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  • 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 ( 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

  • Primary forest cover and forest cover loss in Wallacea for the years 2000-2018 to train a deforestation model and produce maps of projected probability of deforestation until 2053. Full details about this dataset can be found at

  • This dataset contains responses to a set of evaluation questions on flood resilience improvement within communities in the Katakwi District, Uganda. This data were created as part of the NIMFRU project (National-Scale Impact Based Forecasting of Flood Risk in Uganda) and consists of 21 semi-structured interviews. These have been completed by community members from the project target communities of Anyangabella, Agule and Kaikamosing which are all found in the Katakwi district. Five of the interviews were completed by local district officers. The data were collected in December 2020. These data were collected to understand how communities resilience had changed as a result of the NIMFRU project. Full details about this dataset can be found at

  • Data are presented showing for individual seedling, herbivory damage at the leaf level; galls, pathogens, trail herbivory presence/absence qualitative data; and leaf mortality. Data were collected in each leaf from a plot based fertilisation experiment. The experiment was carried out at the Biological Dynamics of Forest Fragments Project (BDFFP) approximately 100 km north of Manaus. Data were collected bimonthly from February 2019 to January 2020, by the dataset first author. Leaf loss in percentage was made using the choice for direct visual estimate. We also followed the recommendations proposed by the authors, sectoring the leaves with a millimetre grid, improving measurement accuracy. The presence of Galls, pathogens and trail herbivory presence/absence qualitative data were also collected in each leaf. The work was carried out as part of the Amazon Fertilization Experiment (AFEX), funded by the Natural Environment Research Council (NERC), Award reference NE/L007223/1, the Brazilian government (Researcher scholarship) and the Biological Dynamics of Forest Fragments Project (BDFFP - logistical support and camps maintanance). Full details about this dataset can be found at

  • Data on timing of breeding, breeding success and diet of the European shag, sampled from the Isle of May population. The data were collected between 1985 and 2015 by visually checking nests and collecting regurgitated diet samples. These data are part of the Isle of May long-term study to assess population trends of seabirds under environmental change (IMLOTS Full details about this dataset can be found at

  • Gridded land use map of Peninsular Malaysia with a resolution of approximate 25 meters for the year 2018. The map includes nine different classes: 1) non-paddy agriculture, 2) paddy fields, 3) rural residential, 4) urban residential, 5) commercial/institutional, 6) industrial/infrastructure, 7) roads, 8) urban and 9) others. The land use map was created as part of the project “Malaysia - Flood Impact Across Scales”. The project is funded under the Newton-Ungku Omar Fund ‘Understanding of the Impacts of Hydrometeorological Hazards in South East Asia’ call. The grant was jointly awarded by the Natural Environment Research Council and the MYPAIR Scheme under the Ministry of Higher Education of Malaysia. Full details about this dataset can be found at

  • This R application is an implementation of state tagging approach for improved quality assurance of environmental data. The application returns state-dependent prediction intervals on input data. The states are determined based on clustering of auxiliary inputs (such as meteorological data) made on the same day. The method provides contextual information to assess the quality of observational data and is applicable to any point-based, daily time series observational data. To use this application, the user will need to input two separate csv files: one for state variables and the other for observations. 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 application can be found at

  • 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

  • The R code "carbon_stock_calculations.R" estimates aboveground carbon stocks for 49 plots in 14 fragmented forest sites and 4 continuous forest sites in Sabah, Malaysian Borneo, using the vegetation dataset 'Vegetation and habitat data for fragmented and continuous forest sites in Sabah, Malaysian Borneo, 2017'. The 14 fragmented sites were all in Roundtable on Sustainable Palm Oil-certified oil palm plantations, and are hereafter termed 'conservation set-asides'. The code also estimates the aboveground carbon stocks of oil palm plantations for comparison. The R code "analyses_and_figures.R" runs analyses and makes figures of aboveground carbon stocks and associated plant diversity for these sites, as presented in Fleiss et al. (2020) This R code was created in order to investigate the following: (1) to establish the value of conservation set-asides for increasing oil palm plantation aboveground carbon stocks; (2) to establish whether set-asides with high aboveground carbon stocks can have co-benefits for plant diversity; (3) to compare the carbon stocks and vegetation structure of conservation set-asides with that of continuous forest, including assessing tree regeneration potential by examining variation in seedling density; (4) to examine potential drivers of variation in aboveground carbon stocks of conservation set-asides (topography, degree of fragmentation, and soil parameters); (5) to scale-up the estimates of the aboveground carbon stocks of conservation set-asides, in order to predict average carbon stocks of oil palm plantations with and without set-asides, and for varying coverage of set-asides across the plantation. Full details about this application can be found at

  • Data comprise tree trait data collected during September and October 2016 (the peak dry season), in the Caxiuanã National Forest Reserve, eastern Amazon, Brazil. 17 traits (including plot type, tree species name, diameter at breast height, tree light score, carboxylation capacity, electron transport capacity, leaf respiration in the dark, stomatal conductance, stem CO2 efflux, leaf mass per area, leaf nitrogen and phosphorus content, branch wood density, leaf water potential, xylem pressure, lumen conductance, percentage loss of conductivity, hydraulic Safety Margin and leaf area to sapwood area ratio) of 176 trees (most common genera) were sampled across two experimental plots: a one-hectare through-fall exclusion plot with a plastic panel structure that excludes 50% of the canopy through-fall and has done since 2002 and a corresponding one-hectare control plot without any drought structure. This data comes from the Caxiuanã through-fall exclusion (TFE) experiment located in the terra firma forest, on yellow oxisol soils at 15 m above sea level, with a mean annual rainfall between 2,000–2,500 mm and a pronounced dry season between June and November. Full details about this dataset can be found at