{"numFound":29,"page":1,"rows":20,"url":"http://data.inms.international:443/inms/documents/","nextPage":"http://data.inms.international:443/inms/documents/?page=2","results":[{"authorAffiliation":["European Commission,  Joint Research Centre"],"authorName":["Rita van Dingenen"],"authorOrcid":["https://orcid.org/0000-0003-2521-4972"],"catalogue":"inms","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","identifier":"19853be7-854e-4d0d-ab47-b64089cfcca2","impScale":["Global"],"incomingCitationCount":0,"individual":["Rita van Dingenen","Claudia Caporusso"],"inmsProject":["INMS"],"keyword":["INMS","Global"],"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))"],"orcid":["https://orcid.org/0000-0003-2521-4972"],"organisation":["European Commission,  Joint Research Centre","UK Centre for Ecology & Hydrology"],"recordType":"Dataset","resourceIdentifier":["https://data.inms.international/id/19853be7-854e-4d0d-ab47-b64089cfcca2"],"resourceStatus":"Available","resourceType":"Dataset","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"],"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..."},{"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,"inmsProject":["INMS"],"keyword":["INMS"],"recordType":"Model (UKCEH)","resourceType":"Model (UKCEH)","state":"published","title":"EMEP4Earth (WRF-EMEP global)","version":1.0,"view":["public","leav","inms-users"],"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,..."},{"authorAffiliation":["Potsdam Institute  for Climate Impact Research"],"authorName":["Benjamin Leon Bodirsky","Michael Crawford","Felicitas Beier","Kristine Karstens"],"authorOrcid":["https://orcid.org/0000-0002-8242-6712"],"catalogue":"inms","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","impScale":["Global"],"incomingCitationCount":0,"individual":["Benjamin Leon Bodirsky","Michael Crawford","Felicitas Beier","Kristine Karstens"],"inmsProject":["INMS"],"keyword":["INMS","Global"],"licence":"Non-Open government licence","locations":["POLYGON((-180 -90, -180 90, 180 90, 180 -90, -180 -90))"],"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"],"resourceStatus":"Available","resourceType":"Dataset","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"],"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..."},{"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,"inmsProject":["INMS"],"keyword":["INMS"],"recordType":"Model (UKCEH)","resourceType":"Model (UKCEH)","state":"published","title":"Emissions Database for Global Atmospheric Research (EDGAR)","version":1.0,"view":["public","leav","inms-users"],"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..."},{"authorAffiliation":["European Commission, Joint Research Centre"],"authorName":["M. Crippa","G. Oreggioni","D.Guizzardi","M. Muntean","E. Schaaf","Solazzo E."],"catalogue":"inms","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,"individual":["M. Crippa","G. Oreggioni","D.Guizzardi","M. Muntean","E. Schaaf","Solazzo E."],"inmsProject":["INMS"],"keyword":["INMS"],"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))"],"organisation":["European Commission, Joint Research Centre"],"recordType":"Third-party dataset","resourceIdentifier":["https://data.inms.international/id/78701875-cfa2-48d6-b8bb-9fd7c4beb966"],"resourceStatus":"Unknown","resourceType":"Third-party dataset","state":"published","title":"Emissions Database for Global Atmospheric Research (EDGAR) Global Greenhouse Gas Emissions, 1970-2015","version":1.0,"view":["public","inms-users","crimar"],"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..."},{"authorAffiliation":["PBL Netherlands Environmental Assessment Agency"],"authorName":["Beusen, A.H.W.","van Grinsven, H.J.M.","Bouwman, A.F."],"authorOrcid":[" https://orcid.org/0000-0003-0104-8615"],"catalogue":"inms","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,"individual":["Beusen, A.H.W.","van Grinsven, H.J.M.","Bouwman, A.F."],"inmsProject":["INMS"],"keyword":["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))"],"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"],"resourceStatus":"Available","resourceType":"Dataset","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"],"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..."},{"catalogue":"inms","documentType":"CEH_MODEL","identifier":"6a6e1c87-3b56-44a0-a576-f65c5e18450e","incomingCitationCount":0,"inmsProject":["INMS"],"keyword":["INMS"],"recordType":"Model (UKCEH)","resourceType":"Model (UKCEH)","state":"published","title":"PCR-GLOBWB","version":1.0,"view":["public","leav","inms-users"]},{"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,"inmsProject":["INMS"],"keyword":["INMS"],"recordType":"Model (UKCEH)","resourceType":"Model (UKCEH)","state":"published","title":"CAPRI Modelling System (Common Agricultural Policy Regionalised Impact)","version":1.0,"view":["public","leav","inms-users"],"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..."},{"catalogue":"inms","documentType":"CEH_MODEL","identifier":"a35efde3-7bc0-4b47-bb69-43e25c5ece49","incomingCitationCount":0,"inmsProject":["INMS"],"keyword":["INMS"],"recordType":"Model (UKCEH)","resourceType":"Model (UKCEH)","state":"published","title":"EPIC The Environmental Policy Integrated Model","version":1.0,"view":["public","leav","inms-users"]},{"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","impScale":["Global"],"impTopic":["Greenhouse Gases","Air Pollution"],"incomingCitationCount":0,"inmsProject":["INMS"],"keyword":["Global","Greenhouse Gases","Air Pollution","INMS"],"recordType":"Model (UKCEH)","resourceType":"Model (UKCEH)","state":"published","title":"GAINS Global","version":1.0,"view":["public","leav","inms-users"],"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..."