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S."],"authorGivenName":["E.","G.","C.","S.","A.","W.","T.","P.","N."],"authorOrcid":["0000-0001-7821-4998","0000-0003-3825-4156","0000-0002-5941-7770"],"authorRor":["https://ror.org/00pggkr55","https://ror.org/03yghzc09","https://ror.org/05v62cm79"],"availability":"Available","catalogue":"eidc","dataFormat":["Plain text"],"description":"This dataset consists of monthly spatial patterns of meteorological change for 34 Global Circulation Models (GCMs). The patterns are a set of regression coefficients, each representing the change per degree of mean global warming over land, for the corresponding meteorological variable. The meteorological variables analysed for each GCM include: surface temperature change per degree global warming (K K-1); surface relative humidity change per degree global warming (percentage of K-1); wind change per degree global warming (m s-1 K-1); longwave change per degree global warming (W m-2 K 1); shortwave change per degree global warming (W m-2 K-1); precipitation change per degree global warming (mm day-1 K-1) and pressure change per degree global warming (hPa K-1).\n\nThe supporting information document associated with this metadata includes parameters for an energy balance model (IMOGEN EBM) that calculates the amount of global warming. Each GCM output has been re-mapped on to UKMO-HadCM3 grid, with resolution of 3.75° longitude and 2.5° latitude; this produces a surface spatial resolution of about 417 km E-W x 278 km N-S, reducing to 295 km E-W x 278 km N-S at 45° North and South. This corresponds to 1631 land points, each of which has a row in the provided data files.\n\nThe data presented here is calibration of IMOGEN EBM parameters and patterns against 34 GCMs in the Coupled Model Intercomparison Project phase 5 (CMIP5) GCM ensemble.\n\nFurther information about the dataset and its derivation can be found in Comyn-Platt, E. et al. (2018). 'Carbon budgets for 1.5 and 2 °C targets lowered by natural wetland and permafrost feedbacks'. Nature Geoscience. http://doi.org/10.1038/s41561-018-0174-9","documentType":"GEMINI_DOCUMENT","familyName":["Comyn-Platt","Hayman","Huntingford","Chadburn","Burke","Harper","Collins","Webber","Powell","Cox","Gedney","Sitch"],"fullName":["Comyn-Platt, E.","Hayman, G.","Huntingford, C.","Chadburn, S.","Burke, E.","Harper, A.","Collins, W.","Webber, C.","Powell, T.","Cox, P.","Gedney, N.","Sitch, S."],"givenName":["E.","G.","C.","S.","A.","W.","T.","P.","N."],"identifier":"343885af-0f5e-4062-88e1-a9e612f77779","incomingCitationCount":1,"keyword":["Modelling","Climate and climate change","Global Circulation Model (GCM)","Global climate-carbon cycle model ","Future climate projections ","Energy Balance Model","EBM","Integrated Model Of Global Effects of climatic aNomalies (IMOGEN)","energy balance","climate","global model","carbon cycle","CMIP5 "],"keywordsOther":["Global Circulation Model (GCM)","Global climate-carbon cycle model ","Future climate projections ","Energy Balance Model","EBM","Integrated Model Of Global Effects of climatic aNomalies (IMOGEN)","energy balance","climate","global model","carbon cycle","CMIP5 "],"keywordsTheme":["Modelling","Climate and climate change"],"licence":"Open government licence","lineage":"The pattern-scaling set is derived from 34 global circulation models (GCM) of the World Climate Research Programme's (WCRP's) Coupled Model Intercomparison Project phase 5 (CMIP5) multi-model dataset. For each GCM emulated, regression is performed for each meteorological variable, each month and each grid box against global mean warming over land. \n\nThe calibration against CMIP5 models has full numerical code documentation, traceability on demand and with scripts utilising open-access software. This meets the ISO standards for numerical model building and is available on request to Chris Huntingford.\n","locations":["POLYGON((-180 -90, -180 90, 180 90, 180 -90, -180 -90))"],"metadataDate":"2026-07-24T20:35:48.000Z","orcid":["0000-0001-7821-4998","0000-0003-3825-4156","0000-0002-5941-7770"],"organisation":["Centre for Ecology & Hydrology","University of Leeds and University of Exeter","Met Office Hadley Centre","University of Exeter","University of Reading","UK Centre for Ecology & Hydrology","NERC Environmental Information Data Centre","NERC EDS Environmental Information Data Centre"],"publicationDate":"2018-06-26T00:00:00.000Z","recordType":"Dataset","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/343885af-0f5e-4062-88e1-a9e612f77779","10.5285/343885af-0f5e-4062-88e1-a9e612f77779","doi:10.5285/343885af-0f5e-4062-88e1-a9e612f77779"],"resourceType":"Dataset","rightsHolder":["UK Centre for Ecology & Hydrology"],"ror":["https://ror.org/00pggkr55","https://ror.org/03yghzc09","https://ror.org/05v62cm79","https://ror.org/04xw4m193"],"shortenedDescription":"This dataset consists of monthly spatial patterns of meteorological change for 34 Global Circulation Models (GCMs). The patterns are a set of regression coefficients, each representing the change per degree of mean global warming over land, for the corresponding meteorological…","state":"published","supplementalDescription":["Comyn-Platt, E., Hayman, G., Huntingford, C., Chadburn, S. E., Burke, E. J., Harper, A. B., … Sitch, S. (2018). Carbon budgets for 1.5 and 2 °C targets lowered by natural wetland and permafrost feedbacks. Nature Geoscience, 11(8), 568–573."],"title":"CMIP5 GCM-based monthly patterns of local meteorological change, per degree of mean land warming, for driving the IMOGEN impacts model","topic":["0/Modelling/","0/Climate and climate change/"],"version":1.0,"view":["public","tinzha"]},{"authorAffiliation":["UK Centre for Ecology & Hydrology"],"authorFamilyName":["Robinson","Blyth","Clark","Comyn-Platt","Rudd","Wiggins"],"authorFullName":["Robinson, E.L.","Blyth, E.M.","Clark, D.B.","Comyn-Platt, E.","Rudd, A.C.","Wiggins, M."],"authorGivenName":["E.L.","E.M.","D.B.","Edward","A.C.","Matthew"],"authorOrcid":["0000-0002-3746-4517","0000-0002-5052-238X","0000-0003-1348-7922","0000-0001-7821-4998","0000-0001-5996-6115","0000-0002-7395-9381"],"authorRor":["https://ror.org/00pggkr55"],"availability":"Available","catalogue":"eidc","dataFormat":["NetCDF"],"description":"Gridded daily meteorological variables over Great Britain for the years 1961-2019 at 1 km resolution. This dataset contains time series of daily mean values of air temperature (K), specific humidity (kg kg-1), wind speed (m s-1), downward longwave radiation (W m-2), downward shortwave radiation (W m-2), precipitation (kg m-2 s-2) and air pressure (Pa), plus daily temperature range (K). These are the variables required to run the JULES land surface model with daily disaggregation.\n\nThe data are provided in gridded netCDF files. There is one file for each variable for each month of the dataset.\n\nThis research has been carried out under national capability funding as part of the NERC Hydro-JULES programme (NE/S017380/1) and under the NERC Changing Water Cycle program (NE/I006087/1).","documentType":"GEMINI_DOCUMENT","familyName":["Robinson","Blyth","Clark","Comyn-Platt","Rudd","Wiggins"],"fdriCategory":["Hydrology","Meteorology"],"fdriSpatialScale":"National scale","fullName":["Robinson, E.L.","Blyth, E.M.","Clark, D.B.","Comyn-Platt, E.","Rudd, A.C.","Wiggins, M."],"funder":["Natural Environment Research Council"],"givenName":["E.L.","E.M.","D.B.","Edward","A.C.","Matthew"],"grant":["NE/S017380/1","NE/I006087/1"],"identifier":"835a50df-e74f-4bfb-b593-804fd61d5eab","incomingCitationCount":0,"keyword":["Floods and Droughts Digital Research Infrastructure (FDRI)","Hydrology","Modelling","air pressure","Specific humidity","air temperature","daily temperature range","Precipitation","Surface downwelling longwave radiation","Surface downwelling shortwave radiation","Wind speed","downward longwave radiation","downward shortwave radiation","Meteorology","National scale"],"keywordsOther":["air pressure","Specific humidity","air temperature","daily temperature range","Precipitation","Surface downwelling longwave radiation","Surface downwelling shortwave radiation","Wind speed","downward longwave radiation","downward shortwave radiation","Hydrology","Meteorology","National scale"],"keywordsProject":["Floods and Droughts Digital Research Infrastructure (FDRI)"],"keywordsTheme":["Hydrology","Modelling"],"licence":"Non-Open government licence","lineage":"Input meteorological data\n\nThe data sources used for the meteorological variables are:\n•\tMet Office Rainfall and Evaporation Calculation System (MORECS) dataset (Hough and Jones, 1997), specifically the daily mean values of air temperature, vapour pressure, hours of bright sunshine and wind speed.\n•\tGridded estimates of daily and monthly areal rainfall for the United Kingdom (1890-2019) [CEH-GEAR] for precipitation (https://doi.org/10.5285/dbf13dd5-90cd-457a-a986-f2f9dd97e93c).\n•\tClimatic Research Unit (CRU) Time-Series TS 3.21, CRU TS 3.24, CRU TS 4.03, CRU TS 4.04 (Jones and Harris, 2013; Harris et al., 2014) daily temperature range.\n•\tWATer and global CHange (WATCH) Forcing Data (Weedon et al., 2011) air pressure. 9https://catalogue.ceh.ac.uk/documents/8ebdd74c-ffec-4d5f-b832-e4916df694c8)\n\nOther input data:\n•\tThe Integrated Hydrological Digital Terrain Model (IHDTM) (Morris and Flavin, 1990) 50m resolution elevation\n•\tUK Energy Technology Support Unit (ETSU) 1 km resolution dataset of mean wind speeds. (Newton and Burch, 1985; Burch and Ravenscroft, 1992)\n\nThe MORECS variables were downscaled from 40 km to 1 km resolution by interpolating and adjusting for local topography using the IHDTM elevation and the ETSU wind speeds, then used to calculate the required Climate hydrology and ecology research support system (CHESS) variables. The Gridded estimates of daily and monthly areal rainfall for the United Kingdom (CEH-GEAR) precipitation was scaled to the appropriate units. The CRU TS daily temperature range was regridded from 0.5 degree to 1 km resolution with no interpolation, the monthly value is used for every day of the relevant month. A mean-monthly climatology of the WATCH Forcing Data air pressure was downscaled from 0.5 degree to 1 km resolution by adjusting for local topography, and the mean monthly value used for each day of the relevant month.","locations":["POLYGON((-8.648 49.864, -8.648 60.861, 1.768 60.861, 1.768 49.864, -8.648 49.864))"],"metadataDate":"2026-09-01T17:41:18.000Z","orcid":["0000-0002-3746-4517","0000-0002-5052-238X","0000-0003-1348-7922","0000-0001-7821-4998","0000-0001-5996-6115","0000-0002-7395-9381"],"organisation":["UK Centre for Ecology & Hydrology","NERC EDS Environmental Information Data Centre"],"publicationDate":"2023-05-09T00:00:00.000Z","recordType":"Dataset","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/835a50df-e74f-4bfb-b593-804fd61d5eab","10.5285/835a50df-e74f-4bfb-b593-804fd61d5eab","doi:10.5285/835a50df-e74f-4bfb-b593-804fd61d5eab"],"resourceType":"Dataset","rightsHolder":["UK Centre for Ecology & Hydrology"],"ror":["https://ror.org/00pggkr55","https://ror.org/04xw4m193"],"shortenedDescription":"Gridded daily meteorological variables over Great Britain for the years 1961-2019 at 1 km resolution. This dataset contains time series of daily mean values of air temperature (K), specific humidity (kg kg-1), wind speed (m s-1), downward longwave radiation (W m-2),…","state":"published","temporalExtentText":"Data collected from 1961 to 2019","title":"Climate hydrology and ecology research support system meteorology dataset for Great Britain (1961-2019) [CHESS-met]","topic":["0/Hydrology/","0/Modelling/"],"version":1.0,"view":["public","danor"]},{"authorAffiliation":["University of Exeter"],"authorFamilyName":["Baker"],"authorFullName":["Baker, D.J."],"authorGivenName":["D.J."],"authorOrcid":["0000-0002-0466-8222"],"availability":"Unknown","catalogue":"eidc","dataFormat":["rd","r","rdata","Plain text"],"description":"The speciesRecordTools R package contains functions for examining the distribution of species records, understanding sampling trends and potential biases, and building correlative presence-background species distribution models for prediction of the