{"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":["Potsdam Institute  for Climate Impact Research"],"authorOrcid":["https://orcid.org/0000-0002-8242-6712"],"availability":"Unknown","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":["European Commission,  Joint Research Centre"],"authorFamilyName":["Van Dingenen"],"authorFullName":["Van Dingenen, R."],"authorGivenName":["Rita"],"authorOrcid":["https://orcid.org/0000-0003-2521-4972"],"availability":"Unknown","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":"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"]},{"catalogue":"inms","description":"Main drivers are population, economic development (income), land use, livestock and crop production.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":"ad4af6af-29b8-42a5-945c-28e3598eb85c","incomingCitationCount":0,"keyword":["Nitrogen","INMS"],"metadataDate":"2024-11-04T09:30:55.000Z","recordType":"Model","resourceIdentifier":["https://data.inms.international/id/ad4af6af-29b8-42a5-945c-28e3598eb85c"],"resourceType":"Model","shortenedDescription":"Main drivers are population, economic development (income), land use, livestock and crop production.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…","state":"published","title":"IMAGE-GLOBIO Aquatic","version":1.0,"view":["public","crimar"]},{"altTitle":["MARINA-Global model for nutrients in water"],"authorAffiliation":["Wageningen University & Research"],"authorOrcid":["https://orcid.org/0000-0002-8063-7743","https://orcid.org/0000-0002-2543-4871","https://orcid.org/0000-0002-8954-6629"],"availability":"Unknown","catalogue":"inms","dataFormat":["Comma-separated values (CSV)","Shapefile"],"description":"River export of nitrogen and phosphorus is provided for 2010 and 2050. This data is computed by the MARINA-Global model: Model to Assess River Inputs of pollutants to seas. Nitrogen and phosphorus are in dissolved inorganic and organic forms. \nThe future follows Shared Socio-economic Pathways (SSP) 1 (sustainability-oriented) and 5 (economic-oriented). SSP1 is combined with Representative Concentration Pathway (RCP) 2.6. SSP5 is combined with RCP8.5 to create the scenarios:\n\n1) Business as usual (SSP5-RCP8.5 Low N ambition)\n2) Bioenergy (SSP1-RCP2.6 High N ambition with low meat & dairy diet)\n\nRiver export of the nutrients is calculated from around ten thousands sub-basins as a function of human activities on the land (e.g., agriculture, sewage) and sub-basin characteristics (e.g., hydrology, terrestrial and aquatic retentions). \n\nThe modelling approaches of MARINA-Global are published and evaluated in earlier studies, please see the documentation below.","documentType":"GEMINI_DOCUMENT","identifier":"933f9d5f-07e1-4197-a201-c0867d949c76","incomingCitationCount":2,"keyword":["INMS","MARINA-Global","Nutrients","River Export","Sub-basins","Globe "],"keywordsOther":["MARINA-Global","Nutrients","River Export","Sub-basins","Globe "],"keywordsTheme":["INMS"],"licence":"Non-Open government licence","lineage":"Sources of the data are indicated in Chapter 11 of the INI book. \n\nAdditional sources\n-\tfor point-source inputs of nutrients to rivers: Strokal, Maryna; Bai, Zhaohai; Franssen, Wietse; Hofstra, Nynke; Koelmans, Albert A.; Ludwig, Fulco; et al. Metadata supporting the article: Urbanization: an increasing source of multiple pollutants to rivers in the 21st century. https://doi.org/10.6084/m9.figshare.13333796  (2020)\n\n-\tHydrological data: https://vic.readthedocs.io/en/master/Overview/ModelOverview/ and ISIMIP2b https://www.isimip.org/\n","locations":["POLYGON((-180 -90, -180 90, 180 90, 180 -90, -180 -90))"],"metadataDate":"2025-12-15T15:57:54.000Z","orcid":["https://orcid.org/0000-0002-8063-7743","https://orcid.org/0000-0002-2543-4871","https://orcid.org/0000-0002-8954-6629"],"organisation":["Wageningen University & Research","INMS ","INMS"],"recordType":"Dataset","resourceIdentifier":["https://data.inms.international/id/933f9d5f-07e1-4197-a201-c0867d949c76"],"resourceType":"Dataset","shortenedDescription":"River export of nitrogen and phosphorus is provided for 2010 and 2050. This data is computed by the MARINA-Global model: Model to Assess River Inputs of pollutants to seas. Nitrogen and phosphorus are in dissolved inorganic and organic forms. \nThe future follows Shared…","state":"published","supplementalName":["The MARINA model (Model to Assess River Inputs of Nutrients to seAs): Model description and results for China "],"title":"Modelled river export of nutrients from sub-basins globally for scenarios SSP1 and SSP5 and for the years 2010 and 2050","version":1.0,"view":["public","inms-users","crimar"]},{"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"]},{"catalogue":"inms","description":"The EMEP4Earth model framework consists of an atmospheric chemistry transport model which simulates hourly to annual average atmospheric composition and deposition