This dataset is a model output, from the Grid-to-Grid hydrological model driven by observed climate data (CEH-GEAR rainfall and Oudin temperature-based potential evaporation). It provides monthly mean flow (m3/s) and soil moisture (mm water/m soil) on a 1 km grid for the period 1891 to 2015. 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 (http://www.mariusdroughtproject.org/). Full details about this dataset can be found at https://doi.org/10.5285/f52f012d-9f2e-42cc-b628-9cdea4fa3ba0

This dataset is a model output from the JULES land surface model driven with the Watch Forcing Data methodology applied to Era-Interim (WFDEI) data. It provides monthly global methane emissions from natural wetlands on 0.5 x 0.5 degree grid between 1980-2014. It includes the following variables: - fch4_wetl: modelled methane flux from natural wetland, in mg CH4 m-2 day-1 - fwetl: fraction of wetland - cs: soil carbon in each of these four soil carbon pools: decomposable plant material, resistant plant material, microbial biomass and humus), in kg m-2 - t_soil: sub-surface temperature of the four modelled soil layers (0-0.1 m, 0.1-0.35 m, 0.35-1.0 m and 1.0-2.0 m), in K Full details about this dataset can be found at https://doi.org/10.5285/6ce61e91-6912-4fe2-a095-12136af86347

This dataset contains modelled vegetation carbon output from the land surface model JULES, along with the temperature and rainfall outputs (which were originally inputted) at a monthly, 1.5km resolution. There are four different JULES simulations, using two different climate projections (global climate sensitivity of 3.5K and highest global climate sensitivity of 7.1K) under a constant, present day atmospheric CO2 and a CO2 pathway that follows the SRES (Special Report on Emissions Scenarios) A1B scenario. Full details about this dataset can be found at https://doi.org/10.5285/f493ad5c-585c-475d-a374-2f77b5866bc4

The dataset contains daily and monthly surface water, energy and carbon fluxes, and state variables for Great Britain over the period between 1961 and 2015. The data was obtained from a 55 years simulation with the JULES Land Surface Model, at 1 km spatial resolution and driven by the meteorological dataset CHESS-met v1.2 (Robinson et al., 2017; https://doi.org/10.5285/b745e7b1-626c-4ccc-ac27-56582e77b900). The data comes in both monthly (all variables) and daily (only variables with no z dimension) averages. The variables are: total evapotranspiration and components (kg m-2 s-1), runoff (kg m-2 s-1), surface temperature (K), soil moisture (kg m-2), soil temperature (K), snow mass (kg m-2). latent and sensible heat (W m-2), net and gross primary productivities (kg C m-2 s-1), plant respiration (kg C m-2 s-1). The z dimension may refer, if present, to tile (surface type), pft (plant functional type) or soil (soil layer). This simulation forms the basis for new research paper by Blyth et al (2017, under review). Full details about this dataset can be found at https://doi.org/10.5285/c76096d6-45d4-4a69-a310-4c67f8dcf096

1 km gridded estimates of daily and monthly rainfall for Great-Britain and Northern Ireland (together with approximately 3000 km2 of catchment in the Republic of Ireland) from 1890 to 2017. The rainfall estimates are derived from the Met Office national database of observed precipitation. To derive the estimates, monthly and daily (when complete month available) precipitation totals from the UK rain gauge network are used. The natural neighbour interpolation methodology, including a normalisation step based on average annual rainfall, was used to generate the daily and monthly estimates. The estimated rainfall on a given day refers to the rainfall amount precipitated in 24 hours between 9am on that day until 9am on the following day. The CEH-GEAR dataset has been developed according to the guidance provided in BS 7843-4:2012. Full details about this dataset can be found at https://doi.org/10.5285/ee9ab43d-a4fe-4e73-afd5-cd4fc4c82556

This dataset presents modelled estimates of soil carbon concentration (g kg-1) at 1km2 resolution across Great Britain. A Generalized Additive Model approach was used with Countryside Survey soil carbon data from 2007 and including climate, atmospheric deposition, habitat, soil and spatial predictors. The model is based on soil carbon data from 2446 locations across Great Britain and is representative of 0-15 cm soil depth. Loss-on-ignition (LOI) was determined by combustion of 10g dry soil at 375 degrees Celsius for 16 hours; carbon concentration was estimated by multiplying LOI by a factor of 0.55. The Countryside Survey looks at a range of physical, chemical and biological properties of the topsoil from a representative sample of habitats across the UK. 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/10.5285/3aaa52d3-918a-4f95-b065-32f33e45d4f6