distribution of species across the landscape. The package is built to work with the Environmental Record Centre for Cornwall and the Isles of Scilly's (ERCCIS) opportunistic species records.","documentType":"GEMINI_DOCUMENT","familyName":["Baker"],"fullName":["Baker, D.J."],"funder":["Natural Environment Research Council"],"givenName":["D.J."],"grant":["NE/V007726/1"],"identifier":"030b49f4-9e1f-46e9-ad98-157d8668a517","incomingCitationCount":0,"keyword":["Biodiversity","Modelling","opportunistic records","spatial sampling bias","presence-background models","species distribution"],"keywordsOther":["opportunistic records","spatial sampling bias","presence-background models","species distribution"],"keywordsTheme":["Biodiversity","Modelling"],"licence":"Open government licence","lineage":"Code supplied here can be used to analyse data held by ERCCIS (Cornwall Wildlife Trust). The data is not included in the package but can be requested https://erccis.org.uk/requesting-data. The tools provided in this package were designed in collaboration with ERCCIS to overcome some of the limitations associated with these data when used to map the distribution of species. In particular, the methods attempt to address sparse sampling and spatial sampling bias associated with sampling for many species. Distribution data is required to support planning decisions and local nature recovery strategies within the region. ","locations":["POLYGON((-6.757 49.504, -6.757 51.052, -4.043 51.052, -4.043 49.504, -6.757 49.504))"],"metadataDate":"2026-07-24T18:53:15.000Z","orcid":["0000-0002-0466-8222"],"organisation":["University of Exeter","NERC EDS Environmental Information Data Centre"],"publicationDate":"2024-10-09T00:00:00.000Z","recordType":"Software","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/030b49f4-9e1f-46e9-ad98-157d8668a517","10.5285/030b49f4-9e1f-46e9-ad98-157d8668a517","doi:10.5285/030b49f4-9e1f-46e9-ad98-157d8668a517"],"resourceType":"Software","rightsHolder":["University of Exeter"],"ror":["https://ror.org/04xw4m193"],"shortenedDescription":"The speciesRecordTools R package contains functions for examining the distribution of species records, understanding sampling trends and potential biases, and building correlative presence-background species distribution models for prediction of the distribution of…","state":"published","title":"Tools for modelling species distributions using data from the Environmental Record Centre for Cornwall and the Isles of Scilly (ERCCIS)","topic":["0/Biodiversity/","0/Modelling/"],"version":1.0,"view":["public","tinzha"]},{"authorAffiliation":["Centre for Ecology & Hydrology","BIOSS","University of St Andrews"],"authorFamilyName":["Searle","Butler","Harris","Purse"],"authorFullName":["Searle, K.","Butler, A.","Harris, C.","Purse, B.V."],"authorGivenName":["K.","A.","C.","B.V."],"authorOrcid":["0000-0003-4624-9023","0000-0001-5140-2710"],"authorRor":["https://ror.org/00pggkr55","https://ror.org/03jwrz939","https://ror.org/02wn5qz54"],"availability":"Available","catalogue":"eidc","dataFormat":["Shapefile"],"description":"These spatial layers contain the predicted occurrence and abundance of three heathland shrubs, Arctostaphylos uva-ursi, Vaccinium myrtillus and Vaccinium vitis-idaea identified as susceptible host species for Phytophthora ramorum and Phytophthora kernoviae in Scotland. The distribution models were developed from quadrat vegetation data kindly provided by Scottish Natural Heritage combined with data on climate and soil conditions as well as deer abundance and were fitted using a Bayesian Generalised Mixed Modelling approach adapted for input data on the DOMIN scale. This research was funded by the Scottish Government under research contract CR/2008/55, 'Study of the epidemiology of Phytophthora ramorum and Phytophthora kernoviae in managed gardens and heathlands in Scotland' and involved collaborators from St Andrews University, Science and Advice for Scottish Agriculture (SASA), Scottish Natural Heritage (SNH), Forestry Commission, the Food and Environment Research Agency (FERA) and the Centre for Ecology & Hydrology (CEH). ","documentType":"GEMINI_DOCUMENT","familyName":["Searle","Butler","Harris","Purse"],"fullName":["Searle, K.","Butler, A.","Harris, C.","Purse, B.V.","Purse, B."],"givenName":["K.","A.","C.","B.V.","Bethan"],"identifier":"5749df3d-000c-445e-a37f-dc0763b4d5ec","incomingCitationCount":0,"keyword":["Modelling","Vaccinium myrtillus","Vaccinium vitis-idaea","Arctostaphylos uva-ursi","Phytophthora ramorum","Phytophthora kernoviae"],"keywordsOther":["Vaccinium myrtillus","Vaccinium vitis-idaea","Arctostaphylos uva-ursi","Phytophthora ramorum","Phytophthora kernoviae"],"keywordsTheme":["Modelling"],"licence":"Open government licence","lineage":"A logit-normal model was developed, structured as a generalized linear mixed model (GLMM) for logit-transformed proportions. An approach was adopted that would deal with overdispersion (the large number of absence records for the species), the unequal numbers of quadrats taken per NVC survey site, and the uncertainty around the true percent cover arising from the unequal size of the DOMIN class intervals - such that the true percent cover is an unobserved latent variable. The supporting documentation contains full details of the GLMM used. Adam Butler from Biomathematics and Statistics Scotland (BioSS) provided statistical advice for habitat suitability modelling.","locations":["POLYGON((-8.648 54.634, -8.648 60.861, -0.728 60.861, -0.728 54.634, -8.648 54.634))"],"metadataDate":"2026-07-24T23:06:09.000Z","orcid":["0000-0003-4624-9023","0000-0001-5140-2710"],"organisation":["Centre for Ecology & Hydrology","BIOSS","University of St Andrews","UK Centre for Ecology & Hydrology","NERC Environmental Information Data Centre","NERC EDS Environmental Information Data Centre"],"publicationDate":"2016-07-07T00:00:00.000Z","recordType":"Dataset","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/5749df3d-000c-445e-a37f-dc0763b4d5ec","10.5285/5749df3d-000c-445e-a37f-dc0763b4d5ec","doi:10.5285/5749df3d-000c-445e-a37f-dc0763b4d5ec"],"resourceType":"Dataset","ror":["https://ror.org/00pggkr55","https://ror.org/03jwrz939","https://ror.org/02wn5qz54","https://ror.org/04xw4m193"],"shortenedDescription":"These spatial layers contain the predicted occurrence and abundance of three heathland shrubs, Arctostaphylos uva-ursi, Vaccinium myrtillus and Vaccinium vitis-idaea identified as susceptible host species for Phytophthora ramorum and Phytophthora kernoviae in Scotland.…","state":"published","temporalExtentText":"Data collected from 2012 to 2012","title":"Distribution maps of Arctostaphylos uva-ursi, Vaccinium myrtillus and Vaccinium vitis-idaea across Scotland as hosts for Phytophthora ","topic":["0/Modelling/"],"version":1.0,"view":["public","hajd"]},{"authorAffiliation":["UK Centre for Ecology & Hydrology"],"authorFamilyName":["Kay","Rudd","Davies","Lane","Bell"],"authorFullName":["Kay, A.L.","Rudd, A.C.","Davies, H.N.","Lane, R.A.","Bell, V.A."],"authorGivenName":["A.L.","A.C.","H.N.","R.A.","V.A."],"authorOrcid":["0000-0002-5526-1756","0000-0001-5996-6115","0000-0001-7783-4853","0000-0003-4176-9214","0000-0002-0792-5650"],"authorRor":["https://ror.org/00pggkr55"],"availability":"Available","catalogue":"eidc","dataFormat":["NetCDF"],"description":"Gridded hydrological model river flow estimates on a 1km grid over Northern Ireland for the period Dec 1980 - Nov 2011. The dataset includes monthly mean river flow, annual maxima of daily mean river flow (water years Oct - Sept) and annual minima of 7-day mean river flow (years spanning Dec-Nov) (units: m3/s). The data are provided in gridded netCDF files. There is one file for each variable.\n\nTo aid interpretation, three additional spatial datasets are provided: a) digitally-derived catchment areas and b) estimated locations of flow gauging stations both on the 1km x 1km grid and c) a 1km x 1km grid identifying majority lake cells.\n\nThe data were produced as part of UK-SCAPE (UK Status, Change And Projections of the Environment, Work Package 2: Case Study - Water) a NERC-funded National Capability Science Single Centre award.","documentType":"GEMINI_DOCUMENT","familyName":["Kay","Rudd","Davies","Lane","Bell"],"fullName":["Kay, A.L.","Rudd, A.C.","Davies, H.N.","Lane, R.A.","Bell, V.A.","Kay, A."],"funder":["Natural Environment Research Council"],"givenName":["A.L.","A.C.","H.N.","R.A.","V.A.","Alison"],"grant":["NE/R016429/1"],"identifier":"f5fc1041-e284-4763-b8b7-8643c319b2d0","incomingCitationCount":1,"keyword":["Northern Ireland","UK-SCAPE","Modelling","Hydrology","Annual minimum","Annual maximum","Grid-to-Grid (G2G)","River flow","hydrology","modelling","river"],"keywordsOther":["Annual minimum","Annual maximum","Grid-to-Grid (G2G)","River flow","hydrology","modelling","river"],"keywordsPlace":["Northern Ireland"],"keywordsProject":["UK-SCAPE"],"keywordsTheme":["Modelling","Hydrology"],"licence":"Open government licence","lineage":"This dataset is a model output from the Grid-to-Grid hydrological model (Bell et al. 2009; https://doi.org10.1016/j.jhydrol.2009.08.031) driven by observed data. The meteorological variables used were: \n - daily precipitation (CEH-GEAR; https://doi.org/10.5285/33604ea0-c238-4488-813d-0ad9ab7c51ca)\n - daily min and max temperature (Met Office; https://doi.org/10.5285/2a62652a4fe6412693123dd6328f6dc8) \n - monthly 40km grids of PE for short grass (MORECS; https://doi.org/10.5194/hess-1-227-1997)\n\nSee accompanying documenation for more details.","locations":["POLYGON((-8.394 53.702, -8.394 55.329, -5.35 55.329, -5.35 53.702, -8.394 53.702))"],"metadataDate":"2026-07-25T18:07:58.000Z","orcid":["0000-0002-5526-1756","0000-0001-5996-6115","0000-0001-7783-4853","0000-0003-4176-9214","0000-0002-0792-5650"],"organisation":["UK Centre for Ecology & Hydrology","NERC EDS Environmental Information Data Centre"],"publicationDate":"2021-08-27T00:00:00.000Z","recordType":"Dataset","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/f5fc1041-e284-4763-b8b7-8643c319b2d0","10.5285/f5fc1041-e284-4763-b8b7-8643c319b2d0","doi:10.5285/f5fc1041-e284-4763-b8b7-8643c319b2d0"],"resourceType":"Dataset","rightsHolder":["UK Centre for Ecology & Hydrology"],"ror":["https://ror.org/00pggkr55","https://ror.org/04xw4m193"],"shortenedDescription":"Gridded hydrological model river flow estimates on a 1km grid over Northern Ireland for the period Dec 1980 - Nov 2011. The dataset includes monthly mean river flow, annual maxima of daily mean river flow (water years Oct - Sept) and annual minima of 7-day mean river…","state":"published","supplementalDescription":["Kay, A.L., Davies, H.N., Lane, R. A., Rudd, A.C., & Bell, V.A. (2021). Grid-based simulation of river flows in Northern Ireland: Model performance and future flow changes. In Journal of Hydrology: Regional Studies (Vol. 38, p. 100967). Elsevier BV."],"supplementalName":["UKSCAPE"],"temporalExtentText":"Data collected from 1980 to 2011","title":"Grid-to-Grid model estimates of river flow for Northern Ireland driven by observed data (1980 to 2011)","topic":["0/Modelling/","0/Hydrology/"],"version":1.0,"view":["public","crimar"]},{"authorAffiliation":["Centre for Ecology & Hydrology","Australian National University","University of Exeter","Marine Biological Laboratory","University of Minnesota","Columbia University","Western Sydney University","University of Canterbury","Met Office","University of Oxford"],"authorFamilyName":["Huntingford","Atkin","Martinez-de la Torre","Mercado","Heskel","Harper","Bloomfield","O'Sullivan","Reich","Wythers","Butler","Chen","Griffin","Meir","Tjoelker","Turnbull","Sitch","Wiltshire","Malhi"],"authorFullName":["Huntingford, C.","Atkin, O.K.","Martinez-de la Torre, A.","Mercado, L.M.","Heskel, M.A.","Harper, A.B.","Bloomfield, K.J.","O'Sullivan, O.S.","Reich, P.B.","Wythers, K.R.","Butler, E.E.","Chen, M.","Griffin, K.L.","Meir, P.","Tjoelker, M.G.","Turnbull, M.H.","Sitch, S.","Wiltshire, A.","Malhi, Y."],"authorGivenName":["C.","O.K.","A.","L.M.","M.A.","A.B.","K.J.","O.S.","P.B.","K.R.","E.E.","M.","K.L.","P.","M.G.","M.H.","S.","Y."],"authorOrcid":["0000-0002-5941-7770","0000-0003-0244-5348"],"authorRor":["https://ror.org/00pggkr55","https://ror.org/019wvm592","https://ror.org/03yghzc09","https://ror.org/046dg4z72","https://ror.org/017zqws13","https://ror.org/00hj8s172","https://ror.org/03t52dk35","https://ror.org/03y7q9t39","https://ror.org/01ch2yn61","https://ror.org/052gg0110"],"availability":"Available","catalogue":"eidc","dataFormat":["NetCDF"],"description":"The