of pollutants, and the weather research and forecast model. Pollutants simulated include PM10, PM2.5, secondary organic aerosols, elemental carbon, secondary inorganic aerosols, SO2, NH3, NOx, and O3. Dry and wet deposition of pollutants are also calculated. \nEMEP4Earth operates at horizontal resolutions ranging from 1°×1° for the global domain down to ~1.1 km2 for specific regional domains (i.e. UK) The model setup is very flexible and can be changed for utilisation in scientific and/or policy applications. The default vertical domain ranges from ~50 m (thickness of the first layer at the surface) up to ~16 km (at the top of the vertical domain - 100 hPa), however setting a more flexible horizontal and vertical resolution is also possible.","documentType":"CEH_MODEL","identifier":"9b8b52fe-df65-48e6-a9f3-25c0a47ff636","incomingCitationCount":0,"keyword":["INMS"],"metadataDate":"2024-11-04T09:28:46.000Z","recordType":"Model","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/e30f10ed-2147-45b6-be94-b94f19487de8","https://data.inms.international/id/9b8b52fe-df65-48e6-a9f3-25c0a47ff636"],"resourceType":"Model","shortenedDescription":"The EMEP4Earth model framework consists of an atmospheric chemistry transport model which simulates hourly to annual average atmospheric composition and deposition of pollutants, and the weather research and forecast model. Pollutants simulated include PM10, PM2.5,…","state":"published","title":"EMEP4Earth (WRF-EMEP global)","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"]},{"catalogue":"inms","description":"GAINS estimates emissions of air pollutants and greenhouse gases in future scenarios based on (1) projections of activity data and (2) rate of implementation of emission reducing technologies. An optimization algorithm allows to minimize costs of measures when intending to arrive at a given ecological “endpoint” (human health, biodiversity, GHG level etc.). \n\nMore than 200 individual abatement technologies and abatement costs are individually defined and implemented in connection with specific “target” emitted compound, interference with other compounds considered. Costs are computed as a function of investments, interest rates and country specific labour/energy/ commodity costs; co-benefits can be integrated in cost factors (e.g. negative energy costs).\n","documentType":"CEH_MODEL","identifier":"289b4b74-3284-4c5d-a9f7-1b22bf51421a","incomingCitationCount":0,"inmsScale":["Global"],"keyword":["Global","Greenhouse Gases","Air Pollution","INMS"],"metadataDate":"2024-11-04T09:36:31.000Z","recordType":"Model","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/951d2c32-3d30-4e87-958a-65378ec1858c","https://data.inms.international/id/289b4b74-3284-4c5d-a9f7-1b22bf51421a"],"resourceType":"Model","shortenedDescription":"GAINS estimates emissions of air pollutants and greenhouse gases in future scenarios based on (1) projections of activity data and (2) rate of implementation of emission reducing technologies. An optimization algorithm allows to minimize costs of measures when intending…","state":"published","title":"GAINS Global","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":["National Oceanography Centre"],"authorOrcid":["https://orcid.org/0000-0002-2081-2693"],"availability":"Unknown","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"]},{"authorAffiliation":["Boston College","Auburn University"],"authorFamilyName":["Tian","Pan"],"authorFullName":["Tian, H.","Pan, N."],"authorGivenName":["Hanqin","Naiqing"],"authorOrcid":["https://orcid.org/0000-0002-1806-4091","https://orcid.org/0000-0002-1465-7738"],"availability":"Unknown","catalogue":"inms","dataFormat":["Text file","NetCDF"],"description":"The dataset includes the effects of N inputs (N fertilizer, N deposition and manure N) on global terrestrial major greenhouse gases (N2O, CO2, CH4) exchange, nitrogen oxide-induced ozone impacts on global terrestrial carbon pools in 2010, which were estimated by the Dynamic Land Ecosystem Model (DLEM). This dataset was prepared for Chapter 13 in the upcoming International Nitrogen Assessment (INA) book.","documentType":"GEMINI_DOCUMENT","familyName":["Tian","Pan"],"fullName":["Tian, H.","Pan, N."],"givenName":["Hanqin","Naiqing"],"identifier":"183ca035-b4e1-4e42-acd0-e3c5a7eb55a9","incomingCitationCount":1,"inmsScale":["Global"],"keyword":["Pollution","Soil","INMS","Global"],"keywordsOther":["INMS","Global"],"keywordsTheme":["Pollution","Soil"],"licence":"Non-Open government licence","lineage":"Dynamic Land Ecosystem Model (DLEM) was used to assess the impacts of nitrogen inputs on GHG (N2O, CO2, CH4) exchange and the transfer of nitrogen from land to water. Gridded data sets used to drive DLEM simulations include climate, CO2 concentration and land use/cover change from NMIP (N2O Model Intercomparison Project), with IMAGE-GNM data on N fertilizer, N deposition and manure N in 2010. MATLAB