Climate change is bringing hotter, drier summers and warmer, wetter winters, intensifying winter floods and causing larger seasonal variations in soil moisture. These shifts place increasing stress on levees – many of which were constructed decades or centuries ago – making their current performance challenging to assess. Levee performance depends on limiting water ingress, as increased seepage can trigger piping and slope failure, potentially leading to catastrophic breaches. Hydraulic conductivity strongly controls water ingress, but is challenging to measure directly and can change as the materials deteriorate. To investigate how environmental loading affects levee performance, a clay levee in northern England was monitored for four years every 48 hours using electrical resistivity tomography (ERT) – a geophysical technique sensitive to fluid changes – alongside environmental sensors (soil moisture, river stage, and meteorological data). Two zones showed clear responses to climatic and hydrological forcing: the foundation strata, where groundwater levels rose with river levels, and the active layer (upper ~1.5 m), which seasonally dried in summer and rewetted in winter. These zones may therefore be particularly vulnerable to future climatic extremes and flood events. Analysis of annual drying-front depths indicates that average summer desiccation depths could increase from ~0.6 m to >1 m in northern England by the late 21st century (UKCP18 data, RCP8.5). This enhanced desiccation may reduce their performance, especially in early autumn when open shrinkage cracks can act as seepage pathways during subsequent floods. Forward planning using geophysical methods will help guide remediation and mitigate the effects of potential failure in levees.

Moisture induced landslides in clay slopes are generally driven by heterogeneity in both saturation levels and material properties and their arising complex and dynamic interactions in the subsurface. The use of time-lapse geophysical imaging can illuminate four-dimensional subsurface moisture dynamics and geotechnical property changes at the slope-scale, thereby complementing conventional geotechnical point sampling and sensing, and geodetic observations of the ground surface. Here we consider: (1) the development of novel time-lapse geoelectrical, seismic and fibre-optic geophysical imaging technologies for landslide monitoring; (2) in-situ and laboratory derived petrophysical relationships to enable geotechnical information to be estimated from geophysical models; (3) surface topography determination and ground deformation tracking using geodetic observations; (4) coupled geophysical-hydrological modelling of slopes; (5) perspectives and recommendations for the incorporation of integrated geophysical-geodetic-geotechnical technologies into landslide early warning systems – illustrated using results from a number of long-term field observatories.

Disused coal tips formed by waste materials from coal mining activities can become unstable over time. Landslides or avalanches of coal waste can occur, especially during heavy rainfall or by other environmental factors, leading to significant safety hazards for nearby residents and infrastructure. The Welsh Government Coal Tip Safety Taskforce has recently identified over 2,500 disused coal tips in Wales potentially posing a risk, following a significant landslip in Tylorstown after the 2020 storms. Ongoing climate change further destabilises these legacies of past mining activities, posing great challenges to land management and hazard remediation, as instability within the coal tip can be invisible to surface surveys and inspections.Wattstown in the Rhondda Cynon Taf County Borough was identified as a preferred location for deploying long-term 4D geoelectrical monitoring, with the aim of observing the moisture dynamics between a heavily vegetated basin area upslope of the coal tip (where a previous landslip has occurred) and the downslope tip materials. A BGS-designed Proactive Infrastructure Monitoring & Evaluation (PRIME) system has been deployed here to characterise this site using eight 32-electrode arrays. PRIME is a low-cost, low-power, non-invasive 4D geo-electrical imaging technology designed for near-real-time infrastructure monitoring. The eight ERT sensor arrays are arranged so that four arrays form two long 2D survey lines to monitor the main slope in directions perpendicular to each other, while a further five arrays cover the landslip region in a 3D configuration, in which one of the arrays is common between the linear and the grid configuration. A full daily measurement schedule allowing for ground motion tracking has been implemented since Mid-2023. Measured data is transferred daily to the BGS servers, and system diagnostics reports are automatically generated to confirm the recent monitoring status and performance of the PRIME system.The baseline resistivity model shows a lower resistivity layer with a variable thickness of 0 - 5 m covering the whole monitored area. This layer is interpreted as spoils that have been deposited and subsequently reprofiled. Our observation also matches with the presence of high clay contents found in the hand-augered soil. Below the reprofiled spoils resistivity values increase significantly, likely to be underlying bedrock that is composed of sandstone with interbedded layers of coal and silt.Time-lapse inversion revealed the influence of effective precipitation on the moisture dynamics of the coal tip. Several anomalies were observed within the gradually decreasing resistivity distribution in the near-surface. Along the line perpendicular to the slope, larger low-resistivity features are observed in both the ditches that run parallel to the slope. This could be the result of preferential infiltration in these areas and the ponding of surface water. In the rotational landslip area, PRIME monitoring data has identified what is potentially a preferential flow path from 5 m to 10 m below ground level.Through continuous monitoring of the disused coal tip, the PRIME system demonstrated its capability for enhanced coal tip assessment, detecting critical hydrogeological processes through minimally-invasive subsurface imaging. Ongoing work aims to establish in-situ petrophysical relationships.

