This dataset contains breakthrough curves of conservative (fluorescein) and reactive (resazurin and resorufin) tracers resulting from instantaneous tracer experiments in a lowland agricultural stream. Breakthrough curves were measured seasonally at four locations within the stream, creating three experimental reaches, in the Wood Brook, Staffordshire from July 2016 to March 2017. Breakthrough curves were measured in-situ using on-line fluorometers configured to measure the excitation of fluorescein, resazurin and resorufin every 10 seconds. The breakthrough curves were measured to determine hydrological metrics of advective transport, transient storage and aerobic respiration. The work was funded by the Natural Environment Research Council, UK through a through a Central England NERC Training Alliance Studentship and grant NE/ L004437/1, with additional funding provided by the European Union through the H2020-MSCA-RISE-2016 project 734317. Full details about this dataset can be found at https://doi.org/10.5285/5b34d963-d0f0-465e-b395-e955b89e1cd7

The dataset describes the data needed for and results produced by the flood risk assessment framework under different development strategies of Luanhe river basin under a changing climate. The Luanhe river basin is located in the northeast of the North China Plain (115°30′ E-119°45′ E, 39°10′ N-42°40′ N) of China, is an essential socio-economic zone on its own in North-Eastern China, and also directly contributes to and influences the socio-economic development of the Beijing-Tianjin-Hebei region. The dataset here used for investigating the flood risk includes (1) uplifts of future climate scenarios to 2030 (2) the validation results of a historical event that happened in 2012; (3) the flood inundation prediction under different development strategies and climate scenarios to 2030; (4) and the spatial resident density map in Luanhe river basin to 2030. Wherein, the uplifts of the future climate change is generated based on the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset and will be applied to the future design rainfall to represent the future climate scenarios; a 2012 event is select to validate the flood model, and the remote sensing data is adopted as real-world observation data; considering the uplifts and future land use data as input, the validated flood model is applied to produce flood inundation prediction under different development strategies and climate scenarios to 2030; and the inundation results are used to overlay the Gridded Population of the World, Version 4 (GPWv4) and then calculate the flood risk map of the local resident. These data are mainly open data or produced by authors. With all these data, the flood risk of the Luanhe river basin in the near future (2030) can be assessed. Full details about this dataset can be found at https://doi.org/10.5285/82055942-386a-4a8b-b2a1-0c3eea12b168

This dataset provides hydro-meteorological timeseries and landscape attributes for 671 catchments across Great Britain. It collates river flows, catchment attributes and catchment boundaries from the UK National River Flow Archive together with a suite of new meteorological timeseries and catchment attributes. Daily timeseries for the time period 1st October 1970 to the 30th September 2015 are provided for a range of hydro-meteorological data (including rainfall, potential evapotranspiration, temperature, radiation, humidity and flow). A comprehensive set of catchment attributes are quantified describing a range of catchment characteristics including topography, climate, hydrology, land cover, soils, hydrogeology, human influences and discharge uncertainty. This dataset is intended for the community as a freely available, easily accessible dataset to use in a wide range of environmental data and modelling analyses. A research paper (Coxon et al, CAMELS-GB: Hydrometeorological time series and landscape attributes for 671 catchments in Great Britain) describing the dataset in detail will be made available in Earth System Science Data (https://www.earth-system-science-data.net/). Full details about this dataset can be found at https://doi.org/10.5285/8344e4f3-d2ea-44f5-8afa-86d2987543a9

Datasets consists of the results of Computational Fluid Dynamics (CFD) flow simulations for a section of the South Saskatchewan River, Canada. The aim of these CFD simulations was to investigate the effect of dunes on the depth-averaged and near-bed flow fields. Modelling was carried out using the open source CFD package OpenFOAM to solve the three-dimensional Navier-Stokes equations. The dataset consists of two files, one with simulation results for a river bed characterised by alluvial bedforms (dunes) and one for a smooth river bed without dunes. This work was part of NERC project NE/L00738X/1. Digital Surface Models (DSMs) were constructed using imagery obtained on four occasions (13th May 2015; 2nd Sept 2016; 8th June 2017; and 12th June 2017). Full details about this dataset can be found at https://doi.org/10.5285/7db04405-2f5e-4543-aa94-948ddbcd588a

This is a web map service of the UKCEH digital river network of Great Britain (1:50,000). It is a river centreline network, based originally on OS 1:50,000 mapping. There are four layers: rivers; canals; surface pipes (man-made channels such as aqueducts and leats) and miscellaneous channels (including estuary and lake centre-lines and some underground channels).

This dataset contains instream dissolved oxygen data collected continuously at one minute intervals for five sites in the Hampshire Avon catchment in the United Kingdom. Data were collected between August 2014 and August 2015 using miniDOT loggers. Full details about this dataset can be found at https://doi.org/10.5285/840228a7-40a1-4db4-aef0-a9fea2079987

This dataset comprises river centrelines, digitised from OS 1:50,000 mapping. It consists of four components: rivers; canals; surface pipes (man-made channels for transporting water such as aqueducts and leats); and miscellaneous channels (including estuary and lake centre-lines and some underground channels). This dataset is a representation of the river network in Great Britain as a set of line segments, i.e. it does not comprise a geometric network.

This dataset is a set of 24 gridded netcdf files, each including data describing a total of 14,400 widespread flooding events across mainland GB, with event selection based on a peaks-over-threshold approach. The data describes peak river flow in m3/s and the associated annual probability of exceedance for each 1km grid-square on the GB river network. The data is extracted from daily time series data from the Grid-to-Grid model, using UKCP18 12km regional projections from 12 members of a perturbed parameter ensemble, over the periods Dec 1980 – Nov 2010 and Dec 2050 – Nov 2080 (based on 360-day years). Multi-day events were summarised by taking cell-wise flow maxima. This data was generated for use in analysis of risk through catastrophe modelling using the Future Flows Explorer. It was generated and interpreted by UKCEH, working with Sayers & Partners Consultancy, as part of the AquaCAT project, part of the UK Climate Resilience Programme. Full details about this dataset can be found at https://doi.org/10.5285/26ce15dd-f994-40e0-8a09-5f257cc1f2ab

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 contains river (fluvial) and surface water (pluvial) flooding maps for the central highlands of Vietnam and surrounding provinces. Flood depth is estimated at 30m horizontal grid spacing for 10 return periods, ranging from the 1 in 5 year to the 1 in 1000 year return period flood. These maps are of relevance to planners and policy makers to estimate which areas of most at risk of flooding and can contribute towards policy such as the sustainable development goals. Full details about this dataset can be found at https://doi.org/10.5285/74e4e6ec-a119-4dc7-8ada-9513252b1b60