{"facets":[{"admin":false,"displayName":"Record type","fieldName":"recordType","hierarchical":false,"results":[{"active":false,"count":14,"name":"Dataset","url":"http://data.inms.international:443/inms/documents?facet=recordType%7CDataset"},{"active":false,"count":15,"name":"Model","url":"http://data.inms.international:443/inms/documents?facet=recordType%7CModel"},{"active":false,"count":3,"name":"Third-party dataset","url":"http://data.inms.international:443/inms/documents?facet=recordType%7CThird-party%20dataset"}]},{"admin":false,"displayName":"Pollutant","fieldName":"inmsPollutant","hierarchical":false},{"admin":false,"displayName":"Model Type","fieldName":"modelType","hierarchical":false},{"admin":false,"displayName":"Demonstration Region","fieldName":"inmsDemonstrationRegion","hierarchical":false},{"admin":false,"displayName":"Project","fieldName":"inmsProject","hierarchical":false}],"nextPage":"http://data.inms.international:443/inms/documents?page=2","numFound":32,"order":"asc","page":1,"results":[{"authorAffiliation":["PBL – Netherlands Environmental  Assessment Agency"],"authorOrcid":["https://orcid.org/0000-0001-6162-9943"],"availability":"Available","catalogue":"inms","dataFormat":["Comma-separated values (CSV)"],"description":"The dataset comprises the simulated loss of freshwater biodiversity intactness (expressed as MSA; scale 0-1) in lakes and rivers worldwide, as well as the simulated concentration of cyanobacteria (harmful algal blooms) in lakes (in range classes related to the WHO standards; Chorus & Welker, 2021), due to nutrient emissions to surface water.\nThe effects have been modelled by the model GLOBIO-Aquatic (Janse et al., 2015; Janse et al., 2023). The data are global at a 30x30 arc minutes resolution, and cover the years 1970, 2010, and 2050 for six INMS scenarios, representing different combinations  of SSPs, RCPs and nitrogen policy options.\n","documentType":"GEMINI_DOCUMENT","identifier":"a349e3ca-fe95-448c-b14d-6a488392bc77","incomingCitationCount":0,"keyword":["INMS"],"keywordsProject":["INMS"],"licence":"Non-Open government licence","lineage":"The nutrient emissions and concentrations in surface water have been modelled by the Global Nutrient Model (GNM; Beusen et al., 2022) combined with the global hydrological model PCR-GLOBWB (Sutanudjaja et al., 2018). The missions are the sum of leaching from (agricultural) land, urban emissions, aquaculture and atmospheric deposition.\nLand-use and climate projections were based on the IMAGE model (Stehfest et al.,  2014).","locations":["POLYGON((-180 -90, -180 90, 180 90, 180 -90, -180 -90))"],"metadataDate":"2025-12-15T15:57:46.000Z","orcid":["https://orcid.org/0000-0001-6162-9943"],"organisation":["PBL – Netherlands Environmental  Assessment Agency"],"recordType":"Dataset","resourceIdentifier":["https://data.inms.international/id/a349e3ca-fe95-448c-b14d-6a488392bc77"],"resourceType":"Dataset","shortenedDescription":"The dataset comprises the simulated loss of freshwater biodiversity intactness (expressed as MSA; scale 0-1) in lakes and rivers worldwide, as well as the simulated concentration of cyanobacteria (harmful algal blooms) in lakes (in range classes related to the WHO standards;...","state":"published","supplementalDescription":["\n"],"supplementalName":["GLOBIO | Global biodiversity model for policy support","Beusen AHW, Doelman JC, Van Beek LPH, et al (2022) Exploring river nitrogen and phosphorus loading and export to global coastal waters in the Shared Socio-economic pathways. Glob Environ Change 72:102426. ","Chorus, I, Welker M; eds. (2021). Toxic Cyanobacteria in Water, 2nd edition. CRC Press, Boca Raton (FL), on behalf of the World Health Organization, Geneva, CH.","Janse, J.H., Kuiper, J.J., Weijters, M.J., Westerbeek, E.P., Jeuken, M.H.J.L., Bakkenes, M.Alkemade, R., Verhoeven, J.T.A., 2015. GLOBIO-aquatic, a global model of human impact on the biodiversity of inland aquatic ecosystems. Env. Sci. Pol. 48: 99-114.","Janse, J.H., Bakkenes, M. and Meijer, J.R. (2016). GLOBIO-AQUATIC, Technical Model Description, Model version 1.3 (Aug. 2014). PBL, Den Haag, publ. nr. 2829.","Stehfest, E., van Vuuren, D., Kram, T., Bouwman, L., Alkemade, R., Bakkenes, M., Biemans, H., Bouwman, A., den Elzen, M., Janse, J., Lucas, P., van Minnen, J., Muller, M., Prins, A., 2014. Integrated assessment of global environmental change with IMAGE 3.0. Model description and policy applications. PBL Netherlands Environmental Assessment Agency.","Janse JH, Bakkenes M, Barbarossa V, Gernaat DEHJ, Giesen P (2023). Future pathways to restore ecological quality of freshwater ecosystems  within SSP2/RCP6.0 boundary conditions. Background report Future Water Challenges. PBL, Den Haag."],"title":"GLOBIO-AQUATIC. Effects of nutrient emissions on freshwater biodiversity and algal blooms for the years 1970, 2010 and 2050.","version":1.0,"view":["jh.janse@gmail.com","public","ccaporusso@gmx.com"]},{"authorAffiliation":["Potsdam Institute  for Climate Impact Research"],"authorOrcid":["https://orcid.org/0000-0002-8242-6712"],"availability":"Available","catalogue":"inms","dataFormat":["NetCDF"],"description":"This dataset provides MAGPIE runs for seven Integrated Nitrogen Management System (INMS) scenarios from 2000 to 2100, with 10 years interval.\n\nThe last historical year is 2020.