NERC Data Catalogue Service
NERC Data Catalogue Service (Natural Environment Research Council)
Continents, countries, sea regions of the world.
Radionuclide biological half-lives for farm animals
Data comprise a compilation of quality-controlled biological half-life values (and associated information) from a literature review for animals (mainly farm livestock) which contribute to the human food chain. The dataset contains almost 650 entries for 12 animal types (cattle, sheep, goats, deer, geese, hens, horses, pigs, rabbits, camels, ducks and red grouse) of importance to the human food chain for 32 elements relevant to radiological protection. Entries include values for milk, muscle (meat), eggs, whole body, carcass and various tissues (e.g. liver and kidney); the number of entries available for each element/food-chain product combination is highly variable. The dataset also contains values for other sample types (e.g. urine, faeces etc.) that are not associated with the human food but are presented as the data appeared in the same source reference as those values for human food chain products and they may be useful for modelling purposes. All the source references consulted during the review are provided. The study was conducted as part of the CONFIDENCE project which is part of the CONCERT EJP, which receives funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 662287. Full details about this nonGeographicDataset can be found at https://doi.org/10.5285/d26ea56a-a692-427c-8f5a-a9bb6eb7da6b
If you reuse this data, you should cite: Barnett, C.L., Wells, C., Fesenko, S., Tagami, K., Beresford, N.A. (2019). Radionuclide biological half-lives for farm animals. NERC Environmental Information Data Centre https://doi.org/10.5285/d26ea56a-a692-427c-8f5a-a9bb6eb7da6b
This resource is made available under the terms of the Open Government Licence
Prior to beginning the review, a recording sheet (in Microsoft Excel), was designed to allow easy collation of the half-life values (and associated information) into the final dataset. Each source reference was consulted and the data quality-controlled prior to inclusion into the dataset; subsequently the majority of the entries have been validated by somebody other than the person making the initial entry. Care should be taken when summarising the biological half-life values presented here. This is because source references often report differing numbers of components of loss (e.g. for Cs in milk between one and five loss components are reported by different authors) which means it is not possible to derive means and associated probability distribution functions for a given component (i.e. a degree of interpretation will be required when trying to derive ‘best estimate’ values).
Soil, vegetation and geology maps of the Moor House National Nature Reserve WMS
Map service of soil types, geology and vegetation in the Moor House region of the Moor House - Upper Teesdale National Nature Reserve. The site lies in the North Pennine uplands of England and has an area of 74 km2. It is England's highest and largest terrestrial National Nature Reserve (NNR), a UNESCO Biosphere Reserve and a European Special Protection Area. Habitats include exposed summits, extensive blanket peatlands, upland grasslands, pastures, hay meadows and deciduous woodland. Altitude ranges from 290 to 850 m. Moor House - Upper Teesdale is part of the Environmental Change Network (ECN) which is the UK's long-term environmental monitoring programme.
The soil map was originally created in 1963 by G.A.L. Johnson from methods outlined in Soil Map of the Moor House National Nature Reserve (1963), Westmorland. Soil Survey for the Nature Conservancy by GAL Johnson in The Geology of Moor House, a National Nature Reserve in north-east Westmorland / Johnson G.A.L., Dunham K.C., Monographs of the Nature Conservancy (No.2) 1963, HMSO. The geological map of the Moor House region was started in 1954. The survey was undertaken by G.A.L. Johnson under a grant by The Nature Conservancy (a forerunner of the Natural Environment Research Council). The original map was mapped on to a base map of 3 inches to the mile. The vegetation map was created by Eddy, A., Welch, D., & Rawes, M. in the early 1960s at the Nature Conservancy's Moor House Field Station. The original field survey was mapped on to a base map on the scale of 6 inches to the mile (1:10560). Note that the reserve has been extended to Upper Teesdale, however these maps cover the Moor House area only. The paper maps were digitised by the Institute of Terrestrial Ecology (now the Centre for Ecology & Hydrology) in the 1990s.