Robust and timely assessment of the condition of geotechnical infrastructure assets (e.g. cuttings, embankments, dams) is essential for cost effective maintenance and engineering interventions to prevent failure events. Infrastructure slopes (in transportation, utilities and water management) are experiencing increasingly high levels of failure and require considerable resources to maintain; in the order of hundreds of millions of pounds per year in the UK alone. The issue of accelerating asset deterioration is being exacerbated by the greater prevalence of extreme weather events. Conventional monitoring techniques are still dominated by surface observations, which provide infrequent information and deliver very few insights into subsurface deterioration processes which typically precede surface expressions of deterioration. Here we describe the development of novel geoelectrical imaging technology to monitor and assess the internal condition of infrastructure slopes in four-dimensions. In particular, we outline a workflow in which time-lapse geophysical models are used to inform estimates of soil moisture and suction distributions, and we consider the challenges associated with the deployment of geophysical monitoring systems on operational geotechnical assets. Examples are given from long-term field experiments on transportation and water management earthworks. We propose that novel geophysical monitoring complements more traditional forms of asset assessment to significantly enhance the resilience of safety critical infrastructure through improved subsurface information provision and decision support.

Understanding the geological and hydrological conditions present within an unstable slope is crucial for assessing the likelihood of failure. Recently, geoelectrical characterization and monitoring of landslides has become increasingly prevalent in this context, due to the spatial sensitivity of electrical methods to critical hydro-mechanical parameters. We explore a situational relationship between resistivity and matric potential (or negative pore pressure), which is a key parameter in estimating the resistance to shear in geological materials, and gravimetric moisture content (GMC). We have chosen a well-characterized active landslide instrumented with geoelectrical monitoring technology, the Hollin Hill Landslide Observatory, situated in Lias rocks in the southern Howardian Hills, United Kingdom. We report on petrophysical relationships between porosity, GMC, electrical resistivity, and matric potential. We trial the application of these petrophysical relationships to inverted resistivity images. Ground model development is achieved through a mixture of clustering resistivity distributions and analysis of surface movements. Our findings show the shrink swell properties of clay result in a variable porosity, which is problematic for applying classic petrophysical relationships documented in the literature. Moreover, directly translating resistivity distributions into matric potential has additional challenges. Nonetheless, volumetric imaging of resistivity suggest that low shear strengths are concentrated downslope of a rotational backscarp. We infer that an accumulation of moisture drives the development of a slip surface at depth, which subsequently manifests in failure at the ground surface. We conclude that the time-lapse resistivity images alone could not be used to infer the pore pressure conditions present within the slope without development of the petrophysical relationships shown here. Therefore, we suggest that the results have practical implications for landslide monitoring with geophysical methods.

