the elements at risk, the assessment of the potential degree of damage and the quantification of the vulnerability of the elements at risk, and those used to perform the quantitative risk analysis are also described. The paper is intended for use by scientists and practising engineers, geologists and other landslide experts.

This paper shows the results of a study aimed at quantitatively estimating—in terms of direct (repair) costs, at large scale (1:5000)—the slow-moving landslide risk to a road network assumed as undamaged as well as the consequences to the same network in damaged conditions. The newly conceived methodological approaches address some challenging tasks concerning (i) the hazard analysis, which is expressed in terms of probability of occurrence of slow-moving landslides with a given intensity level that, in turn, is established based on empirical fragility curves, and (ii) the consequence analysis, which brings to the generation of time-dependent vulnerability curves. Their applicability is successfully tested in a case study in the Campania region (southern Italy) for which both very high-resolution DInSAR data and information gathered from in situ surveys on the severity of damage sustained by the selected road sections are available. Benefits associated with the use of the obtained results in informed decision-making processes are finally discussed.

Monitoring slow-moving landslides is a crucial task for socioeconomic risk prevention and/or mitigation. Persistent scatterer interferometric synthetic aperture radar (PS-InSAR) is an advanced remote sensing method for monitoring ground deformation. In this research, PS-InSAR time series derived from COSMO-SkyMed (descending orbit) and Sentinel-1 (ascending orbit) are analyzed for a region in Central Apennines in Italy. The sequential turning point detection method (STPD) is implemented to detect the trend turning dates and their directions in the PS-InSAR time series within areas of interest susceptible to landslides. The monthly maps of significant turning points and their directions for years 2018, 2019, 2020, and 2021 are produced and classified for four Italian administrative regions, namely, Marche, Umbria, Abruzzo, and Lazio. Monthly global precipitation measurement (GPM) images at 0.1∘×0.1∘ spatial resolution and four local precipitation time series are also analyzed by STPD to investigate when the precipitation rate has changed and how they might have reactivated slow-moving landslides. Generally, a strong correlation (r≥0.7) is observed between GPM (satellite-based) and local precipitation (station-based) with similar STPD results. Marche and Abruzzo (the coastal regions) have an insignificant precipitation rate while Umbria and Lazio have a significant increase in precipitation from 2017 to 2023. The coastal regions also exhibit relatively lower precipitation amounts. The results indicate a strong correlation between the trend turning dates of the accumulated precipitation and displacement time series, especially for Lazio during summer and fall 2020, where relatively more significant precipitation rate of change is observed. The findings of this study may guide stakeholders and responsible authorities for risk management and mitigating damage to infrastructures.

Slow-moving landslides yearly induce huge economic losses worldwide in terms of damage to facilities and interruption of human activities. Within the landslide risk management framework, the consequence analysis is a key step entailing procedures mainly based on identifying and quantifying the exposed elements, defining an intensity criterion and assessing the expected losses. This paper presents a two-scale (medium and large) procedure for vulnerability assessment of buildings located in areas affected by slow-moving landslides. Their intensity derives from Differential Interferometric Synthetic Aperture Radar (DInSAR) satellite data analysis, which in the last decade proved to be capable of providing cost-effective long-term displacement archives. The analyses carried out on two study areas of southern Italy (one per each of the addressed scales) lead to the generation, as an absolute novelty, of both empirical fragility and vulnerability curves for buildings in slow-moving landslide-affected areas. These curves, once further validated, can be valuably used as tools for consequence forecasting purposes and, more in general, for planning the most suitable slow-moving landslide risk mitigation strategies.

Landslides are widespread geomorphological features on the North Island of New Zealand, where they represent one of the primary landscape-forming processes. This study focuses on the steepland terrain surrounding Gisborne, a city located on the east coast of the North Island, at the Hikurangi subduction margin. This terrain consists of young, weak, sedimentary rocks and soils; the most common modes of slope failures are soil creep, slides and flows in shallow, clay-rich soil and regolith, triggered by heavy rainfall. Based on observational data from Sentinel-1 imagery, this study leverages results from interferometric synthetic aperture radar (InSAR) processing to reveal the distribution of deformation across Gisborne’s steepland periphery from January 2016 to December 2021. Velocities in the line of sight were obtained from the stack of interferograms and projected along the direction of maximum slope, to extract the true displacement on the slopes. The ascending and descending data sets were combined to reveal the vertical and horizontal components of the deformation. The results were combined with a regional LiDAR data set, aerial imagery and field observations to delineate areas of slope deformation. Finally, slope deformation time series data was compared with rainfall records to identify seasonal changes, due to shrinking and swelling of expansive soils. Although the InSAR displacement data contains some noise, results could be used to identify 132 unstable slopes within the study area, caused by soil creep and earthflows. Also, the shrink-swell of expansive soils causes a seasonal pattern of displacements, which varied by 10–20 mm/year between Austral winter and summer, strongly correlated to rainfall.

The reconnaissance, mapping and analysis of kinematic features of slow-moving landslides evolving along medium-deep sliding surfaces in urban areas can be a difficult task due to the presence and interactions of/with anthropic structures/infrastructures and human activities that can conceal morphological signs of landslide activity. The paper presents an integrated approach to investigate the boundaries, type of movement, kinematics and interactions (in terms of damage severity distribution) with the built environment of a roto-translational slow-moving landslide affecting the historic centre of Lungro town (Calabria region, southern Italy). For this purpose, ancillary multi-source data (e.g. geological-geomorphological features and geotechnical properties of geomaterials), both conventional inclinometer monitoring and innovative non-invasive remote sensing (i.e. A-DInSAR) displacement data were jointly analyzed and interpreted to derive the A-DInSAR-geotechnical velocity (DGV) map of the landslide. This result was then cross-compared with detailed information available on the visible effects (i.e. crack pattern and width) on the exposed buildings along with possible conditioning factors to displacement evolution (i.e. remedial works, sub-services, etc.). The full integration of multi-source data available at the slope scale, by maximizing each contribution, provided a comprehensive outline of kinematic-geometric landslide features that were used to investigate the damage distribution and to detect, if any, anomalous locations of damage severity and relative possible causes. This knowledge can be used to manage landslide risk in the short term and, in particular, is propaedeutic to set up an advanced coupled geotechnical-structural model to simulate both the landslide displacements and the behavior of interacting buildings and, therefore, to implement appropriate risk mitigation strategies over medium/long period.

Landslides represent a serious worldwide hazard, especially in Italy, where exposure to hydrogeological risk is very high; for this reason, a landslide quantitative risk assessment (QRA) is crucial for risk management and for planning mitigation measures. In this study, we present and describe a novel methodological approach of QRA for slow-moving landslides, aiming at national replicability. This procedure has been applied at the basin scale in the Arno River basin (9100 km2, Central Italy), where most landslides are slow-moving. QRA is based on the application of the equation risk = hazard (H) × vulnerability (V) × exposure (E) and on the use of open data with uniform characteristics at the national scale. The study area was divided into a grid with a 1 km2 cell size, and for each cell, the parameters necessary for the risk assessment were calculated. The obtained results show that the total risk of the study area amounts to approximately 7 billion €. The proposed methodology presents several novelties in the risk assessment for the regional/national scale of the analysis, mainly concerning the identification of the datasets and the development of new methodologies that could be applicable over such large areas. The present work demonstrates the feasibility of the methodology and discusses the obtained results.