dataset contains annual global plant respiration (and related diagnostics, such as Net Primary Productivity, Gross Primary Productivity and soil respiration), applicable for pre-industrial times (taken as year 1860) through to the end of the 21st Century (year 2100). The spatial resolution of the data is 2.5 degrees latitude x 3.75 degrees longitude. These diagnostics are outputs from the Joint UK Land Environment Simulator (JULES land surface model) under four different approaches to calcluate leaf respiration. \n\nEach of four sets contains a total of 34 runs, each driven by a different CMIP5 model climate pattern, using the Integrated Model Of Global Effects of climatic aNomalies (IMOGEN) system. These are for a \"business-as-usual\" approach to fossil fuel usage, as the Representative Concentration Pathway scenario RCP8.5. These simulations form the basis for new research paper by Huntingford et al (2017, under review).","documentType":"GEMINI_DOCUMENT","familyName":["Huntingford","Atkin","Martinez-de la Torre","Mercado","Heskel","Harper","Bloomfield","O'Sullivan","Reich","Wythers","Butler","Chen","Griffin","Meir","Tjoelker","Turnbull","Sitch","Wiltshire","Malhi"],"fullName":["Huntingford, C.","Atkin, O.K.","Martinez-de la Torre, A.","Mercado, L.M.","Heskel, M.A.","Harper, A.B.","Bloomfield, K.J.","O'Sullivan, O.S.","Reich, P.B.","Wythers, K.R.","Butler, E.E.","Chen, M.","Griffin, K.L.","Meir, P.","Tjoelker, M.G.","Turnbull, M.H.","Sitch, S.","Wiltshire, A.","Malhi, Y."],"givenName":["C.","O.K.","A.","L.M.","M.A.","A.B.","K.J.","O.S.","P.B.","K.R.","E.E.","M.","K.L.","P.","M.G.","M.H.","S.","Y.","Chris"],"identifier":"24489399-5c99-4050-93ee-58ac4b09341a","incomingCitationCount":1,"keyword":[" Joint UK Land Environment Simulator","JULES","Modelling","Large-scale respiration","global warming","carbon cycle"],"keywordsOther":["Large-scale respiration","global warming","carbon cycle"],"keywordsProject":[" Joint UK Land Environment Simulator","JULES"],"keywordsTheme":["Modelling"],"licence":"Open government licence","lineage":"The annual data are time means of the JULES fluxes, calculated every 3600 s. JULES is a Land Surface Model widely used in climate research. The JULES runs were driven by 34 different GCM climate patterns as described in the IMOGEN system. The 4 approaches for the calculation of plant respiration are:\n(i) \"standard\" - this is using the standard version of the JULES model\n(ii) \"RD25\" - this is with revised estimates of respiration at 25 degrees Celsius.\n(iii) \"RD25 plus b,c\" - this is with new 25 degrees respiration values, plus a revised temperature response across temperature ranges, dependent on two empiric parameters.\n(iv) \"RD25 plus b,c plus acclim\" - as for (iii) above, with additional temperature acclimation effects.\n\nSee supporting information accompanying this dataset for further details. ","locations":["POLYGON((-180 -90, -180 90, 180 90, 180 -90, -180 -90))"],"metadataDate":"2026-07-24T19:24:40.000Z","orcid":["0000-0002-5941-7770","0000-0003-0244-5348"],"organisation":["Centre for Ecology & Hydrology","Australian National University","University of Exeter","Marine Biological Laboratory","University of Minnesota","Columbia University","Western Sydney University","University of Canterbury","Met Office","University of Oxford","UK Centre for Ecology & Hydrology","NERC Environmental Information Data Centre","NERC EDS Environmental Information Data Centre"],"publicationDate":"2017-07-12T00:00:00.000Z","recordType":"Dataset","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/24489399-5c99-4050-93ee-58ac4b09341a","10.5285/24489399-5c99-4050-93ee-58ac4b09341a","doi:10.5285/24489399-5c99-4050-93ee-58ac4b09341a"],"resourceType":"Dataset","rightsHolder":["UK Centre for Ecology & Hydrology"],"ror":["https://ror.org/00pggkr55","https://ror.org/019wvm592","https://ror.org/03yghzc09","https://ror.org/046dg4z72","https://ror.org/017zqws13","https://ror.org/00hj8s172","https://ror.org/03t52dk35","https://ror.org/03y7q9t39","https://ror.org/01ch2yn61","https://ror.org/052gg0110","https://ror.org/04xw4m193"],"shortenedDescription":"The dataset contains annual global plant respiration (and related diagnostics, such as Net Primary Productivity, Gross Primary Productivity and soil respiration), applicable for pre-industrial times (taken as year 1860) through to the end of the 21st Century (year 2100).…","state":"published","temporalExtentText":"Data collected from 1860 to 2100","title":"Plant Respiration Modelling with JULES for a changing climate (1860-2100)","topic":["0/Modelling/"],"version":1.0,"view":["public","nuri"]},{"authorAffiliation":["Air Quality Management Resource Centre","University of Leicester","UK Health and Security Agency","Imperial College London","Cranfield University","University of Plymouth","The Open University"],"authorFamilyName":["Hayes","Hansell","Douglas","Roca-Barcelo","Williams","Tyrell","Jackson","Nasar","Rolph","Khera","Bennett","Gladding"],"authorFullName":["Hayes, E.T.","Hansell, A.","Douglas, P.","Roca-Barcelo, A.","Williams, B.","Tyrell, S.","Jackson, S.","Nasar, Z.","Rolph, C.","Khera, S.","Bennett, A.","Gladding, T."],"authorGivenName":["E.T.","A.","P.","B.","S.","Z.","C.","T."],"authorRor":["https://ror.org/02nwg5t34","https://ror.org/04h699437","https://ror.org/041kmwe10","https://ror.org/05cncd958","https://ror.org/008n7pv89","https://ror.org/05mzfcs16"],"availability":"Available","catalogue":"eidc","dataFormat":["Plain text"],"description":"These files represent the model build used to generate postcode level concentrations to estimate Aspergillus fumigatus exposure from outdoor composting activities in England between 2005 and 2014. Each file, named after the nearest SCAIL-Agriculture validated meteorological station used to model the outputs, contains modelled concentrations at composting sites within 4km of each composting site. These files, presented as.txt, are the .APL files used to model bioaerosol dispersion from every composting site in England, using ADMS 5. To use this file, please convert the .txt file extension to .APL and upload into ADMS. From there, press run. Model runs are likely to generate over 40GB of data per model run.\n\nThe work was supported by the Natural Environment Research Council grants (NE/P010806/1 and NE/M011631/1).","documentType":"GEMINI_DOCUMENT","familyName":["Hayes","Hansell","Douglas","Roca-Barcelo","Williams","Tyrell","Jackson","Nasar","Rolph","Khera","Bennett","Gladding"],"fullName":["Hayes, E.T.","Hansell, A.","Douglas, P.","Roca-Barcelo, A.","Williams, B.","Tyrell, S.","Jackson, S.","Nasar, Z.","Rolph, C.","Khera, S.","Bennett, A.","Gladding, T."],"funder":["Natural Environment Research Council"],"givenName":["E.T.","A.","P.","B.","S.","Z.","C.","T."],"grant":["NE/M011631/1","NE/P010806/1"],"identifier":"9f1b307b-9b47-4a11-8e5b-e14008ad0032","incomingCitationCount":0,"keyword":["Modelling","Bioaerosols","ADMS","Aspergillus fumigatus","modelling","aerosol"],"keywordsOther":["Bioaerosols","ADMS","Aspergillus fumigatus","modelling","aerosol"],"keywordsTheme":["Modelling"],"licence":"Open government licence","lineage":"The model build is derived from inputs from academic literature (for particulate matter), meteorological information (BADC) and the authors considerations of site operating hours.","locations":["POLYGON((-6.452 49.864, -6.452 55.812, 1.768 55.812, 1.768 49.864, -6.452 49.864))"],"metadataDate":"2026-08-26T12:26:32.000Z","organisation":["Air Quality Management Resource Centre","University of Leicester","UK Health and Security Agency","Imperial College London","Cranfield University","University of Plymouth","The Open University","NERC EDS Environmental Information Data Centre","University of the West of England"],"publicationDate":"2022-05-18T00:00:00.000Z","recordType":"Dataset","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/9f1b307b-9b47-4a11-8e5b-e14008ad0032","10.5285/9f1b307b-9b47-4a11-8e5b-e14008ad0032","doi:10.5285/9f1b307b-9b47-4a11-8e5b-e14008ad0032"],"resourceType":"Dataset","rightsHolder":["University of the West of England"],"ror":["https://ror.org/02nwg5t34","https://ror.org/04h699437","https://ror.org/041kmwe10","https://ror.org/05cncd958","https://ror.org/008n7pv89","https://ror.org/05mzfcs16","https://ror.org/04xw4m193"],"shortenedDescription":"These files represent the model build used to generate postcode level concentrations to estimate Aspergillus fumigatus exposure from outdoor composting activities in England between 2005 and 2014. Each file, named after the nearest SCAIL-Agriculture validated meteorological…","state":"published","temporalExtentText":"Data collected from 2005 to 2014","title":"Estimated Aspergillus fumigatus concentrations at postcode level within close proximity of outdoor composting facilities in England, 2005-2014","topic":["0/Modelling/"],"version":1.0,"view":["public","margla"]},{"authorAffiliation":["University of Hull"],"authorFamilyName":["Delorme","Murphy","McLelland"],"authorFullName":["Delorme, P.","Murphy, B.J.","McLelland, S.J."],"authorGivenName":["P.","B.J.","S.J."],"authorOrcid":["0000-0002-5865-714X","0000-0002-1223-0917"],"authorRor":["https://ror.org/04nkhwh30"],"availability":"Available","catalogue":"eidc","dataFormat":["Comma-separated values (CSV)"],"description":"This dataset contains the elevation data of a 10m long, 20cm wide experimental river. This river was generated in an experimental flume (Total Environment Simulator) of the University of Hull. This river was formed spontaneously by imposing water and sediment flux at the upstream boundary of a flat sand bed. This model doesn’t attempt to reproduce any natural river, it’s a generic model design to explore the influence of discharge variation on the river evolution. We regularly measure the topography of the experimental area to follow the evolution of the river width, depth, slope, and conveyance capacity. The elevation is measured in metres.  The associated table contains the experimental characteristics: time (seconds), water discharge (L/min), and morphological characteristics extracted from the DEM. This is the raw data, so the width is in pixels, the depth is in metres and the conveyance capacity is in metre pixel.","documentType":"GEMINI_DOCUMENT","familyName":["Delorme","Murphy","McLelland"],"fullName":["Delorme, P.","Murphy, B.J.","McLelland, S.J."],"funder":["Natural Environment Research Council"],"givenName":["P.","B.J.","S.J."],"grant":["NE/S015795/2"],"identifier":"7b2071d6-da02-4f48-a87e-ccab19a737bd","incomingCitationCount":0,"keyword":["Hydrology","Modelling","flood sequencing","morphological evolution","experimental rivers","elevation"],"keywordsOther":["flood sequencing","morphological evolution","experimental rivers","elevation"],"keywordsTheme":["Hydrology","Modelling"],"licence":"Open government licence","lineage":"The data have been generated using a terrestrial laser scanner (Faro Focus X330). The laser scanner is in a fixed position above the experimental area. Seven targets (coordinates known) are erected in the area. Each scan is therefore reoriented using the location of the target, using the Faro software (Scene). We only accept scans with a re-orientation error lower than 0.5 mm. If the error is higher, we redo the scan.\n\nThe duration of a scan is about 10 minutes and the time between each scan depends on the experimental conditions (the exact duration between each scan can be found in the experimental data file).\n\nOnce the scan has been reoriented, we pre-process it using CloudCompare to select only the experimental area (remove the wall and instrument), we then convert this into a tiff file to analyse with python. \n\nThe scanner can’t see the surface under water, consequently we have to ensure that the experimental area is dry before we start scanning. If the surface is not dry the scan returns NaN values.