\n\nThe climate scenarios are represented as combination of SSP-RCP-N (shared socioeconomic pathways - representative concentration pathways - nitrogen pathways) :\n\n1) Business as usual (SSP5-RCP8.5 Low N ambition)\n2) Low N regulation (SSP2-RCP4.5 Low N ambition)\n3) Medium N regulation (SSP2-RCP4.5 Moderate N ambition)\n4) High N regulation (SSP2-RCP4.5 High N ambition)\n5) Best Case (SSP1-RCP4.5 High N ambition)\n6) Best Case + (SSP1-RCP4.5 High N ambition with ambitious diet shift and food loss/waste reductions)\n7) Bioenergy (SSP1-RCP2.6 High N ambition with low meat & dairy diet)","documentType":"GEMINI_DOCUMENT","identifier":"6ca8bc1e-c491-451f-a216-0f4869227dc4","incomingCitationCount":0,"inmsScale":["Global"],"keyword":["INMS","Global"],"keywordsOther":["Global"],"keywordsProject":["INMS"],"licence":"Non-Open government licence","locations":["POLYGON((-180 -90, -180 90, 180 90, 180 -90, -180 -90))"],"metadataDate":"2025-12-15T15:57:49.000Z","orcid":["https://orcid.org/0000-0002-8242-6712"],"organisation":["Potsdam Institute  for Climate Impact Research","INMS"],"recordType":"Dataset","resourceIdentifier":["https://data.inms.international/id/6ca8bc1e-c491-451f-a216-0f4869227dc4"],"resourceType":"Dataset","shortenedDescription":"This dataset provides MAGPIE runs for seven Integrated Nitrogen Management System (INMS) scenarios from 2000 to 2100, with 10 years interval.\n\nThe last historical year is 2020.\n\nThe climate scenarios are represented as combination of SSP-RCP-N (shared socioeconomic...","state":"published","supplementalDescription":["More information on the open-source model MAgPIE can be found at the link below.","Please cite the model using the following references:","Bodirsky, B. L. et al. Reactive nitrogen requirements to feed the world in 2050 and potential to mitigate nitrogen pollution. Nature Communications 5, (2014)."],"supplementalName":["Dietrich, J. P. et al. MAgPIE 4 – a modular open-source framework for modeling global land systems. Geoscientific Model Development 12, 1299–1317 (2019).","Dietrich, J. P. et al. MAgPIE - An Open Source land-use modeling framework - Version 4.3.0. (2020).","Bodirsky, B. L. et al. N2O emissions from the global agricultural nitrogen cycle – current state and future scenarios. Biogeosciences 9, 4169–4197 (2012)."],"title":"Results of the Model of Agricultural Production and its Impact on the Environment (MAgPIE) for seven Integrated Nitrogen Management System (INMS) global scenarios, 2000-2100","version":1.0,"view":["bodirsky@pik-potsdam.de","public","ccaporusso@gmx.com"]},{"authorAffiliation":["PBL Netherlands Environmental Assessment Agency","PBL Netherlands Environmental Assessment Agency "],"availability":"Available","catalogue":"inms","dataFormat":["NetCDF"],"description":"This dataset provides global and regional estimates of all components of nitrogen budgets in agricultural and natural ecosystems with a temporal resolution of five years that for the past and future period that goes from 2000 to 2100 at a 10 years interval. The last historical year is 2010.\n\nThe IMAGE-GNM model outputs were run for six different INMS scenarios:\n \nThe climate scenarios are represented as combination of SSP-RCP-N (shared socioeconomic pathways - representative concentration pathways - nitrogen pathways) :\n\n1) Business as usual (SSP5-RCP8.5 Low N ambition)\n2) Low N regulation (SSP2-RCP4.5 Low N ambition)\n3) Medium N regulation (SSP2-RCP4.5 Moderate N ambition)\n4) High N regulation (SSP2-RCP4.5 High N ambition)\n5) Best Case (SSP1-RCP4.5 High N ambition)\n6) Bioenergy (SSP1-RCP2.6 High N ambition with low meat & dairy diet)\n\nThis dataset has a spatial resolution of 0.5 x 0.5 degrees.","documentType":"GEMINI_DOCUMENT","identifier":"d97a8f08-94a4-4143-9714-b50b5b3d9225","incomingCitationCount":0,"keyword":["INMS"],"keywordsOther":["INMS"],"licence":"Non-Open government licence","lineage":" This dataset is the result of IMAGE-GNM output runs. The dataset was created with the IMAGE-GNM model version published by Beusen et al. (2015) in Geoscientific Model Development. Main inputs for IMAGE-GNM are from IMAGE3.2 (https://eartharxiv.org/repository/view/2759/). Validation of the model results with observations are shown in Beusen et al. (2022) in Global Environmental Change (https://doi.org/10.1016/j.gloenvcha.2021.102426).","locations":["POLYGON((-180 -90, -180 90, 180 90, 180 -90, -180 -90))"],"metadataDate":"2025-12-15T15:57:51.000Z","organisation":["PBL Netherlands Environmental Assessment Agency","PBL Netherlands Environmental Assessment Agency "],"recordType":"Dataset","resourceIdentifier":["https://data.inms.international/id/d97a8f08-94a4-4143-9714-b50b5b3d9225"],"resourceType":"Dataset","shortenedDescription":"This dataset provides global and regional estimates of all components of nitrogen budgets in agricultural and natural ecosystems with a temporal resolution of five years that for the past and future period that goes from 2000 to 2100 at a 10 years interval. The last...","state":"published","supplementalName":["Coupling global models for hydrology and nutrient loading to simulate nitrogen and phosphorus retention in surface water – description of IMAGE–GNM and analysis of performance"],"title":"Integrated Model to Assess the Global Environment (IMAGE) Global Nutrient Model (GNM) simulations of past and future (2000-2100) nitrogen budgets and individual budget terms for agricultural and natural ecosystems","version":1.0,"view":["public","inms-users","crimar"]},{"authorAffiliation":["NASA"],"availability":"Available","catalogue":"inms","description":"The NASA Goddard Institute for Space Studies (GISS) Earth System Model (ModelE) dataset comprises outputs from a coupled atmosphere-ocean general circulation model used to simulate past, present, and future climate conditions. ModelE integrates key components of the Earth system, including atmosphere, ocean, sea ice, and land surface, with optional modules for atmospheric chemistry, aerosols, and the carbon cycle.