This is the land parcels (polygon) dataset for the UKCEH Land Cover Map of 2018(LCM2018) representing Great Britain. It describes Great Britain's land cover in 2018 using UKCEH Land Cover Classes, which are based on UK Biodiversity Action Plan broad habitats. This dataset was derived from the corresponding LCM2018 20m classified pixels dataset. All further LCM2018 datasets for Great Britain are derived from this land parcel product. A range of land parcel attributes are provided. These include the dominant UKCEH Land Cover Class given as an integer value, and a range of per-parcel pixel statistics to help to assess classification confidence and accuracy; for a full explanation please refer to the dataset documentation. LCM2018 represents a suite of geospatial land cover datasets (raster and polygon) describing the UK land surface in 2018. These were produced at the UK Centre for Ecology & Hydrology by classifying satellite images from 2018. LCM2018 was simultaneously released with LCM2017 and LCM2019. These are the latest in a series of UKCEH land cover maps, which began with the 1990 Land Cover Map of Great Britain (now usually referred to as LCM1990) followed by UK-wide land cover maps LCM2000, LCM2007 and LCM2015. 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/10.5285/9f7f7f70-5137-4bfc-a6a3-f91783d5a6a6

The dataset describes the inundation results simulated by high-performance integrated hydrodynamic modelling system (HiPIMS) model for the pluvial flooding and fluvial flooding in Can Tho city Vietnam. Wherein, the pluvial flooding results simulated by HiPIMS are driven by the design rainfall in the 2, 5, 10, 20, 50, and 100 years return period, respectively, and the fluvial flooding results simulated by HiPIMS are driven by the river water level boundary in 2011. Full details about this dataset can be found at https://doi.org/10.5285/585ce4f2-0070-490f-adb2-ed7f1207605b

Data comprise radiocaesium concentrations in soil, vegetation, wildlife and fungi analysed from samples collected from throughout Great Britain after the 1986 Chernobyl accident by the Centre for Ecology & Hydrology (CEH), formerly the Institute of Terrestrial Ecology (ITE). National level vegetation surveys were conducted in May 1986, October 1986 and Spring 1987. More intensive surveys of vegetation (grass and heather) and wildlife (grouse, fox, etc.) in restricted areas were carried out in Cumbria, Wales and North Yorkshire in 1989, 1990, 1991 and 1993. Surveys of fungi were carried out between 1994 and 1997. The data are suitable for interpolation to create spatially variable surfaces suitable for input into models. Full details about this dataset can be found at https://doi.org/10.5285/d0a6a8bf-68f0-4935-8b43-4e597c3bf251

The dataset includes data on vegetation composition, flower counts, berry availability over winter, pollinator visitation rates, invertebrate, hedge structure and hedgerow regrowth from a set of long running hedgerow experiments. There were three experiments in total. Experiment 1 was based in Monks Wood, Cambridgeshire, and was used to investigate the long-term effects of timing and frequency of cutting on resource provision for wildlife. Experiment 2 was based at 5 sites across Oxfordshire, Buckinghamshire and Devon and was used to investigate the effect of timing, intensity and frequency of hedgerow cutting. Experiment 3 was based at 5 sites across Cambridgeshire, Northamptonshire, Buckinghamshire and Oxfordshire and was used to investigate the effects of different rejuvenation techniques on hedgerows. All three experiments were randomised plot experiments (full details of plots and their treatments can be found in the supporting documentation. The majority of the data was collected between 2010 and 2016 but for one experiment there is data from 2005. The long running hedgerow experiments had two linked aims focused on management to maintain and restore the hedgerow resource under the agri-environment schemes: • to examine the effects of simple cutting management regimes promoted by Entry Level Stewardship (ELS) and Higher Level Stewardship (HLS) on the quality and quantity of wildlife habitat, and food resources in hedgerows; and • to identify, develop and test low-cost, practical options for hedgerow restoration and rejuvenation applicable at the large-scale under both ELS and HLS. This research was funded by Defra (project number BD2114: Effects of hedgerow management and restoration on biodiversity) and managed by the UK Centre for Ecology & Hydrology (UKCEH). Full details about this dataset can be found at https://doi.org/10.5285/95259623-f0b6-4328-a0e3-4aec09ede5b5

This data collection results from abundance surveys of 7 species of weeds in ca. 500 lowland arable fields in 49 farms over three years. Each field was divided into large grids of 20x20 metre cells, and the density of seven species was estimated three times a year. The study is part of the NERC Rural Economy and Land Use (RELU) programme. In the context of changing external and internal pressures on UK agriculture, particularly those associated with the ongoing reform of the EU Common Agricultural Policy, it is imperative to determine whether all of the various dimensions of sustainability - including the relevant economic and environmental objectives as well as social and cultural values - can be integrated successfully at the farm and landscape levels. Although the ways in which economic, technological, and regulatory changes are likely to affect the profitability and management of farms of varying size are reasonably well understood, there is not the knowledge or understanding to predict the resulting effects on biodiversity. For example, the effect of changes in arable farming practices on field weeds and, in turn, on habitats and food supply required to sustain farm birds is a case in point. This knowledge is critical, however, if we are to understand the ecological consequences of changes in agricultural policy. Furthermore, it is also important if we are to design and justify changes in farming methods that can not only enhance nature conservation, but do this is ways that are practical and appealing from a farmer's point of view. This understanding is essential if we are to achieve an agriculture that is sustainable in both economic and environmental terms and is widely perceived to have social and cultural value. A consistent theme in all components of this research project is to understand the behaviour (of farmers, weeds or birds) and then use this information to produce predictive models. Whilst there have been a number of models of economic behaviour, weed populations and bird populations - including many by the research team here - the really novel component of this research is to integrate these within one framework. Farmer interviews on economic attitudes and preferences associated with and importance of different land-use objectives to lowland arable farmers are available at the UK Data Archive under study number 6728 (see online resources). Further documentation for this study may be found through the RELU Knowledge Portal and the project's ESRC funding award web page (see online resources).