\n","metadataDate":"2026-07-25T06:56:49.000Z","orcid":["0000-0002-5865-714X","0000-0002-1223-0917"],"organisation":["University of Hull","NERC EDS Environmental Information Data Centre"],"publicationDate":"2024-04-30T00:00:00.000Z","recordType":"Dataset","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/7b2071d6-da02-4f48-a87e-ccab19a737bd","10.5285/7b2071d6-da02-4f48-a87e-ccab19a737bd","doi:10.5285/7b2071d6-da02-4f48-a87e-ccab19a737bd"],"resourceType":"Non-geographic dataset","rightsHolder":["University of Hull"],"ror":["https://ror.org/04nkhwh30","https://ror.org/04xw4m193"],"shortenedDescription":"This dataset contains the elevation data of a 10m long, 20cm wide experimental river. This river was generated in an experimental flume (Total Environment Simulator) of the University of Hull. This river was formed spontaneously by imposing water and sediment flux at…","state":"published","title":"Digital Elevation Model of an experimental river generated in a flume under variable discharge conditions","topic":["0/Hydrology/","0/Modelling/"],"version":1.0,"view":["public","jenroe"]},{"authorAffiliation":["University of East Anglia"],"authorFamilyName":["Yu"],"authorFullName":["Yu, D.W."],"authorGivenName":["D.W."],"authorOrcid":["0000-0001-8551-5609"],"availability":"Available","catalogue":"eidc","dataFormat":["Comma-separated values (CSV)","Plain text"],"description":"This dataset contains information about the locations and local environmental conditions of 123 Malaise trap samples collected in November-December 2021 in the 908 km2 forested ‘leakage belt’ buffer zone of the Gola Rainforest National Park (GRNP) in eastern Sierra Leone, where cocoa, a driver of deforestation, is the main cash crop. Each trap was set out for five days with >99% ethanol. The samples were transported from Sierra Leone to the UK, where they have been sent for metabarcoding for arthropods (using Leray2 PCR primers). \n\nThe work was supported by the Natural Environment Research Council (Grant NE/S014063/1).","documentType":"GEMINI_DOCUMENT","familyName":["Yu"],"fullName":["Yu, D.W."],"funder":["Natural Environment Research Council"],"givenName":["D.W."],"grant":["NE/S014063/1"],"identifier":"161315e4-71c1-481d-906c-149ab2e9705c","incomingCitationCount":0,"keyword":["West Africa","Sierra Leone","Gola Rainforest National Park","Modelling","metabarcoding","environmental DNA","metagenomics","biodiversity indices","systematic conservation planning","machine learning","cocoa","joint species distribution modelling","DNA","conservation","arthropod","earth observation","modelling"],"keywordsOther":["metabarcoding","environmental DNA","metagenomics","biodiversity indices","systematic conservation planning","machine learning","cocoa","joint species distribution modelling","DNA","conservation","arthropod","earth observation","modelling"],"keywordsPlace":["West Africa","Sierra Leone","Gola Rainforest National Park"],"keywordsTheme":["Modelling"],"licence":"Open government licence","lineage":"This file came into being from handwritten field datasheets from when we set out the Malaise traps. To ensure accurate recording and labelling of samples, all handwritten field sheets were transcribed to a spreadsheet on the day of collection, while sites were still fresh in the memory, and samples were clearly and multiply labelled before handing over to the sequencing service. ","locations":["POLYGON((-12.041 6.872, -12.041 10.012, -10.25 10.012, -10.25 6.872, -12.041 6.872))"],"metadataDate":"2026-09-15T08:38:41.000Z","orcid":["0000-0001-8551-5609"],"organisation":["University of East Anglia","NERC EDS Environmental Information Data Centre"],"publicationDate":"2023-02-06T00:00:00.000Z","recordType":"Dataset","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/161315e4-71c1-481d-906c-149ab2e9705c","10.5285/161315e4-71c1-481d-906c-149ab2e9705c","doi:10.5285/161315e4-71c1-481d-906c-149ab2e9705c"],"resourceType":"Dataset","rightsHolder":["University of East Anglia"],"ror":["https://ror.org/04xw4m193"],"shortenedDescription":"This dataset contains information about the locations and local environmental conditions of 123 Malaise trap samples collected in November-December 2021 in the 908 km2 forested ‘leakage belt’ buffer zone of the Gola Rainforest National Park (GRNP) in eastern Sierra…","state":"published","supplementalName":["NCBI Short Read Archive Reviewer Link to for the amplicon sequence data"],"temporalExtentText":"Data collected from 2021 to 2021","title":"Environmental and location data for Malaise trap samples in the leakage belt of the Gola Rainforest National Park, Sierra Leone, November to December 2021","topic":["0/Modelling/"],"version":1.0,"view":["public","hajd"]},{"authorAffiliation":["Centre for Ecology & Hydrology","SASA","University of St Andrews"],"authorFamilyName":["Purse","Schlenzig","Harris","Searle"],"authorFullName":["Purse, B.V.","Schlenzig, A.","Harris, C.","Searle, K."],"authorGivenName":["B.V.","Alexandra","C.","K."],"authorRor":["https://ror.org/00pggkr55","https://ror.org/02wn5qz54"],"availability":"Available","catalogue":"eidc","dataFormat":["Shapefile","Comma-separated values (CSV)"],"description":"These spatial layers contain risk factors and overall risk scores, representing relative risk of Phytophthora infection (Phytophthora ramorum and P. kernoviae), for Core Native Woodland and known larch fragments across Scotland. Risk factors include climate suitability, proximity to road and river networks and suitability of habitat for key hosts of Phytophthora and were broadly concurrent with the period between 2007 and 2013. This research was funded by the Scottish Government under research contract CR/2008/55, 'Study of the epidemiology of Phytophthora ramorum and Phytophthora kernoviae in managed gardens and heathlands in Scotland' and involved collaborators from St Andrews University, Science and Advice for Scottish Agriculture (SASA), Scottish Natural Heritage (SNH), Forestry Commission, the Food and Environment Research Agency (FERA) and the Centre for Ecology & Hydrology (CEH).","documentType":"GEMINI_DOCUMENT","familyName":["Purse","Schlenzig","Harris","Searle"],"fullName":["Purse, B.V.","Schlenzig, A.","Harris, C.","Searle, K.","Purse, B."],"givenName":["B.V.","Alexandra","C.","K.","B."],"identifier":"29726cda-09f5-4661-8fd4-ddaa5555466a","incomingCitationCount":1,"keyword":["Scotland","Modelling","Phytophthora ramorum","Phytophthora kernoviae ","Rhododendron ponticum","Vaccinium myrtillus"],"keywordsOther":["Phytophthora ramorum","Phytophthora kernoviae ","Rhododendron ponticum","Vaccinium myrtillus"],"keywordsPlace":["Scotland"],"keywordsTheme":["Modelling"],"licence":"Open government licence","lineage":"Risk factors were extracted and summarised for Core Native Woodland and Larch fragments, and included habitat suitability for the principal sporulation host, Rhododendron ponticum, and predicted abundance for the additional host, Vaccinium myrtillus, climatic suitability for growth and sporulation, proximity to inspected garden or trade premises, proximity to infected premises, and proximity to larch and infected larch. Final weighting for risk factors were agreed with partners for the final calculation of risk scores. Newly developed methods for modelling habitat suitability of invasive species were used to predict habitat suitability for the invasive shrub Rhododendron ponticum.A suite of climate, soil, biotic and abiotic factors were used to predict percent cover for Vaccinium myrtillus. Data on outbreaks in 2011, 2012 and up to May 2013 were provided by SASA and locations of larch infections up to April 2013 were provided by the Forestry Commission. A small proportion of core native woodland fragments were missing data for climate and Vaccinium myrtillus habitat suitability. Adam Butler from Biomathematics and Statistics Scotland (BioSS) provided statistical advice for habitat suitability modelling.","locations":["POLYGON((-8.648 54.634, -8.648 60.861, -0.728 60.861, -0.728 54.634, -8.648 54.634))"],"metadataDate":"2026-07-24T19:29:50.000Z","organisation":["Centre for Ecology & Hydrology","SASA","University of St Andrews","UK Centre for Ecology & Hydrology","NERC Environmental Information Data Centre","NERC EDS Environmental Information Data Centre"],"publicationDate":"2016-07-07T00:00:00.000Z","recordType":"Dataset","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/29726cda-09f5-4661-8fd4-ddaa5555466a","10.5285/29726cda-09f5-4661-8fd4-ddaa5555466a","doi:10.5285/29726cda-09f5-4661-8fd4-ddaa5555466a"],"resourceType":"Dataset","ror":["https://ror.org/00pggkr55","https://ror.org/02wn5qz54","https://ror.org/04xw4m193"],"shortenedDescription":"These spatial layers contain risk factors and overall risk scores, representing relative risk of Phytophthora infection (Phytophthora ramorum and P. kernoviae), for Core Native Woodland and known larch fragments across Scotland. Risk factors include climate suitability,…","state":"published","temporalExtentText":"Data collected from 2007 to 2013","title":"Risk of Phytophthora infection in woodland and larch fragments across Scotland","topic":["0/Modelling/"],"version":1.0,"view":["public","hajd"]},{"authorAffiliation":["ADAS UK Ltd","Rothamsted Research North Wyke"],"authorFamilyName":["Nicholson","Chambers","Lord","Bessey","Misselbrook"],"authorFullName":["Nicholson, F.","Chambers, B.","Lord, E.","Bessey, R.","Misselbrook, T."],"authorGivenName":["F.","B.","E.","R. ","T."],"authorRor":["https://ror.org/006d11e04","https://ror.org/0347fy350"],"availability":"Available","catalogue":"eidc","dataFormat":["Shapefile"],"description":"Estimates of annual volumes of manure produced by six broad farm livestock types for England and Wales at 10 km resolution, modelled with MANURES-GIS [1]. \n\nThe farm livestock classes are: dairy cattle; beef cattle; pigs; sheep and other livestock; laying hens; broilers and other poultry. The quantities produced by each type are subsequently apportioned into managed and field-deposited manure. The managed manure sources are categorised as beef farmyard manure, beef slurry, dairy farmyard manure, dairy slurry, broiler litter, layer manure, pig farmyard manure, pig slurry and sheep farmyard manure. The destinations are recorded as grass, winter arable, spring arable and direct excreta when grazing. For each 10 km square, the quantity of manure going from each source to each destination is estimated. The values specify amount of excreta, in kilograms for solid manure and in litres for liquid manure.