Above-ground carbon density derived from LiDAR data over oil palm plantations in Malaysian Borneo, 2014
This data set provides above-ground carbon density derived from LiDAR data over oil palm plantations in the Stability of Altered Forest Ecosystems (SAFE) project site located in Sabah, Malaysian Borneo in 2014. This includes the number of trees in plots and the average forest canopy per hectare at different heights. Data were collected during a project which was included in the NERC Human-modified tropical forest (HMTF) Programme. Full details about this dataset can be found at https://doi.org/10.5285/6e18121c-2184-49df-a852-f3227c28d82f
© Copyright University of Cambridge 2017
If you reuse this data, you should cite: Nunes, M.H., Ewers, R.M., Turner, E.C., Coomes, D.A. (2017). Above-ground carbon density derived from LiDAR data over oil palm plantations in Malaysian Borneo, 2014 . NERC Environmental Information Data Centre https://doi.org/10.5285/6e18121c-2184-49df-a852-f3227c28d82f
The work was carried out in the oil palm plantations within the Stability of Altered Forest Ecosystem (SAFE) Project, located within lowland dipterocarp forest regions of East Sabah in Malaysian Borneo. Airborne LiDAR data were acquired on 5 November 2014 using a Leica LiDAR50-II flown at 1850 m altitude on a Dornier 228-201 travelling at 135 knots. The LiDAR sensor emitted pulses at 83.1 Hz with a field of view of 12.0°, and a footprint of about 40 cm diameter. The average pulse density was 7.3/m2. The Leica LiDAR50-II sensor records full waveform LiDAR, but for the purposes of this study the data were discretised, with up to four returns recorded per pulse. The LiDAR data were pre-processed by NERC's Data Analysis Node and delivered in standard LAS format. All further processing was undertaken using LAStools (Rapidlasso GmbH, LAStools). Points were classified as ground and non-ground, and a digital elevation model (DEM) was fitted to the ground returns, producing a raster of 1 m resolution. The DEM elevations were subtracted from elevations of all non-ground returns to produce a normalised point cloud, and a canopy height model (CHM) was constructed from this on a 0.5 m raster by averaging the first returns. Finally, holes in the raster were filled by averaging neighbouring cells. See supporting documentation for further information.
Zinc tolerance measurements for Silene uniflora populations in the UK and Ireland, 2021
The dataset contains: (i) estimates of zinc tolerance for 50 populations of Silene uniflora in the UK and Ireland generated between 03/2021 and 09/2021. The data were collected using cuttings from wild collected specimens. Root growth of cuttings in zinc rich media was assessed using deep water culture experiments. The data set contains the zinc tolerance (mean and standard deviations of root growth scores) and the number of cuttings assessed for each population; (ii) GPS positions describing the locations of 56 populations of Silene uniflora in the UK and Ireland which were observed between 2018 and 2021. Basic habitat type (montane, serpentine, mine, coastal) information is also included. The work was supported by the Natural Environment Research Council NE/R001081/1. Full details about this dataset can be found at https://doi.org/10.5285/af4735e3-b5ba-4e0b-8a41-503eeff89a82
If you reuse this data, you should cite: Papadopulos, A., Wood, D., Giles, L. (2022). Zinc tolerance measurements for Silene uniflora populations in the UK and Ireland, 2021. NERC EDS Environmental Information Data Centre https://doi.org/10.5285/af4735e3-b5ba-4e0b-8a41-503eeff89a82
This resource is available under the terms of the Open Government Licence
The data were collected using cuttings from wild collected specimens which had been acclimatised in common conditions. Cuttings were propagated and acclimatised to standard deep water culture conditions before root staining and transfer to high zinc media. Root growth for each cutting was scored after 48 hours in zinc rich media. For each population, the zinc tolerance was estimated as the mean root growth score. Location observations were generated during field work between 02/2018 and 09/2021. Sites which potentially had been colonised by Silene uniflora were identified from Botanical Society of the British Isles records, existing literature and communication with county recorders. Sites were visited and the approximate geographic extent of the populations was recorded by GPS based on the location of observed specimens.
This is a vector data set representing the land surface of Great Britain, classified into 21 UKCEH land cover classes, based upon Biodiversity Action Plan broad habitats. This vector Land Parcel dataset is the result of intersecting the 10m raster classified pixel datasets with the UKCEH Land Parcel Spatial Framework to generate summary land parcel attributes for each land cover parcel. A full description of this and all UKCEH LCM2021 products are available from the LCM2021 product documentation accompanying this dataset. Full details about this dataset can be found at https://doi.org/10.5285/398dd41e-3c08-47f5-811f-da990007643f
Bespoke licensing conditions apply to these data. If you choose to download the data, the UKCEH Data Licensing team will contact you to negotiate a licence.
