This resource comprises two Jupyter notebooks that includes the model code in python to train a random forest model to predict long-term seasonal nitrate and orthophosphate concentrations at each river reach in Great Britain. The input features considered are catchment descriptors and land cover matched to the reaches. The training data is obtained from the Environmental Agency Water Quality Archive, 2010-2020. This method provides an effective way to map water quality data from stations to the river network. A live demo of a web application to visualize the dataset can be viewed at https://moisture-wqmlviewer.datalabs.ceh.ac.uk/wqml_viewer Full details about this application can be found at https://doi.org/10.5285/ba208b6c-6f1a-43b1-867d-bc1adaff6445

Results of a survey undertaken in 2018 involving a range of open and closed questions intended to elicit local residents’ values they attach to the importance of coastal attributes and their perceptions of various tidal and wave energy development characteristics. Three case study sites were selected: Weston-super-Mare, Minehead, and the Taw-Torridge Estuary, South-West UK. Full details about this dataset can be found at https://doi.org/10.5285/e5190fd0-2995-42aa-aca0-80714abde768

This dataset consists of the 1km raster, dominant target class version of the Land Cover Map 1990 (LCM1990) for Northern Ireland. The 1km dominant coverage product is based on the 1km percentage product and reports the habitat class with the highest percentage cover for each 1km pixel. The 21 target classes are based on the Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompass the entire range of UK habitats. This dataset is derived from the vector version of the Land Cover Map, which contains individual parcels of land cover and is the highest available spatial resolution. LCM1990 is a land cover map of the UK which was produced at the UK Centre for Ecology & Hydrology by classifying satellite images (mainly from 1989 and 1990) into 21 Broad Habitat-based classes. It is the first in a series of land cover maps for the UK, which also includes maps for 2000, 2007, 2015, 2017, 2018 and 2019. LCM1990 consists of a range of raster and vector products and users should familiarise themselves with the full range (see related records, the UKCEH web site and the LCM1990 Dataset documentation) to select the product most suited to their needs. 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/d33593d7-5c4d-419e-924c-b341847fd6ae

This model code for object oriented data analysis of surface motion time series in peatland landscapes provides the procedure to assess peatland condition using object oriented data analysis. The model code assesses peatland condition according to which cluster each surface motion time series is assigned, based on key measures capturing differences between the time series. It can be run on any machine with R. Full details about this application can be found at https://doi.org/10.5285/dbdb9f19-c039-4a73-b590-e1acc7f79df4

The dataset includes six files of UK physical river characteristics including five files of gridded data at 1km × 1km resolution and one comma separated table. The data includes: • Drainage directions (D8 flow method), ESRI coding • Drainage directions (D8 flow method), unifhy (python hydrology framework) coding • Catchment areas (km2) • Widths of bankfull rivers (m) • Depths of bankfull rivers (m) • NRFA gauging station locations (easting (m), northing (m)) Two versions of drainage directions are provided, both have the same drainage directions but different numbering systems. The comma separated NRFA (National River Flow Archive) gauging station locations table provides the best locations of 1499 river flow gauging stations on the 1km grids, together with the approximate error in the 1km × 1km gridded delineation of the upstream catchment area. All datasets are provided on the British National Grid. Full details about this dataset can be found at https://doi.org/10.5285/8df65124-68e9-4c68-8659-1c6b82c735e9

[This dataset is embargoed until September 1, 2024]. Vegetation data from field surveys undertaken at two experimental trials at Martin Down NNR, to investigate the potential for reducing dense Brachypodium pinnatum cover (experiment 1) and preventing further expansion of sparse cover (experiment 2). Experiment 1 explores the use of herbicide and reseeding, whilst experiment 2 examines cutting and grazing in the spring, autumn and both seasons. Percentage cover of all vascular plant species were recorded in 50 cm x 50 cm quadrats in each treatment replicate for both experiments. Surveys were undertaken in 2019 as a baseline before the experiments commenced, and post treatment in 2020, 2021 and 2022. Full details about this dataset can be found at https://doi.org/10.5285/f15e64c0-db65-40ec-8b6d-50573f5f6694

This is a 25m pixel data set representing the land surface of Northern Ireland, classified into 21 UKCEH land cover classes, based upon Biodiversity Action Plan broad habitats. It is a three-band raster in GeoTiff format, produced by rasterising three properties of the classified land parcels dataset. The first band gives the most likely land cover type; the second band gives the per-parcel probability of the land cover, the third band is a measure of parcel purity. The probability and purity bands (scaled 0 to 100) combine to give an indication of uncertainty. A full description of this and all UKCEH LCM2020 products are available from the LCM2020 product documentation. Full details about this dataset can be found at https://doi.org/10.5285/c1d73bd3-33aa-4a5f-aac5-47c403c2a0e6

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

[This dataset is embargoed until January 2, 2024]. The data set contains grain size distributions, organic matter (OM) content and trace metal distribution (including Fe, Zn, Cu, Cr and Pb) of 37 shallow cores of sediments sampled from dams across the Limpopo River Basin. The dams include: Gaborone, Lotsane and Shashe dams in Botswana; Houtrivier, Nwanedi and Mutshedzi dams in South Africa; Ripple Creek and Zhovhe dams in Zimbabwe; and Massingir Dam in Mozambique. Data from 2 cores sampled from an oxbow lake in Mozambique are also included. The cores were collected with a gravity corer using PVC pipes of 5 cm diameter by a team from Botswana International University of Science and Technology (BIUST) led by Dr. Franchi between July 2018 and April 2021. Full details about this dataset can be found at https://doi.org/10.5285/b8db8239-3bde-454a-aa75-d1cec24c8763

This dataset comprises operational data for a novel decentralised drinking water treatment platform (DWTP) and water quality data for three sampling points within the treatment system. The dataset captures a period of 12 weeks between November 2019 and February 2020. The DWTP was continuously operational throughout this time period with samples from across the system being collected at nine timepoints throughout this three-month period. Operational data from the remote telemetry unit on the DWTP reports flow rate through the system, transmembrane pressure for the ultrafiltration and both the oxidation-reduction potential (ORP) and the free chlorine of the treated water. Data is also provided for a suite of biological, physicochemical, chemical, metals, hydrocarbons and disinfection byproducts. This data was produced from water samples which were sent to an accredited UK laboratory. The data reports for all samples for each of the 3 sampling locations within the treatment system are summarised into .csv files. Information about the location of each sampling point and the level of treatment received at each point can be found in the supporting documentation. Full details about this dataset can be found at https://doi.org/10.5285/98148025-e8ef-40f9-907e-dae8b28106d2