\n\n [1] ADAS (2008) The National Inventory and Map of Livestock Manure Loadings to Agricultural Land: MANURES-GIS. Final Report for Defra Project WQ0103","documentType":"GEMINI_DOCUMENT","familyName":["Nicholson","Chambers","Lord","Bessey","Misselbrook","Gooday"],"fullName":["Nicholson, F.","Chambers, B.","Lord, E.","Bessey, R.","Misselbrook, T.","Gooday, R."],"givenName":["F.","B.","E.","R. ","T.","Richard"],"identifier":"517717f7-d044-42cf-a332-a257e0e80b5c","incomingCitationCount":1,"keyword":["Modelling","Pollution","agriculture","livestock farming","manure","beef cattle","agriculture and cattle industry","poultry","piggery"],"keywordsOther":["agriculture","livestock farming","manure","beef cattle","agriculture and cattle industry","poultry","piggery"],"keywordsTheme":["Modelling","Pollution"],"licence":"Open government licence","lineage":"Results are derived from the MANURES-GIS model (Defra WQ0103) developed by ADAS [1]. \n\nMANURES-GIS creates a conceptual framework of manure/excreta flow pathways, using Node and Link tree diagrams, similar to the National Ammonia Reduction Strategy Evaluation System (NARSES) model [2]. The framework is divided into six broad livestock classes (dairy cattle; beef cattle; pigs; sheep and other livestock; laying hens; broilers and other poultry). Each livestock class is further divided into stages representing: excretion, housing, grazing, hardstandings, storage, export and land spreading. These stages are then further subdivided, for example, housing may be on a slurry or solid manure based system, slurry storage may be in a lagoon or above-ground tank etc. At each stage, alterations to the composition of the manure may occur through loss processes (e.g. ammonia or methane emissions), gains (e.g. straw added during housing) or transformations (e.g. immobilisation of ammonium-N during farmyard manure storage). \nThe livestock and cropping data used to parametrise MANURES-GIS are sourced from the 2010 June Survey from Defra and the Welsh Government. \n\n[1] ADAS (2008) The National Inventory and Map of Livestock Manure Loadings to Agricultural Land: MANURES-GIS. Final Report for Defra Project WQ0103\n[2] Webb, J. and Misselbrook, T. (2004). A mass flow model of ammonia emissions from UK livestock production. Atmospheric Environment, 38, 2163-2176","locations":["POLYGON((-6.452 49.864, -6.452 55.812, 1.768 55.812, 1.768 49.864, -6.452 49.864))"],"metadataDate":"2026-07-24T23:03:17.000Z","organisation":["ADAS UK Ltd","Rothamsted Research North Wyke","NERC Environmental Information Data Centre","NERC EDS Environmental Information Data Centre"],"publicationDate":"2016-03-17T00:00:00.000Z","recordType":"Dataset","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/517717f7-d044-42cf-a332-a257e0e80b5c","10.5285/517717f7-d044-42cf-a332-a257e0e80b5c","doi:10.5285/517717f7-d044-42cf-a332-a257e0e80b5c"],"resourceType":"Dataset","ror":["https://ror.org/006d11e04","https://ror.org/0347fy350","https://ror.org/04xw4m193"],"shortenedDescription":"Estimates of annual volumes of manure produced by six broad farm livestock types for England and Wales at 10 km resolution, modelled with MANURES-GIS [1]. \n\nThe farm livestock classes are: dairy cattle; beef cattle; pigs; sheep and other livestock; laying hens; broilers…","state":"published","temporalExtentText":"Data collected from 2010 to 2010","title":"Estimates of manure volumes by livestock type and land use for England and Wales","topic":["0/Modelling/","0/Pollution/"],"version":1.0,"view":["public","nuri"]},{"altTitle":["LWdown WFD","Surface incident longwave radiation WFD","Downwards long-wave radiation flux WFD"],"availability":"Available","catalogue":"eidc","dataFormat":["NetCDF"],"description":"The WATCH Forcing data is a twentieth century meteorological forcing dataset for land surface and hydrological models. It consists of three/six-hourly states of the weather for global half-degree land grid points. It was generated as part of the EU FP 6 project \"WATCH\" (WATer and global CHange\") which ran from 2007-2011. The data was generated in 2 tranches with slightly different methodology: 1901-1957 and 1958-2001, but generally the dataset can be considered as continuous. More details regarding the generation process can be found in the associated WATCH technical report and paper in J. Hydrometeorology. To understand how the data grid is formed it is necessary to read the attached WFD-land-long-lat-z files either in NetCDF or DAT formats. The data covers land points only and excludes the Antarctica. LWdown or surface incident longwave radiation (also known as downwards long-wave radiation flux ) is the surface incident longwave radiation averaged over the next six hours, measured in W/m2 at 6 hourly resolution and 0.5 x 0.5 degrees spatial resolution. ","documentType":"GEMINI_DOCUMENT","familyName":["Warnaars"],"fullName":["Warnaars, T."],"givenName":["Tanya"],"identifier":"e5d00d66-dcf5-4b0e-9346-f6205a336385","incomingCitationCount":0,"keyword":["Modelling","land surface","atmosphere"],"keywordsOther":["land surface","atmosphere"],"keywordsTheme":["Modelling"],"licence":"Non-Open government licence","lineage":"These data were created as part of the EU FP6 project WATCH which was coordinated by NERC-CEH and ran from 2007-2011. The data were generated in two tranches according to time interval. The data from 1958-2001 were based on ECMWF (European Centre for Medium Range Weather Forecasting) \"ERA-40\" reanalysis data via interpolation, elevation correction and monthly bias correction from gridded observations. The data from 1901-1957 were created via re-ordering of the ERA-40 reanalysis one year at a time. For full details of the process see the WATCH technical report No 22 and Weedon (2011, J. Hydrometeorology). ","locations":["POLYGON((-180 -90, -180 90, 180 90, 180 -90, -180 -90))"],"metadataDate":"2026-07-25T14:50:00.000Z","organisation":["UK Centre for Ecology & Hydrology","NERC EDS Environmental Information Data Centre"],"publicationDate":"2011-08-01T00:00:00.000Z","recordType":"Dataset","resourceIdentifier":["1314892215339","CEH:EIDC::1314892215339","https://catalogue.ceh.ac.uk/id/e5d00d66-dcf5-4b0e-9346-f6205a336385"],"resourceType":"Dataset","ror":["https://ror.org/00pggkr55","https://ror.org/04xw4m193"],"shortenedDescription":"The WATCH Forcing data is a twentieth century meteorological forcing dataset for land surface and hydrological models. It consists of three/six-hourly states of the weather for global half-degree land grid points. It was generated as part of the EU FP 6 project \"WATCH\"…","state":"published","supplementalDescription":["Weedon, G.P.; Gomes, S.; Viterbo, P.; Shuttleworth, W.J.; Blyth, E.; Osterle, H.; Adam, J.C.; Bellouin, N.; Boucher, O.; Best, M.. 2011 Creation of the WATCH forcing data and its use to assess global and regional reference crop evaporation over land during the twentieth century. Journal of Hydrometeorology, 12 (5). 823-848. "],"temporalExtentText":"Data collected from 1911 to 2001","title":"WATCH forcing data (WFD) - 20th Century LWdown (surface incident longwave radiation) 1901-2001","topic":["0/Modelling/"],"version":1.0,"view":["public","rjsc"]},{"authorAffiliation":["University College London","An-Najah National University","Global Earthquake Model (GEM) Foundation","People in Need","National Society for Earthquake Technology","Chittagong University of Engineering and Technology","Chittagong University of Engineering & Technology","Lund University","Boğaziçi University","Ardhi University"],"authorFamilyName":["Cremen","Gentile","Dabeek","Aljawhari","Manandhar","Rawal","Nocera","Rana","Zisan","Leandro","Çaktı","Kombe"],"authorFullName":["Cremen, G.","Gentile, R.","Dabeek, J.","Aljawhari, K.","Manandhar, V.","Rawal, P.","Nocera, F.","Rana, S.","Zisan, B.","Leandro, I.","Çaktı, E.","Kombe, W."],"authorGivenName":["G.","r.","J.","K","V.","P.","F","S.","B.","I.","E.","W."],"authorOrcid":["0000-0002-6699-7312","0000-0002-7682-4490","0000-0001-8369-4105","0000-0003-4891-8702","0000-0001-8274-1416","0000-0001-7940-8014","0000-0002-3383-7662","0000-0002-5343-9215","0000-0002-6959-0235","0000-0002-1946-3940","0000-0003-1381-1833","0000-0001-9117-9496"],"authorRor":["https://ror.org/02jx3x895","https://ror.org/0046mja08","https://ror.org/02xh1tf34","https://ror.org/058m30p27","https://ror.org/052qsay17","https://ror.org/012a77v79","https://ror.org/03z9tma90","https://ror.org/05k903r09"],"availability":"Available","catalogue":"eidc","dataFormat":["GeoJSON","Comma-separated values (CSV)"],"description":"This dataset comprises spatially explicit risk analysis outputs for 10 study areas, representing potential disaster impacts across various future urban development scenarios, that are created by different community groups within each study area. Study areas include Istanbul (Türkiye), Nablus (Palestine), Chattogram (Bangladesh), Cox's Bazaar (Bangladesh), Nairobi (Kenya), Nakuru (Kenya), Quito (Ecuador), Kokhana (Nepal), Rapti (Nepal) and Darussalam (Tanzania). The data quantify the physical, social, and economic risks resulting from seismic activity or floods or landslides interacting with projected land-use plans and building typologies.\n\nKey components of the dataset include attributes on damage states for each building and eight metrics related to socio-demographic characteristics: \n-\tNumber of workers unemployed,  \n-\tNumber of children with no Access to education\n-\tNumber of households with no Access to hospital\n-\tNumber of individuals with no Access to hospital\n-\tNumber of households displaced\n-\tNumber of homeless individuals\n-\tPopulation displacement\n-\tNumber of casualties\n\nDetails on the computation of each metric is provided in the readme file.\nThis dataset was created as case studies for the Tomorrows Cities: Tomorrowville virtual testbed. It is supported by NERC as part of the GCRF Urban Disaster Risk Hub (NE/S009000/1).","documentType":"GEMINI_DOCUMENT","familyName":["Cremen","Gentile","Dabeek","Aljawhari","Manandhar","Rawal","Nocera","Rana","Zisan","Leandro","Çaktı","Kombe"],"fullName":["Cremen, G.","Gentile, R.","Dabeek, J.","Aljawhari, K.","Manandhar, V.","Rawal, P.","Nocera, F.","Rana, S.","Zisan, B.","Leandro, I.","Çaktı, E.","Kombe, W."],"funder":["Natural Environment Research Council"],"givenName":["G.","r.","J.","K","V.","P.","F","S.","B.","I.","E.","W."],"grant":["NE/S009000/1"],"identifier":"30a025cc-4482-48a0-a0f9-9f5b71d7ef83","incomingCitationCount":0,"keyword":["Modelling","disaster","risk","loss","environmental impact","hazard","urbanisation","GCRF Urban Disaster Risk Hub","Tomorrow's Cities"],"keywordsOther":["disaster","risk","loss","environmental impact","hazard","urbanisation","GCRF Urban Disaster Risk Hub","Tomorrow's Cities"],"keywordsTheme":["Modelling"],"licence":"Open government licence","lineage":"The dataset was generated using the Tomorrow's Cities Decision Support Environment (TCDSE) workflow, designed to assess disaster risks in future urban planning scenarios. The process involved the integration of three primary components:\n1. Hazard Models: Physics-based simulations or probabilistic models for relevant hazards (e.g., earthquakes, floods, landslides) specific to the project location.\n2. Future Exposure Scenarios: Spatially explicit projections of future urban growth, land use, and building typologies, co-developed with local stakeholders and planning authorities.\n3. Vulnerability Functions: Engineering-based curves defining the susceptibility of proposed infrastructure to specific hazard intensities.\nThese components were processed through a computational risk engine (served under https://webapp.tomorrowscities.org) to calculate potential impacts (e.g., physical damage, displacement, economic loss) for various future development trajectories.\nQuality Assurance (QA) Steps:\n1. Input Validation: Hazard models and exposure data were validated by local domain experts and compared against historical baseline data where applicable.\n2. Internal Consistency Checks: The computational workflow underwent code reviews and unit testing to ensure data integrity during the integration of hazard and exposure layers.