If you reuse this data, you should cite: Marston, C., Rowland, C.S., O'Neil, A.W., Morton, R.D. (2022). Land Cover Map 2021 (land parcels, GB). NERC EDS Environmental Information Data Centre https://doi.org/10.5285/398dd41e-3c08-47f5-811f-da990007643f
UKCEH's automated land cover algorithms classify 10 m pixels across the whole of UK. Training data were automatically selected from stable land covers over the interval of 2018 to 2020. A Random Forest classifier used these to classify four composite images representing per season median surface reflectance. Seasonal images were integrated with context layers (e.g., height, aspect, slope, coastal proximity, urban proximity and so forth) to reduce confusion among classes with similar spectra. Land cover was validated by organising the 10 m pixel classification into a land parcel framework (the LCM2021 classified land parcels product). The classified land parcels were compared to known land cover producing a confusion matrix to determine overall and per class accuracy. Details are available from the product documentation accompanying this dataset.
Seasonal changes in mosquito population densities in irrigated and non-irrigated areas in Bura, Tana River County, Kenya (2013-2014)
These data comprise apparent densities, species and sex and of mosquitos collected in irrigated and non-irrigated areas in Bura, Tana River County Kenya, between September 2013 and November 2014. Sampling was repeated four times over the period to cover the wet season, dry season, irrigation season and fallow periods. Mosquitoes were trapped using carbon dioxide-baited (CDC) light traps. Mosquitoes harvested from each of these traps were immobilized using 99.5% triethyleamine (Sigma-Aldrich, St. Louis, Missouri) and transferred to distinct bar-coded centrifuge tubes or cryogenic vials. The samples were transported in liquid nitrogen to the entomology section of Arbovirus/Viral haemorrhagic fever (VHF) laboratory at the Kenya Medical Research Institute (KEMRI) where they were sorted by species, sex, village, collection date and counted. The study was implemented to assess the impact of land use change (specifically the conversion of pastoral rangeland into crop land) on the suitability of the habitats to mosquito development and colonization. It also aimed to identify relative abundance of mosquitoes associated with Rift Valley fever virus transmission. The data were collected and analysed by experienced researchers from the International Centre of Insect Physiology and Ecology (Kenya), the International Livestock Research Institute (Kenya) and the Kenya Medical Research Institute. This dataset is part of a wider research project, the Dynamic Drivers of Disease in Africa Consortium (DDDAC). The research was funded by NERC project no NE-J001570-1 with support from the Ecosystem Services for Poverty Alleviation Programme (ESPA). Additional funding was provided by the Consultative Group on International Agricultural Research (CGIAR) Program Agriculture for Nutrition and Health. Full details about this dataset can be found at https://doi.org/10.5285/813f99c4-d07a-42dc-993a-1c35df9f028e
© International Livestock Research Institute
If you reuse this data, you should cite: Sang, R., Lutomiah, J., Lindahl, J.F., Grace, D., Bett, B. (2017). Seasonal changes in mosquito population densities in irrigated and non-irrigated areas in Bura, Tana River County, Kenya (2013-2014). NERC Environmental Information Data Centre https://doi.org/10.5285/813f99c4-d07a-42dc-993a-1c35df9f028e
During each visit, mosquitoes were trapped using carbon dioxide-baited (CDC) light traps that were set between 6PM and 6AM for three consecutive days in defined sampling sites (farms and human settlements). All the 10 villages in the Bura irrigation scheme and an additional site outside the scheme (Murukani) were sampled during all these visits. A total of 10 traps were used per village. Each trap was allocated a unique number and mosquitoes harvested from each of these traps were immobilized using 99.5% triethyleamine (Sigma-Aldrich, St. Louis, Missouri) and transferred to distinct bar-coded centrifuge tubes or cryogenic vials. The samples were transported in liquid nitrogen to the entomology section of the VHF laboratory at KEMRI where they were sorted by species, sex, village, collection date and counted. Identified mosquitoes were pooled in sizes of up to 25 per pool and kept in liquid nitrogen for future analyses. Data on mosquito counts per trap-night were entered into a database designed using Microsoft Excel. These data were then converted into a csv file for ingestion purposes. The data are in their original formats as no transformations or editing has been done. Mosquito speciation and data analysis was supervised by an internationally recognised entomologist for quality assurance.
