Keyword

Groundwater

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  • 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 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).

  • 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 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.

  • On December 1, 1965, an underground blowout during an exploratory drill with a catastrophic outcome occurred near Sleen, The Netherlands. During approximately 2.5 months, near-continuous leakage of large amounts of natural gas was released into the subsurface. After the blowout, the local drinking water production company installed a network of groundwater monitoring wells to monitor for possible adverse effects on groundwater quality at the blowout site. Today, more than 50 years after the blowout, the groundwater is still impaired. Data has been correlated with previously published data by Schout et al. (2018) covering description of geology and well depths. During October 2019 we sampled from 12 groundwater wells covering: - Inorganic parameters (hydrocarbons, anions, cations, DOC, alkalinity, nitrate and ammonium) - DNA (quantification of total bacteria by qPCR 16S, aerobic methane oxidation by qPCR pmoA, and anaerobic methane oxidation by qPCR mcrA) The dataset was created within SECURe project (Subsurface Evaluation of CCS and Unconventional Risks) - https://www.securegeoenergy.eu/. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 764531

  • 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: [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] 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).

  • The data sets contain the daily record of meters of groundwater columns for 7 Heron logger transducers installed in different boreholes and wells in the study area. Missing data denoted -9999. The Barlog data for atmospheric pressure (Atmospheric Pressure data measured by Heron Barologger for the period of April 2014 to November 2018 at Munje Jabalini.) is also included. "Uncomp.HT.WTR. Above Transducer" corresponds to the actual pressure the dipperLog is measuring. "Barologger Data" corresponds to the Barlog data for atmospheric pressure at Munje Jabalini "Comp.Depth.WTR Below the Datum" is the "Depth below datum" entered in the logger setup less "Comp.HT.WTR. Above Transducer". The data was collected by Albert Folch and Nuria Ferrer (UPC), Mike Lane and Calvince Wara (Rural Focus Ltd). The PI on the Gro for GooD project was Prof. Rob Hope, University of Oxford.

  • 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).

  • The Groundwater Vulnerability 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. The Groundwater Vulnerability Scotland dataset version 2 (2015) shows the relative vulnerability of groundwater to contamination across Scotland. Groundwater vulnerability is the tendency and likelihood for general contaminants to move vertically through the unsaturated zone and reach the uppermost water table after introduction at the ground surface. The groundwater vulnerability dataset was developed as a screening tool to support groundwater management at a regional scale across Scotland, and specifically to aid groundwater risk assessment. The data can be used to show the relative threat to groundwater quality from contamination, by highlighting areas at comparatively higher risk of groundwater contamination. The dataset is delivered at 1: 100 000 scale; the resolution of the dataset being 50m and the smallest detectable feature 100 m