This dataset is a model output from the European Monitoring and Evaluation Programme (EMEP) model applied to the UK (EMEP4UK) driven by Weather and Research Forecast model meteorology (WRF). It provides annual total atmospheric deposition of cadmium, copper, nickel, lead, zinc, sulphur, and nitrogen over the UK on a 3x3 km2 grid for the years 2040, 2070 and 2100 under the Shared Socioeconomic Pathways (SSPs) 1-5. SSPs are future scenarios based on narratives of possible socio-economic pathways. The EMEP4UK model version used here is rv4.36, and the WRF model version is the 4.2.2. 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/eec9634d-ba2b-4553-a65d-ef6ce4c40ebc

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° grid (approximate grid of 10 km × 10 km) across West Africa for historical (1950 to 2005) and projected future (2006 to 2099) periods. This dataset is the output from the Hydrological Modelling Framework for West Africa, or "HMF-WA" model. The ensembles correspond to historical and three projected future climate scenarios (RCP2.6, RCP4.5 and RCP8.5) 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/10.5285/6429828f-6a06-4d2d-8f50-4910b18f7ff4

This dataset includes relative surface soil moisture across the Thames Valley, between October 2015 and September 2021, using backscatter radar data collected using the ESA Sentinel-1 Constellation. Radar backscatter was normalised to 40 incidence angle, using a novel monthly normalisation parameterisation. Full details about this dataset can be found at https://doi.org/10.5285/b23d63d1-dcc5-4c49-a6b5-67154f3739b7

[THIS DATASET HAS BEEN WITHDRAWN]. Gridded potential evapotranspiration calculated from HadUK-Grid gridded observed meteorological data at 1 km resolution over the United Kingdom for the years 1969-2020. 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. These data were generated as part of NERC grant NE/S017380/1 (Hydro-JULES: Next generation land surface and hydrological prediction.) Full details about this dataset can be found at https://doi.org/10.5285/470d9bf9-8c82-487c-956e-f15f9d8aac64

This dataset contains two gridded potential evapotranspiration variables for Great Britain from 1961-2019: 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 variables were calculated from the Climate Hydrology and Ecology research Support System meteorology dataset for Great Britain (1961-2019) [CHESS-met] gridded observed meteorological data at 1 km resolution. 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/bcec9c33-f863-464e-ac28-73b981bd40a4

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

[THIS DATASET HAS BEEN WITHDRAWN]. Gridded hydrological model river flow estimates on a 1km grid over Great Britain for the period Dec 1980 - Nov 2080. The dataset includes monthly mean river flow, annual maxima of daily mean river flow (water years Oct - Sept), along with the date of occurrence, and annual minima of 7-day mean river flow (years spanning Dec-Nov), along with the date of occurrence (units: m3/s). The data are provided in gridded netCDF files. There is one file for each variable and ensemble member. To aid interpretation, two additional spatial datasets are provided: a) digitally-derived catchment areas and b) estimated locations of flow gauging stations both on the 1km x 1km grid. The data were produced as part of UK-SCAPE (UK Status, Change And Projections of the Environment; www.ceh.ac.uk/ukscape, Work Package 2: Case Study – Water) programme, a NERC-funded National Capability Science Single Centre award number NE/R016429/1. Full details about this dataset can be found at https://doi.org/10.5285/b7a98440-8742-40d5-a518-46dc6420416e

Data comprise a set of broadleaf afforestation scenarios (provided as netCDF files) that may be run with the Joint UK Land Environment Simulator (JULES), a community land surface model. The scenarios are based on the CEH Land Cover 2000 classification. Afforestation takes place according to catchment structure and existing land cover. Scenarios cover twelve river catchments in Great Britain: Dee, Tay, Ouse, Ure, Derwent, Thames, Avon, Tamar, Severn at Bewdley , Severn at Haw Bridge, Ribble and Clyde. Afforestation scenarios relate to two catchment properties: - (1) River network structure and (2) Land use. By using these two catchment properties, in conjunction with different extents of afforestation, up to 288 afforestation scenarios per catchment are generated. This dataset was created as part of the NERC doctoral training partnerships (grant number NE/L002612/1). Full details about this dataset can be found at https://doi.org/10.5285/f484ff54-9139-462e-b37a-347a69f78500

A new monthly long term average (climatology) of Leaf Area Index (LAI) has been developed for use as ancillary data with the Joint UK Land Environment Simulator (JULES) Land Surface Model and the UK Met Office Unified Model. It is derived from an improved version of long time series of LAI from the original Global LAnd Surface Satellite (GLASS) products (http://www.glass.umd.edu/LAI/MODIS/0.05D/). The GLASS data consists of a time series of LAI from Moderate Resolution Imaging Spectroradiometer (MODIS) surface-reflectance data for the period 2000-2014. The MODIS data was provided in a spatial resolution of 1km in a sinusoidal projection and is interpolated into 0.5deg on a geographic latitude/longitude projection in this dataset. The total LAI from MODIS is segregated into five different Plant Functional Types (PFTs) using the fractional coverage of each PFT from the Climate Change Initiative (CCI) Land Cover data. For this reason this new LAI climatology should be used in combination with the CCI PFT data, which is also provided here. Two variables are provided with the dataset containing LAI, each covering the same spatial and time extent. The PFT data provided with this dataset covers a time span of only one year, 2010. - Leaf Area Index (LAI) - LAI is an important parameter in land-surface models, influencing the surface roughness, transpiration rate and the soil water content and temperature. Numerous outputs of vegetation models such as net primary productivity (NPP), evapotranspiration (ET), light absorption by plants (FAPAR), nutrient dynamics etc., are influenced by LAI where it is a key variable in energy and water balance calculations. - Vegetation Canopy Height (H) - H plays an important role in the interface between the atmosphere and land surface and it impacts weather and climate at local to global scales by modulating aerodynamic conductance and vegetation dynamics. Therefore, H is fundamentally needed for the calculation of turbulent exchanges of energy and mass between the atmosphere and the terrestrial ecosystem. One variable is provided with the dataset containing CCI PFTs: - Fractional coverage of 5 PFTS or vegetation classes and 4 land use classes – The 5 PFTs are Broad Leaf, Needle Leaf, C3 Grass, C4 Grass and Shrub. The 4 land use classes are Urban area, Inland Water, Bare Soil and Snow/Ice. Full details about this dataset can be found at https://doi.org/10.5285/6d07d60a-4cb9-44e4-be39-89ea40365236

This dataset is a model output from the European Monitoring and Evaluation Programme (EMEP) model applied to the UK (EMEP4UK) driven by Weather and Research Forecast model meteorology (WRF). It provides UK estimates daily averaged atmospheric composition at approximately 5 km grid for the years 2001 to 2015. The data consists of atmospheric composition and deposition values of various pollutants; including PM10, PM2.5, secondary organic aerosols (SOA), elemental carbon (EC), secondary inorganic aerosols (SIA), sulfur dioxide (SO2), ammonia (NH3), nitrogen oxides (NOX) , and ozone (O3). The EMEP model version used here is rv4.17 and the WRF model version is the 3.7.1. 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. A version of this dataset is available (https://doi.org/10.5285/ca302d30-7b8b-46ec-90b6-67b79df00c92), run with the latest release of the Atmospheric Chemistry Transport Model EMEP (rv4.36) and with the latest NAEI emissions. The new set of model runs covers an extended time period from 2002 until 2021 and is available in a higher resolution (3 km2 x 3km2). Full details about this dataset can be found at https://doi.org/10.5285/b0545f67-e47c-4077-bf3c-c5ffcd6b72c8