Electrical resistivity tomography (ERT), a geophysical imaging method, is commonly used on flood embankments (dykes or levees) to characterize their internal structure and look for defects. These surveys often use a single line of electrodes to enable 2-D imaging through the embankment crest, an approach that enables rapid and efficient surveying compared to 3-D surveys. However, offline variations in topography can introduce artefacts into these 2-D images, by affecting the measured resistivity data. Such topographic effects have only been explored on a site-specific basis. If the topographic effects can be assessed for a distribution of embankment geometries (e.g. slope angle and crest width) and resistivity variations, it would allow for targeted correction procedures and improved survey design. To investigate topographic effects on ERT measurements, we forward-modelled embankments with different trapezoidal cross-sections sat atop a flat foundation layer with contrasting resistivity values. Each was compared to a corresponding flat model with the same vertical resistivity distribution. The modelling workflow was designed to minimize the effect of forward modelling errors on the calculation of topographic effect. We ran 1872 unique embankment forward models, representing 144 geometries, each with 13 different resistivity contrasts. Modelling results show that offline topography affects the tested array types (Wenner-Schlumberger, Dipole-Dipole and Multiple-Gradient) in slightly different ways, but the magnitudes are similar, so all are equally suitable for embankment surveys. Three separate mechanisms are found to cause topographic effects. The dominant mechanism is caused by the offline topography confining the electrical current flow, increasing the measured transfer resistance from the embankment model. The two other mechanisms, previously unidentified, decrease the measured transfer resistances from the embankment model compared to a layered half-space but only affect embankments with specific geometries and resistivity distributions. Overall, we found that for typical embankment geometries and resistivity distributions, the resistivity distribution has a greater control on the magnitude of the topographic effect than the exact embankment geometry: the subsurface resistivity distribution cannot be neglected. 2-D inversions are suitable when both the embankment is more resistive than the foundations and when the embankment's cross-sectional area is greater than 4 m2 m-2 (area scaled to an embankment with a height of 1 m). Topographic corrections, 3-D data acquisition or 3-D forward models are required when these conditions are not met. These are demonstrated using field data from an embankment at Hexham, Northumberland, UK. Improving the accuracy of the resistivity values in ERT models will enable more accurate ground models, better integration of resistivity data with geotechnical data sets, and will improve the translation of resistivity values into geotechnical properties. Such developments will contribute to a better characterized and safer flood defence network.

Slope failures are an ongoing global threat leading to significant numbers of fatalities and infrastructure damage. Landslide impact on communities can be reduced using efficient early warning systems to plan mitigation measures and protect elements at risk. This manuscript presents an innovative geophysical approach to monitoring landslide dynamics, which combines Electrical Resistivity Tomography (ERT) and low-frequency Distributed Acoustic Sensing (DAS), and was deployed on a slope representative of many landslides in clay rich lowland slopes. ERT is used to create detailed, dynamic moisture maps that highlight zones of moisture accumulation leading to slope instability. The link between ERT derived soil moisture and the subsequent initiation of slope deformation is confirmed by low-frequency DAS measurements, which were collocated with the ERT measurements and provide changes in strain at unprecedented spatiotemporal resolution. Auxiliary hydrological and slope displacement data support the geophysical interpretation. By revealing critical zones prone to failure, this combined ERT and DAS monitoring approach sheds new light on landslide mechanisms. This study demonstrates the advantage of including subsurface geophysical monitoring techniques to improve landslide early warning approaches, and highlights the importance of relying on observations from different sources to build effective landslide risk management strategies.

Vegetation is important for managing shallow geotechnical assets. However, root water uptake-driven changes in slope hydrology and the near-surface (soil water content, matric suction, and hydraulic conductivity) are highly complex. Improved knowledge of these processes is increasingly important as society faces the threat of a greater prevalence of climate-driven extreme rainfall and drought events. Intrinsic factors affect slope stability, including geometry, soil properties, groundwater, and vegetation-driven matric suction. Field evidence shows that engineered slopes are susceptible to hydrometeorological instability mechanisms and pose a potential failure hazard to asset operation and public safety. This study considers the combination of a novel geophysical monitoring system and geotechnical point sensors for use in controlled laboratory conditions to assess the influence of vegetation on soil-water dynamics in the context of geotechnical infrastructure. The geophysical monitoring system, referred to here as PRIME (Proactive Infrastructure Monitoring and Evaluation system), uses electrical resistivity tomography (ERT) technology to non-invasively image changing subsurface moisture-driven processes. The PRIME system and point sensor arrays are being developed for near real-time data acquisition of transient soil moisture conditions in a suite of soil column experiments. Through addressing the challenges associated with designing integrated geophysical-geotechnical laboratory-scale monitoring experiments, this research aims to provide new tools and approaches to further our understanding of vegetation-driven soil moisture movement to better assess slope instability risk.

