The dataset contains groundwater levels from ten boreholes located in the Gandak Basin, Bihar, North India. The data was collected using automatic level loggers recording at 15-minute intervals between April 2017 and February 2019. This data set quantifies the effects of groundwater abstraction on, and seasonal changes in groundwater levels. The data were collected as part of the NERC sponsored project Coupled Human and Natural Systems Environment (CHANSE), grant number NE/N01670X/1 Full details about this dataset can be found at https://doi.org/10.5285/21df678b-6eb6-4559-9005-8eb7953b48ef

This dataset contains the results of a laboratory study investigating the dissolution of UO3•nH2O particles in dynamic sediment/groundwater column systems, representative of the shallow subsurface at the Sellafield Ltd. site, UK. Measurements were carried out to determine the extent of uranic particle dissolution and the speciation of dissolved uranium within the columns under contrasting biogeochemical conditions (oxic and electron-donor amended). Columns effluents were analysed periodically for key biogeochemical indicators (nitrate, sulfate) and trace metals (iron, manganese, uranium) and systems were sacrificed after 6 and 12 months of groundwater flow. Upon sacrifice, columns were cross-sectioned, and the sediment structure preserved for synchrotron micro-focus X-ray Fluorescence (XRF) mapping, and uranium L-edge X-ray Absorption Spectroscopy (XAS) measurements. Sub-samples of column sediments were also analysed for acid extractable metals, microbial abundance and classification and bioavailable Fe(II) concentrations. Experiments were performed between March 2016 and March 2017. Subsequent analyses were performed between March 2017 and December 2018. This data was collected as part of the project: Understanding radioactive ‘hot’ particle evolution in the environment funded by the UK Natural Environment Research Council (grant NE/M014088/1). Full details about this nonGeographicDataset can be found at https://doi.org/10.5285/2702e1b0-13df-4ae4-9f91-4ac4bd07bbf1

This dataset includes dissolved organic radiocarbon content and dissolved organic carbon concentration data for river waters around the globe. The riverine dataset contains already published (n=1163) and new (n=101) data between the years 1962 and 2015. Soil solution data (n=139) from North American and European natural and semi-natural ecosystems are also included, which cover the period 1988 to 2008. Groundwater data containing 49 data points from boreholes in Europe and North America are also provided. Extra data including sampling dates, locations, stable isotope (13C), water quality and qualitative descriptions of the catchments are included in the dataset. Full details about this dataset can be found at https://doi.org/10.5285/06b219a8-b3ff-4db7-870a-4b1038ff53e2

This dataset contains water chemistry for inlet samples for remediation systems in Bihar, India and associated remediation system efficiency for arsenic removal. The dataset contains paired inlet-outlet data for 31 household and community groundwater remediation systems of different technology types (split into reverse osmosis/RO and non-reverse osmosis) and settings (household and non-household). The chemical data includes the composition of inlet water (concentrations of Fe, P, As, Ca, Mg, Na and Si) and associated arsenic removal. This data was generated as part of the Indo-UK Water Quality Programme Project FAR-GANGA (NE/R003386/1 and DST/TM/INDO-UK/2K17/55(C) & 55(G)). Full details about this dataset can be found at https://doi.org/10.5285/77700f8e-5da6-45ab-9c12-df1a7d20bc32

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 https://doi.org/10.5285/40a80d95-5a8a-4586-aa24-d6c87f9968b6

Future Flows Hydrology (FF-HydMod-PPE) is an 11-member ensemble projections of river flow and groundwater levels time series for 283 catchments and 24 boreholes in Great Britain. It is derived from Future Flows Climate, an 11-member 1-km bias-corrected and downscaled climate projection products based on the SRES A1B emission scenario. River Flows data are at a daily time step: Groundwater Levels data are at a monthly time step. Future Flows Hydrology span from 1951 to 2098. The development of Future Flows Hydrology was made during the partnership project 'Future Flows and Groundwater Levels' funded by the Environment Agency for England and Wales, Defra, UK Water Research Industry, NERC (Centre for Ecology & Hydrology and British Geological Survey) and Wallingford HydroSolutions. Full details about this dataset can be found at https://doi.org/10.5285/f3723162-4fed-4d9d-92c6-dd17412fa37b

The dataset contains 2 km resolution gridded daily potential groundwater recharge time series covering the British mainland from the Enhanced Future Flows and Groundwater (eFLaG) project. The data include simulations driven with historical observed climate data (1962-2018) and simulations driven with bias-corrected UKCP18 'Regional' 12km projections. Full details about this dataset can be found at https://doi.org/10.5285/b14839e5-03e0-43ff-9382-1be2daf3baba

The dataset contains stable isotope data from surface and groundwater samples collected in the Gandak Basin, north India. The data was collected between March 2017 and February 2019. These measurements were taken to improve understanding of surface and subsurface water interconnections and movement through river and canal networks and underlying aquifers. The data were collected as part of the NERC sponsored project Coupled Human and Natural Systems Environment (CHANSE), grant number NE/N01670X/1 Full details about this dataset can be found at https://doi.org/10.5285/09ae86d6-896f-430f-aab4-c5b46c265213

This dataset includes weekly data from monitoring of stream, rainfall and groundwater hydrochemistry in the Vyrnwy research catchment between 1994 and 2001. Data for over 50 chemical determinands are presented alongside data for some in-situ measurements such as water temperature. Full descriptions of the analytical methods used for each determinand is included. Intensive and long-term monitoring within the catchments underpins a wealth of hydrological and hydro-chemical research; other linked datasets include river flow, meteorology and a variety of detailed spatial datasets representing the topography, soils and rivers of the catchments. Monitoring is funded by the Centre for Ecology & Hydrology. Full details about this dataset can be found at https://doi.org/10.5285/68f4a12f-740d-4705-9c27-6a7fb7127046

This datasets contains the anonymised results of a survey of well owners in Kisumu, Kenya. Data includes information on the amount of water abstracted daily from the well and ways in which this water was used and handled, information on other sources of water (e.g. piped utility water and rainwater) and how this is used, and the assets and services that the well owner has access to. Answers from questions to assess food poverty are also included. The surveys were carried out during February and March 2014 and include data from 51 well owners. The data were collected as part of the Groundwater2030 project, which aims to reduce the health problems that result from consumption of contaminated groundwater in urban areas of Africa. The project was co-ordinated by the University of Southampton, with partners at the University of Surrey, the Victoria Institute of Research on Environment and Development (VIRED) International, and the Jaramogi Oginga Odinga University of Science and Technology. The project was funded by the Natural Environment Research Council and the Department for International Development as part of the Unlocking the Potential of Groundwater for the Poor (UPGro) programme. Full details about this dataset can be found at https://doi.org/10.5285/4ca855a3-752c-4492-8e26-3438652dd35c