Seedlings were grown in soil collected from an experiment at Hambleton Forest, North Yorkshire. Soils originated from plots containing either Scots pine or birch monocultures, or a mixture of both species. In the lab, pairs of seedlings of birch and Scots pine were grown as monocultures and mixtures from each of the three plot treatments. The data represent the biomass, leaf C:N ratio and mycorrhizal status determined after 20 weeks. Full details about this dataset can be found at https://doi.org/10.5285/7991871e-382f-422f-b5b2-91b4f6d8598f

This dataset provides the projections of meteorological, hydrological, and agricultural droughts for the near-future period (2021-2050) for the Mun River basin, in Northeast Thailand. Near future drought characteristics (duration, intensity, and severity) are projected for climate change (CC) scenario using 8 CMIP6 climate models (CNRM-CM6-1, CNRM-CM6-1-HR, EC-Earth3P, EC-Earth3P-HR, HadGEM3-GC31-HH, HadGEM3-GC31-HM, HadGEM3-GC31-MM, HadGEM3-GC31-LL) for SSP5-8.5 scenario. Full details about this dataset can be found at https://doi.org/10.5285/b11c040d-c3c0-43c5-a7c0-442b067dc526

The dataset contains carbon dioxide and methane emissions, as well as resorufin production (as a proxy for microbial metabolic activity) and dissolved oxygen concentrations, resulting from laboratory incubation experiments of streambed sediments. The sediments were collected from the upper 10 centimetres of the streambed in the River Tern and the River Lambourn in September 2015, with three samples collected from each river. These samples were collected from three areas: silt-dominated sediment underneath vegetation (fine), sand-dominated sediment from unvegetated zones (medium) and gravel-dominated sediment from unvegetated zones (coarse). The sediment was used in laboratory incubation experiments to determine the effect of temperature, organic matter content, substrate type and geological origin on streambed microbial metabolic activity, and carbon dioxide and methane production. The work was carried out as part of a Natural Environment Research Council (NERC) funded PhD (NERC award number 1602135). The work was also part funded through the Seventh Framework Programme (EU grant number 607150). Full details about this dataset can be found at https://doi.org/10.5285/3a0a5132-797c-4ed5-98b9-1c17eaa2f2b7

This dataset contains time series observations of surface-atmosphere exchanges of net ecosystem carbon dioxide exchange (NEE), sensible heat (H) and latent heat (LE), and momentum (τ) measured at a short rotation coppice willow plantation in Lincolnshire, UK. Turbulent flux densities were monitored using the micrometeorological eddy covariance (EC) technique between 13th October 2009 and 15th May 2013. The dataset includes ancillary weather and soil physics observations, as well as variables describing atmospheric turbulence and the quality of the turbulent flux 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 dataset can be found at https://doi.org/10.5285/9e14539e-b376-4a70-87a0-f8725f84bae9

The dataset contains monthly measurements of chlorophyll fluorescence measured using a Walz MINI-PAM II. The chlorophyll flouresence of growing tips of moss samples were measured following saturating pulses three times in the field before dark adaptation three further measurements. Yield of photosystem II, non-photochemical quenching and electron transport rate were calculated. Field work was carried out approximately monthly from April 2017 until September 2018. Full details about this dataset can be found at https://doi.org/10.5285/249034d3-2f4d-42c6-a3cd-113ff3a960c5

This dataset contains GPS data fixes (WGS84 format) from 32 European nightjars (Caprimulgus europaeus) . The data contains additional information on identity of the bird, date, time of fix acquisition, and the associated site and night number (between 6 and 17 nights of data, varying between individuals). These data were collected on the Humberhead peatlands NNR, South Yorkshire, from 2015 until 2018. Birds were caught in mist nets and tail-mounted miniaturised GPS tags (Pathtrack, Otley, UK; <3% bird bodyweight) were fitted by BTO-licensed researchers. Data were collected as part of a NERC ACCE-funded PhD. Full details about this dataset can be found at https://doi.org/10.5285/aa20f8c4-bbdb-4dfa-82b4-b9b3fd8f34eb

This dataset contains the areas affected by landslides triggered by Typhoon Parma in the area of Itogon (Benguet, Philippines) between the 2nd and 5th October 2009. The polygons were mapped using Google Earth imagery dated 31 December 2003 for pre-event and images and 31 December 2009 for post-event images. The area has an extension of 150 km2. Full details about this dataset can be found at https://doi.org/10.5285/2e15dbd2-71c3-4e86-aa90-6029d37bd417

This dataset contains pH, turbidity, conductivity and viral concentration information in river and estuarine water, wastewater, sediment and mussel samples collected in the Conwy River and estuary. The aim of data collection was to monitor wastewater contamination in the freshwater-marine continuum. Samples were collected by trained members of staff from Bangor University at four weekly between March 2016 and August 2017. Treated and untreated wastewater samples were collected at four wastewater treatment plants along the Conwy River. Surface water samples were collected at four sites, sediments at three sites and mussels at two sites. The VIRAQUA project was funded by the Natural Environment Research Council (NERC) under the Environmental Microbiology and Human Health (EMHH) Programme (NE/M010996/1) Full details about this dataset can be found at https://doi.org/10.5285/5d19f6e2-1383-41ed-92d2-138d95bf4c72

[THIS DATASET HAS BEEN WITHDRAWN]. This dataset contains daily and sub-daily hydrometeorological and soil observations from COSMOS-UK (cosmic-ray soil moisture) monitoring network from October 2013 to the end of 2019. These data are from 51 sites across the UK recording a range of hydrometeorological and soil variables. Each site in the network records the following hydrometeorological and soil data at 30 minute resolution: Radiation (short wave, long wave and net), precipitation, atmospheric pressure, air temperature, wind speed and direction, humidity, soil heat flux, and soil temperature and volumetric water content (VWC), measured by point senors at various depths. Each site hosts a cosmic-ray sensing probe; a novel sensor technology which counts fast neutrons in the surrounding atmosphere. In combination with the recorded hydrometeorological data, neutron counts are used to derive VWC over a field scale (COSMOS VWC), at two temporal resolutions (hourly and daily). The presence of snow leads to erroneously high measurements of COSMOS VWC due to all the extra water in the surrounding area. Included in the daily data are indications of snow days, on which, the COSMOS VWC are adjusted and the snow water equivalent (SWE) is given. The potential evapotranspiration (PE), derived from recorded hydrometeorological and soil are also included at daily resolution. Two levels of quality control are carried out, firstly data is run through a series of automated checks, such as range tests and spike tests, and then all data is manually inspected each week where any other faults are picked up, including sensor faults or connection issues. Quality control flags are provided for all recorded (30 minute) data, indicating the reason for any missing data. 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/b5c190e4-e35d-40ea-8fbe-598da03a1185

The dataset provides raster gridded estimates of open water and inundated vegetation for the Barotseland Region in Western Zambia. There are a total of 55 images covering the period 2016-2019 at a spatial resolution of 10m. The images were generated using an automatic classification routine applied to Sentinel-1 radar imagery, with classification refinements made using ancillary datasets such as the Global Urban Footprint, and the Height Above Nearest Drainage terrain derivative generated using SRTM digital elevation data. These data are valuable for a range of applications including public health and water resources. Full details about this dataset can be found at https://doi.org/10.5285/4ef558d2-05d4-4ae2-988e-a5c2450b95dd