\n\nThe dataset typically includes variables such as surface and atmospheric temperature, precipitation, radiation fluxes, winds, humidity, ocean temperature and circulation, sea ice extent, and biogeochemical tracers (e.g., aerosols and greenhouse gases), depending on the specific model configuration and experiment (e.g., CMIP simulations).\n\nMethodologically, ModelE is a physically based, process-driven numerical model that solves equations governing fluid dynamics, thermodynamics, and radiative transfer on a global grid. Simulations are driven by prescribed boundary conditions (e.g., greenhouse gas concentrations, solar forcing) or fully coupled interactions between system components. Many outputs are produced as part of international intercomparison projects such as CMIP, enabling standardized evaluation of climate model performance and projections.","documentType":"GEMINI_DOCUMENT","identifier":"86f223bd-88ab-4a49-af0d-2e8e899e93f8","incomingCitationCount":0,"licence":"Non-Open government licence","locations":["POLYGON((-180 -90, -180 90, 180 90, 180 -90, -180 -90))"],"metadataDate":"2026-03-24T13:16:32.000Z","organisation":["NASA"],"recordType":"Third-party dataset","resourceIdentifier":["https://data.inms.international/id/86f223bd-88ab-4a49-af0d-2e8e899e93f8"],"resourceType":"Third-party dataset","shortenedDescription":"The NASA Goddard Institute for Space Studies (GISS) Earth System Model (ModelE) dataset comprises outputs from a coupled atmosphere-ocean general circulation model used to simulate past, present, and future climate conditions. ModelE integrates key components of the...","state":"published","title":"GISS Earth System Model: ModelE","version":1.0,"view":["public","inms-users","ccaporusso@gmx.com"]},{"catalogue":"inms","description":"GLOBIOM is a global economic bottom-up agricultural and forest sector model. The model is based on a detailed spatially explicit grid and estimates economic and environmental impacts, incl. nutrient balances, tightly linked with bio-physical process based models like EPIC. The model is typically used for scenario analysis in medium (2030), long (2050), and very long (2100) time horizon.","documentType":"CEH_MODEL","identifier":"681deb6d-25f4-45e1-86bc-74935aa29fdf","incomingCitationCount":0,"keyword":["INMS"],"metadataDate":"2024-11-04T09:24:25.000Z","recordType":"Model","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/5e6e20a8-db5f-4aa0-b11a-44e5838827b1","https://data.inms.international/id/681deb6d-25f4-45e1-86bc-74935aa29fdf"],"resourceType":"Model","shortenedDescription":"GLOBIOM is a global economic bottom-up agricultural and forest sector model. The model is based on a detailed spatially explicit grid and estimates economic and environmental impacts, incl. nutrient balances, tightly linked with bio-physical process based models like...","state":"published","title":"GLOBIOM","version":1.0,"view":["inms-users","public","leav","leclere@iiasa.ac.at"]},{"authorAffiliation":["International Institute for Applied Systems Analysis (IIASA)"],"authorOrcid":["https://orcid.org/0000-0003-4463-7778"],"availability":"Available","catalogue":"inms","dataFormat":["NetCDF"],"description":"This dataset is a model output from the Global Biosphere Management Model (GLOBIOM). It provides global estimates of annual nitrogen cycle in the agricultural sector (including cropland, pasture, and livestock systems) for the period of 2010-2100 with 10 years interval under seven scenarios of nitrogen future described in Kanter et al. (2020), with 2010 as the only historical year.\nThe scenarios are represented as combination of SSP-RCP-N (shared socioeconomic pathways - representative concentration pathways - nitrogen pathways) :\n\nThe climate scenarios are represented as combination of SSP-RCP-N (shared socioeconomic pathways - representative concentration pathways - nitrogen pathways) :\n\n1) Business as usual (SSP5-RCP8.5 Low N ambition)\n2) Low N regulation (SSP2-RCP4.5 Low N ambition)\n3) Medium N regulation (SSP2-RCP4.5 Moderate N ambition)\n4) High N regulation (SSP2-RCP4.5 High N ambition)\n5) Best Case (SSP1-RCP4.5 High N ambition)\n6) Best Case + (SSP1-RCP4.5 High N ambition with ambitious diet shift and food loss/waste reductions)\n7) Bioenergy (SSP1-RCP2.6 High N ambition with low meat & dairy diet)\n\n\nThe data was produced as part of the INMS (International Nitrogen Managements System) project that is implemented by the UN Environment with funding through the Global Environment Facility (GEF) and executed through UK Centre for Ecology & Hydrology (UKCEH).","documentType":"GEMINI_DOCUMENT","identifier":"1f96dfea-b859-4196-a14e-4fb4a499f3f9","incomingCitationCount":0,"keyword":["INMS"],"keywordsProject":["INMS"],"licence":"Non-Open