Ross Stirling

Groundwater models form the basis for investigating subsurface processes that relate to groundwater flow. Urban cover, however, usually inhibits the collection of new subsurface or geological data. Therefore, models usually depend on existing, poor-quality, or scarce datasets. The geological domain is an integral part of any groundwater model, and as such, understanding the model’s sensitivity to the geological interpretation is key to constraining uncertainty. This research uses a recent advancement in mitigating uncertainty in geological modeling to investigate how different geological interpretations affect groundwater model uncertainty. Using the Ouseburn catchment, Newcastle upon Tyne, UK, as a case study, it estimates baseflows and uses them to develop an ensemble of coupled distributed groundwater recharge and groundwater flow models using SWAc and MODFLOW, and performs a Monte Carlo analysis on the different model formulations. Results indicate that even though river baseflows are not highly affected, there is a connection between simulated groundwater level sensitivity and areas of high geological uncertainty. As the interest in the urban subsurface grows, constraining uncertainty in groundwater models is especially important for urban planning, policy making, water resources, and groundwater flooding protection. Therefore, constraining uncertainty from geological datasets is key to robust groundwater modeling.

Geological models form the basis for scientific investigations of both the surface and subsurface of urban environments. Urban cover, however, usually prohibits the collection of new subsurface data. Therefore, models depend on existing subsurface datasets that are often of poor quality and have an uneven spatial and temporal distribution, introducing significant uncertainty. This research proposes a novel method to mitigate uncertainty caused by clusters of uncertain data points in kriging-based geological modelling. This method estimates orientations from clusters of uncertain data and randomly selects points for geological interpolation. Unlike other approaches, it relies on the spatial distribution of the data and translating geological information from points to geological orientations. This research also compares the proposed approach to locally changing the accuracy of the interpolator through data-informed local smoothing. Using the Ouseburn catchment, Newcastle upon Tyne, UK, as a case study, results indicate good correlation between both approaches and known conditions, as well as improved performance of the proposed methodology in model validation. Findings highlight a trade-off between model uncertainty and model precision when using highly uncertain datasets. As urban planning, water resources, and energy analyses rely on a robust geological interpretation, the modelling objective ultimately guides the best modelling approach.

This review examines the application of the geophysical methods for Transportation Infrastructure Slope Monitoring (TISM). In contrast to existing works, which address geophysical methods for natural landslide monitoring, this study focuses on their application to infrastructure assets. It addresses the key aspects regarding the geophysical methods most employed, the subsurface properties revealed, and the design of monitoring systems, including sensor deployment. It evaluates the benefits and challenges associated with each geophysical approach, explores the potential for integrating geophysical techniques with other methods, and identifies the emerging technologies. Geophysical techniques such as Electrical Resistivity Tomography (ERT), Multichannel Analysis of Surface Waves (MASW), and Fiber Optic Cable (FOC) have proven effective in monitoring slope stability and detecting subsurface features, including soil moisture dynamics, slip surfaces, and material heterogeneity. Both temporary and permanent monitoring setups have been used, with increasing interest in real-time monitoring solutions. The integration of advanced technologies like Distributed Acoustic Sensing (DAS), UAV-mounted sensors, and artificial intelligence (AI) promises to enhance the resolution, accessibility, and predictive capabilities of slope monitoring systems. The review concludes with recommendations for future research, emphasizing the need for integrated monitoring frameworks that combine geophysical data with real-time analysis to improve the safety and efficiency of transportation infrastructure management.

Growing media in green infrastructure (GI) designed for stormwater management plays a critical role in providing hydrological benefits. However, sediment deposition by urban stormwater can cause clogging and reduce the infiltration capacity. This study introduces a new approach to characterise the impact of clogging of GI that uses fluorescent tracer particles. Results are compared for two contrasting growing media: Grey to Green Substrate (G2G) and Marie Curie Substrate (MCS). Results showed that most sediment particles were retained on the surface of both growing media, and surface clogging in G2G caused a decrease in infiltration capacity. Sediment vertical movement was observed in both growing media, but particles travelled deeper in MCS. Vertical sediment movement is influenced by the growing media’s pore size distribution, and a high clogging risk is expected in growing media with a pore size distribution that is close to the particle size distribution of the incoming sediment particles.

Vegetation is an important tool for managing urban surface water and shallow geotechnical assets. However, root water uptake driven changes in slope hydrology (soil water content, matric suction, and hydraulic conductivity) are poorly understood in heterogeneous soils and under extreme climatic conditions. Slope stability is affected by intrinsic factors, including geometry, soil properties, groundwater and vegetation driven matric suction. Field evidence indicates that engineered slopes are susceptible to hydrometeorological slope instability mechanisms and that these pose a potential failure hazard to asset operation and public safety. The UK hosts 15,800 km of railway network and 7100 km of strategic road network, accounting for 49,000 slopes. This is a significant portfolio of slopes that must be managed and maintained at considerable expense.To better understand the influence of vegetation on soil water dynamics in geotechnical infrastructure, Electrical Resistivity Tomography (ERT) is being used. ERT is a non-invasive tool for measuring and imaging subsurface soil moisture dynamics volumetrically. ERT can be used to quantitatively establish how the presence of roots influences transient soil moisture content and suction to assess the effectiveness of vegetation in managing slope hydrology and excess surface water issues in the built environment. This research aims to use 4-D ERT to determine the impact of vegetation on the hydrological behaviour of a high plasticity clay derived sub-soil used in the construction of infrastructure slopes in the southern half of the UK. Laboratory-scale experiments are underway at the UK National Green Infrastructure Facility, Newcastle, using a controlled environment chamber. A suite of soil columns is planted with vegetation, False Oat Grass (Arrhenatherum elatius) and Common Bent (Agrostis capillaris) and feature a 3D ERT electrode array and point sensors for measurement of volumetric water content, matric suction, and electrical conductivity throughout the profile. Through frequent imaging of soil-water-plant interactions and correlation with destructive root architecture imaging, this research aims to highlight how these relationships change over time and respond to extreme weather conditions (drought/inundation) to better predict, manage, and mitigate the occurrence of slope failure. Furthermore, the work aims to improve understanding of vegetation-driven soil moisture movement in the near-surface to better assess seasonal and longer-term slope stability to inform asset management strategies.

Dispersed space heating alone accounts for 40% of UK energy use and 20% of carbon dioxide (CO₂) emissions. Tackling heating and building cooling demands is therefore critical to achieve net-zero ambitions in the UK. The most energy-efficient way to reduce the carbon dioxide emissions of heating and cooling is through the use of ground-source heat pumps and district heating technology. However, capital costs are often high, sometimes prohibitively so. To reduce investment costs, it is proposed to use buried infrastructure as sources and stores of thermal energy. Barriers to this innovative approach include lack of knowledge about the actual net amount of recoverable energy and impacts on the primary function of any buried infrastructure, as well as the need for new investment and governance strategies integrated across the energy and infrastructure sectors. Additional opportunities from thermal utilisation in buried infrastructure include the potential mitigation of damaging biological and/or chemical processes that may occur. This paper presents a first assessment of the scale of the opportunity for thermal energy recovery and storage linked to new and existing buried infrastructure, along with strategic measures to help reduce barriers and start the UK on the journey to achievement of its infrastructure energy potential.