Daily and sub-daily hydrometeorological and soil data (2013-2016) [COSMOS-UK] v2
[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 the start of the network, in October 2013, to the end of 2016. These data are from 42 sites active across UK during this time, recording a range of hydrometeorological and soil variables. Each site in the network hosts a cosmic-ray sensing probe; a novel sensor technology which can be used, in combination with hydrometeorological data, to calculate the volumetric water content of soil over a field scale. The hydrometeorological and soil data are recorded at a 30 minute resolution and they include neutron counts from the Cosmic-ray sensing probe, humidity and atmospheric pressure data that are used to derive volumetric water content at two temporal resolutions (hourly and daily). Also included are soil heat flux, air temperature, wind speed and net radiation data which are used to derive potential evapotranspiration at a daily resolution. Full details about this dataset can be found at https://doi.org/10.5285/486f049b-c51b-4496-97e3-1d66137e4296
If you reuse this data, you should cite: COSMOS-UK (2018). Daily and sub-daily hydrometeorological and soil data (2013-2016) [COSMOS-UK] v2. NERC Environmental Information Data Centre https://doi.org/10.5285/486f049b-c51b-4496-97e3-1d66137e4296
Hydrometeorological and soil variables are recorded on site and telemetered to a central data system at CEH Wallingford every hour. These data are then loaded to an Oracle database and subject to quality control tests. Soil volumetric water content data are derived from 30 minute hydrometeorological data and data from the cosmic-ray sensing probe by python scripts run every hour and at the end of the day a daily value is calculated. The data are calculated using the method outlined in Evans et al. (2016). Potential evapotranspiration data are derived by python scripts at the end of the day from the hydrometeorological data according to the Penman-Monteith method (FAO 56). See supporting documentation for further information.
Boulder movement in two debris flow channels and a landslide in the Bhote Koshi catchment, Nepal, May to October 2019
The data contains location and x,y,z accelerometer readings from trackers embedded into 23 boulders in the upper Bhote Koshi catchment, Nepal before the 2019 monsoon season. The data was transmitted in real time via a long-range wide-area network (LoRaWAN®) gateway to a server. The data presented cover the period May 2019 to October 2019. The data from this study was used to demonstrate how cost-effective technology can be used to monitor boulder movement in hazard-prone sites, and to show the potential for active sensors connected through a long-range wide-area network (LoRaWAN®) to be used in an early warning system in the future. Data was collected by the data authors. This was carried out as part the BOULDER: Accounting for BOUlders in Landslide-flood Disaster Evaluation and Resilience project, funded by the Natural Environment Research Council (NERC), Award reference NE/S005951/1 Full details about this dataset can be found at https://doi.org/10.5285/93518ac3-4ded-47fa-b260-38184c09dfc8
If you reuse this data, you should cite: Dini, B., Bennett, G.L., Franco, A.M.A., Whitworth, M.R.Z. (2023). Boulder movement in two debris flow channels and a landslide in the Bhote Koshi catchment, Nepal, May to October 2019. NERC EDS Environmental Information Data Centre https://doi.org/10.5285/93518ac3-4ded-47fa-b260-38184c09dfc8
The data was transmitted from the trackers in real time (unless gateway connectivity was lost) via a long-range wide-area network (LoRaWAN®) gateway to a server. The active devices had been set to send regular GPS and accelerometer acquisitions but also to activate, record and send accelerometer data if movement was sensed. Data was downloaded as a comma separated file which was deposited into the EIDC.
Energy and mass balance characteristics of Peruvian glaciers
Code to compare the mass and energy balance of five Peruvian glaciers, based on outputs from the energy balance model Tethys-Chloris. Also includes code to compare the results of climate sensitivity experiments (where the air temperature and precipitation were varied). The main outputs of the analysis at each of the sites are also stored. Full details about this dataset can be found at https://doi.org/10.5285/5f6661e4-1d34-4b01-8f3a-9fc86c546f73
If you reuse this data, you should cite: Fyffe, C.L., Potter, E., Fugger, S., Orr, A., Fatichi, S., Loarte, E., Medina, K., Hellström, R.Å., Bernat, M., Aubry-Wake, C., Gurgiser, W., Perry, L.B., Suarez, W., Quincey, D.J., Pellicciotti, F. (2021). Energy and mass balance characteristics of Peruvian glaciers. NERC EDS Environmental Information Data Centre https://doi.org/10.5285/5f6661e4-1d34-4b01-8f3a-9fc86c546f73
The inputs for the analysis code were generated by running the Tethys-Chloris energy balance melt model using weather station data from five Peruvian glaciers. These input data and code are described in the related dataset ‘The physically-based melt model Tethys-Chloris and meteorological input data for five Peruvian glaciers’. The outputs of the analysis saved in this dataset are a result of running the analysis code provided (the code can be run using the inputs provided in Main_runs_for_analysis to produce the output data).
