Optimising subsurface use for future cities

The subsurface is a dynamic environmental system influenced by the surface through the interaction of heat, water, chemical and biological phenomena and physical stresses. The urban environment modifies the natural link between the surface and the subsurface by interacting and changing the surface drivers or by directly changing the structure of the subsurface. Similar to the concept of ‘ecosystem services’ (see Ehrlich and Ehrlich, 1981) the urban subsurface may be considered as a resource that can provide several services (Bobylev, 2009). Although we consider the urban subsurface as a single resource, it may be subdivided into four resources relating to: construction space, geo-materials, groundwater and geothermal (Parriaux, 2007). It has long been recognised that the urban subsurface is a complex, scarce and valuable resource

A solution (data architecture) for handling time-series data - sensor data (4D), its visualisation and the questions around uncertainty of this data

Geo-environmental research is increasingly in the age of data-driven research. It has become necessary to collect, store, integrate and visualise more subsurface data for environmental research. The information required to facilitate data-driven research is often characterised by its variability, volume, complexity and frequency. This has necessitated the development of suitable data workflows, hybrid data architectures, and multiple visualisation solutions to provide the proper context to scientists and to enable their understanding of the different trends that the data displays for their many scientific interpolations. However this data, predominantly time-series (4D) acquired through sensors and being mostly telemetered, poses significant challenges/questions in quantifying the uncertainty of the data. To validate the research answers including the data methodologies, the following open questions around uncertainty will need addressing, i.e. uncertainty generated from: • the instruments used for data capture; • the transfer process of the data often from remote locations through telemetry; • the data processing techniques used for harmonising and integration from multiple sensor outlets; • the approximations applied to visualize such data from various conversion factors to include units standardisation The main question remains: How do we deal with the issues around uncertainty when it comes to the large and variable amounts of time-series data we collect, harmonise and visualise for the data-driven geo-environmental research that we undertake today

Probabilistic estimates of climate change impacts on UK water resources

Climate change will increase temperatures and change rainfall across the UK. In turn, this will modify patterns of river flow and groundwater recharge, affecting the availability of water. There have been many studies of the impact of climate change on river flows in the UK, but coverage has been uneven and methods have varied. Consequently, it has been very difficult to compare different locations and hard to identify appropriate adaptation responses

Simulating the mesoscale impacts of sea wall defences on coastal morphology

Solid coastal defences are deployed in many countries to halt or slow coastal erosion. Although the impacts on local sediment fluxes have been studied in detail, the non-local impact of a modified sediment flux regime on mesoscale coastal morphology has received less attention. Morphological changes imparted by defensive structures at these scales (decadal processes over tens of kilometres) can be difficult to quantify or even identify with field data. Difficulties in assessing the impact of these structures arise in the separation of natural and anthropogenic influences, both of which can be highly dynamic and non-linear. Numerical modelling allows these influences to be separated and the impacts of coastal defensive structures to be assessed. We extend previous work (Barkwith et al., 2013) to explore the influences of sea walls on the evolution and morphological sensitivity of a pinned, soft-cliff, sandy coastline under a changing wave climate. The Holderness coast of East Yorkshire, UK, is one of the fastest eroding coastlines in Europe and is used as a case study for this research. Using a mesoscale numerical coastal evolution model, stochastic wave climate data are perturbed gradually to assess the sensitivity of the coastal morphology to changing wave climate for both the defended and natural scenarios. Comparative analysis of the simulated output suggests that sea walls in the south of the region have a greater impact on sediment flux due to the increased sediment availability along this part of the coast. Multiple defended structures, including those separated by several kilometres, were found to interact with each other, producing a complex imprint on coastal morphology under a changing wave climate. Although spatially and temporally heterogeneous, sea walls generally slowed coastal recession and accumulated sediment on their up-drift side

The effectiveness of beach mega-nourishment, assessed over three management epochs

Resilient coastal protection requires adaptive management strategies that build with nature to maintain long-term sustainability. With increasing pressures on shorelines from urbanisation, industrial growth, sea-level rise and changing storm climates soft approaches to coastal management are implemented to support natural habitats and maintain healthy coastal ecosystems. The impact of a beach mega-nourishment along a frontage of interactive natural and engineered systems that incorporate soft and hard defences is explored. A coastal evolution model is applied to simulate the impact of different hypothetical mega-nourishment interventions to assess their impacts’ over 3 shoreline management planning epochs: present-day (0–20 years), medium-term (20–50 years) and long-term (50–100 years). The impacts of the smaller interventions when appropriately positioned are found to be as effective as larger schemes, thus making them more cost-effective for present-day management. Over time the benefit from larger interventions becomes more noticeable, with multi-location schemes requiring a smaller initial nourishment to achieve at least the same benefit as that of a single-location scheme. While the longer-term impact of larger schemes reduces erosion across a frontage the short-term impact down drift of the scheme can lead to an increase in erosion as the natural sediment drift becomes interrupted. This research presents a transferable modelling tool to assess the impact of nourishment schemes for a variety of sedimentary shorelines and highlights both the positive and negative impact of beach mega-nourishment

Complex coastlines responding to climate change: do shoreline shapes reflect present forcing or “remember” the distant past?