government licence","lineage":"The GLOBIOM model outputs on the nitrogen flows and budgets of agricultural sector for the period of 2000-2100 (historical period of 2000 and 2010, and projections of 2020 to 2100 ) have been produced under the INMS (Towards the Establishment of an International Nitrogen Management System) project. The data is produced on a 2 by 2 degree resolution over the world. Projections of the agricultural nitrogen cycle are produced under the nitrogen scenarios in the Kanter et al. (2020), which presents a framework for nitrogen future with combination of different shared socioeconomic pathways (SSP), representative concentration pathways (RCP), and nitrogen pathways (N). The SSP data can be found in the SSP database (https://tntcat.iiasa.ac.at/SspDb/).\n","locations":["POLYGON((-180 -90, -180 90, 180 90, 180 -90, -180 -90))"],"metadataDate":"2025-12-15T15:57:54.000Z","orcid":["https://orcid.org/0000-0003-4463-7778"],"organisation":["International Institute for Applied Systems Analysis (IIASA)"],"recordType":"Dataset","resourceIdentifier":["https://data.inms.international/id/1f96dfea-b859-4196-a14e-4fb4a499f3f9"],"resourceType":"Dataset","shortenedDescription":"This dataset is a model output from the Global Biosphere Management Model (GLOBIOM). It provides global estimates of annual nitrogen cycle in the agricultural sector (including cropland, pasture, and livestock systems) for the period of 2010-2100 with 10 years interval...","state":"published","supplementalName":["A framework for nitrogen futures in the shared socioeconomic pathways"],"title":"Global Biosphere Management Model (GLOBIOM) global simulations of agricultural nitrogen cycle for the period of 2010-2100 under various SSP-RCP-N (shared socioeconomic pathways - representative concentration pathways - nitrogen pathways ) scenario combinations","version":1.0,"view":["inms-users","crimar","public","changj@iiasa.ac.at","leclere@iiasa.ac.at"]},{"catalogue":"inms","description":"The IMAGE-GNM is a global, spatially explicit, distributed model that couples IMAGE (with the global hydrological model PCRaster Global Water Balance (PCR-GLOBWB)) as the basis for describing flow and retention/removal of N and P delivery from soils to surface waters. IMAGE-GNM is a coupled nutrient input–hydrology–in-stream nutrient retention model to quantitatively track the changes in the global freshwater N and P cycles. It includes the interactions between human-induced changes in climate, hydrology and nutrient loading. The hydrological system incorporates a distributed river model that merges both terrestrial and aquatic aspects and includes groundwater and upland areas,  wetlands, riparian zones and floodplains, and reservoirs.","documentType":"CEH_MODEL","identifier":"9bf0cca0-49d3-4119-afa5-f16a2f2491e9","incomingCitationCount":0,"inmsScale":["Global"],"keyword":["Water Quality","Global","Soil","Nitrogen","Phosphorous","Scenario Model","INMS"],"metadataDate":"2024-11-04T09:31:10.000Z","recordType":"Model","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/29b66950-385f-43d6-8a55-7e431fcad0c9","https://data.inms.international/id/9bf0cca0-49d3-4119-afa5-f16a2f2491e9"],"resourceType":"Model","shortenedDescription":"The IMAGE-GNM is a global, spatially explicit, distributed model that couples IMAGE (with the global hydrological model PCRaster Global Water Balance (PCR-GLOBWB)) as the basis for describing flow and retention/removal of N and P delivery from soils to surface waters....","state":"published","title":" IMAGE GNM - Integrated Model to Assess the Global Environment - Global Nutrient Model","version":1.0,"view":["public","leav","inms-users"]},{"catalogue":"inms","description":"Main drivers are population, economic development (income), land use, livestock and crop production. Main inputs are land use, use of synthetic and organic fertilizers, atmospheric N deposition, biological N2 fixation, total population, population with sewage connection, nutrient removal during treatment, water discharges, runoff, dams.  Inputs for land use, population, diffuse and point sources of nutrients are from IMAGE; inputs for hydrology are from WBM. Inputs for future years were derived from IMAGE and WBM as well. These models quantitatively interpreted Millennium Ecosystem Assessment scenarios to produce the required inputs for Global NEWS-2","documentType":"CEH_MODEL","identifier":"6ca8d695-7e1e-4008-bedc-26ef2ab541d0","incomingCitationCount":0,"inmsScale":["Basin"],"keyword":["Basin","Water Quality","INMS"],"metadataDate":"2024-11-04T09:39:20.000Z","recordType":"Model","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/26db611a-351b-4eea-851e-922d1409061b","https://data.inms.international/id/6ca8d695-7e1e-4008-bedc-26ef2ab541d0"],"resourceType":"Model","shortenedDescription":"Main drivers are population, economic development (income), land use, livestock and crop production. Main inputs are land use, use of synthetic and organic fertilizers, atmospheric N deposition, biological N2 fixation, total population, population with sewage connection,...","state":"published","title":"Global NEWS (Nutrient Export from WaterSheds)","version":1.0,"view":["public","leav"]},{"authorAffiliation":["PBL Netherlands Environmental Assessment