This dataset comprises seven ensembles of hydrological model estimates of monthly mean and annual maximum river flows (m3s-1) on a 0.1° × 0.1° grid (approximate grid of 10 km × 10 km) across West Africa for historical (1950 to 2005) and projected future (2006 to 2099) periods. This dataset is the output from the Hydrological Modelling Framework for West Africa, or “HMF-WA” model. The ensembles correspond to historical and three projected future climate scenarios (RCP2.6, RCP4.5 and RCP8.5) with two future scenarios of water use. The scenarios of water use are (i) future water use that varies in line with projected population increases, and (ii) future water use is the same as present day. This dataset is an output from the regional scale hydrological modelling study from African Monsoon Multidisciplinary Analysis-2050 (AMMA-2050) project. Full details about this dataset can be found at https://doi.org/10.5285/6429828f-6a06-4d2d-8f50-4910b18f7ff4

The dataset is the lake polygons from the UK Lakes Portal (https://eip.ceh.ac.uk/apps/lakes/), originally based on OS PANORAMA but this dataset includes data from a number of sources. It has a basic set of attributes including the water body ID (WBID) as well as the computed area and perimeter of each lake. The WBID is commonly used across research institutions and is the same ID as used on the UK Lakes Portal, where more information can be found on each lake in this dataset. This is v3.6, which follows the same versioning as the underlying database. Although the database has seen the majority of the changes since version 1, the polygons have also been changed and improved over that time, mostly fixing issues with lake outlines, but also some new sites being added. Full details about this dataset can be found at https://doi.org/10.5285/b6b92ce3-dcd7-4f0b-8e43-e937ddf1d4eb

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

This dataset presents plant percentage cover by species, average plant cover and species richness for sites along the foredune area of sites distributed between Cape Canaveral (Florida) and Tybee Island (Georgia), USA. Plant cover by species was sampled on three occasions using 0.5 x 0.5m quadrats distributed along 3 transects at up to 28 sites. Observations were conducted in February 2018, July 2018, and January 2019. The coastline was impacted by Hurricane Irma in October 2017 and the data were collected to look at plant composition in coastal foredunes undergoing recovery from the hurricane. The data were collected as part of NERC grant NE/R016593/1, Resilience of a coastal ecosystem following hurricane Irma. Full details about this dataset can be found at https://doi.org/10.5285/100af68f-78e2-4b9d-86b9-5777a5ef38fa

This dataset contains weather conditions, water quality, water chemistry and crustacean zooplankton counts sampled at Loch Leven throughout the year 2019. Loch Leven is a lowland lake in Scotland, United Kingdom. The data were collected as part of a long-term monitoring programme, which began in 1968 and is still underway. Sampling occurs roughly every 2 weeks with laboratory analysis and data processing being performed at the UK Centre for Ecology & Hydrology Edinburgh site. The sampling and processing 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/e404f64c-ddbc-4e3e-8dca-9bea3d68959a

[THIS DATASET HAS BEEN WITHDRAWN]. This dataset consists of measures of topsoil (0-15cm) physico-chemical properties from soils sampled from 49 x 1km squares across Great Britain in 2020 as part of a rolling soil and vegetation monitoring program of 500 1km squares repeated every 5 years. The properties included are: soil organic matter (loss on ignition (LOI)), derived carbon concentration, total soil organic carbon (SOC), nitrogen, Olsen-phosphorous, pH, electrical conductivity, soil bulk density of fine earth and fine earth volumetric water content. The UKCEH Countryside Survey is a unique study or 'audit' of the natural resources of the UK's countryside. The sample sites are chosen from a stratified random sample, based on a 15 by 15 km grid of GB. Surveys have been carried out in 1978, 1984, 1990, 1998 and 2007 by the UK Centre for Ecology & Hydrology (UKCEH) and predecessors, with repeated visits to the majority of squares. The countryside is sampled and surveyed using rigorous scientific methods, allowing us to compare new results with those from previous surveys. In this way, we can detect the gradual and subtle changes that occur in the UK's countryside over time. In addition to soil data, vegetation species data are also gathered by the current phase of the Countryside Survey. 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/cc2aa8f3-95cb-4b85-b883-8ac26e69bdbe

The data comprise measurements of abundance of invasive species, illuminance, air temperature, air humidity, specific leaf area, species richness, species diversity, vegetation cover, biomass, decomposition rate and, carbon efflux that was made during filed campaigns to assess the impact of an invasive grass (Urochloa decumbens) on a tropical savanna (Cerrado) in two nature reserve areas in Brazil. The two experimental areas were located within natural reserves in Southeast and Central Brazil; Estação Ecológica de Itirapina in the municipalities of Brotas and Itirapina in São Paulo State, and Parque Nacional de Brasília in the Distrito Federal. Data were collected during multiple field excursions between March and September 2019. Full details about this dataset can be found at https://doi.org/10.5285/abcabfe2-612c-4cab-b626-641002fc442e

This application is an implementation of a Fuzzy changepoint based approach to evaluate how well numerical models capture local scale temporal shifts in environmental time series. A changepoint in a time series represents a change in the statistical properties of the time series (either mean, variance or mean and variance in this case). These can often represent important local events of interest that numerical models should accurately capture. The application detects the locations of changepoints in two time series (typically one representing observations and one representing a model simulation) and estimates uncertainty on the changepoint locations using a bootstrap approach. The changepoint locations and associated confidence intervals are then converted to fuzzy numbers and fuzzy logic is used to evaluate how well the timing of any changepoints agree between the time series. The app returns individual similarity scores for each changepoint with higher scores representing a better performance of the numerical model at capturing local scale temporal changes seen in the observed record. To use this application, the user will upload a csv file containing the two time series to be compared. This work was supported by Engineering and Physical Sciences Research Council (EPSRC) Data Science for the Natural Environment (DSNE) project (EP/R01860X/1) and the Natural Environment Research Council (NERC) as part the UK-SCAPE programme (NE/R016429/1). Full details about this application can be found at https://doi.org/10.5285/49d04d55-90a7-4106-b8fe-2e75aba228e4

This is a dataset of environmental variables, total invertebrate abundance, and mean invertebrate body mass, sampled at 60 soil habitat patches in the Hengill geothermal valley, Iceland, from May to July 2015. The habitat patches span a temperature gradient of 5-22 °C on average over the sampling period, yet they occur within 2 km of each other and have similar soil moisture, pH, total carbon, and total nitrogen. Full details about this dataset can be found at https://doi.org/10.5285/e00770f3-4acf-4fd5-ba29-0a4dbdca09a4

[This dataset is embargoed until September 30, 2023]. The data provide information on a number of male cricket behaviours organized according to time and duration of the behaviour. Also included are the mean temperature at the ground level for the duration of each observation. Full details about this dataset can be found at https://doi.org/10.5285/f56d3d1c-28f2-4667-90b0-ef352243dd2a