Monthly ammonia measurements from passive samplers using ALPHA® PTFE1020 membrane at four UK Eutrophying and Acidifying Pollutants (UKEAP) network sites, 2021
This dataset consists of monthly ammonia (NH3) measurements from ALPHA® (Adapted Low-cost Passive High Absorption) samplers co-located with UKEAP DELTA sites from January 2021 to December 2021. The UK Eutrophying and Acidifying Atmospheric Pollutants (UKEAP) network measures air pollutants at rural sites across the UK. Originally the purpose of these measurements was for validation of new PTFE membrane for ALPHA® samplers prepared at UKCEH Edinburgh research laboratory. Full details about this dataset can be found at https://doi.org/10.5285/3590613d-c462-4972-a27b-d2245e0e93a0
If you reuse this data, you should cite: Iwanicka, A., Stephens, A., Duarte, F., Tang, S. (2023). Monthly ammonia measurements from passive samplers using ALPHA® PTFE1020 membrane at four UK Eutrophying and Acidifying Pollutants (UKEAP) network sites, 2021. NERC EDS Environmental Information Data Centre https://doi.org/10.5285/3590613d-c462-4972-a27b-d2245e0e93a0
Local site operator duties are completed by UK Centre for Ecology and Hydrology (UKCEH) staff, Agri-Food and Biosciences Institute (AFBI) staff at Hillsborough and Ricardo AEA staff at Chilbolton. Analysis is completed by the UKCEH Edinburgh. Data analysis and preparation is conducted using the AAGA (Ammonia Acids Gases and Aerosols) data processing tool. AAGA is a database designed to introduce more reliable, reproductible and traceable way of handling data from ALPHA® samplers.
Russian Institute of Radiology and Radioecology
National Institute of Radiological Sciences
NERC EDS Environmental Information Data Centre
International Livestock Research Institute (Kenya)
Swiss Federal Institute for Forest, Snow and Landscape Research
Instituto Nacional de Investigación en Glaciares y Ecosistemas de Montaña
Servicio Nacional de Meteorología e Hidrología del Perú
Breeding success, population size, and site quality data for a population of common guillemots (Uria aalge) on the Isle of May, Scotland, 1981-2018
This dataset contains information on the breeding outcome, breeding site occupancy, and breeding site quality for a sample of common guillemots breeding on the Isle of May, Scotland. Data is available for all attributes from 1981-2018. These data are part of the Isle of May long-term study to assess population trends of seabirds under environmental change (IMLOTS https://www.ceh.ac.uk/our-science/projects/isle-may-long-term-study). Full details about this dataset can be found at https://doi.org/10.5285/33b42f0a-12a5-47fe-aaaf-25f4ee5e13a5
If you reuse this data, you should cite: Bennett, S., Wanless, S., Harris, M., Newell, M.A., Searle, K.R., Green, J.A., Daunt, F. (2022). Breeding success, population size, and site quality data for a population of common guillemots (Uria aalge) on the Isle of May, Scotland, 1981-2018. NERC EDS Environmental Information Data Centre https://doi.org/10.5285/33b42f0a-12a5-47fe-aaaf-25f4ee5e13a5
All data derives from field observations of common guillemots on the Isle of May by experienced observers from the UK Centre for Ecology & Hydrology. Observations of breeding success, site quality and site occupancy were carried out in five long-term study areas (sub-colonies). All observation data were proofed before entering the full dataset and processed as per the accompanying metadata and supporting information.