\n3. Output Verification: Preliminary risk results were reviewed in stakeholder workshops to ensure plausibility and alignment with local knowledge before finalization.","locations":["POLYGON((-82.26 -13.582, -82.26 44.214, 77.696 44.214, 77.696 -13.582, -82.26 -13.582))"],"metadataDate":"2026-07-24T20:33:47.000Z","orcid":["0000-0002-6699-7312","0000-0002-7682-4490","0000-0001-8369-4105","0000-0003-4891-8702","0000-0001-8274-1416","0000-0001-7940-8014","0000-0002-3383-7662","0000-0002-5343-9215","0000-0002-6959-0235","0000-0002-1946-3940","0000-0003-1381-1833","0000-0001-9117-9496"],"organisation":["University College London","An-Najah National University","Global Earthquake Model (GEM) Foundation","People in Need","National Society for Earthquake Technology","Chittagong University of Engineering and Technology","Chittagong University of Engineering & Technology","Lund University","Boğaziçi University","Ardhi University","NERC EDS Environmental Information Data Centre"," University of Edinburgh"],"publicationDate":"2026-04-09T00:00:00.000Z","recordType":"Dataset","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/30a025cc-4482-48a0-a0f9-9f5b71d7ef83","10.5285/30a025cc-4482-48a0-a0f9-9f5b71d7ef83","doi:10.5285/30a025cc-4482-48a0-a0f9-9f5b71d7ef83"],"resourceType":"Dataset","rightsHolder":[" University of Edinburgh"],"ror":["https://ror.org/02jx3x895","https://ror.org/0046mja08","https://ror.org/02xh1tf34","https://ror.org/058m30p27","https://ror.org/052qsay17","https://ror.org/012a77v79","https://ror.org/03z9tma90","https://ror.org/05k903r09","https://ror.org/04xw4m193","https://ror.org/01nrxwf90"],"shortenedDescription":"This dataset comprises spatially explicit risk analysis outputs for 10 study areas, representing potential disaster impacts across various future urban development scenarios, that are created by different community groups within each study area. Study areas include…","state":"published","supplementalDescription":["Cremen, G., Galasso, C., McCloskey, J., Barcena, A., Creed, M., Filippi, M. E., Gentile, R., Jenkins, L. T., Kalaycioglu, M., Mentese, E. Y., Muthusamy, M., Tarbali, K., & Trogrlić, R. Š. (2023). A state-of-the-art decision-support environment for risk-sensitive and pro-poor urban planning and design in Tomorrow's cities. International Journal of Disaster Risk Reduction, 85, 103400.","Cremen, G., Galasso, C., & McCloskey, J. (2022). A Simulation‐Based Framework for Earthquake Risk‐Informed and People‐Centered Decision Making on Future Urban Planning. Earth's Future, 10(1)."],"title":"Multi-hazard disaster impact analysis outputs from synthetic future urban scenarios in 10 cities","topic":["0/Modelling/"],"version":1.0,"view":["public","jashun"]},{"authorAffiliation":["Loughborough University"],"authorFamilyName":["Zhao","Liang","Chen"],"authorFullName":["Zhao, J.H.","Liang, Q.H.","Chen, H.L."],"authorGivenName":["J.H.","Q.H.","H.L."],"authorOrcid":["0000-0002-0494-1743"],"authorRor":["https://ror.org/04vg4w365"],"availability":"Available","catalogue":"eidc","dataFormat":["Comma-separated values (CSV)","TIFF"],"description":"The dataset describes the data needed for and results produced by the flood risk assessment framework under different development strategies of Luanhe river basin under a changing climate. The Luanhe river basin is located in the northeast of the North China Plain (115°30' E-119°45' E, 39°10' N-42°40'N) of China, is an essential socio-economic zone on its own in North-Eastern China, and also directly contributes to and influences the socio-economic development of the Beijing-Tianjin-Hebei region. The dataset here used for investigating the flood risk includes:\n (1) uplifts of future climate scenarios to 2030\n (2) the validation results of a historical event that happened in 2012\n (3) the flood inundation prediction under different development strategies and climate scenarios to 2030\n (4) and the spatial resident density map in Luanhe river basin to 2030. \n\nWherein, the uplifts of the future climate change is generated based on the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset and will be applied to the future design rainfall to represent the future climate scenarios; a 2012 event is select to validate the flood model, and the remote sensing data is adopted as real-world observation data; considering the uplifts and future land use data as input, the validated flood model is applied to produce flood inundation prediction under different development strategies and climate scenarios to 2030; and the inundation results are used to overlay the Gridded Population of the World, Version 4 (GPWv4) and then calculate the flood risk map of the local resident. These data are mainly open data or produced by authors. With all these data, the flood risk of the Luanhe river basin in the near future (2030) can be assessed. ","documentType":"GEMINI_DOCUMENT","familyName":["Zhao","Liang","Chen"],"fullName":["Zhao, J.H.","Liang, Q.H.","Chen, H.L.","Zhao, J."],"funder":["Natural Environment Research Council"],"givenName":["J.H.","Q.H.","H.L.","Jiaheng"],"grant":["NE/S012427/1"],"identifier":"82055942-386a-4a8b-b2a1-0c3eea12b168","incomingCitationCount":0,"keyword":["Climate and climate change","Hydrology","Modelling","hydrology","modelling","climate change"],"keywordsOther":["hydrology","modelling","climate change"],"keywordsTheme":["Climate and climate change","Hydrology","Modelling"],"licence":"Open government licence","lineage":"The base data (population, climate scenarios) are mainly based on the open data (GPWv4, NEX-GDDP), and the flood inundation results are produced by the in-house high-performance integrated hydrodynamic modelling system (HiPIMS).\n\nThe uplifts of climate scenarios are calculated by fitting a Log-Pearson type III distribution to the NEX-GDDP dataset to calculate the magnitudes of 100-year design rainfall at each pixel within the domain for the 'retrospective run'/historical period (i.e. 1950~2005) without climate change to give (mm/day), and future periods (2006~2030) with climate change (RCP4.5 and RCP8.5) to provide (mm/day). And this pixel-based climate change uplift is then averaged to the city-wide mask to create a city-based uplift (rcp45.csv, rcp85.csv). \n\nThe parameters of the flood model are calibrated to the 2012 event, wherein the remote sensing data is considered as real-world observation (RemoteSensing_validation.tif and Validation.tif). And this calibrated model is then applied to the future development strategies (reflected by land use data) and climate change scenarios (reflected by uplifts) to calculate the flood inundation map for the future scenarios in Luanhe river basin (Baseline.tif, Conservation*.tif, Expansion*.tif, Sustainability*.tif, Trend*.tif).\n\nBase on the inundation map, the flood risk map of the local resident can be calculated by overlaying it on the GPWv4 data (Popu_2000.tif, Popu_2005.tif, Popu_2010.tif, Popu_2015.tif, Popu_2020.tif, Popu_2025.tif, Popu_2030.tif).","locations":["POLYGON((104.028 32.951, 104.028 43.858, 120.99 43.858, 120.99 32.951, 104.028 32.951))"],"metadataDate":"2026-07-25T08:36:26.000Z","orcid":["0000-0002-0494-1743"],"organisation":["Loughborough University","NERC EDS Environmental Information Data Centre"],"publicationDate":"2021-08-09T00:00:00.000Z","recordType":"Dataset","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/82055942-386a-4a8b-b2a1-0c3eea12b168","10.5285/82055942-386a-4a8b-b2a1-0c3eea12b168","doi:10.5285/82055942-386a-4a8b-b2a1-0c3eea12b168"],"resourceType":"Dataset","rightsHolder":["Loughborough University"],"ror":["https://ror.org/04vg4w365","https://ror.org/04xw4m193"],"shortenedDescription":"The dataset describes the data needed for and results produced by the flood risk assessment framework under different development strategies of Luanhe river basin under a changing climate. The Luanhe river basin is located in the northeast of the North China Plain (115°30'…","state":"published","title":"Flood risk assessment of the Luanhe river basin under different development strategies and climate scenarios","topic":["0/Climate and climate change/","0/Hydrology/","0/Modelling/"],"version":1.0,"view":["public","tinzha"]},{"altTitle":["MultiMOVE Version 2.0.1","MultiMOVE: A Package of niche models for British vegetation v.2.0.1"],"authorAffiliation":["Centre for Ecology & Hydrology","Biomathematics & Statistics Scotland (BIOSS)"],"authorFamilyName":["Henrys","Butler","Jarvis","Smart","Fang"],"authorFullName":["Henrys, P.A.","Butler, A.","Jarvis, S.G.","Smart, S.M.","Fang, Z."],"authorGivenName":["P.A.","A.","S.G.","S.M.","Z."],"authorOrcid":["0000-0003-4758-1482","0000-0001-5382-5135","0000-0003-2750-7832"],"authorRor":["https://ror.org/00pggkr55"],"availability":"Available","catalogue":"eidc","dataFormat":["R Script"],"description":"MultiMOVE is an R package that contains fitted niche models for almost 1500 plant species in Great Britain. This package allows the user to access these models, which have been fitted using multiple statistical techniques, to make predictions of species occurrence from specified environmental data. It also allows plotting of relationships between species' occurrence and individual covariates so the user can see what effect each environmental variable has on the specific species in question. The package is built under R 3.1.2 and depends on R packages 'leaps', 'earth', 'fields', 'mgcv', 'stringr', 'gsubfn', 'randomForest' and 'nnet'. ","documentType":"GEMINI_DOCUMENT","familyName":["Henrys","Butler","Jarvis","Smart","Fang"],"fullName":["Henrys, P.A.","Butler, A.","Jarvis, S.G.","Smart, S.M.","Fang, Z.","Henrys, P."],"givenName":["P.A.","A.","S.G.","S.M.","Z.","Pete"],"identifier":"94ae1a5a-2a28-4315-8d4b-35ae964fc3b9","incomingCitationCount":3,"keyword":["Great Britain","Modelling","Vegetation science","ecology"],"keywordsOther":["Vegetation science","ecology"],"keywordsPlace":["Great Britain"],"keywordsTheme":["Modelling"],"licence":"Open government licence","lineage":"Modelling methods used: Generalised Additive Models (GAMs) (Hastie and Tibshirani, 1990) Generalised Linear Models (GLMs) (McCullagh and Nelder, 1989) Random Forests (RF) (Liaw and Wiener, 2002) Multiple Adaptive Regression Splines (MARS) (Friedman, 1991). Neural Networks (NNet) (Venables and Ripley, 2002) Data sources used in the models: National Vegetation Classification (Rodwell 1991 et seq). Data collected at 4-250 m2 using targeted homogenous stands to census full range of plant community variation across Britain. Sampled from 1950-1980. The Key Habitats surveys of Britain (Hornung 1996). Data collected at 4m2 using random sampling within a random selection of 1-km squares stratified by three landscape GIS masks for upland, calcareous grassland and lowland heathland ecosystems. Sampled in 1992. Broadleaved woodland survey of Britain (Kirby et al. 2005; Corney et al. 2006). Data collected at 200 m2 using 16 plots located at random within each of 103 representative sites in Britain. Sampled in 1971. Countryside Survey of Great Britain (Smart et al. 2003). Data collected at 4-200 m2 using randomly located plots within a random selection of 569 1-km squares across Britain stratified by ITE Land Class. Data collected in 1998/1999. ","locations":["POLYGON((-9.227 49.837, -9.227 60.85, 2.6876 60.85, 2.6876 49.837, -9.227 49.837))"],"metadataDate":"2026-07-25T10:18:20.000Z","orcid":["0000-0003-4758-1482","0000-0001-5382-5135","0000-0003-2750-7832"],"organisation":["Centre for Ecology & Hydrology","Biomathematics & Statistics Scotland (BIOSS)","UK Centre for Ecology & Hydrology","NERC Environmental Information Data Centre","NERC EDS Environmental Information Data Centre"],"publicationDate":"2015-05-13T00:00:00.000Z","recordType":"Model","resourceIdentifier":["1431527379383","CEH:EIDC::1431527379383","10.5285/94ae1a5a-2a28-4315-8d4b-35ae964fc3b9","doi:10.5285/94ae1a5a-2a28-4315-8d4b-35ae964fc3b9","https://catalogue.ceh.ac.uk/id/94ae1a5a-2a28-4315-8d4b-35ae964fc3b9"],"resourceType":"Model","rightsHolder":["UK Centre for Ecology & Hydrology"],"ror":["https://ror.org/00pggkr55","https://ror.org/04xw4m193"],"shortenedDescription":"MultiMOVE is an R package that contains fitted niche models for almost 1500 plant species in Great Britain. This package allows the user to access these models, which have been fitted using multiple statistical techniques, to make predictions of species occurrence from…","state":"published","supplementalName":["Manual for R Statistical Environment extensions"],"title":"MultiMOVE Model: Ensemble niche modelling of British vegetation v2.0.1","topic":["0/Modelling/"],"version":1.0,"view":["public","clamw"]},{"altTitle":["SWDown WFD","Surface incident shortwave radiation WFD"],"availability":"Available","catalogue":"eidc","dataFormat":["NetCDF"],"description":"The WATCH Forcing data is a twentieth century meteorological forcing dataset for land surface and hydrological models. It consists of three/six-hourly states of the weather for global half-degree land grid points. It was generated as part of the EU FP 6 project \"WATCH\" (WATer and global CHange\") which ran from 2007-2011. The data was generated in 2 tranches with slightly different methodology: 1901-1957 and 1958-2001, but generally the dataset can be considered as continuous. More details regarding the generation process can be found in the associated WATCH technical report and paper in J. Hydrometeorology. To understand how the data grid is formed it is necessary to read the attached WFD-land-long-lat-z files either in NetCDF or DAT formats. The data covers land points only and excludes the Antarctica. Qair or 2m specific humidity (instantaneous) is the instantaneous specific humidity at 2m measured in kg/kg at 6 hourly resolution and 0.5 x 0.5 degrees spatial resolution. ","documentType":"GEMINI_DOCUMENT","familyName":["Warnaars"],"fullName":["Warnaars, T."],"givenName":["Tanya"],"identifier":"2303498e-c499-42d1-9950-1b1f763a20fa","incomingCitationCount":0,"keyword":["Modelling","Hydrology","land