5km gridded Standardised Precipitation Index (SPI) data for Great Britain, which is a drought index based on the probability of precipitation for a given accumulation period as defined by McKee et al [1]. There are seven accumulation periods: 1, 3, 6, 9, 12, 18, 24 months and for each period SPI is calculated for each of the twelve calendar months. Note that values in monthly (and for longer accumulation periods also annual) time series of the data therefore are likely to be autocorrelated. The standard period which was used to fit the gamma distribution is 1961-2010. The dataset covers the period from 1862 to 2015. This version supersedes previous versions (version 2 and 3) of the same dataset due to minor errors in the data files. NOTE: the difference between this dataset with the previously published dataset "Gridded Standardized Precipitation Index (SPI) using gamma distribution with standard period 1961-2010 for Great Britain [SPIgamma61-10]" (Tanguy et al., 2015; https://doi.org/10.5285/94c9eaa3-a178-4de4-8905-dbfab03b69a0) , apart from the temporal and spatial extent, is the underlying rainfall data from which SPI was calculated. In the previously published dataset, CEH-GEAR (Tanguy et al., 2014; https://doi.org/10.5285/5dc179dc-f692-49ba-9326-a6893a503f6e) was used, whereas in this new version, Met Office 5km rainfall grids were used (see supporting information for more details). The methodology to calculate SPI is the same in the two datasets. [1] McKee, T. B., Doesken, N. J., Kleist, J. (1993). The Relationship of Drought Frequency and Duration to Time Scales. Eighth Conference on Applied Climatology, 17-22 January 1993, Anaheim, California. Full details about this dataset can be found at https://doi.org/10.5285/233090b2-1d14-4eb9-9f9c-3923ea2350ff

[THIS DATASET HAS BEEN WITHDRAWN]. The topographic index is a hydrological quantity describing the propensity of the soil at landscape points to become saturated with water as a result of topographic position (i.e. not accounting for other factors such as climate that also affect soil moisture but are accounted for separately). Modern land surface models require a characterisation of the land surface hydrological regime and this parameter allows the use of the TOPMODEL hydrological model to achieve this .This Geographic Information System layer is intended for use as topographic ancillary files for the TOPMODEL routing model option within the Joint UK Land Environment Simulator (JULES) land surface model. The topographic index variable here is directly comparable to the compound topographic index available from United States Geological Survey's Hydro1K at 30 sec resolution. Full details about this dataset can be found at https://doi.org/10.5285/ce391488-1b3c-4f82-9289-4beb8b8aa7da

The dataset contains 1km gridded estimates of hourly rainfall for Great-Britain for the period 1990-2014. The estimates are derived by applying the nearest neighbour interpolation method to a national database of hourly raingauge observations collated by Newcastle University and the Centre for Ecology & Hydrology (CEH). These interpolated hourly estimates were then used to temporally disaggregate the CEH-GEAR daily rainfall dataset. The estimated rainfall on a given hour refers to the rainfall amount accumulated in the previous hour. The dataset also contains data indicating the distance between the grid point and the closest recording raingauge used in its interpolation. When this distance is greater than 50km, or there is zero rainfall recorded in the closest gauge, the daily value is disaggregated using a design storm. The dataset therefore also contains a flag indicating if the design storm was used. These data are provided as an indicator of the quality of the estimates. Full details about this dataset can be found at https://doi.org/10.5285/d4ddc781-25f3-423a-bba0-747cc82dc6fa

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 annual averaged atmospheric composition at approximately 5Km grid for the year 2015 for a set of vegetation removal experiments. * UK current vegetation * UK no vegetation * UK Urban current vegetation * UK No urban vegetation * UK Urban 25% open greenspace conversion * UK Urban 50% open greenspace conversion The EMEP model version used here is the rv4.10 and 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 and the Office of National Statistics to support the ONS-Defra natural capital accounting programme in the UK. Full details about this dataset can be found at https://doi.org/10.5285/bad6721c-574b-4229-b023-c7b13ae4c099