This dataset contains exponential parameters from fitting an exponential curve to Delayed Flow Index (DFI or Characteristic Delay) curves using river discharge data from CAMELS-GB (Catchment attributes and hydro-meteorological timeseries for 671 catchments across Great Britain). The DFI curve at each catchment describes the response to rainfall over different time windows. The exponential parameters summarise the shape of the DFI curve at each site. Full details about this dataset can be found at https://doi.org/10.5285/d1df0a31-d329-4b21-8aa0-ba135d6a8042

Hourly precipitation (mm) recorded at distributed points around Kampala between April 2019 and March 2020. Only timestamps where data were available from all sensors have been included. There are 8094 records in total and no missing values. Timestamps are recorded as “YYYY-MM-DD hh:mm:ss”. The geographic coordinates of the sensors are provided in GeoJSON format. The column names in the CSV file correspond to the “id” field in the GeoJSON file. Full details about this dataset can be found at https://doi.org/10.5285/3df031ad-34ec-4abc-8528-f8f20bad12b8

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.

Automated measurements of water level and temperature at half-hourly intervals spanning parts of 2018, 2019 and 2020, from seven wetland sites in the Pastaza-Marañón Basin, Amazonian Peru. Full details about this dataset can be found at https://doi.org/10.5285/0d1d15da-e356-492d-88db-2dba3b9ec9b4

This dataset contains time series observations of land surface-atmosphere exchanges of sensible heat (H) and latent heat fluxes (LE), together with supporting meteorological and soil physics data obtained at six eddy covariance (EC) flux tower monitoring sites located across England and Wales. The flux monitoring sites include three croplands (two on peat, one on mineral soils), one grassland on peat, one lowland fen under conservation management, and a relatively intact upland raised bog. Data collection started in January 2023 and ended in January 2024, except for one site in Wales which ended in December 2023. Vegetation data was also collected and consist of manual measurements of canopy height, leaf area index , and biomass. This work was supported by the Natural Environment Research Council award number NE/R016429/1 as part of the UK-SCAPE programme delivering National Capability. Full details about this dataset can be found at https://doi.org/10.5285/b50e7291-b668-49bb-8226-f6371e707714

This CD-ROM set contains the Volume 1 hydrology and soil data collection. The data covers a 24 month period, 1987-1988, and all but one are mapped to a common spatial resolution and grid (1 degree x 1 degree). Temporal resolution for most datasets is monthly; however, a few are at a finer resolution (e.g., 6-hourly). This dataset contains data covering: * Precipitation * Hydrology cover * River basin streamflow * Global soil properties

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 contains daily soil moisture estimates using prototype cosmic ray neutron sensors. The sensors were deployed in a centre-pivot irrigation site in the state of Bahia, Brazil, and measurements were taken hourly between June and December, 2023. The dataset includes daily averages of volumetric water containment, which have been corrected for environmental effects. This dataset was created to study the potential for using distributed cosmic ray sensors to support data driven irrigation optimisation and is supported by NERC (NE/W004364/1). Full details about this dataset can be found at https://doi.org/10.5285/a2c87e47-6f85-4bee-9a54-ecb1bb5b3573

The dataset includes six files of UK physical river characteristics including five files of gridded data at 1 km x 1 km resolution and one comma separated table. The data includes: • Drainage directions (D8 flow method), ESRI coding • Drainage directions (D8 flow method), unifhy (python hydrology framework) coding • Catchment areas (km2) • Widths of bankfull rivers (m) • Depths of bankfull rivers (m) • NRFA gauging station locations (easting (m), northing (m)) Two versions of drainage directions are provided, both have the same drainage directions but different numbering systems. The comma separated NRFA (National River Flow Archive) gauging station locations table provides the best locations of 1499 river flow gauging stations on the 1km grids, together with the approximate error in the 1km × 1km gridded delineation of the upstream catchment area. All datasets are provided on the British National Grid. Full details about this dataset can be found at https://doi.org/10.5285/8df65124-68e9-4c68-8659-1c6b82c735e9

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