surface","atmosphere","hydrology","modelling","meteorology"],"keywordsOther":["land surface","atmosphere","hydrology","modelling","meteorology"],"keywordsTheme":["Modelling","Hydrology"],"licence":"Non-Open government licence","lineage":"These data were created as part of the EU FP6 project WATCH which was coordinated by NERC-CEH and ran from 2007-2011. The data were generated in two tranches according to time interval. The data from 1958-2001 were based on ECMWF (European Centre for Medium Range Weather Forecasting) \"ERA-40\" reanalysis data via interpolation, elevation correction and monthly bias correction from gridded observations. The data from 1901-1957 were created via re-ordering of the ERA-40 reanalysis one year at a time. For full details of the process see the WATCH technical report No 22 and Weedon (2011, J. Hydrometeorology). ","locations":["POLYGON((-180 -90, -180 90, 180 90, 180 -90, -180 -90))"],"metadataDate":"2026-07-24T19:23:30.000Z","organisation":["UK Centre for Ecology & Hydrology","NERC EDS Environmental Information Data Centre"],"publicationDate":"2011-08-01T00:00:00.000Z","recordType":"Dataset","resourceIdentifier":["1315212150538","CEH:EIDC::1315212150538","https://catalogue.ceh.ac.uk/id/2303498e-c499-42d1-9950-1b1f763a20fa"],"resourceType":"Dataset","ror":["https://ror.org/00pggkr55","https://ror.org/04xw4m193"],"shortenedDescription":"The WATCH Forcing data is a twentieth century meteorological forcing dataset for land surface and hydrological models. It consists of three/six-hourly states of the weather for global half-degree land grid points. It was generated as part of the EU FP 6 project \"WATCH\"…","state":"published","supplementalDescription":["Weedon, G.P.; Gomes, S.; Viterbo, P.; Shuttleworth, W.J.; Blyth, E.; Osterle, H.; Adam, J.C.; Bellouin, N.; Boucher, O.; Best, M.. 2011 Creation of the WATCH forcing data and its use to assess global and regional reference crop evaporation over land during the twentieth century. Journal of Hydrometeorology, 12 (5). 823-848."],"temporalExtentText":"Data collected from 1911 to 2001","title":"WATCH forcing data (WFD) - 20th century Qair (specific humidity) 1901-2001","topic":["0/Modelling/","0/Hydrology/"],"version":1.0,"view":["public","rjsc"]},{"altTitle":["MaRIUS-G2G-WAH2-monthly"],"authorAffiliation":["Centre for Ecology & Hydrology"],"authorFamilyName":["Bell","Rudd","Kay","Davies"],"authorFullName":["Bell, V.A.","Rudd, A.C.","Kay, A.L.","Davies, H.N."],"authorGivenName":["V.A.","A.C.","A.L.","H.N."],"authorOrcid":["0000-0002-0792-5650","0000-0001-5996-6115","0000-0002-5526-1756","0000-0001-7783-4853"],"authorRor":["https://ror.org/00pggkr55"],"availability":"Available","catalogue":"eidc","dataFormat":["NetCDF"],"description":"This dataset is a model output, from the Grid-to-Grid hydrological model driven by weather@home2 climate model data. It provides a 100-member ensemble of monthly mean flow (m3/s) and soil moisture (mm water/m soil) on a 1 km grid for the following time periods: historical baseline (HISTBS: 1900-2006), near-future (NF: 2020-2049) and far-future (FF: 2070-2099). It also includes a baseline period (BS: 1975-2004). \n\nTo aid interpretation, two additional spatial datasets are provided:\n -\tDigitally-derived catchment areas on a 1km x 1km grid\n -\tEstimated locations of flow gauging stations on a 1km x 1km grid and as a csv file.\n\nThe data were produced as part of MaRIUS (Managing the Risks, Impacts and Uncertainties of drought and water Scarcity), which was a UK NERC-funded research project (2014-2017) that developed a risk-based approach to drought and water scarcity. ","documentType":"GEMINI_DOCUMENT","familyName":["Bell","Rudd","Kay","Davies"],"fullName":["Bell, V.A.","Rudd, A.C.","Kay, A.L.","Davies, H.N.","Bell, V."],"funder":["Natural Environment Research Council"],"givenName":["V.A.","A.C.","A.L.","H.N.","Vicky"],"grant":["NE/L010208/1"],"identifier":"3b90962e-6fc8-4251-853e-b9683e37f790","incomingCitationCount":2,"keyword":["Great Britain","MaRIUS","Hydrology","Modelling","G2G","Grid-to-Grid","drought","river","flow","soil moisture"],"keywordsOther":["G2G","Grid-to-Grid","drought","river","flow","soil moisture"],"keywordsPlace":["Great Britain"],"keywordsProject":["MaRIUS"],"keywordsTheme":["Hydrology","Modelling"],"licence":"Non-Open government licence","lineage":"The flow and soil moisture estimates are output from the Grid-to-Grid hydrological model (Bell et al. 2009; https://doi.org/10.1016/j.jhydrol.2009.08.031). The meteorological inputs to the model were the weather@home2 bias-corrected precipitation and potential evaporation (Guillod et al. 2018; https://doi.org/10.5285/0cea8d7aca57427fae92241348ae9b03). See Supporting Information for more details. ","locations":["POLYGON((-8.902 49.771, -8.902 58.882, 3.181 58.882, 3.181 49.771, -8.902 49.771))"],"metadataDate":"2026-07-24T20:40:07.000Z","orcid":["0000-0002-0792-5650","0000-0001-5996-6115","0000-0002-5526-1756","0000-0001-7783-4853"],"organisation":["Centre for Ecology & Hydrology","UK Centre for Ecology & Hydrology","NERC Environmental Information Data Centre","NERC EDS Environmental Information Data Centre"],"publicationDate":"2018-03-12T00:00:00.000Z","recordType":"Dataset","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/3b90962e-6fc8-4251-853e-b9683e37f790","10.5285/3b90962e-6fc8-4251-853e-b9683e37f790","doi:10.5285/3b90962e-6fc8-4251-853e-b9683e37f790"],"resourceType":"Dataset","ror":["https://ror.org/00pggkr55","https://ror.org/04xw4m193"],"shortenedDescription":"This dataset is a model output, from the Grid-to-Grid hydrological model driven by weather@home2 climate model data. It provides a 100-member ensemble of monthly mean flow (m3/s) and soil moisture (mm water/m soil) on a 1 km grid for the following time periods: historical…","state":"published","supplementalDescription":["Bell, V. A., Kay, A. L., Jones, R. G., Moore, R. J., & Reynard, N. S. (2009). Use of soil data in a grid-based hydrological model to estimate spatial variation in changing flood risk across the UK. Journal of Hydrology, 377(3–4), 335–350. ","Guillod, B.P.; Jones, R.G.; Kay, A.L.; Massey, N.R.; Sparrow, S.; Wallom, D.C.H.; Wilson, S.S. (2017): Managing the Risks, Impacts and Uncertainties of drought and water Scarcity (MaRIUS) project: Large set of potential past and future climate time series for the UK from the weather@home2 model . Centre for Environmental Data Analysis, 2018. doi:10.5285/0cea8d7aca57427fae92241348ae9b03. "],"supplementalName":["Marius drought project website"],"temporalExtentText":"Data collected from 1900 to 2099","title":"Grid-to-Grid model estimates of monthly mean flow and soil moisture for Great Britain: weather@home2 (climate model) driving data [MaRIUS-G2G-WAH2-monthly]","topic":["0/Hydrology/","0/Modelling/"],"version":1.0,"view":["public","nuri"]},{"authorAffiliation":["Swansea University","Université de Lausanne","NIRAS UK","Carl von Ossietzky Universität Oldenburg","University of Glasgow","Universidad de Cantabria Inicio"],"authorFamilyName":["Ladd","Cremin","Stolpmann","Balke","Vovides","Maza Fernandez"],"authorFullName":["Ladd, C.","Cremin, E.","Stolpmann, L.","Balke, T.","Vovides, A.","Maza Fernandez, M.E."],"authorGivenName":["Cai","Emilie","Lea","Thorsten","Alejandra","M.E."],"authorOrcid":["0000-0001-5437-6474","0000-0003-4732-2376","0000-0003-4251-9532","0000-0001-7733-5069","0000-0002-3293-5770"],"authorRor":["https://ror.org/053fq8t95","https://ror.org/033n9gh91","https://ror.org/00vtgdb53"],"availability":"Available","catalogue":"eidc","dataFormat":["Comma-separated values (CSV)","jpeg"],"description":"Raw acceleration data and images are provided for 20 locations spaced at ~500 m on the mudflat ~2 m in front of eroding or expanding edges of Caerlaverock saltmarsh, Solway Firth, Scotland. Acceleration data was gathered by the Mini Buoy (B4+ design) at rates of either 1 second (monthly redeployments) or 10 seconds (quarterly redeployments) between March 2021-2022. The data has been cleaned to remove any data gathered before/after deployment. Coordinates of each location and the status of the marsh edge (eroding or expanding), and images of the marsh edge at each Mini Buoy deployment are also present. The data is being used to identify hydrological thresholds (inundation duration, current and wave orbital velocities) that determine whether saltmarshes expand or erode.","documentType":"GEMINI_DOCUMENT","familyName":["Ladd","Cremin","Stolpmann","Balke","Vovides","Maza Fernandez"],"fullName":["Ladd, C.","Cremin, E.","Stolpmann, L.","Balke, T.","Vovides, A.","Maza Fernandez, M.E.","Cai Ladd"],"funder":["Natural Environment Research Council"],"givenName":["Cai","Emilie","Lea","Thorsten","Alejandra","M.E."],"grant":["NE/S008926/1"],"identifier":"3845e060-2a43-4b7e-845a-726f54a4867d","incomingCitationCount":0,"keyword":["Environmental survey","Hydrology","Modelling","Saltmarsh","Hydrodynamics","Tipping points","Mini buoy","Caerlaverock","Scotland"],"keywordsOther":["Saltmarsh","Hydrodynamics","Tipping points","Mini buoy","Caerlaverock","Scotland"],"keywordsTheme":["Environmental survey","Hydrology","Modelling"],"licence":"Open government licence","lineage":"Data were obtained using Mini Buoys (acceleration data logger in a bottom-mounted float) between 01-03-2021 and 01-03-2022 at Caerlaverock saltmarsh, Solway Firth, Scotland. Caerlaverock is a dynamic saltmarsh undergoing areas of expansion and erosion - the deployment of Mini Buoys here allows identification of hydrological thresholds for expansion or erosion. B4+ Mini Buoys were deployed. Each individual logger sampled y-axis acceleration at 1Hz. Coordinates showing the location of each Mini Buoy deployment are expressed in WGS84 standard.\n\nIn total, 20 Mini Buoys were deployed - location data (as WGS84) are stored in Coordinates.csv. Acceleration data are stored in csv files and each contain acceleration measurements of an individual Mini Buoy (MB04, MB05, etc.) at a particular time period (i.e. Apr-May 2021; June-Sep 2021, etc.). There are 118 .csv Mini Buoy files in total. Data are provided as sub-minute measurements (DD/MM/YYYY HH:MM:SS) of acceleration date (Yacc) expressed as gravity-compensated acceleration values in g-force (G).\n\nImages are provided of each of the 20 Mini Buoy sites as .JPEG.