Data were collected in 2015, 2016 and 2017 to provide information on the distribution of flow depth and depth-averaged flow velocity at cross-sections on the South Saskatchewan River, Canada. Data were obtained using a Sontek M9 acoustic Doppler current profiler (aDcp) mounted onto either a small zodiac boat or a SonTek Hydroboard. Data for each cross-section is recorded in a single file. Individual points within each file represent single locations on the particular cross-section. Data were collected as part of NERC project NE/L00738X/1. Full details about this dataset can be found at https://doi.org/10.5285/e4fe2ebe-b207-47d5-8c77-9873afc63da9

These data were collected from a preliminary investigation on the interaction between turbulence and biofilms, using the particle image velocimetry (PIV) technique, which provides spatially- and temporally-resolved velocity vector fields in water for different flow configurations. Seventeen different experiments were conducted with different boundary conditions for each one. The biofilm was developed on a 30-cm-long section permeable bed, the biofilm-covered section was then placed in the water channel test section for flow experiments. Flow rate was regulated by a variable frequency drive controlling the pump speed. Data was recorded at four pump frequencies. Full details about this nonGeographicDataset can be found at https://doi.org/10.5285/4fecb4cc-e751-4752-9687-09ef92626f63

This dataset contains data on geomorphological characteristics and flow-related variables along the Beas River (Punjab, India) between Pong dam and Harike barrage in January 2020. The variables provided include cross-sectional area, water depth, river channel width, river flow velocity and dry-season discharge measured at ten reference sites with stable banks and straight, linear channels without islands or other mid-channel structures. Full details about this dataset can be found at https://doi.org/10.5285/f899fbc5-7034-45c0-a15c-9ee1d92a693f

This dataset comprises seven ensembles of hydrological model estimates of monthly mean and annual maximum river flows (m3s-1) on a 0.1 × 0.1 deg grid (approximate grid of 10 km × 10 km) across West Africa for historical (1950 to 2014) and projected future (2015 to 2100) periods. This dataset is the output from the Hydrological Modelling Framework for West Africa, or “HMF-WA” model. The ensembles correspond to CMIP6 (Coupled Model Inter-comparison Project Phase 6) historical and three projected future climate scenarios (SSP126, SSP245 and SSP585) with two future scenarios of water use. The scenarios of water use are (i) future water use that varies in line with projected population increases, and (ii) future water use is the same as present day. This dataset is an output from the regional scale hydrological modelling study from African Monsoon Multidisciplinary Analysis-2050 (AMMA-2050) project. Full details about this dataset can be found at https://doi.org/10.5285/346124fd-a0c6-490f-b5af-eaccbb26ab6b

This dataset provides a coherent, quality-controlled collection of 15-minute river flow observations from across the United Kingdom. It brings together more than 1,300 gauging stations and over 50,000 station-years of data collected by the main UK measuring authorities - the Environment Agency (England), Scottish Environment Protection Agency, Natural Resources Wales, the Department for Infrastructure (Northern Ireland), and the UK Centre for Ecology & Hydrology. The records span from 1948 to the present day and represent the first national-scale compilation of sub-daily flow data in a consistent format. The dataset was created by assembling raw hydrometric records from open APIs and data requests to measuring authorities, then standardising them to a uniform 15-minute time step. A structured quality control framework was applied to identify and flag potential issues such as missing or duplicated values, irregular time steps, and implausible flow events. Each record includes a detailed quality code indicating the outcome of these checks, and a suite of accompanying metadata files provides full traceability of data provenance, quality control results, and any adjustments made during processing. The resource is designed to support large-sample and national-scale hydrological research, particularly for applications requiring high-resolution data such as flood analysis, catchment response studies, and model calibration. Full details about this dataset can be found at https://doi.org/10.5285/211710ac-f01b-4b52-807f-373babb1c368

This dataset includes catchment stream inflow and outflow rates, secchi depth, chlorophyll, phytoplankton counts and nutrient concentrations for the lake, inflow, outflow and groundwater spring. The measurements are from a PhD research project at Rostherne Mere in Cheshire. These data were collected to show the relationship between the catchment hydrology and in-lake nutrient loads for assessment of the current catchment nutrient budget. The monitoring study covered a period from January 2016 to January 2017. All data is presented with date, flow rate, nutrient and chlorophyll concentrations and phytoplankton species abundance. The work was carried out as part of a Natural Environment Research Council (NERC) funded PhD [grant number NE/L002493/1]. Full details about this dataset can be found at https://doi.org/10.5285/5c6b2bcb-6b10-4c57-a595-ce94a655e709

Velocity and along-flow stress states were modelled for Larsen C ice shelf, before and after the calving of iceberg A68 in July 2017. The archive contains two sets of model outputs: i) flow velocity before and after calving, and the difference between these periods, and ii) along-flow stress before and calving, and the difference between these periods. The models are produced with the BISICLES ice sheet model. Additionally to high-resolution geo-referenced model outputs, a low-resolution image of each is provided for reference. The maps were produced by Dr Stephen Cornford, Swansea University. The data is part of the NERC RACE project, NE/R012334/1.

Velocity maps were derived, for regions of Larsen C ice shelf, from satellite imagery spanning the period November 2017 to April 2019. This period was selected to monitor any change in the velocity field of Larsen C, in the months following the calving of iceberg A68 from the front of the ice shelf. The archive contains two sets of maps. The first are derived from Sentinel-1 satellite data, and span the complete ice shelf for the full 18-month epoch. The second are derived from TerraSAR-X data, and show high-resolution velocity trends between 2017 and 2018, covering the frontal region of Larsen C ice shelf. The maps were produced by Professor Adrian Luckman, Swansea University. The data is part of the NERC RACE project, NE/R012334/1.

This dataset is a model output, from the Grid-to-Grid hydrological model driven by weather@home2 climate model data. It provides a 100-member ensemble of monthly mean flow (m3/s) and soil moisture (mm water/m soil) on a 1 km grid for the following time periods: historical baseline (HISTBS: 1900-2006), near-future (NF: 2020-2049) and far-future (FF: 2070-2099). It also includes a baseline period (BS: 1975-2004). To aid interpretation, two additional spatial datasets are provided: - Digitally-derived catchment areas on a 1km x 1km grid - Estimated locations of flow gauging stations on a 1km x 1km grid and as a csv file. The data were produced as part of MaRIUS (Managing the Risks, Impacts and Uncertainties of drought and water Scarcity), which was a UK NERC-funded research project (2014-2017) that developed a risk-based approach to drought and water scarcity. Full details about this dataset can be found at https://doi.org/10.5285/3b90962e-6fc8-4251-853e-b9683e37f790

Enhanced Future Flows and Groundwater (eFLaG) is an 12-member ensemble projection of river flow, groundwater level, and groundwater recharge time series for 200 catchments, 54 boreholes and 558 groundwater bodies in Great Britain and Northern Ireland. It is derived from the UKCP18 dataset, specifically the 'Regional' 12km projections, to which a bias correction is applied. River flows, groundwater level and groundwater recharge data are at a daily time step. To be consistent with the driving meteorological dataset, eFLaG data use a simplified 360-day year, consisting of twelve 30-day months. eFLaG data span from 1981 to 2080. The development of eFLaG was made during the partnership project funded by the Met Office-led component of the Strategic Priorities Fund Climate Resilience programme under contract P107493 (CR19_4 UK Climate Resilience). Full details about this dataset can be found at https://doi.org/10.5285/1bb90673-ad37-4679-90b9-0126109639a9