Agency"],"authorFamilyName":["Schulte-Uebbing"],"authorFullName":["Lena Schulte-Uebbing"],"authorGivenName":["Lena"],"authorOrcid":["https://orcid.org/0000-0002-5511-1432"],"availability":"Available","catalogue":"inms","dataFormat":["TIFF","NetCDF","Comma-separated values (CSV)"],"documentType":"GEMINI_DOCUMENT","familyName":["Schulte-Uebbing"],"fullName":["Lena Schulte-Uebbing"],"givenName":["Lena"],"identifier":"86fdc38c-444e-4cdf-ad80-3f82080f3515","incomingCitationCount":0,"licence":"Non-Open government licence","locations":["POLYGON((-180 -90, -180 90, 180 90, 180 -90, -180 -90))"],"metadataDate":"2026-04-02T08:35:09.000Z","orcid":["https://orcid.org/0000-0002-5511-1432"],"organisation":["PBL Netherlands Environmental Assessment Agency"],"recordType":"Dataset","resourceIdentifier":["https://data.inms.international/id/86fdc38c-444e-4cdf-ad80-3f82080f3515"],"resourceType":"Dataset","state":"published","title":"Static Nitro Climate Model (SNCM)","version":1.0,"view":["public","inms-users","ccaporusso@gmx.com"]},{"catalogue":"inms","description":"Miterra-Global is an environmental impact assessment model at global scale. The model can be used to assess the effects of the implementation of ammonia (NH3) and nitrate (NO3) measures and policies on the emissions of NH3, (nitrous oxide) N2O, N oxides (NOx), and methane (CH4) to the atmosphere, leaching of N (including nitrate) to ground water and surface waters, and on the phosphorus (P) balance at regional level. The emission and leaching factors are used to calculate greenhouse gas emissions (CO2, CH4, N2O) in a deterministic and annual basis. Model inputs refer to: (1) activity data, such as animal numbers, crop yields and N fertilizer amounts, and (2) spatial environmental data, such as climate and land use.","documentType":"CEH_MODEL","identifier":"5765d43a-458d-48ed-a7b2-5bf1d208ba73","incomingCitationCount":0,"inmsScale":["Global"],"keyword":["Global","Greenhouse Gases","INMS","mitigation practices"],"locations":["POLYGON((-180 -90, -180 90, 180 90, 180 -90, -180 -90))"],"metadataDate":"2024-11-04T09:37:50.000Z","recordType":"Model","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/bb33db1a-324e-4be6-8114-9d7f5bd18c08","https://data.inms.international/id/5765d43a-458d-48ed-a7b2-5bf1d208ba73"],"resourceType":"Model","shortenedDescription":"Miterra-Global is an environmental impact assessment model at global scale. The model can be used to assess the effects of the implementation of ammonia (NH3) and nitrate (NO3) measures and policies on the emissions of NH3, (nitrous oxide) N2O, N oxides (NOx), and methane...","state":"published","title":"MITERRA Global","version":1.0,"view":["inms-users","chantal.hendriks@wur.nl","public","leav"]},{"authorAffiliation":["European Commission, Joint Research Centre"],"authorFamilyName":["Crippa","Oreggioni","Guizzardi","Muntean","Schaaf","Solazzo"],"authorFullName":["Crippa, M.","Oreggioni, G.","Guizzardi, D.","Muntean, M.","Schaaf, E.","Solazzo, E."],"authorGivenName":["Monica","Gabriel","Diego","Marilena","Edwin","Efisio"],"availability":"Unknown","catalogue":"inms","dataFormat":["Comma-separated values (CSV)","NetCDF"],"description":"It includes emissions time series (1970-2015)  and gridmaps for Carbon Monoxide (CO), Nitrogen Oxides (NOx), Non-Methane Volatile Organic Compounds (NMVOC), Methane (CH4), Ammonia (NH3), Nitrogen oxides (NOx), Sulfur Dioxide (SO2), Fine Particulate Matter (PM10 and PM2.5) and Carbonaceous speciation (BC, OC).\nEmission gridmaps are expressed in ton substance / 0.1degree x 0.1degree / year for the .txt files and in kg substance /m2 /s for the .nc files.\nEDGAR aims to inform scientists and policy makers on the evolution of the emission inventories over time for all world countries and to provide the scientific community 0.1degX0.1deg gridmaps representing the emissions sources. ","documentType":"GEMINI_DOCUMENT","familyName":["Crippa","Oreggioni","Guizzardi","Muntean","Schaaf","Solazzo"],"fullName":["Crippa, M.","Oreggioni, G.","Guizzardi, D.","Muntean, M.","Schaaf, E.","Solazzo, E."],"givenName":["Monica","Gabriel","Diego","Marilena","Edwin","Efisio"],"identifier":"0466dd11-0b69-4c66-b82b-a017d9714bf9","incomingCitationCount":0,"licence":"Non-Open government licence","lineage":"The emissions in EDGAR are estimated based on the activity data from the international statistics (e.g. IEA, USGS, IFA, FAO etc.) and emissions factors from the official guidebooks such as IPCC and EEA/EMEP, and scientific literature. For emissions distribution on global gridmaps we use proxy data such as population, location of the point sources etc.","locations":["POLYGON((-180 -90, -180 90, 180 90, 180 -90, -180 -90))"],"metadataDate":"2026-03-23T10:13:35.000Z","organisation":["European Commission, Joint Research Centre"],"recordType":"Third-party dataset","resourceIdentifier":["https://data.inms.international/id/0466dd11-0b69-4c66-b82b-a017d9714bf9"],"resourceType":"Third-party dataset","shortenedDescription":"It includes emissions time series (1970-2015)  and gridmaps for Carbon Monoxide (CO), Nitrogen Oxides (NOx), Non-Methane Volatile Organic Compounds (NMVOC), Methane (CH4), Ammonia (NH3), Nitrogen oxides (NOx), Sulfur Dioxide (SO2), Fine Particulate Matter (PM10 and...","state":"published","title":"Emissions Database for Global Atmospheric Research (EDGAR) Global Air