A range of planform morphologies emerge along sandy coastlines as a function of offshore wave climate. It has been implicitly assumed that the morphological response time is rapid compared to the timescales of wave climate change, meaning that coastal morphologies simply reflect the extant wave climate. This assumption has been explored by focussing on the response of two distinctive morphological coastlines – flying spits and cuspate capes – to changing wave climates, using a coastline evolution model. Results indicate that antecedent conditions are important in determining the evolution of morphologies, and that sandy coastlines can demonstrate hysteresis behaviour. In particular, antecedent morphology is particularly important in the evolution of flying spits, with characteristic timescales of morphological adjustment on the order of centuries for large spits. Characteristic timescales vary with the square of aspect ratios of capes and spits; for spits, these timescales are an order of magnitude longer than for capes (centuries vs. decades). When wave climates change more slowly than the relevant characteristic timescales, coastlines are able to adjust in a quasi-equilibrium manner. Our results have important implications for the management of sandy coastlines where decisions may be implicitly and incorrectly based on the assumption that present-day coastlines are in equilibrium with current conditions

Exploring the sensitivities of crenulate-bay shorelines to wave climates using a new vector-based one-line model

We use a new exploratory model that simulates the evolution of sandy coastlines over decadal to centennial timescales to examine the behavior of crenulate-shaped bays forced by differing directional wave climates. The model represents the coastline as a vector in a Cartesian reference frame, and the shoreface evolves relative to its local orientation, allowing simulation of coasts with high planform-curvature. Shoreline change is driven by gradients in alongshore transport following newly developed algorithms that facilitate dealing with high planform-curvature coastlines. We simulated the evolution of bays from a straight coast between two fixed headlands with no external sediment inputs to an equilibrium condition (zero net alongshore sediment flux) under an ensemble of directional wave climate conditions. We find that planform bay relief increases with obliquity of the mean wave direction, and decreases with the spread of wave directions. Varying bay size over 2 orders of magnitude (0.1–16 km), the model predicts bay shape to be independent of bay size. The time taken for modeled bays to attain equilibrium was found to scale with the square of the distance between headlands, so that, all else being equal, small bays are likely to respond to and recover from perturbations more rapidly (over just a few years) compared to large bays (hundreds of years). Empirical expressions predicting bay shape may be misleading if used to predict their behavior over planning timescales

Aeolian sediment reconstructions from the Scottish Outer Hebrides: Late Holocene storminess and the role of the North Atlantic Oscillation

Northern Europe can be strongly influenced by winter storms driven by the North Atlantic Oscillation (NAO), with a positive NAO index associated with greater storminess in northern Europe. However, palaeoclimate reconstructions have suggested that the NAO-storminess relationship observed during the instrumental period is not consistent with the relationship over the last millennium, especially during the Little Ice Age (LIA), when it has been suggested that enhanced storminess occurred during a phase of persistent negative NAO. To assess this relationship over a longer time period, a storminess reconstruction from an NAO-sensitive area (the Outer Hebrides) is compared with Late Holocene NAO reconstructions. The patterns of storminess are inferred from aeolian sand deposits within two ombrotrophic peat bogs, with multiple cores and two locations used to distinguish the storminess signal from intra-site variability and local factors. The results suggest storminess increased after 1000 cal yrs BP, with higher storminess during the Medieval Climate Anomaly (MCA) than the LIA, supporting the hypothesis that the NAO-storminess relationship was consistent with the instrumental period. However the shift from a predominantly negative to positive NAO at c.2000 cal yrs BP preceded the increased storminess by 1000 years. We suggest that the long-term trends in storminess were caused by insolation changes, while oceanic forcing may have influenced millennial variability

Towards integrated flood inundation modelling in groundwater-dominated catchments

Traditionally in flood inundation modelling the contribution of groundwater is either neglected or highly simplified. Long-duration groundwater-induced events, such as those that occur across Chalk catchments of northern Europe, can, however, incur significant economic and social cost. We present a new methodology for integrated flood inundation modelling by coupling the 2D hydrodynamic model LISFLOOD-FP with the 3D finite-difference groundwater model ZOOMQ3D. We apply the model to two adjacent Chalk catchments in southern England, the Lambourn and Pang, over two flooding events, during the winters of 2000/01 and 2013/14. A dense network of monitoring boreholes reveals local-scale heterogeneities in the aquifer not captured by the model. However, we demonstrate through inundation extent and streamflow comparisons that, on a regional scale, groundwater levels are simulated sufficiently well to capture groundwater inundation extent. The role of the unsaturated zone is discussed and contrasted between the two events. Currently, predictive tools to simulate groundwater flood events are limited, and this new, computationally efficient methodology will help to fill this gap

PhiGO seasonal groundwater forecasting system

This report provides an overview of the seasonal (90-day) groundwater level forecasting system for the Philippines that was developed as part of the NERC-Newton fund Philippines Groundwater Outlook (PhiGO) project (NE/S003118/1). The system builds on other operational groundwater level forecasting systems developed at the BGS, such as the UK Hydrological Outlook (Prudhomme et al., 2017). Forecasts are made at the borehole scale across a network of observation boreholes using the BGS AquiMod groundwater model (Mackay et al., 2014a) driven by numerical weather prediction forecasts. However, this system has been customised with the Philippine case study in mind. More specifically, it has been developed to work alongside a telemetered network of boreholes operated by Ateneo de Manila University (ADMU) and owned by the National Water Resources Board (NWRB) under the Groundwater Management Plan (GMP) project. Groundwater level data have only been collected since 2019 and the network is continually expanding. Accordingly, a number of additional features have been included, such as automated recalibration of the AquiMod models as more observation data become available; and the ability to automatically generate new AquiMod groundwater models as the telemetered network expands. Climate observation data are also relatively sparse and, therefore, the system makes use of freely-available global gridded datasets. This report outlines the scientific background and methodology of the forecasting system. It also summarises the principal outputs, which are published online via a free-to-view web-delivery platform (https://mapapps.bgs.ac.uk/phigo/). The report begins with an overview of the process-based BGS AquiMod groundwater model which underpins the groundwater level forecasts as well as the model calibration and evaluation approach used in the forecasting system. Section 3 provides more details of the input data used to drive the forecasts before Section 4 gives an overview of the underlying processes in the forecasting system. Finally, a description of the main system outputs is given in Section 5