This is the 25m rasterised land parcels dataset for the UKCEH Land Cover Map of 2018(LCM2018) representing Northern Ireland. It describes Northern Ireland's land cover in 2018 using UKCEH Land Cover Classes, which are based on UK Biodiversity Action Plan broad habitats. This dataset was derived by rasterising the corresponding LCM2018 land parcels dataset into 25m pixels. It is provided as a 3-band, 8-bit integer raster. The first band is the UKCEH Land Cover Class identifier. Bands 2 and 3 are indicators of classification confidence. For a fuller description please refer to the product documentation. LCM2018 represents a suite of geospatial land cover datasets (raster and polygon) describing the UK land surface in 2018. These were produced at the UK Centre for Ecology & Hydrology by classifying satellite images from 2018. LCM2018 was simultaneously released with LCM2017 and LCM2019. These are the latest in a series of UKCEH land cover maps, which began with the 1990 Land Cover Map of Northern Ireland (now usually referred to as LCM1990) followed by UK-wide land cover maps LCM2000, LCM2007 and LCM2015. 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/10.5285/396c1249-84f0-4ca0-891d-4188d750c1ee

The Digital Elevation Model (DEM) domain includes the tidally influenced Conwy estuary, downstream of the Cwmlanerch river gauge on the River Conwy and extending offshore into Conwy Bay and the Menai Strait at the coastal boundary. A number of sources were combined to generate the land elevation data, including (a) seabed bathymetry, (b) land elevations and (c) location and heights of existing flood defences. The domain topography was based on the marine DEM, Lidar Digital Terrain Model (DTM) and Ordnance Survey Terrain 5m DTM. The Lidar DTM data was used to check and, where necessary, augment the flood defences vector database. Full details about this dataset can be found at https://doi.org/10.5285/7217e6c0-46c7-4f87-bc36-589f884d3b02

This is the land parcels (polygon) dataset for the UKCEH Land Cover Map of 2017 (LCM2017) representing Northern Ireland. It describes Northern Ireland's land cover in 2017 using UKCEH Land Cover Classes, which are based on UK Biodiversity Action Plan broad habitats. This dataset was derived from the corresponding LCM2017 20m classified pixels dataset. All further LCM2017 datasets for Northern Ireland are derived from this land parcel product. A range of land parcel attributes are provided. These include the dominant UKCEH Land Cover Class given as an integer value, and a range of per-parcel pixel statistics to help to assess classification confidence and accuracy; for a full explanation please refer to the dataset documentation. LCM2017 represents a suite of geospatial land cover datasets (raster and polygon) describing the UK land surface in 2017. These were produced at the UK Centre for Ecology & Hydrology by classifying satellite images from 2017. LCM2017 was simultaneously released with LCM2018 and LCM2019. These are the latest in a series of UKCEH land cover maps, which began with the 1990 Land Cover Map of Great Britain (now usually referred to as LCM1990) followed by UK-wide land cover maps LCM2000, LCM2007 and LCM2015. 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/efb98222-5b9a-4d56-990d-5ab85eaf187e

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 at a Miscanthus x. giganteus Greef et Deu plantation in Lincolnshire, UK. Turbulent flux densities were monitored using the micrometeorological eddy covariance (EC) technique between 30th April 2008 and 18th February 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/10.5285/00487c70-b74e-4c91-ab0c-31735c2e3b13

This dataset consists of computer code transcripts for two proprietary flood risk models from a study as part of the NERC Rural Economy and Land Use (RELU) programme. This project was conceived in order to address the public controversies generated by the risk management strategies and forecasting technologies associated with diffuse environmental problems such as flooding and pollution. Environmental issues play an ever-increasing role in all of our daily lives. However, controversies surrounding many of these issues, and confusion surrounding the way in which they are reported, mean that sectors of the public risk becoming increasingly disengaged. To try to reverse this trend and regain public trust and engagement, this project aimed to develop a new approach to interdisciplinary environmental science, involving non-scientists throughout the process. Examining the relationship between science and policy, and in particular how to engage the public with scientific research findings, a major diffuse environmental management issue was chosen as a focus - flooding. As part of this approach, non-scientists were recruited alongside the investigators in forming Competency Groups - an experiment in democratising science. The Competency Groups were composed of researchers and laypeople for whom flooding is a matter of particular concern. The groups worked together to share different perspectives - on why flooding is a problem, on the role of science in addressing the problem, and on new ways of doing science together. We aimed to achieve four substantive contributions to knowledge: 1. To analyse how the knowledge claims and modelling technologies of hydrological science are developed and put into practice by policy makers and commercial organisations (such as insurance companies) in flood risk management. 2. To develop an integrated model for forecasting the in-river and floodplain effects of rural land management practices. 3. To experiment with a new approach to public engagement in the production of interdisciplinary environmental science, involving the use of Competency Groups. 4. To evaluate this new approach to doing public science differently and to identify lessons learnt that can be exported beyond this particular project to other fields of knowledge controversy. This dataset consists of computer code transcripts for two proprietary flood risk models. Flood risk and modelling interview transcripts from this study are available at the UK Data Archive under study number 6620 (see online resources). Further documentation for this study may be found through the RELU Knowledge Portal and the project's ESRC funding award web page (see online resources).