Pollutant Emissions, 1970-2015","version":1.0,"view":["public","inms-users","crimar"]},{"authorAffiliation":["National Oceanography Centre"],"authorOrcid":["https://orcid.org/0000-0002-2081-2693"],"availability":"Available","catalogue":"inms","dataFormat":["NetCDF"],"description":"Model concentrations of winter nitrate, growing season chlorophyll-a and monthly minimum oxygen for the Northwest European Shelf Seas derived from simulations of the Nucleus for a European Model of the Ocean (NEMO) coupled to the European Regional Seas Ecosystem Model (ERSEM). \nData for mean conditions between 1998 and 2007 and for 2010 are derived from a reanalysis (https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_BGC_004_011, accessed May 2021). \nClimate projections for mean conditions from 2005 to 2015 and from 2045 to 2055 are for the RCP8.5 climate scenario (https://doi.org/10.5281/zenodo.3953801). ","documentType":"GEMINI_DOCUMENT","identifier":"b6a14d00-8ec0-4936-a4d6-e175766cd319","incomingCitationCount":0,"keyword":["INMS"],"keywordsProject":["INMS"],"licence":"Non-Open government licence","lineage":"This dataset is based on two NEMO-ERSEM simulations for the Northwest European Continental shelf.  \n\nThe reanalysis study was conducted using E.U. Copernicus Marine Service Information: https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_BGC_004_011. Validation of the reanalysis data is in https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-NWS-QUID-004-011.pdf  (accessed May 2021). An evaluation of the RCP8.5 climate projection is described by Wakelin et al. (2020) (https://doi.org/https://doi.org/10.1016/j.pocean.2020.102400). ","locations":["POLYGON((-14.093 44.089, -14.093 63.234, 12.274 63.234, 12.274 44.089, -14.093 44.089))"],"metadataDate":"2025-12-15T15:57:46.000Z","orcid":["https://orcid.org/0000-0002-2081-2693"],"organisation":["National Oceanography Centre"],"recordType":"Dataset","resourceIdentifier":["https://data.inms.international/id/b6a14d00-8ec0-4936-a4d6-e175766cd319"],"resourceType":"Dataset","shortenedDescription":"Model concentrations of winter nitrate, growing season chlorophyll-a and monthly minimum oxygen for the Northwest European Shelf Seas derived from simulations of the Nucleus for a European Model of the Ocean (NEMO) coupled to the European Regional Seas Ecosystem Model...","state":"published","title":"Concentrations of Nitrate, Chlorophyll-a and Oxygen for the Northwest European Shelf Seas (1998-2007, 2010, 2005-2015 and 2045-2055)  ","version":1.0,"view":["public","ccaporusso@gmx.com","slwa@noc.ac.uk"]},{"catalogue":"inms","documentType":"CEH_MODEL","identifier":"d65a5816-585d-4899-9723-bdacd24246db","incomingCitationCount":0,"keyword":["INMS"],"metadataDate":"2024-11-04T09:26:46.000Z","recordType":"Model","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/2788461a-3a34-4350-8b9c-9e2b6025bbba","https://data.inms.international/id/d65a5816-585d-4899-9723-bdacd24246db"],"resourceType":"Model","state":"published","title":"TM5","version":1.0,"view":["public","leav","inms-users"]},{"catalogue":"inms","documentType":"CEH_MODEL","identifier":"6a6e1c87-3b56-44a0-a576-f65c5e18450e","incomingCitationCount":0,"keyword":["INMS"],"metadataDate":"2024-11-04T09:24:45.000Z","recordType":"Model","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/f520356a-aed0-4c73-acdf-b7e01aa88f99","https://data.inms.international/id/6a6e1c87-3b56-44a0-a576-f65c5e18450e"],"resourceType":"Model","state":"published","title":"PCR-GLOBWB","version":1.0,"view":["public","leav","inms-users"]},{"authorAffiliation":["European Commission, Joint Research Centre"],"availability":"Unknown","catalogue":"inms","dataFormat":["Comma-separated values (CSV)","NetCDF"],"description":"It includes emissions time series and gridmaps for CO2, CH4 and N2O. Carbon dioxide emissions are provided for the period 1970-2018, and CH4 and N2O for 1970-2015. \nEDGAR aims to inform scientists and policy makers on the evolution of the emission inventories over time for all world countries and to provide the scientific community 0.1degX0.1deg gridmaps representing the emissions sources. \nEmission gridmaps are expressed in ton substance / 0.1degree x 0.1degree / year for the .txt files and in kg substance /m2 /s for the .nc files.","documentType":"GEMINI_DOCUMENT","identifier":"78701875-cfa2-48d6-b8bb-9fd7c4beb966","incomingCitationCount":0,"licence":"Non-Open government licence","lineage":"The emissions in EDGAR are estimated based on the activity data from the international statistics (e.g. IEA, USGS, IFA, FAO etc.) and emissions factors from the official guidebooks such as IPCC and EEA/EMEP, and scientific literature. For emissions distribution on global gridmaps we use proxy data such as population, location of the point sources etc.","locations":["POLYGON((-180 -90, -180 90, 180 90, 180 -90, -180 -90))"],"metadataDate":"2024-11-01T10:28:15.000Z","organisation":["European Commission, Joint Research Centre"],"recordType":"Third-party dataset","resourceIdentifier":["https://data.inms.international/id/78701875-cfa2-48d6-b8bb-9fd7c4beb966"],"resourceType":"Third-party dataset","shortenedDescription":"It includes emissions time series and gridmaps for CO2, CH4 and N2O. Carbon dioxide emissions are provided for the period 1970-2018, and CH4 and N2O for 1970-2015. \nEDGAR aims to