Occurrence of exotic plant species in oil palm dominated landscapes with embedded rainforest remnants in Sabah, Malaysian Borneo, 2019
This dataset contains records of exotic plant occurrence within 21 oil palm-dominated sites in Sabah, Malaysian Borneo. Within each site, exotic plants were recorded along 100m transects in up to four habitats that represented a gradient of disturbance. The four habitats included oil palm, forest-oil palm edge, disturbed forest and intact forest. In addition to exotic plant data, the dataset contains measurements of forest structure for each transect, including canopy cover and number of large native trees. These data were collected in order to examine the change in exotic species composition along a disturbance gradient from oil palm areas into intact rainforest remnants. Full details about this dataset can be found at https://doi.org/10.5285/372861ee-67c5-4bb3-b765-cf8c03c06199
If you reuse this data, you should cite: Waddell, E.H., Chapman, D.S., Hill, J.K., Hughes, M., Bin Sailim, A., Tangah, J., Banin, L.F. (2020). Occurrence of exotic plant species in oil palm dominated landscapes with embedded rainforest remnants in Sabah, Malaysian Borneo, 2019. NERC Environmental Information Data Centre https://doi.org/10.5285/372861ee-67c5-4bb3-b765-cf8c03c06199
Field surveys were conducted in July to October 2017 and February to April 2019. The 21 sites were all in Roundtable on Sustainable Palm Oil-certified oil palm plantations. Rainforest remnants, where the forest transects were conducted, were mostly isolated forest fragments (n=15 sites) and six sites were connected to larger tracts of forest reserves outside the oil palm plantation boundary. At each site, exotic plants were surveyed along 100 m transect in up to four habitats, oil palm (n=21 transects), forest-oil palm edge (n=21), disturbed forest (n=20) and intact forest (n=16). Four rainforest remnants were very small and disturbed and therefore had no ‘intact forest’ and in one site the forest interior was not accessible. All plants were identified to species and their exotic status confirmed with the help of experienced local botanists and species lists compiled from literature. In total 18 exotic species were recorded, made up of forbs, climbers and shrubs, no exotic trees were detected and graminoids were not included due to difficulties with identification. Intentionally planted exotic species (i.e. leguminous cover crop, Calopogonium mucunoides) were also excluded. The dataset also includes measurements of forest structure (e.g. canopy cover and number of native large trees) along the length of each transect.
Surface temperature, surface oxygen, water clarity, water chemistry and phytoplankton chlorophyll a data from Windermere North Basin, 1945 to 2013
This is a long-term monitoring dataset of surface temperature, surface oxygen, water clarity, water chemistry and phytoplankton chlorophyll a from weekly or fortnightly sampling at the North Basin of Windermere in Cumbria, England. Monitoring began in 1945 for some variables. The data comprise surface temperature, surface oxygen saturation, Secchi depth, alkalinity, ammonium, nitrate, soluble reactive phosphate, total phosphorus, dissolved reactive silicon and phytoplankton chlorophyll a. Water samples are based on a sample integrated from 0 to 5 m (1945-1962), 0 to 10 m (1962-1964) and 0 to 7 m (1964 onwards). Measurements are made from a boat at a marked location at the deepest part of the lake. All data are from February 1945 until the end of 2013 and were initially collected by the Freshwater Biological Association but have been collected by the Centre for Ecology & Hydrology (CEH) and its predecessor Institute of Freshwater Ecology since 1989. Full details about this dataset can be found at https://doi.org/10.5285/f385b60a-2a6b-432e-aadd-a9690415a0ca
If you reuse this data, you should cite: Maberly, S.C., Brierley, B., Carter, H.T., Clarke, M.A., De Ville, M.M., Fletcher, J.M., James, J.B., Keenan, P., Kelly, J.L., Mackay, E.B., Parker, J.E., Patel, M., Pereira, M.G., Rhodes, G., Tanna, B., Thackeray, S.J., Vincent, C.J., Feuchtmayr, H. (2017). Surface temperature, surface oxygen, water clarity, water chemistry and phytoplankton chlorophyll a data from Windermere North Basin, 1945 to 2013. NERC Environmental Information Data Centre https://doi.org/10.5285/f385b60a-2a6b-432e-aadd-a9690415a0ca
Registration is required to access this data
Measurements are made from a boat at a marked location (buoy) at the deepest part of the lake. All data are from February 1945 until the end of 2013. Samples were analysed in Freshwater Biological Association/Centre for Ecology & Hydrology laboratories.
