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Aquifers

29 record(s)

 

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  • 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.

  • 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.

  • 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.

  • (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.

  • 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)

  • 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

  • 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.

  • The map shows the potential for the rocks to supply groundwater and the type of groundwater flow within the rocks. The dataset reattributes polygons in the Digital Geological Map Data of Great Britain - 625k (DiGMapGB-625) Bedrock version 5 dataset to indicate whether the bedrock is an aquifer, the type of flow through the aquifer (fracture and fissure flow or intergranular flow) and how productive the aquifer is likely to be. The dataset is based on the known hydrogeological properties of rock types. The dataset covers just the bedrock formations for the UK and the Isle of Man. The data can be used for planning, environmental analysis, water supply and hazards.

  • The Environment Agency and Natural Resources Wales have updated its groundwater vulnerability map to reflect improvements in data mapping, modelling capability and understanding of the factors affecting vulnerability. Two new maps are available which show the vulnerability of groundwater to a pollutant discharged at ground level. The potential impact of groundwater pollution is considered using the aquifer designation status which provides an indication of the scale and importance of groundwater for potable water supply and/or in supporting baseflow to rivers, lakes and wetlands. This dataset for Wales has shared intellectual property (IP) between Natural Resources Wales and British Geological Survey.