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  • Joint BGS/Environment Agency dataset of aquifer designations for England and Wales at 1:50 000. The dataset identifies different types of aquifer - underground layers of water-bearing permeable rock or drift deposits from which groundwater can be extracted. These designations reflect the importance of aquifers in terms of groundwater as a resource (drinking water supply) but also their role in supporting surface water flows and wetland ecosystems. The maps are split into two different type of aquifer designation: superficial - permeable unconsolidated (loose) deposits (for example, sands and gravels), and bedrock - solid permeable formations e.g. sandstone, chalk and limestone.

  • Matlab m-file code to generate a probabilistic model of aquifer-body occurrence in the subsurface of the Indo-Gangetic foreland basin, northwestern India. The accompanying ArcGIS ASCII matrix files give aquifer-body percentages in successive 10 m depth slices for use within the model. File xxx_01.txt is for depths 0-10 m, file xxx_02.txt for depths 10-20 m, etc.

  • Thicknesses of aquifer units in the subsurface of the Indo-Gangetic foreland basin, northwestern India. Data are organised by borehole and indicate the thickness of aquifer units, separated by non-aquifer material.

  • The Superficial Aquifer Productivity Scotland dataset forms part of the BGS Hydrogeological Maps of Scotland data product. This product is comprised of three datasets: Bedrock Aquifer Productivity Scotland; Superficial Aquifer Productivity Scotland; and Groundwater Vulnerability Scotland. Aquifer productivity is a measure of the potential of aquifers to sustain a borehole water supply. The Superficial Aquifer Productivity Scotland dataset version 2 (2015) indicates the location and productivity of superficial aquifers across Scotland, and their groundwater flow characteristics. Developed as a tool to support groundwater resource management, the dataset provides a guide to aquifer characteristics at a regional scale, and may be useful to anyone interested in learning more about, assessing or managing groundwater resources across Scotland. The dataset is delivered at 1: 100 000 scale; the resolution of the dataset being 50 m and the smallest detectable feature 100 m.

  • Data for Uganda includes analytical, field, isotope and borehole data. Data for Tanzania includes chemistry, field, isotope and borehole data. Borehole data from the Makutopora Wellfield is also included. This data was collected to investigate the resilience to climate change in sub-Saharan Africa (Tanzania and Uganda) of intensive groundwater abstraction from weathered crystalline rock aquifer systems. The sustainability of such abstractions was investigated by examining historical aquifer responses to climate and intensive (> 1 l/s) abstraction, and investigating groundwater residence times at sites of intensive groundwater abstraction using multiple tracers. The project was DFID funded. Project partners include: University College London, the British Geological Survey and the Overseas Development Institute

  • The Bedrock Aquifer Productivity Scotland dataset forms part of the BGS Hydrogeological Maps of Scotland data product. This product is comprised of three datasets: Bedrock Aquifer Productivity Scotland; Superficial Aquifer Productivity Scotland; and Groundwater Vulnerability Scotland. Aquifer productivity is a measure of the potential of aquifers to sustain a borehole water supply. The Bedrock Aquifer Productivity Scotland dataset version 2 (2015) indicates the location and productivity of bedrock aquifers across Scotland, and their groundwater flow characteristics. Developed as a tool to support groundwater resource management, the dataset provides a guide to aquifer characteristics at a regional scale, and may be useful to anyone interested in learning more about, assessing or managing groundwater resources across Scotland. The dataset is delivered at 1: 100 000 scale; the resolution of the dataset being 50 m and the smallest detectable feature 100 m.

  • This dataset was generated with a novel process-based stochastic modelling approach to investigate the productivity and sustainability of groundwater abstractions in the Precambrian basement aquifer in Ghana. The statistical distribution of the generated synthetic yield data was found in very good agreement with observed yield data from the same Ghanaian aquifer. The dataset includes more than 40,000 simulated values of maximum allowable yield and corresponding transmissivity values for different realisations of aquifer heterogeneity, net recharge values, and borehole depth. Further details about the dataset and the method of generation and collection can be found in the article by Bianchi et al. (2020) "Investigating the productivity and sustainability of weathered basement aquifers in tropical Africa using numerical simulation and global sensitivity analysis" published in the Water Resources Research journal. This research was supported by the UKRI British Geological Survey NC-ODA grant NE/R000069/1 and NE/M008827/1.

  • Joint BGS/Natural Resources Wales (NRW) dataset of aquifer designations for Wales at 1:50 000. The dataset identifies different types of aquifer - underground layers of water-bearing permeable rock or drift deposits from which groundwater can be extracted. These designations reflect the importance of aquifers in terms of groundwater as a resource (drinking water supply) but also their role in supporting surface water flows and wetland ecosystems. The maps are split into two different type of aquifer designation: superficial - permeable unconsolidated (loose) deposits (for example, sands and gravels), and bedrock - solid permeable formations e.g. sandstone, chalk and limestone.

  • These maps provide an overview, at the national scale, of the spatial relationships between principal aquifers and some of the major shale and clay units in England and Wales. The data comprises a series of occurrence maps shows the distribution of rock units that form the principal aquifers and some major shale and clay units in England and Wales. In addition, a series of separation maps show the vertical separation between pairs of shales or clays and overlying aquifers. If shale gas resources are to be developed in the UK, the implications for groundwater will need to be considered as part of any risk assessment. A step in such an assessment will be to understand and quantify the spatial relationships between the potential shale gas source rocks (including both shales and some clay units) and overlying aquifers. The datasets used to produce the aquifer maps, the shale and clay occurrence maps and the separation maps are available to download for your own use. As with other BGS data sets available for download, this will enable you to work offline to develop your own systems and methodologies using BGS data. The data used to produce the aquifer, shale and clay maps are available below as ESRI GIS and KML files.

  • (I) Handpump Vibration Data For each handpump, data is organized in one CSV file per day. These files are grouped together over batches, where each batch approximately corresponds to three months. (II) Borehole Water Level Data Water level data at the borehole of each handpump is recorded in one CSV file per handpump. Both uncompensated (raw) and compensated (with respect to atmospheric pressure) data are available. (III) Data Time Logs A separate Excel file lists the locations of the monitoring sites and the time logs corresponding to both (I) and (II) per handpump. References: [1] P. Thomson, R. Hope, and T. Foster, “GSM-enabled remote monitoring of rural handpumps: a proof-of-concept study,” Journal of Hydroinformatics, vol. 14, no. 4, pp. 829–839, 05 2012. [Online]. Available: https://doi.org/10.2166/hydro.2012.183 [2] F. Colchester, “Smart handpumps: a preliminary data analysis,” IET Conference Proceedings, pp. 7–7(1). [Online]. Available: https://digital-library.theiet.org/content/conferences/10.1049/cp.2014.0767 [3] H. Greeff, A. Manandhar, P. Thomson, R. Hope, and D. A. Clifton, “Distributed inference condition monitoring system for rural infrastructure in the developing world,” IEEE Sensors Journal, vol. 19, no. 5, pp.1820–1828, March 2019. [4] F. E. Colchester, H. G. Marais, P. Thomson, R. Hope, and D. A. Clifton, “Accidental infrastructure for groundwater monitoring in africa,” Environmental Modelling Software, vol. 91, pp. 241 – 250, 2017. [Online]. Available:http://www.sciencedirect.com/science/article/pii/S1364815216308325 [5] A. Manandhar, H. Greeff, P. Thomson, R. Hope, and D. A. Clifton, “Shallow Aquifer Monitoring Using Handpump Vibration Data,” In-review, 2019.