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The BGS groundwater levels dataset is a gridded interpolation of depth to groundwater. The dataset is a raster grid, with 50 × 50 metre pixels holding values that represent the probable maximum depth, in metres, to the phreatic water table. This represents the likely lowest water level, under natural conditions, in an open well or borehole drilled into the uppermost parts of a rock unit. The dataset has been modelled from topography and hydrology, assuming that surface water and groundwater are hydraulically connected. It has not used observations of groundwater level in wells or boreholes directly, but they have been used to validate its performance.
Monthly time-series data of GRACE (Gravity Recovery and Climate Experiment) total terrestrial water storage (TWS), GLDAS (Global Land Data Assimilation System) soil moisture, surface water (surface runoff), snow water storage, and basin-aggregated observations from piezometric data for the Makutapora Basin (Tanzania) and Limpopo Basin (South Africa).
In this study data were collected from viruses in groundwater from urban poor settlements in Arusha, Tanzania, Dodowa (Accra), Ghana, and Kampala, Uganda. The published Open Access paper can be viewed here https://pubs.acs.org/doi/10.1021/acsestwater.0c00306
The dataset consists of data for the UK for the Sustainable Development Goal 6.6.1: groundwater sub-indicator for the period 1990 to 2019. The dataset reports for the UK against Sub-Indicator 5 of Goal 6.6.1, following the recommended procedures in the UNEP report on monitoring methodologies for that sub-indicator. The sub-indicator is defined as the change in mean groundwater levels, averaged over a five-year period, from a mean in levels over a previous five-year reference period. Groundwater level data was obtained from BGS’ WellMaster database. 192 groundwater level monitoring stations were processed and, following quality control, 154 were used to provide estimates of regional variation in groundwater levels for 19 of the 34 HydroBASINS at Level 6 in Great Britain and Northern Ireland. As required by the guidance in the monitoring methodology, the sites chosen are representative of local and regional groundwater systems and are observation and monitoring boreholes where groundwater levels are not systematically affected by abstraction. All sites chosen have average monitoring frequencies of greater than one observation a month. The dataset is provided as a .csv file with the following headers: Column A: HYBAS_ID, HydroBASIN unique identification number. Column B: reference period (1990 to 1994). Columns C to AA: reporting periods (five-year periods starting in 1991) with data reported as percentage change (relative to reference period) in running mean five-year groundwater level by HydroBasin.
This data was produced to support a project looking at low permeability rocks in sub-Saharan Africa. Multiple boreholes were drilled for the project with geology identified from chippings. Groundwater chemistry was analysed from the resulting boreholes. The data contained within this record is from the CD that accompanies the report: J Davies and J Cobbing, 2002. An assessment of the hydrogeology of the Afram Plains, Eastern Region, Ghana. British Geological Survey Internal Report, CR/02/137N. 66pp http://nora.nerc.ac.uk/id/eprint/505607/1/CR_02_137N.pdf The CD has not been uploaded in full elsewhere.
This data was produced to support a project looking at low permeability rocks in sub-Saharan Africa. Multiple boreholes were drilled for the project with geology identified from chippings. Groundwater chemistry was analysed from the resulting boreholes. The data contained within this record is from the CD that accompanies the report: J Davies and B É Ó Dochartaigh. 2002. Low Permeability Rocks In Sub-Saharan Africa. Groundwater development in the Tabora Region, Tanzania. British Geological Survey Internal Report, CR/02/191N. 71pp http://nora.nerc.ac.uk/id/eprint/505608/1/CR_02_191N.pdf The CD has not been uploaded in full elsewhere.
The data includes field chemistry, major and minor ions (ICP-MS and IC), nutrients (DOC), and tracers (Tritium, CFCs, SF6, δ18O, δ2H, δ13CDIC) collected in Nigeria and Mali in 2010. There is a brief description of the source, depth and completion date of the borehole, type of pump, estimated village population and estimated rainfall. Work funded by UK Department for International Development.
Collection of data from the PhD Thesis 'Development of coupled processes numerical models of tracer, colloid and radionuclide tranpsort in field migration experiments', submitted as part of the RATE HydroFrame WP5. This collection of data includes blank model files in COMSOL Multiphysics and PHREEQC, as described in the PhD thesis. Also included in this data package are different spreadsheets with model outputs from the model files that describe the transport of conservative tracers, colloids and radionuclides in experiments carried out at the Grimsel Test Site, Switzerland as part of the Colloid Radionuclide and Retardation (CRR) and the Colloid Formation and Migration (CFM) experiments (www.grimsel.com).
This dataset represents the raw reads from sequencing the V4 hyper-variable region of the 16S rRNA gene on an Illumina MiSeq platform. The samples are filtered groundwater samples from 8 boreholes from a sandy-dominated site and a clay-dominated site in Cambodia that show arsenic concentrations above the WHO recommended limit, and were collected in May 2019.
This dataset contains a summary of the weekly volumetric output of pumps monitored using Smart Handpump sensors for 2014 and 2015. Grants that permitted the data collection include: Groundwater Risk Management for Growth and Development project (NE/M008894/1) funded by NERC/ESRC/DFID’s UPGro programme; New mobile citizens and waterpoint sustainability in rural Africa (ES/J018120/1) ESRC-DFID; Groundwater Risks and Institutional Responses for Poverty Reduction in Rural Africa (NE/L001950/1) funded by NERC/ESRC/DFID’s UPGro programme Notes: 1. The accuracy of these volume figures should be considered to be +/- 20%. 2. The dataset has gaps due to variable signal, and some attrition due to damage and vandalism. 3. Not all pumps in the study area were under monitoring. References:  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  Behar, J., Guazzi, A., Jorge, J., Laranjeira, S., Maraci, M.A., Papastylianou, T., Thomson, P., Clifford, G.D. and Hope, R.A., 2013. Software architecture to monitor handpump performance in rural Kenya. In Proceedings of the 12th International Conference on Social Implications of Computers in Developing Countries, Ochos Rios, Jamaica. pp. 978 (Vol. 991).