and ARCGIS were used to extract and process DLEM outputs.","locations":["POLYGON((-180 -90, -180 90, 180 90, 180 -90, -180 -90))"],"metadataDate":"2026-04-02T08:33:14.000Z","orcid":["https://orcid.org/0000-0002-1806-4091","https://orcid.org/0000-0002-1465-7738"],"organisation":["Boston College","Auburn University"],"recordType":"Dataset","resourceIdentifier":["https://data.inms.international/id/183ca035-b4e1-4e42-acd0-e3c5a7eb55a9"],"resourceType":"Dataset","shortenedDescription":"The dataset includes the effects of N inputs (N fertilizer, N deposition and manure N) on global terrestrial major greenhouse gases (N2O, CO2, CH4) exchange, nitrogen oxide-induced ozone impacts on global terrestrial carbon pools in 2010, which were estimated by the…","state":"published","title":"Dynamic Land Ecosystem Model (DLEM) estimates of nitrogen inputs impact on global terrestrial major greenhouse gases (N2O, CO2, CH4) exchange in 2010","topic":["0/Pollution/","0/Soil/"],"version":1.0,"view":["public","ccaporusso@gmx.com"]},{"authorAffiliation":["PBL Netherlands Environmental Assessment Agency","PBL Netherlands Environmental Assessment Agency "],"availability":"Unknown","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"]},{"catalogue":"inms","documentType":"CEH_MODEL","identifier":"a35efde3-7bc0-4b47-bb69-43e25c5ece49","incomingCitationCount":0,"keyword":["INMS"],"metadataDate":"2024-11-04T09:28:29.000Z","recordType":"Model","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/91a040ef-e465-44ff-ae17-a5d17e08059e","https://data.inms.international/id/a35efde3-7bc0-4b47-bb69-43e25c5ece49"],"resourceType":"Model","state":"published","title":"EPIC The Environmental Policy Integrated Model","version":1.0,"view":["public","leav","inms-users"]},{"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"]},{"catalogue":"inms","documentType":"CEH_MODEL","identifier":"6b24ce1c-8a2d-4d9b-817f-804fa16007e7","incomingCitationCount":0,"keyword":["INMS","Hydrological","Water","Land"],"metadataDate":"2024-11-04T09:27:51.000Z","recordType":"Model","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/4b0ad695-7335-4355-ae4e-7d1cd33f86d1","https://data.inms.international/id/6b24ce1c-8a2d-4d9b-817f-804fa16007e7"],"resourceType":"Model","state":"published","title":"WBM/VIC","version":1.0,"view":["public","leav","inms-users"]},{"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","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"]},{"authorAffiliation":["PBL Netherlands Environmental Assessment Agency"],"authorOrcid":[" https://orcid.org/0000-0003-0104-8615"],"availability":"Unknown","catalogue":"inms","dataFormat":["Comma-separated values (CSV)"],"description":"This dataset contains global Nitrogen balances by country or IMAGE region. Balances are calculated for 1970, 1990, 2010, 2020 and 2050 for three different Shared Socio-economic Pathways (SSP). \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)","documentType":"GEMINI_DOCUMENT","identifier":"ef3a1118-2001-4c85-aa22-a899ca8ec868","incomingCitationCount":0,"keyword":["INMS"],"keywordsOther":["INMS"],"licence":"Non-Open government licence","lineage":"This dataset is a summary file from the grid-based output of IMAGE-GNM which provides the area (km2) and the major N balance terms (in kg N) for most countries or IMAGE regions. The grid-based dataset was aggregated to country level and the IMAGE world regions. This dataset is important as most INMS users will be mainly interested in the balance files and not in the grid-based IMAGE-GNM output in NetCDF format. ","locations":["POLYGON((-180 -90, -180 90, 180 90, 180 -90, -180 -90))"],"metadataDate":"2025-12-15T15:57:52.000Z","orcid":[" https://orcid.org/0000-0003-0104-8615 "," https://orcid.org/0000-0003-0104-8615"],"organisation":["PBL Netherlands Environmental Assessment Agency"],"recordType":"Dataset","resourceIdentifier":["https://data.inms.international/id/ef3a1118-2001-4c85-aa22-a899ca8ec868"],"resourceType":"Dataset","shortenedDescription":"This dataset contains global Nitrogen balances by country or IMAGE region. Balances are calculated for 1970, 1990, 2010, 2020 and 2050 for three different Shared Socio-economic Pathways (SSP). \n\nThe climate scenarios are represented as combination of SSP-RCP-N (shared…","state":"published","title":"Integrated Model to Assess the Global Environment (IMAGE) Global Nutrient Model (GNM) global simulations for Nitrogen balances by country, for the years 1970, 1990, 2010, 2020 and 2050","version":1.0,"view":["public","crimar"]},{"authorAffiliation":["PBL Netherlands Environmental Assessment Agency"],"authorFamilyName":["Schulte-Uebbing"],"authorFullName":["Lena Schulte-Uebbing"],"authorGivenName":["Lena"],"authorOrcid":["https://orcid.org/0000-0002-5511-1432"],"availability":"Unknown","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"]}],"rows":20,"url":"http://data.inms.international:443/inms/documents"}