inform scientists and policy makers on the evolution of the emission inventories over time...","state":"published","title":"Emissions Database for Global Atmospheric Research (EDGAR) Global Greenhouse Gas Emissions, 1970-2015","version":1.0,"view":["public","inms-users","crimar"]},{"authorAffiliation":["European Commission,  Joint Research Centre"],"authorFamilyName":["Van Dingenen"],"authorFullName":["Van Dingenen, R."],"authorGivenName":["Rita"],"authorOrcid":["https://orcid.org/0000-0003-2521-4972"],"availability":"Available","catalogue":"inms","dataFormat":["NetCDF"],"description":"The dataset consists of 5 files including each global 0.5°x0.5° grids of annual deposition of NOx (nitric oxide and nitrogen dioxide), NOy (the sum of NOx and all oxidized atmospheric odd-nitrogen species), and total N (nitrogen) for the following scenarios and years:\n\n1)\tcurrent status (year 2010),  \n2)\tHigh N regulation (SSP2-RCP4.5 High N ambition), year 2030\n3)\tHigh N regulation (SSP2-RCP4.5 High N ambition), year 2050\n4)\tBusiness as usual (SSP5-RCP8.5 Low N ambition), year 2030 \n5)\tBusiness as usual (SSP5-RCP8.5 Low N ambition), year 2050 \n\nEach file contains 3 global fields (layers) at a resolution of 0.5°x0.5° with annual total (wet and dry) deposition expressed in milligram N (nitrogen) per square meter for NOy and NHx separately, as well as the sum of both depositions.\n\n","documentType":"GEMINI_DOCUMENT","familyName":["Van Dingenen"],"fullName":["Van Dingenen, R."],"givenName":["Rita"],"identifier":"19853be7-854e-4d0d-ab47-b64089cfcca2","incomingCitationCount":0,"inmsScale":["Global"],"keyword":["INMS","Global"],"keywordsOther":["Global"],"keywordsProject":["INMS"],"licence":"Non-Open government licence","lineage":"The TM5-FASST Model only requires pollutant emissions as input.\nPresent dataset was computed from IMAGE pollutant emission scenarios prepared for the International Nitrogen Assessment. In addition, large scale biomass burning emissions, not included in IMAGE, were taken from CMIP6 harmonized set for the SSP2-RCP4.5 scenario for the respective years evaluated (Van Marle et al., 2017, DOI: 10.5194/gmd-10-3329-2017; Feng et al., 2020, DOI: 10.5194/gmd-13-461-2020). ","locations":["POLYGON((-180 -90, -180 80, 180 80, 180 -90, -180 -90))"],"metadataDate":"2026-03-23T15:42:48.000Z","orcid":["https://orcid.org/0000-0003-2521-4972"],"organisation":["European Commission,  Joint Research Centre","UK Centre for Ecology & Hydrology"],"publicationDate":"2023-06-21T00:00:00.000Z","recordType":"Dataset","resourceIdentifier":["https://data.inms.international/id/19853be7-854e-4d0d-ab47-b64089cfcca2"],"resourceType":"Dataset","shortenedDescription":"The dataset consists of 5 files including each global 0.5°x0.5° grids of annual deposition of NOx (nitric oxide and nitrogen dioxide), NOy (the sum of NOx and all oxidized atmospheric odd-nitrogen species), and total N (nitrogen) for the following scenarios and years:\n\n1)\tcurrent...","state":"published","supplementalDescription":["Data were obtained with the reduced-form source receptor model TM5-FASST (Van Dingenen et al., 2018, DOI: 10.5194/acp-18-16173-2018 ). \nThis model bypasses expensive chemical and physical process modelling by using grid-wise linear extrapolations of pre-computed emission-deposition sensitivities. \nThe computation of the sensitivities was performed with the global chemistry-transport model TM5 (Krol et al., 2005; DOI: 10.5194/acp-5-417-2005), using RCP emissions of year 2000 and meteorology of year 2001. \n\nThe reduced-form approach, building on linearized responses, strongly improves computation speed however at the cost of accuracy. For instance, output does not take into account specific meteorology for the year 2010, and does not include non-linear responses to changing chemistry regimes between 2000 and 2010/2030/2050. Therefore the data have to be considered as indicative and should be interpreted at regional scale, not at the individual grid cell level.\n\nDeposition from natural emissions (as of year 2000) are included in the total, but do not change between emission scenarios, therefore they are eliminated when making the difference between scenarios. Large scale fire emissions are taken from CMIP6 for the year 2010 (historical data), and CMIP6 SSP2-RCP4.5 for years 2030 and 2050 projections (Van Marle et al., 2017, DOI: 10.5194/gmd-10-3329-2017; Feng et al., 2020, DOI: 10.5194/gmd-13-461-2020).\n"],"title":"Modelled global N deposition fields (TM5-FASST) from natural, anthropogenic and large scale biomass burning emissions as a total of wet and dry deposition for 2010, with projections for best and worst case scenarios in 2030 and 2050","version":1.0,"view":["public","ccaporusso@gmx.com","shofer"]},{"catalogue":"inms","description":"The Common Agricultural Policy Regional Impact Analysis (CAPRI) modelling system is a large-scale comparative static, global multi-commodity,  partial equilibrium model for the agricultural sector. It has been developed for policy impact assessment of the European Common Agricultural Policy (CAP) and other policies affecting agriculture from global to regional and farm type scale, focusing on Europe .