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  • Year-long mooring dataset from beneath George VI Ice Shelf between January 2012 and January 2013. Contains turbulence mooring data from two instruments initially at 2.57 m and 13.57 m beneath the ice base. High resolution 3D velocity data and temperature data at two instruments are included, as well as low-resolution thermistor data. The processing of this data was funded by a grant from the Natural Environment Research Council, NE/L002507/1.

  • The dataset contains a scaled, semi-quantitative conceptual hydrogeological model of the Gaborone catchment along a general WSW-ENE direction including; (1) the geographic coordinates of the extremities of each segment of the polyline transect; (2) the raster, scaled image of the conceptual hydrogeological cross-section. Full details about this dataset can be found at

  • The dataset contains borehole groundwater levels and physico-chemical parameters for the period May 2017 to June 2018 including; (1) near-monthly measurements of water table depth, groundwater temperature, pH, electrical conductivity and total dissolved solids obtained from manual sampling of 22 boreholes; and (2) higher temporal resolution (5-min time-step) timeseries of water table depth, groundwater temperature and electrical conductivity obtained from automatic dataloggers in 3 of the abovementioned boreholes. Full details about this dataset can be found at

  • This dataset is a model output created using the BGS AquiMod model. It provides monthly groundwater level relative to the Ordnance Datum (maOD) from 1891 to 2015, reconstructed for 54 observation boreholes across the UK. Based on the Generalised Likelihood Uncertainty Estimation (GLUE) methodology, 90th percentile and 10th percentile confidence bounds have been estimated and are given for each of reconstructed groundwater level time series. Full details about this dataset can be found at

  • Monthly Standardised Groundwater level Index (SGI) for observation boreholes across the UK from 1891 to 2015, based on reconstructed groundwater level time series (Bloomfield et al., 2018; Standardised groundwater levels have been estimated using a non-parametric normal scores transform of groundwater level data for each calendar month. Probability estimates of an SGI being less than 0, -1, -1.5 and -2 are also provided. Full details about this dataset can be found at