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 contains GR6J (which stands for modèle du Génie Rural à 6 paramètres Journalier) modelled daily river flow time series for 95 river catchments in Great Britain and driving data (precipitation and potential evapotranspiration calculated from temperature) from the EC-Earth Single Model Initial Condition Large Ensemble (SMILE) climate model. The large ensemble is based on the EC-Earth Global Climate Model (GCM) v2.3 and is run for present day (equivalent to present day climate with observed global mean surface temperature for the period 2011-2015) and pre-industrial plus 2°C and 3°C global warming conditions. In total, they make up 2000 years of data for each global warming level (i.e. 16 ensemble members x 25 realizations x 5 years = 2000 years). All ensemble members are pooled to form a continuous 2000-year time series of meteorological input and daily modelled river flows. Full details about this dataset can be found at https://doi.org/10.5285/d966ae7f-a29b-4c57-835d-f4efd0d65c88

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

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

This data contains the time series flow discharge results of hydrological simulation of the River Trent at Colwick using UKCP09 Weather Generator inputs for a variety of time slices and emissions scenarios. The Weather Generator (WG) inputs were run on a hydrological model (Leathard et al., unpublished), calibrated using the observed record 1961-2002. Each simulation is derived from 100 30-year time series of weather at the WG location 4400355 for Control, Low, Medium and High emissions scenarios for the 2020s, 2030s, 2040s, 2050s and 2080s time slices. The datasets include the relevant accompanying input WG data. Full details about this dataset can be found at https://doi.org/10.5285/986d3df3-d9bf-42eb-8e18-850b8d54f37b

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

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

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

Gridded potential evapotranspiration calculated from HadUK-Grid gridded observed meteorological data at 1 km resolution over the United Kingdom for the years 1969-2022. This dataset contains two potential evapotranspiration variables: daily total potential evapotranspiration (PET; kg m-2 d-1) and daily total potential evapotranspiration with interception correction (PETI; kg m-2 d-1). The units kg m-2 d-1 are equivalent to mm d-1. The data are provided in gridded netCDF files. There is one file for each variable, for each calendar month. Full details about this dataset can be found at https://doi.org/10.5285/beb62085-ba81-480c-9ed0-2d31c27ff196

The WATCH Forcing data is a twentieth century meteorological forcing dataset for land surface and hydrological models. It consists of three/six-hourly states of the weather for global half-degree land grid points. It was generated as part of the EU FP 6 project "WATCH" (WATer and global CHange") which ran from 2007-2011. The data was generated in 2 tranches with slightly different methodology: 1901-1957 and 1958-2001, but generally the dataset can be considered as continuous. More details regarding the generation process can be found in the associated WATCH technical report and paper in J. Hydrometeorology. To understand how the data grid is formed it is necessary to read the attached WFD-land-long-lat-z files either in NetCDF or DAT formats. The data covers land points only and excludes the Antarctica. Snowf or snowfall is the snowfall rate based on the GPCC bias corrected, undercatch corrected measured in kg/m2/s at 3 hourly resolution averaged over the next 3 hours and at 0.5 x 0.5 degrees spatial resolution. Please note that there is also a WFD Snowf CRU bias corrected dataset, but as the GPCC dataset is the preferred dataset only this snowfall dataset is available from the EIDC. These snowfall datasets contain snowfall data only and need to be combined with the respective WFD rainfall datasets to obtain precipitation data.