\n\nCAPRI simulates changes in global agricultural trade and EU supply of agricultural commodities under given technological, economic and policy constraints. Strengths of CAPRI include the possibility of good representation of EU policies, the detailed description of farm management in EU supply models, and the bio-physical approach based on nutrient mass-flow approach, including life-cycle assessment with regard to GHGs (operational) and nitrogen (operational end 2017) for agricultural commodities.\n\nThe code for the model is stored at Bonn University. For access contact Adrian Leip who will forward the request. \n","documentType":"CEH_MODEL","identifier":"dbab4238-3736-4797-859a-0fe8c41f28e9","incomingCitationCount":0,"keyword":["INMS"],"metadataDate":"2024-11-04T09:22:18.000Z","recordType":"Model","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/0f717c5c-653f-4992-8dc5-56bf4d880d3c","https://data.inms.international/id/dbab4238-3736-4797-859a-0fe8c41f28e9"],"resourceType":"Model","shortenedDescription":"The Common Agricultural Policy Regional Impact Analysis (CAPRI) modelling system is a large-scale comparative static, global multi-commodity,  partial equilibrium model for the agricultural sector. It has been developed for policy impact assessment of the European Common...","state":"published","title":"CAPRI Modelling System (Common Agricultural Policy Regionalised Impact)","version":1.0,"view":["public","leav","inms-users"]},{"authorAffiliation":["PBL – Netherlands Environmental  Assessment Agency"],"availability":"Available","catalogue":"inms","dataFormat":["TIFF"],"description":"The datasets display the loss of plant biodiversity due to nitrogen deposition in the world in two past years (1970 and 2010) and six modelled scenarios for 2050. \nThe datasets have been created by the GLOBIO 4 model.","documentType":"GEMINI_DOCUMENT","identifier":"3fb43167-0d9b-43ba-bea4-230c64701f10","incomingCitationCount":1,"inmsScale":["Global"],"keyword":["INMS","Global"],"keywordsOther":["Global"],"keywordsProject":["INMS"],"licence":"Non-Open government licence","locations":["POLYGON((-180 -90, -180 90, 180 90, 180 -90, -180 -90))"],"metadataDate":"2025-12-15T15:57:46.000Z","organisation":["PBL – Netherlands Environmental  Assessment Agency","UK Centre for Ecology & Hydrology","INMS"],"recordType":"Dataset","resourceIdentifier":["https://data.inms.international/id/3fb43167-0d9b-43ba-bea4-230c64701f10"],"resourceType":"Dataset","shortenedDescription":"The datasets display the loss of plant biodiversity due to nitrogen deposition in the world in two past years (1970 and 2010) and six modelled scenarios for 2050. \nThe datasets have been created by the GLOBIO 4 model.","state":"published","title":"GLOBIO-terrestrial nitrogen deposition impacts on plant mean species abundance (MSA) for the years 1970, 2010 and 2050","version":1.0,"view":["public","ccaporusso@gmx.com"]},{"catalogue":"inms","description":"The Emissions Database for Global Atmospheric Research (EDGAR) provides independent estimates of the global anthropogenic emissions and emission trends, based on publicly available statistics, for the atmospheric modeling community as well as for policy makers. This scientific independent emission inventory is characterized by a coherent world historical trend from 1970 to 2012, including emissions of all greenhouse gases, air pollutants, aerosols and toxic pollutants (Hg). Data are presented for all countries, with emissions provided per main source category, and spatially allocated on a 0.1x0.1 grid over the globe.","documentType":"CEH_MODEL","identifier":"43a222b0-b7dc-4b54-90e9-855d2f52c950","incomingCitationCount":0,"keyword":["INMS"],"metadataDate":"2024-11-04T09:27:02.000Z","recordType":"Model","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/41178be8-1daa-4511-8648-011e4277549a","https://data.inms.international/id/43a222b0-b7dc-4b54-90e9-855d2f52c950"],"resourceType":"Model","shortenedDescription":"The Emissions Database for Global Atmospheric Research (EDGAR) provides independent estimates of the global anthropogenic emissions and emission trends, based on publicly available statistics, for the atmospheric modeling community as well as for policy makers. This...","state":"published","title":"Emissions Database for Global Atmospheric Research (EDGAR)","version":1.0,"view":["public","leav","inms-users"]},{"catalogue":"inms","description":"GLOBIO uses all biophysical information from IMAGE, such as climate, vegetation, agricultural land use, and it uses soil information. Atmospheric deposition is from an ensemble of chemistry transport models","documentType":"CEH_MODEL","identifier":"177e2331-17a6-49c6-9825-f6af3d6ccef3","incomingCitationCount":0,"inmsScale":["Global"],"keyword":["Impact Model","Global","Biodiversity","Agricultural Practices","INMS"],"metadataDate":"2024-11-04T09:32:44.000Z","recordType":"Model","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/31f92f89-9c1b-475f-9b04-303020907bbf","https://data.inms.international/id/177e2331-17a6-49c6-9825-f6af3d6ccef3"],"resourceType":"Model","shortenedDescription":"GLOBIO uses all biophysical information from IMAGE, such as climate, vegetation, agricultural land use, and it uses soil information. Atmospheric deposition is from an ensemble of chemistry transport models","state":"published","title":"IMAGE-GLOBIO terrestrial","version":1.0,"view":["public","leav","inms-users"]}],"rows":20,"url":"http://data.inms.international:443/inms/documents"}