Abstract The Dumfries Basin aquifer supports groundwater abstraction for public supply, agriculture and industry. Abstraction is concentrated in the western part of the basin, where falling groundwater levels and deteriorating water quality both reflect the effects of intense pumping. There are two bedrock units: a predominantly breccia-coarse sandstone sequence in the west, interfingering with a predominantly sandstone sequence in the NE and east. The basin is bounded by weakly permeable Lower Palaeozoic rocks, and is largely concealed by variable superficial deposits. Surface water flows onto the basin from the surrounding catchment via the Nith and the Lochar Water and their respective tributaries. Direct rainfall recharge occurs via superficial sands and gravels, especially in the north, and discharge is predominantly to the rivers in the central area rather than the sea. A picture is developing of two main aquifer types within the basin: the high-transmissivity western sector underlain by a fracture-flow system with younger water and active recharge and a high nitrate content, compared with the east where groundwater residence times are longer and the storage capacity is higher.
Abstract. Groundwater recharge is a key hydrogeological variable that informs the renewability of groundwater resources. Long-term average (LTA) groundwater recharge provides a measure of replenishment under the prevailing climatic and landuse conditions and is therefore of considerable interest in assessing the sustainability of groundwater withdrawals globally. This study builds on the modelling results of MacDonald et al. (2021) who produced the first LTA groundwater recharge map across Africa using a linear mixed model (LMM) rooted in 134 ground-based studies. Here, continent-wide predictions of groundwater recharge were generated using Random Forest (RF) regression employing five variables (precipitation, potential evapotranspiration, soil moisture, NDVI and aridity index) at a higher spatial resolution (0.1° resolution) to explore whether an improved model might be achieved through machine learning. Through the development of a series of RF models, we confirm that a RF model is able to generate maps of higher spatial variability than LMM; the performance of final RF models in terms of the goodness of fit (R2 = 0.83, 0.88 with residual kriging) is comparable to the LMM (R2 = 0.86). The higher spatial scale of the predictor data (0.1°) in RF models better preserves small-scale variability from predictor data, than the values provided via interpolated LMM; these may provide useful in testing global-to-local scale models. The RF model remains, nevertheless, constrained by its representation of focused recharge and by the limited range of recharge studies in tropical Africa, especially in the areas of high precipitation. This confers substantial uncertainty in model estimates.
There are currently few reliable data available for the concentrations of trace elements in
Scottish groundwaters. A new project Baseline Scotland, jointly funded by the British
Geological Survey (BGS) and the Scottish Environment Protection Agency (SEPA), seeks to
improve the data availability and general understanding of the chemistry of Scotland’s
groundwater. However, this is a major undertaking and these new data will take several years
to collect and interpret across the whole of Scotland.
In the interim, SEPA have asked BGS to use their existing knowledge and data to give a
rough estimate of where certain elements are more likely to be elevated in groundwater. This
information will be used to help focus future monitoring and give background for Baseline
Scotland. Predicting trace element concentrations is difficult, in part due to lack of
knowledge on the distribution of mineral phases, the reactivity of different minerals and the
geochemical environment, particularly the redox status.
This report scopes the potential scale of naturally elevated trace elements in Scottish
groundwater, in particular those elements that are potentially harmful to health: e.g.
aluminium, arsenic, barium, cadmium, chromium, lead, manganese, nickel, uranium and zinc.
The problems and limitations of prediction are discussed in the report and this work does not
replace a proper assessment based on actual chemical analyses of groundwater.
The method uses information on the geochemistry of the Scottish environment derived from
the most comprehensive geochemical data set for Scotland, the BGS Geochemical Baseline
Survey of the Environment (G-BASE), combined with the limited data available on the
chemistry of Scottish groundwaters. The conditions under which each of the elements can
become elevated in groundwater are discussed and the geological and geochemical
information interpreted to produce a series of maps highlighting areas where each trace
element may be elevated in groundwater relative to the Scottish average.
The maps are based primarily on the 1:625 000 scale bedrock geology map of Scotland. In
order to make the scheme and the maps simple and manageable, we have used the same
numbers to describe the individual rock units (1 to 114) that are usedd on the Geological map
of the UK (Solid Geology): North sheet. Some rock units have been subdivided, and other
small areas highlighted where additional information is known, either from G-BASE or
previous studies.
After assessing the results of the exercise the following conclusions can be drawn:
1. The study has provided a useful summary of geochemical information for trace
elements in Scotland, and detail the conditions in which these elements may become
elevated in groundwater. This provides essential background to the Baseline Scotland
project, which aims to improve the availability of groundwater chemistry data and the
general understanding of the chemistry of Scotland’s groundwater.
2. The predictions can be used as a first pass to help focus and prioritise additional
monitoring and for helping to interpret groundwater chemistry data from different
areas. The predictions are only preliminary and will be modified in the future by
detailed groundwater sampling and interpretation.
There are several caveats:
• For all of the trace elements considered, the lack of available groundwater chemistry
data with detailed analysis of trace elements, and their restricted spatial distribution, means that it is not possible to rigorously test whether the groundwater quality
predictions are accurate or not.
• More groundwater chemistry data are available for three elements, barium, manganese
and zinc, allowing a rudimentary test of the predictive maps. For barium the
prediction appears to work well, but there is poor correlation for zinc. For manganese,
some correlation is evident, but the complexity and variability of local conditions are
such that much variation is observed.
• This approach, using broad, national scale geological and environmental data, cannot
account for the complexity of the controls on groundwater chemistry: i.e. the
heterogeneous nature of the Scottish environment, not least the aquifer mineralogy and
glacial history, and the complex behaviour of trace elements in groundwater,
determined by aspects such as flow pathways, residence times, and the geochemical
environment (for example, oxidising/reducing or acidic/alkaline conditions).
In summary, this approach appears to be a useful first step in trying to estimate the likely
distribution of trace elements in Scottish groundwater, in the absence of much reliable
groundwater quality data. However, only by systematically collecting reliable groundwater
chemistry data, across different aquifers and regions and from different depths, can the
variation in trace elements in groundwater across Scotland be understood. Careful modelling
and interpretation of these new data in the context of the geology and environmental
conditions will help make future predictions of groundwater quality more reliable and provide
reference information for the Water Framework Directive.
'%3e%3cpath fill='%23012169' d='M0 0v30h60V0z'/%3e%3cpath stroke='%23fff' stroke-width='6' d='m0 0 60 30m0-30L0 30'/%3e%3cpath stroke='%23C8102E' stroke-width='4' d='m0 0 60 30m0-30L0 30' clip-path='url(%23b)'/%3e%3cpath stroke='%23fff' stroke-width='10' d='M30 0v30M0 15h60'/%3e%3cpath stroke='%23C8102E' stroke-width='6' d='M30 0v30M0 15h60'/%3e%3c/g%3e%3c/svg%3e)