Land Cover Map 2021 (1km summary rasters, GB and N. Ireland)
This 1 km summary pixel data set represents the land surface of Great Britain and Northern Ireland, classified using two classification schemas, target and aggregate classes. The target class schema comprise 21 UKCEH land cover classes based upon Biodiversity Action Plan broad habitats. The aggregate class schema comprises 10 aggregate classes that are groupings of the 21 target classes. The aggregate classes group some of the more specialised target classes into more general classes. For example, the five coastal classes in the target class are grouped into a single aggregate class. The 1km percentage product provides the percentage cover for each of the 21 land cover classes for 1km x 1km pixels. This product contains one band per habitat class, producing 21 and 10 band images for the target and aggregate class products respectively. The 1km dominant coverage product is based on the 1km percentage product, and reports the land cover class with the highest percentage cover for each 1km pixel. A full description of this and all UKCEH LCM2021 products are available from the LCM2021 product documentation. Full details about this dataset can be found at https://doi.org/10.5285/a3ff9411-3a7a-47e1-9b3e-79f21648237d
If you reuse this data, you should cite: Marston, C., Rowland, C.S., O'Neil, A.W., Morton, R.D. (2022). Land Cover Map 2021 (1km summary rasters, GB and N. Ireland). NERC EDS Environmental Information Data Centre https://doi.org/10.5285/a3ff9411-3a7a-47e1-9b3e-79f21648237d
UKCEH's automated land cover algorithms classify 10 m pixels across the whole of UK. A Random Forest classifier used these to classify four composite images representing per season median surface reflectance. Seasonal images were integrated with context layers (e.g., height, aspect, slope, coastal proximity, urban proximity and so forth) to reduce confusion among classes with similar spectra. Land cover was validated by organising the 10 m pixel classification into a land parcel framework (the LCM2021 classified land parcels product). The classified land parcels were compared to known land cover producing a confusion matrix to determine overall and per class accuracy. Details are available from the documentation accompanying the data. The 1 km percentage and dominant products are created by calculating the percentage cover for each land cover classes for every 1km x 1km pixel, then reporting the dominant land cover class with the highest percentage cover for each 1km pixel.
Land Cover Map 2022 (land parcels, N. Ireland)
This is a vector data set representing the land surface of Northern Ireland, classified into 21 UKCEH land cover classes, based upon Biodiversity Action Plan broad habitats. This vector Land Parcel dataset is the result of intersecting the 10m raster classified pixel datasets with the UKCEH Land Parcel Spatial Framework to generate summary land parcel attributes for each land cover parcel. A full description of this and all UKCEH LCM2022 products are available from the LCM2022 product documentation. Full details about this dataset can be found at https://doi.org/10.5285/5a28a303-abde-4bdd-b170-942b50d831fd
If you reuse this data, you should cite: Marston, C.G., Morton, R.D., O'Neil, A.W., Rowland, C.S. (2024). Land Cover Map 2022 (land parcels, N. Ireland). NERC EDS Environmental Information Data Centre https://doi.org/10.5285/5a28a303-abde-4bdd-b170-942b50d831fd
UKCEH's automated land cover algorithms classify 10 m pixels across the whole of UK. Training data were automatically selected from stable land covers over the interval of 2019 to 2021. A Random Forest classifier used these to classify four composite images representing per season median surface reflectance. Seasonal images were integrated with context layers (e.g., height, aspect, slope, coastal proximity, urban proximity and so forth) to reduce confusion among classes with similar spectra. Land cover was validated by organising the 10 m pixel classification into a land parcel framework (the LCM2022 classified land parcels product). The classified land parcels were compared to known land cover producing a confusion matrix to determine overall and per class accuracy. Details are available from the product documentation. The 25 m rasterised land parcels product is created by pixelating the corresponding land parcel product.
Modelled topographic change of the River Liang catchment, Philippines following Typhoon Mangkhut, 2018
The datasets contain maps of the total change in topography along the river Liang (Philippines) after the Typhoon Mangkhut event in September 2018. The maps have been generated using multi-phase mass model r.avaflow to simulate the change in channel and landscape. Full details about this dataset can be found at https://doi.org/10.5285/5d17ff4b-fc98-47e5-9d61-956b17254681
If you reuse this data, you should cite: Panici, D. (2023). Modelled topographic change of the River Liang catchment, Philippines following Typhoon Mangkhut, 2018. NERC EDS Environmental Information Data Centre https://doi.org/10.5285/5d17ff4b-fc98-47e5-9d61-956b17254681
This resource is available under the terms of the Creative Commons license
The data was produced using multi-phase mass model r.avaflow 2.4; the model generates, individually, maps of erosion and deposition. The outputs are in the form of a numerical value indicating the depth that each cell has had for both deposition and erosion (in metres). The two maps have then been added together by a GIS software, through a “raster calculator” function, whereby a new raster has been produced with the total topographic change (in metres). Values between -0.01 and 0.01 have been filtered out to remove noise in the final map. The final output has been compared to the observed post-event topography for controlling consistency with observed values. Overall raster statistics have also been analysed to identify whether any spurious or outlier values of individual cells were observed.
