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.

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.

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.

The borehole is located at the UK Centre for Ecology and Hydrology (UKCEH), screened between 2 and 4.5 m in the Thames gravels, and drilled to a total depth of 4.8m. It is located on an actively managed grass verge with popular and sycamore trees within 10 m. The stilling well is positioned 420 m west of the borehole in the River Thames. Both stage and groundwater level were monitored at 1-minutre frequency to investigate hydrological fractal scaling of high frequency data between 2012 and 2016. An automatic weather station is present between the borehole and stilling well and the data are available separately from UKCEH (stetur@ceh.ac.uk). Further site description is provided in: Habib, A. et al. 2017. Journal of Hydrology, 549, 715-730. Habib, A. et al. 2022. Hydrological Sciences Journal

The hydrogeological map indicates aquifer potential in generalised terms using a threefold division of geological formations: those in which intergranular flow in the saturated zone is dominant, those in which flow is controlled by fissures or discontinuities and less permeable formations including aquifers concealed at depth beneath covering layers. Highly productive aquifers are distinguished from those that are only of local importance or have no significant groundwater. Within each of these classes the strata are grouped together according to age or lithology. The 1:625 000 scale data may be used as a guide to the aquifers at a regional or national level, but should not be relied on for local information.

Monthly anomalies (August 2002 to July 2016) of total terrestrial water storage (TWS), soil moisture storage (SMS), surface water storage (SWS), snow water storage (SNS), groundwater storage (GWS) derived from an ensemble mean of 3 gridded GRACE products (CSR, JPL-Mascons and GRGS) and an ensemble mean 4 land surface models (CLM, NOAH, VIC and MOSAIC), provided by the NASA’s Global Land Data Assimilation System (GLDAS). Monthly precipitation (CRU) data, derived from the Climatic Research Unit (CRU), were aggregated over each aquifer system. GRACE, GLDAS and CRU datasets are publicly available at the global scale. (NERC grant NE/M008932/1)

Digitised versions of a set of 1:100,000 scale maps of aquifer vulnerability for England and Wales. The dataset identifies the vulnerability to pollution of major and minor aquifers as defined by the Environment Agency, utilising a combination of geological, hydrogeological and soils data. The maps are designed to be used by planners, developers, consultants and regulatory bodies to ensure that developments conform to the Policy and Practice of the Environment Agency for the protection of Groundwater. Please note that these maps are based on data from the late 1980's and early 1990's, more up-to-date digital data may now be available from the Environment Agency. Flat maps may be purchased from the BGS, some sheets are now out of print.

This dataset consists of reconstructions of daily groundwater levels for eight boreholes in Burkina Faso. Data for each borehole is provided in an individual csv file, with reconstructed groundwater level time series reported in metres above sea level (GWL, mASL). The groundwater level reconstructions were derived in 2019 as a part of the BRAVE project (NE/M008827/1 and NE/M008983/1) to develop an improved understanding of temporal variability in groundwater levels in sub-Saharan Africa. The reconstructions were derived using the lumped conceptual groundwater model AquiMod. Observed groundwater level time series for the eight boreholes were modelled using AquiMod, and the calibrated models were used with historic precipitation and potential evapotranspiration data to derive the reconstructions. The length of the time series of reconstructed groundwater levels varies between the boreholes due to differences in the length of the precipitation time series used to derive the reconstructions. Full details of this dataset are reported by Ascott et al. (2020). Ascott, M.J., Macdonald, D.M.J., Black, E., Verhoef, A., Nakohoun, P., Tirogo, J., Sandwidi, W.J.P., Bliefernicht, J., Sorensen, J.P.R., Bossa, A.Y., 2020. In Situ Observations and Lumped Parameter Model Reconstructions Reveal Intra-Annual to Multidecadal Variability in Groundwater Levels in Sub-Saharan Africa. Water Resour. Res., 56(12): e2020WR028056. DOI:https://doi.org/10.1029/2020WR028056

(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.

2 published papers from NERC grant NE/G016879/1. Palaeosol Control of Arsenic Pollution:The Bengal Basin in West Bengal, India by by U. Ghosal, P.K. Sikdar, and J.M. McArthur. Tracing recharge to aquifers beneath an Asian megacity with Cl/Br and stable isotopes: the example of Dhaka, Bangladesh by M. A. Hoque, J. M. McArthur, P. K. Sikdar, J. D. Ball and T. N. Molla (DOI 10.1007/s10040-014-1155-8)