Globally, earth embankments are used to protect against flooding. Raised above the surrounding water table, these embankments make ideal habitats for many burrowing animals whose burrows can impact their structural integrity. Ground Penetrating Radar (GPR) is commonly used to identify and map animal burrows and other small cavities. However, the depth of investigation of a GPR survey can be severely limited in saline and clay-rich environments, soil properties commonly associated with flood embankments. In contrast, Electrical Resistivity Tomography (ERT) can image subsurface voids in conductive ground conditions but has been rarely used to image animal burrows. Here we aim to assess the efficacy of ERT and GPR to image two badger burrow networks, called 'setts', located in clay embankments on the River Ouse, Yorkshire, UK. The two setts were excavated to validate the geophysical results, and the soil was characterised through logging and geotechnical analysis to develop a ground model of the site. We find that ERT can accurately resolve tunnels down to 1.5 m depth, map the structure of a multi-entrance badger sett and successfully identify the end of the tunnels. This result compares favourably to the GPR surveys, which mapped all but the deepest tunnels, limited by its penetration depth due to clay soils. Our results show that ERT can be used as a primary survey tool for animal burrows in clay-rich en-vironments and can be validated using co-located GPR surveys if penetration depth is sufficient. The implications of this study may allow embankment managers to map burrow networks, assess flood embankment stability, minimise repair costs, and reduce unexpected failures during flood events. Additionally, a better understanding of how, for example, local heterogeneities impact badgers' burrow geometry may be achievable using these geophysical methods, as they provide a non-destructive, repeatable method for imaging setts.

Flood embankments (levees or dykes) are used worldwide to protect homes, industry and farmland from flooding caused by extreme weather events and tidal surges. Their role is becoming increasingly important for two key reasons: climate change is causing larger and more frequent floods, and the number of people living on floodplains is increasing globally. Both these factors necessitate that flood defences are well maintained to minimise failure during flood events, and reduce disruption, damage, and even loss of life. There are more than 10,000 km of flood embankments in UK alone, so condition monitoring must be rapid. Current monitoring relies on qualitative walkover surveys every 6-12 months, but this can only detect the surface features that form in response to subsurface processes or characteristics. If we could detect subsurface properties and deterioration features directly it would enable us to identify areas at risk significantly earlier, minimising both risk and mitigation costs. Two complementary geophysical methods stand out: Electrical Resistivity Tomography (ERT) and Multi Channel Analysis of Surface Waves (MASW). These are sensitive to different hydro-mechanical properties of the materials that make up flood embankments and their foundations. ERT is sensitive to moisture content, clay content and porosity, whereas MASW is sensitive to elastic properties controlled by material strength, density, porosity and saturation. In this work we combine co-located ERT and MASW surveys with time-lapse airborne lidar on three contrasting embankments on the River Thames, River South Tyne, and the Humber Estuary. Each site was selected based on known anomalies or the availability of existing geotechnical information to ground truth the geophysical measurements. The three embankments represent a range of different soil types, ages and varying foundation materials, making an ideal suite of targets to test the different geophysical methods. In total c. 1 km of embankment was surveyed. Preliminary analysis shows good spatial agreement between units imaged by the ERT and those identified in the borehole data for each site. Areas of greatest settlement identified using time-lapse lidar also correlate with low resistivity anomalies indicating areas of soft clay and peat. Further data analysis will incorporate the MASW results and use clustering to quantitatively divide the subsurface into units with similar electrical and seismic properties. Geotechnical properties will then be attributed to each of the clusters, allowing more accurate fragility analysis of the embankment during flood conditions to be conducted.