\n\n1st processing stage only (to remove data before/after deployment). Future work will involve extracting metrics on inundation duration, current and wave orbital velocity values.","locations":["POLYGON((-3.575 54.962, -3.575 55.011, -3.424 55.011, -3.424 54.962, -3.575 54.962))"],"metadataDate":"2026-07-24T20:38:21.000Z","orcid":["0000-0001-5437-6474","0000-0003-4732-2376","0000-0003-4251-9532","0000-0001-7733-5069","0000-0002-3293-5770"],"organisation":["Swansea University","Université de Lausanne","NIRAS UK","Carl von Ossietzky Universität Oldenburg","University of Glasgow","Universidad de Cantabria Inicio","NERC EDS Environmental Information Data Centre","Newcastle University"],"publicationDate":"2026-05-28T00:00:00.000Z","recordType":"Dataset","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/3845e060-2a43-4b7e-845a-726f54a4867d","10.5285/3845e060-2a43-4b7e-845a-726f54a4867d","doi:10.5285/3845e060-2a43-4b7e-845a-726f54a4867d"],"resourceType":"Dataset","rightsHolder":["Newcastle University"],"ror":["https://ror.org/053fq8t95","https://ror.org/033n9gh91","https://ror.org/00vtgdb53","https://ror.org/04xw4m193"],"shortenedDescription":"Raw acceleration data and images are provided for 20 locations spaced at ~500 m on the mudflat ~2 m in front of eroding or expanding edges of Caerlaverock saltmarsh, Solway Firth, Scotland. Acceleration data was gathered by the Mini Buoy (B4+ design) at rates of either…","state":"published","supplementalDescription":["This dataset is part of a wider Mini Buoy series - where acceleration and hydrodynamics data were recorded across the Indian Ganges Brahmaputra Meghna, Mekong and Red River Deltas. Mini Buoys offer a practical and open-source way to assess the hydrological dynamics (in terms of inundation, currents and waves) of mangrove forest - allowing assessment of suitability of mangrove plantation schemes. Mini Buoys cost less than $400 to assemble, and the material to build one can be purchased around the globe.","Data can also be uploaded as part of an App, where anyone can upload Mini Buoy data and find out if a mangrove site is suitable for restoration."],"temporalExtentText":"Data collected from 2021 to 2022","title":"Hydrological data for 20 locations spaced at 500m intervals along Caerlaverock saltmarsh (UK) between March 2021 and 2022","topic":["0/Environmental survey/","0/Hydrology/","0/Modelling/"],"version":1.0,"view":["public","katwri"]},{"authorAffiliation":["Biomathematics and Statistics Scotland","UK Centre for Ecology & Hydrology","Norwegian Institute for Nature Research","SLR Consulting Limited","ABP Marine Environmental Research Ltd."],"authorFamilyName":["Frantsuzova","Butler","Searle","Bogdanova","Daunt","Fayet","Furness","Reiertsen","Skeate"],"authorFullName":["Frantsuzova, A.","Butler, A.","Searle, K.","Bogdanova, M.","Daunt, F.","Fayet, A.L.","Furness, R.","Reiertsen, T.K.","Skeate, E."],"authorGivenName":["Anastasia","Adam","Kate","Maria","Francis","Annette L.","Robert","Tone Kristin","Eleanor"],"authorRor":["https://ror.org/03jwrz939","https://ror.org/00pggkr55","https://ror.org/04aha0598"],"availability":"Available","catalogue":"eidc","dataFormat":["Comma-separated values (CSV)"],"description":"This dataset contains information about outputs of a video-conference expert elicitation exercise carried out in March 2023 to estimate demographic consequences of potential seabird compensatory measures within the context of protected breeding populations in the North Sea. The outputs comprise of expert judgements from 6 experts given as quantitative values (median, quantiles and range) for the impact of each potential compensation measure on seabird demography (survival and productivity) for a range of species or species groups, and the estimated parameters of a probability distribution fitted using these judgements. The list of measures and species to consider was decided by the experts. The elicitation exercise was organised and carried out by UKCEH and Biomathematics and Statistics, Scotland (BioSS). Distribution fitting was carried out by BioSS. One expert gave incomplete quantitative judgements, which were not included in the final estimation of distribution parameters.\n\nThis work was funded by NERC, The Crown Estate, Crown Estate Scotland and DEFRA  as part of the ECOWINGS project within the ECOWind programme","documentType":"GEMINI_DOCUMENT","familyName":["Frantsuzova","Butler","Searle","Bogdanova","Daunt","Fayet","Furness","Reiertsen","Skeate"],"fullName":["Frantsuzova, A.","Butler, A.","Searle, K.","Bogdanova, M.","Daunt, F.","Fayet, A.L.","Furness, R.","Reiertsen, T.K.","Skeate, E.","Anastasia Frantsuzova"],"funder":["Natural Environment Research Council"],"givenName":["Anastasia","Adam","Kate","Maria","Francis","Annette L.","Robert","Tone Kristin","Eleanor"],"grant":["NE/X009068/1"],"identifier":"8847e78c-8778-474b-b2a2-3931beeb0e1b","incomingCitationCount":0,"keyword":["Modelling","Environmental risk","expert elicitation","compensatory measure","species conservation","population viability","demography","conservation management","mitigation measure","ecological compensation","offshore wind farm","black-legged kittiwake","common guillemot","Atlantic puffin","razorbill","northern gannet","ecowings"],"keywordsOther":["expert elicitation","compensatory measure","species conservation","population viability","demography","conservation management","mitigation measure","ecological compensation","offshore wind farm","black-legged kittiwake","common guillemot","Atlantic puffin","razorbill","northern gannet","ecowings"],"keywordsTheme":["Modelling","Environmental risk"],"licence":"Open government licence","lineage":"The dataset consists of two CSV files containing expert judgements and fitted Beta distribution parameters. Values represent expert estimates of the potential change in breeding success and adult survival resulting from compensatory measures at bird colonies.  Expert judgements were collected using the Sheffield Elicitation Framework (SHELF) through a structured online elicitation process. Six experts were selected based on their expertise, career stage, gender and availability. They provided ranges and quantiles for each scenario, which were discussed within the expert group for internal consistency and physical plausibility. Individual judgements were anonymised and combined by the facilitators after each elicitation round. A Beta distribution was fitted to the judgements for each expert, measure and species. Experts reviewed the resulting distributions and probability curves and could refine their judgements following discussion.\n\nThe main limitations are that results depend on the selection and judgement of the experts and may be affected by cognitive biases, differences in interpretation or group dynamics. The fitted Beta distributions also impose assumptions about the shape of expert beliefs, particularly in the tails, and the available ranges and quantiles provide limited information about the underlying distributions. Consequently, fitted distributions may be sensitive to small changes in the elicited values and may not fully represent expert beliefs. Experts generally considered the Beta distribution to provide a reasonable representation. \n\nQuality control included checks for internal consistency, ensuring quantiles followed a valid order and fell within the specified ranges, and checks that ranges and quantiles were within physically reasonable limits. Overlap between expert ranges and the spread of quantiles were also assessed. Experts received training and example judgements, with facilitator oversight supporting adherence to the elicitation protocol.","locations":["POLYGON((-3.58 54.765, -3.58 58.404, 3.059 58.404, 3.059 54.765, -3.58 54.765))"],"metadataDate":"2026-09-22T07:57:47.000Z","organisation":["Biomathematics and Statistics Scotland","UK Centre for Ecology & Hydrology","Norwegian Institute for Nature Research","SLR Consulting Limited","ABP Marine Environmental Research Ltd.","NERC EDS Environmental Information Data Centre"],"publicationDate":"2026-09-17T00:00:00.000Z","recordType":"Dataset","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/8847e78c-8778-474b-b2a2-3931beeb0e1b","10.5285/8847e78c-8778-474b-b2a2-3931beeb0e1b","doi:10.5285/8847e78c-8778-474b-b2a2-3931beeb0e1b"],"resourceType":"Dataset","rightsHolder":["UK Centre for Ecology & Hydrology","Biomathematics and Statistics Scotland"],"ror":["https://ror.org/03jwrz939","https://ror.org/00pggkr55","https://ror.org/04aha0598","https://ror.org/04xw4m193"],"shortenedDescription":"This dataset contains information about outputs of a video-conference expert elicitation exercise carried out in March 2023 to estimate demographic consequences of potential seabird compensatory measures within the context of protected breeding populations in the North…","state":"published","temporalExtentText":"Data collected from 2023 to 2023","title":"Demographic consequences of potential seabird compensatory measures relating to protected breeding populations in the North Sea, UK, from an expert elicitation exercise","topic":["0/Modelling/","0/Environmental risk/"],"version":1.0,"view":["public","katwri"]},{"authorAffiliation":["Centre for Ecology & Hydrology","Woodland Trust","SASA","St Andrews University","Met Office","FERA"],"authorFamilyName":["Purse","Searle","Elliot","Schlenzig","Harris","Vieno","Chapman","Burgin","Turner","Thorp","Jennings"],"authorFullName":["Purse, B.V.","Searle, K.","Elliot, M.","Schlenzig, A.","Harris, C.","Vieno, M.","Chapman, D.","Burgin, L.","Turner, J.","Thorp, G.","Jennings, P."],"authorGivenName":["B.V.","K.","M.","A.","C.","D.","L.","J.","G.","P."],"authorOrcid":["0000-0001-5140-2710","0000-0001-7741-9377","0000-0003-1836-4112"],"authorRor":["https://ror.org/00pggkr55","https://ror.org/05e9eyh13","https://ror.org/02wn5qz54","https://ror.org/01ch2yn61","https://ror.org/05299tt38"],"availability":"Available","catalogue":"eidc","dataFormat":["TIFF"],"description":"These spatial layers map the suitability of climate conditions for infection of Phytophthora ramorum and Phytophthora kernoviae across the UK. The model is based on laboratory data of environmental responses of these pathogens and hourly temperature and relative humidity regimes between 2007 and 2011, and focuses on how many times infection could have been completed within running 48 hour periods through the year. Average suitability of conditions are mapped for the whole period and for individual years from 2007 to 2011. This research was funded by the Scottish Government under research contract CR/2008/55, 'Study of the epidemiology of Phytophthora ramorum and Phytophthora kernoviae in managed gardens and heathlands in Scotland' and involved collaborators from St Andrews University, Science and Advice for Scottish Agriculture (SASA), Scottish Natural Heritage (SNH), Forestry Commission, the Food and Environment Research Agency (FERA) and the Centre for Ecology & Hydrology (CEH). This dataset forms part of a series.","documentType":"GEMINI_DOCUMENT","familyName":["Purse","Searle","Elliot","Schlenzig","Harris","Vieno","Chapman","Burgin","Turner","Thorp","Jennings"],"fullName":["Purse, B.V.","Searle, K.","Elliot, M.","Schlenzig, A.","Harris, C.","Vieno, M.","Chapman, D.","Burgin, L.","Turner, J.","Thorp, G.","Jennings, P."],"givenName":["B.V.","K.","M.","A.","C.","D.","L.","J.","G.","P.","B. V."],"identifier":"a13d5821-824f-41ba-a930-26a5f0f4d2a6","incomingCitationCount":0,"keyword":["Modelling","Sudden oak death","plant pathogen","Ramorum disease","Ramorum dieback","pathogen","oak","Beech","Rhododendron","Quercus","Phytophthora ramorum","Phytophthora kernoviae"],"keywordsOther":["Sudden oak death","plant pathogen","Ramorum disease","Ramorum dieback","pathogen","oak","Beech","Rhododendron","Quercus","Phytophthora ramorum","Phytophthora kernoviae"],"keywordsTheme":["Modelling"],"licence":"Open government licence","lineage":"Laboratory data of environmental responses of Phytophthora ramorum and Phytophthora kernoviae was provided by FERA and hourly temperature and relative humidity gridded data was provided by the UK Met Office. Additional maps of quarterly suitability and for the period from 2012 to 2014 are available on request to point of contact. Daniel Chapman (NERC Centre for Ecology & Hydrology) and Massimo Vieno (NERC Centre for Ecology & Hydrology) provided invaluable help with setting up the climate algorithm.","locations":["POLYGON((-12.622 47.654, -12.622 60.985, 6.982 60.985, 6.982 47.654, -12.622 47.654))"],"metadataDate":"2026-07-25T13:16:16.000Z","orcid":["0000-0001-5140-2710","0000-0001-7741-9377","0000-0003-1836-4112"],"organisation":["Centre for Ecology & Hydrology","Woodland Trust","SASA","St Andrews University","Met Office","FERA","UK Centre for Ecology & Hydrology","NERC Environmental Information Data Centre","NERC EDS Environmental Information Data Centre"],"publicationDate":"2015-09-01T00:00:00.000Z","recordType":"Dataset","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/a13d5821-824f-41ba-a930-26a5f0f4d2a6","10.5285/a13d5821-824f-41ba-a930-26a5f0f4d2a6","doi:10.5285/a13d5821-824f-41ba-a930-26a5f0f4d2a6"],"resourceType":"Dataset","ror":["https://ror.org/00pggkr55","https://ror.org/05e9eyh13","https://ror.org/02wn5qz54","https://ror.org/01ch2yn61","https://ror.org/05299tt38","https://ror.org/04xw4m193"],"shortenedDescription":"These spatial layers map the suitability of climate conditions for infection of Phytophthora ramorum and Phytophthora kernoviae across the UK. The model is based on laboratory data of environmental responses of these pathogens and hourly temperature and relative humidity…","state":"published","temporalExtentText":"Data collected from 2007 to 2011","title":"Climate suitability for Phytophthora ramorum and Phytophthora kernoviae infection in the UK","topic":["0/Modelling/"],"version":1.0,"view":["public","hajd"]}],"rows":20,"semanticEnabled":false,"url":"https://catalogue.ceh.ac.uk/eidc/documents?facet=topic%7C0/Modelling/"}