Daily and sub-daily hydrometeorological and soil data (2013-2018) [COSMOS-UK]
[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 2018. These data are from 50 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. Full details about this dataset can be found at https://doi.org/10.5285/37702a54-b7a4-40ff-b62e-d14b161b69ca
If you reuse this data, you should cite: Stanley, S., Antoniou, V., Askquith-Ellis, A., Ball, L.A., Bennett, E.S., Blake, J.R., Boorman, D.B., Brooks, M., Clarke, M., Cooper, H.M., Cowan, N., Cumming, A., Evans, J.G., Farrand, P., Fry, M., Hitt, O.E., Lord, W.D., Morrison, R., Nash, G.V., Rylett, D., Scarlett, P.M., Swain, O.D., Szczykulska, M., Thornton, J.L., Trill, E.J., Warwick, A.C., Winterbourn, J.B. (2020). Daily and sub-daily hydrometeorological and soil data (2013-2018) [COSMOS-UK]. NERC Environmental Information Data Centre https://doi.org/10.5285/37702a54-b7a4-40ff-b62e-d14b161b69ca
Hydrometeorological and soil observations are recorded at sites around the UK using an array of sensors. Every hour, the raw data collected is sent to CEH in Wallingford, where it is subject to quality control. Soil volumetric water content data are derived from 30 minute hydrometeorological data and data from the cosmic-ray sensing probe by Python scripts run every hour. The data are calculated using the method outlined in Evans et al. (2016, https://doi.org/10.1002/hyp.10929). Snow water equivalent are derived by Python scripts at the end of the day from the hydrometeorological data according to Wallbank et al. (2020, https://ui.adsabs.harvard.edu/abs/2019EGUGA..2116420W/abstract). Potential evapotranspiration data are derived by Python scripts at the end of the day from the hydrometeorological data according to the Penman-Monteith method (FAO 56, http://www.fao.org/3/X0490E/x0490e00.htm). All data, that is raw, quality controlled and derived, is saved in an Oracle database. See supporting documentation for further information.
Impacts of increasing land use under energy crops - hydrology data, 2006-2009
This data set consists of various hydrological measurements taken over two years of instrumental monitoring in fields of willow and Miscanthus crops from a study as part of the NERC Rural Economy and Land Use (RELU) programme. Future policies are likely to encourage more land use under energy crops: principally willow, grown as short rotation coppice, and a tall exotic grass Miscanthus. These crops will contribute to the UK's commitment to reduce CO2 emissions. However, it is not clear how decisions about appropriate areas for growing the crops, based on climate, soil and water, should be balanced against impacts on the landscape, social acceptance, biodiversity and the rural economy. This project integrated social, economic, hydrology and biodiversity studies in an interdisciplinary approach to assessing the impact of converting land to Miscanthus grass and short-rotation coppice (SRC) willows. Two contrasting farming systems were focused on: the arable-dominated East Midlands; and grassland-dominated South West England. This data set consists of various hydrological measurements taken over two years of instrumental monitoring in fields of both crops. GIS and biodiversity survey datasets are also available. The public attidues questionnaire data from this study are available at the UK Data Archive under study number 6615 (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).
Standard conditions of use of the Economic and Social Data Service apply
Research funded by Economic and Social Research Council, Natural Environment Research Council and Biotechnology and Biological Sciences Research Council. Award Number: RES-227-25-0020 Logged and manual measurements of 56 hydrological variables. Dataset consists of two files; one for each sampling site. Sampling carried out 26 Apr 2006 - 21 Feb 2007 for Miscanthus and 12 May 2006 - 6 Dec 2007 for SRC willow. Continuously logged, at hourly intervals, measurements of: downward global solar radiation, net radiation, air temperature, wet bulb temperature, wind speed, wind direction, rainfall, soil, latent and sensible heat fluxes and two sets of soil water contents measured at 6 depths. Intermittent measurements of canopy height and two sets of soil water content measured at 17 depths. Measurements were made using standard, commercially available instruments. Full details of the hydrological measurements, their units and the instruments used are found in the data files, along with information on the two sample sites.
The South East Asia Rainforest Research Partnership
Rothamsted Research, Plant and Invertebrate Ecology Division