Landslides are complex geological hazards that affect all globally settled areas; hence the necessity to understand this hazard for purposes of studying failure mechanisms and managing risk levels. Numerous methods have been explored for characterising the geomorphology and geology of active landslides. In this study we characterise and monitor a well understood field site, Hollin Hill (situated in Lias Group rocks in the southern Howardian Hills, UK), using geomatics (UAV and LiDAR surveys), near-surface geophysics and petrophysical relationships. Time-lapse electrical resistivity tomography (ERT) is an effective tool for monitoring hydrological processes, given that the Hollin Hill landslide is moisture-induced, the field site is instrumented with a permanent (shallow buried) 3D ERT monitoring array. However, monitoring active landslides poses specific challenges regarding time-lapse geophysical methods as the surface topography is distorted with slope movements, which in this case are expressed as centimetre to metre scale lateral and vertical movements that complicate time-lapse resistivity processing. To compensate for the changing slope topography, we incorporate terrestrial LiDAR and aerial photogrammetry surveys to capture the changing slope surface through time. Additionally, lateral movements are periodically recorded with RTK corrected GNSS surveys. For each geophysical survey the topography and positions of the electrodes are interpolated using thin plate splines, and a modelling mesh with unique surface topography is created for each time step in the time-lapse ERT scheme (which uses a baseline constraint). Hence, we develop a time-lapse geophysical model spanning approximately 8 years, which captures both changes in the electrical properties of Hollin Hill and the slope’s geomorphology. To further understand the hydrological state of the landslide, we observe a direct relationship between electrical conductivity (the inverse of resistivity), gravimetric moisture content and soil suction for the relevant lithologies present at Hollin Hill. The resistivity models are partitioned into different lithologies using k-means clustering, and subsequently resistivity is converted to matric suction via a petrophysical relationship. Areas of consistently low resistivity, and by extension high moisture content and low suction, correspond to areas on the landslide which exhibit the most movement. Furthermore, the movements of electrodes are used to estimate the depth of the landslide surface via the balanced cross section method (after Bishop). Low soil suctions occur at the location of the likely slip surface, thus offering insights into the failure mechanisms occurring at the Hollin Hill landslide. This suggests that a combination of the techniques demonstrated in this study could be used to assess active landslide dynamics and hence improve our capacity to forecast movements on unstable slopes.

With the advances of ESA’s Sentinel-1 InSAR (Interferometric Synthetic Aperture Radar) mission, there are freely available remote sensing ground deformation observations all over the globe that allow continuous monitoring of natural hazards and structural instabilities. The Digital Environment initiative in the UK aims to include these remote sensing data in the effort at forecasting and mitigating hazards across the UK. In this paper, we present a case study of the Hollin Hill landslide in North Yorkshire where a variety of ground-based geophysical measurements are available for comparison with InSAR data. To include Sentinel-1 data in the UK’s Digital Environment, it is important to understand the advantages and limitations of these observations and interpret them appropriately. The Hollin Hill landslide observatory (HHLO) is used by the British Geological Survey to understand landslide processes, and to trial new technologies and methodologies for slope stability characterisation and monitoring. In July 2019, six corner reflectors were installed to improve the coherence of the InSAR measurements. We use Sentinel-1 InSAR data acquired between October 2015 and January 2019 to study the behaviour of this landslide, and find that the line-of-sight component of the down-slope movement is 2.7 mm/year in the descending track, and 7.5–7.7 mm/year in the ascending track. The InSAR measurements also highlight the seasonal behaviour of this landslide. Using InSAR data after the installation of the six corner reflectors, we are able to track the most recent movement on the landslide that occurred in January 2021. This result is in agreement with other ground-based measurements such as tracking of pegs, and soil moisture data derived from electrical resistivity tomography.

Developments in time-lapse electrical resistivity tomography (ERT) technology are transforming our ability to monitor the subsurface due to purpose-built monitoring instruments, advances in automation and modeling, and the resulting improvements in spatial and temporal resolution. We describe the development of a novel ERT-based remote monitoring system called PRIME that integrates new low-power measurement instrumentation with data delivery, automated data processing and image generation, and web-based information delivery. Due to the sensitivity of ERT to hydrologic processes in the near surface, we focus on the application of PRIME for moisture-driven landslide monitoring. Case examples are considered of landslides in engineered and natural slopes, including those impacting geotechnical assets in rail and highways, where slope hydrology is seen to be controlled by lithology, vegetation, fissuring, and drainage structures. We conclude by taking a forward look at emerging developments in ERT monitoring relating to hardware, software and modeling, and applications.

