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  • Two scripts for classifying remotely sensed data used to produce maps of peatland distribution and predicted peat thickness, using random forest classification and regression. Written in JavaScript for use with Google Earth Engine. These are versions of the scripts used in Hastie et al. (2022), https://doi.org/10.1038/s41561-022-00923-4. Users should also cite Rodríguez-Veiga et al. (2020), https://doi.org/10.3390/rs12152380 . Full details about this application can be found at https://doi.org/10.5285/e337de58-df5e-4412-8aef-28875870f965

  • The data comprises river section, zone and test site delineation, winter Season average NDVI by section and zone 1989-2020, land cover maps seasonally 1989-2020, and derived land cover fractions by section and zone 1989-2020. The data was produced as part of a study to determine how changes in geomorphic form and dynamics due to human alteration to river flows and riparian land management relate to changes in vegetation communities in the Sutlej and Beas Rivers, India. Vegetated and other land cover, including water area, were quantified by winter season NDVI trends (in the plains of Punjab) and seasonal supervised classification of Landsat data for over a 30-year period. The work was supported by the Natural Environment Research Council (Grant NE/S01232X/1). Full details about this dataset can be found at https://doi.org/10.5285/9a96e199-34d0-46f9-9a64-140d300a2531

  • This dataset presents modelled estimates of soil pH at 1km2 resolution across Great Britain. A Generalized Additive Model approach was used with Countryside Survey soil pH data from 2007 and including climate, atmospheric deposition, habitat, soil and spatial predictors. The model is based on soil pH data from 2446 locations across Great Britain and is representative of 0-15 cm soil depth. Soil pH was measured using 10g of field moist soil with 25ml de-ionised water giving a ratio of soil to water of 1:2.5 by weight. 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/10.5285/4b0e364d-61e6-48fb-8973-5eb18fb454cd

  • These data are GIS shapefiles which contain geospatial information describing the location and condition of bridges, buildings and roads in Chamoli District, Uttarakhand, India, following the 7th February 2021 avalanche and debris flow hazard cascade (the so-called ‘Chamoli event’). The dataset also contains a GIS shapefile which contains polygon outlines supporting geomorphological analysis of change in river valleys between the avalanche source and the town of Joshimath. The latter is designed to be used in conjunction with the other data resources contained in this data collection. Full details about this dataset can be found at https://doi.org/10.5285/a763e254-c249-4934-b0fb-c3b808b37db6

  • This data set includes a range of physico-chemical properties measured from topsoil within a wide range of land use types across Wales, collected as part of the Glastir Monitoring and Evaluation Programme (GMEP). The properties included are: soil organic matter (loss on ignition (LOI)), derived carbon concentration, total soil organic carbon (SOC), nitrogen, total soil phosphorous, Olsen-phosphorous (within improved land only), pH, electrical conductivity, soil bulk density of fine earth, fine earth volumetric water content when sampled and soil water repellency - water drop penetration time. The monitoring programme was set up by the Welsh Government in 2013 to monitor the effects of the Glastir agri-environment scheme on the environment and ran from 2013 to 2016. The field survey element was based on a stratified random sampling design of 300 x 1km square sites across Wales, and was managed by the Centre for Ecology & Hydrology. Full details about this dataset can be found at https://doi.org/10.5285/0fa51dc6-1537-4ad6-9d06-e476c137ed09

  • 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 https://doi.org/10.5285/1de712d3-081e-4b44-b880-b6a1ebf9fcd8

  • The dataset contains concentrations of total soil organic carbon, soil carbon fractions, soil CO2 fluxes, soil temperature and moisture in the Peruvian Andes. Measurements and sampling took place between 2010 and 2013. Data were generated as part of a larger NERC project: 'Are tropical uplands regional hotspots for methane and nitrous oxide' Full details about this dataset can be found at https://doi.org/10.5285/3813aef3-71cc-49e6-ba21-495a43363001

  • Measurements of sediment properties (incl. organic and carbonate content), radionuclides (210Pb, 137Cs, 241Am) and elements (including mercury, nickel, copper, zinc, and lead) in lake sediment successions. Radionuclide dating provides a reliable chronology of sediment ages from the mid-19th century (sometimes only 20th century) to the present (2016). The dataset comprises a standardised matrix of multiple measured sediment variables (element values per mass) against stratigraphic depth for 8 lakes. In these water bodies multiple core datasets exist, one collected from the littoral zone, one of intermediate depth and one from the deepest area. The deepest core was used for 210Pb dating. The intermediate and littoral depth cores are not dated, except at Esthwaite where the littoral core (29328_ESTH_LITT.csv) had been previously collected, 210Pb dated and measured for organic and carbonate content. Full details about this dataset can be found at https://doi.org/10.5285/87dec506-ca7f-4b57-a605-486ec9d8cca2

  • The dataset details global positioning system (GPS) locations and elevations recorded for 1323 sampling sites across UK saltmarshes. Between 2018 and 2021, soil was sampled at 1323 locations as part of the Carbon Storage in Intertidal Environments (C-SIDE) project to facilitate the calculation of saltmarsh soil organic carbon stocks and burial rates. Sites were chosen to represent contrasting habitat types in the UK, in particular sediment types, vegetation and sea level history. The work was carried out under the NERC programme - Carbon Storage in Intertidal Environment (C-SIDE), NERC grant reference NE/R010846/1. Full details about this dataset can be found at https://doi.org/10.5285/d61b6033-be45-4682-b4dc-a2f95feefa7d

  • This dataset presents modelled estimates of soil nitrogen concentration (% dry weight soil) at 1km2 resolution across Great Britain. A Generalized Additive Model approach was used with Countryside Survey soil nitrogen data from 2007 and including climate, atmospheric deposition, habitat, soil and spatial predictors. The model is based on soil nitrogen data from 913 locations across Great Britain and is representative of 0-15 cm soil depth. Soil N concentration was determined using a total elemental analyser. 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/10.5285/8ec2d5ae-5d19-4b58-8cf6-aafdad485bb2