Landslides in the Thompson River Valley, British Columbia, Canada, threaten the serviceability of two railway lines that connect Vancouver to the rest of Canada and the US. To minimise the impact of slope instability on vital transport infrastructure, as well as on terrestrial and aquatic ecosystems, public safety, communities, local heritage, and the economy, and to better inform decision making, there is a need for monitoring. Since 2013, the Ripley Landslide – a small, slow-moving, translational landslide – has been the focus of monitoring efforts in the Thompson River Valley transportation corridor. In November 2017, a novel Electrical Resistivity Tomography (ERT) monitoring system was installed on the site, providing near-real-time data collection via a telemetric link. 4-Dimensional resistivity models are presented in the context of moisture content and soil suction, two parameters known to influence slope stability in the Thompson River Valley. Here, we discuss the development of laboratory-based petrophysical relationships that relate electrical resistivity to moisture content and soil suction directly, building on relationships developed in the field. The 4-D ERT models were calibrated using these petrophysical relationships to provide insights into the complex spatial and temporal variations in moisture content and soil suction. This study highlights the utility of geoelectrical monitoring for assessing slope stability in the context of moisture-driven landslides.

Landslides display heterogeneity in movement types and rates, ranging from creeping motion to catastrophic acceleration. In most of the catastrophic events, rocks, debris, or soil can travel at several tens of meters per year speed, causing significant cost in life losses, infrastructure, economy, and ecosystem of the region. In contrast, slow-moving landslides display typical velocities scaling from few centimeters to several meters per year. Although slow-moving landslides rarely claim life losses, they can still cause considerable damage to public and private infrastructure. Sometimes these slow, persistent landslides eventually lead to catastrophic acceleration, e.g., clayey landslides are prone to these transitions. Such events need to be detected by Early Warning Systems (EWS) in advance to take timely actions to reduce life and economic losses. Several approaches are proposed to forecast the time of failure; still, there is a need to improve prediction strategies and EWS’s. Here we present state and parameter estimation for a simplified viscoplastic sliding model of a landslide using a Kalman filter approach, which is termed as an observer problem in control theory. The model under investigation is based on underlying mechanics (physics-based model) that portray a landslide behavior. In this model, a slide block is assumed to be placed on an inclined surface, where landslide (slide block) motion is regulated by basal pore fluid pressure and opposed by sliding resistance governed by friction, cohesion, and viscosity. This model is described by an Ordinary Differential Equation (ODE) with displacement as a state and landslide material and geometrical properties as parameters. In this approach, known parameter values (landslide geometrical parameters and some material properties) and water table height time-series are provided as input. Finally, two illustrative examples validate the presented approach: i) a synthetic case study and ii) Hollin hill landslide (Uhlemann et al., 2016) field data. In both examples, displacement, friction angle, and viscosity are well estimated from known parameter values, water table height time-series, and displacement measurements. In the simulation results for the Hollin Hill field data, it is observed that friction angle almost remains constant while viscosity varies significantly through time. Uhlemann, S., Smith, A., Chambers, J., Dixon, N., Dijkstra, T., Haslam, E., Meldrum P., Merritt, A., Gunn, D., and Mackay, J., (2016). Assessment of ground-based monitoring techniques applied to landslide investigations. Geomorphology, 253, 438-451. doi:10.1016/j.geomorph.2015.10.027.

ABSTRACT The Ripley Landslide is a small (0.04 km 2 ), slow‐moving landslide in the Thompson River Valley, British Columbia, that is threatening the serviceability of two national railway lines. Slope failures in this area are having negative impacts on railway infrastructure, terrestrial and aquatic ecosystems, public safety, communities, local heritage and the economy. This is driving the need for monitoring at the site, and in recent years there has been a shift from traditional geotechnical surveys and visual inspections for monitoring infrastructure assets toward less invasive, lower cost, and less time‐intensive methods, including geophysics. We describe the application of a novel electrical resistivity tomography system for monitoring the landslide. The system provides near‐real time geoelectrical imaging, with results delivered remotely via a modem, avoiding the need for costly repeat field visits, and enabling near‐real time interpretation of the four‐dimensional electrical resistivity tomography data. Here, we present the results of the electrical resistivity tomography monitoring alongside field sensor‐derived relationships between suction, resistivity, moisture content and continuous monitoring single‐frequency Global Navigation Satellite System stations. Four‐dimensional electrical resistivity tomography data allows us to monitor spatial and temporal changes in resistivity, and by extension, in moisture content and soil suction. The models reveal complex hydrogeological pathways, as well as considerable seasonal variation in the response of the subsurface to changing weather conditions, which cannot be predicted through interrogation of weather and sensor data alone, providing new insight into the subsurface processes active at the site of the Ripley Landslide.