},{"catalogue":"inms","description":"This model was designed to make large-scale representations and combines terrestrial vegetation dynamics and land-atmosphere carbon and water exchanges under climate change. It is the only DGVM that uses dynamic land use at global scale. Also simulates the production of woody and herbaceous short-rotation bioenergy plantations.","documentType":"CEH_MODEL","identifier":"bdaacc6e-762a-42bd-9f7f-7834bfd4fd6b","incomingCitationCount":0,"inmsProject":["INMS"],"keyword":["INMS"],"recordType":"Model (UKCEH)","resourceType":"Model (UKCEH)","state":"published","title":"LPJmL","version":1.0,"view":["public","leav","inms-users"],"shortenedDescription":"This model was designed to make large-scale representations and combines terrestrial vegetation dynamics and land-atmosphere carbon and water exchanges under climate change. It is the only DGVM that uses dynamic land use at global scale. Also simulates the production..."},{"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","impScale":["Global"],"impTopic":["Biodiversity","Agricultural Practices"],"incomingCitationCount":0,"inmsProject":["INMS"],"keyword":["Impact Model","Global","Biodiversity","Agricultural Practices","INMS"],"recordType":"Model (UKCEH)","resourceType":"Model (UKCEH)","state":"published","title":"IMAGE-GLOBIO terrestrial","version":1.0,"view":["public","leav","inms-users"],"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"},{"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","impScale":["Global"],"impTopic":["Soil"],"impWaterPollutant":["Nitrogen","Phosphorous"],"incomingCitationCount":0,"inmsProject":["INMS"],"keyword":["Water Quality","Global","Soil","Nitrogen","Phosphorous","Scenario Model","INMS"],"modelType":["Scenario Model"],"recordType":"Model (UKCEH)","resourceType":"Model (UKCEH)","state":"published","title":" IMAGE GNM - Integrated Model to Assess the Global Environment - Global Nutrient Model","version":1.0,"view":["public","leav","inms-users"],"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...."},{"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","impWaterPollutant":["Nitrogen"],"incomingCitationCount":0,"inmsProject":["INMS"],"keyword":["Nitrogen","INMS"],"recordType":"Model (UKCEH)","resourceType":"Model (UKCEH)","state":"published","title":"IMAGE-GLOBIO Aquatic","version":1.0,"view":["public","crimar"],"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..."},{"authorAffiliation":["National Oceanography Centre"],"authorName":["Wakelin, S.L."],"authorOrcid":["https://orcid.org/0000-0002-2081-2693"],"catalogue":"inms","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,"individual":["Wakelin, S.L."],"inmsProject":["INMS"],"keyword":["INMS"],"licence":"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))"],"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"],"resourceStatus":"Available","resourceType":"Dataset","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"],"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..."},{"catalogue":"inms","documentType":"CEH_MODEL","identifier":"6b24ce1c-8a2d-4d9b-817f-804fa16007e7","impTopic":["Hydrological","Water","Land"],"incomingCitationCount":0,"inmsProject":["INMS"],"keyword":["INMS","Hydrological","Water","Land"],"recordType":"Model (UKCEH)","resourceType":"Model (UKCEH)","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,"inmsProject":["INMS"],"keyword":["INMS"],"recordType":"Model (UKCEH)","resourceType":"Model (UKCEH)","state":"published","title":"TM5","version":1.0,"view":["public","leav","inms-users"]},{"authorAffiliation":["PBL – Netherlands Environmental  Assessment Agency"],"authorName":["Jan H. Janse","Michel Bakkenes"],"authorOrcid":["https://orcid.org/0000-0001-6162-9943"],"catalogue":"inms","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,"individual":["Jan H. Janse","Michel Bakkenes"],"inmsProject":["INMS"],"keyword":["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))"],"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"],"resourceStatus":"Available","resourceType":"Dataset","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"],"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;..."},{"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","impScale":["Basin"],"incomingCitationCount":0,"inmsProject":["INMS"],"keyword":["Basin","Water Quality","INMS"],"recordType":"Model (UKCEH)","resourceType":"Model (UKCEH)","state":"published","title":"Global NEWS (Nutrient Export from WaterSheds)","version":1.0,"view":["public","leav"],"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,..."},{"authorAffiliation":["National Oceanography Centre","Plymouth Marine Laboratory"],"authorName":["Wakelin, S.L.","Katavouta, A.","Jardine, J.","Partridge, D."],"authorOrcid":["https://orcid.org/0000-0002-2081-2693","https://orcid.org/0000-0002-1587-4996","https://orcid.org/0000-0002-8949-4785","https://orcid.org/0000-0003-1813-056X"],"catalogue":"inms","description":"Model concentrations of July-to-September mean nitrate and August-to-October mean chlorophyll-a for South Asia seas (62.4°E to 98.3°E, 2.0°N to 26.2°N) derived from a hindcast of the Nucleus for a European Model of the Ocean (NEMO) coupled to the European Regional Seas Ecosystem Model (ERSEM). Data are averaged over the period 1998 to 2007 and for the year 2010. \nNEMO-ERSEM climate projection of nitrate for Southeast Asia seas (77.8°E to 133.0°E, 19.3°S to 24.0°N) under the RCP8.5 climate scenario; average July-to-September concentrations over the periods 2005 to 2015 and 2045 to 2055. ","documentType":"GEMINI_DOCUMENT","identifier":"36bff017-345e-43c8-a470-6d605e15c1dc","incomingCitationCount":0,"individual":["Wakelin, S.L.","Katavouta, A.","Jardine, J.","Partridge, D."],"inmsProject":["INMS"],"keyword":["INMS"],"licence":"Open government licence","lineage":"This dataset is based on two NEMO-ERSEM simulations: a hindcast of South Asia Seas and an RCP8.5 climate projection for Southeast Asia. The model setups are described by Polton et al. (2023) (labelled SANH for South Asia and SEAsia for Southeast Asia in https://doi.org/10.5194/gmd-16-1481-2023). ","locations":["POLYGON((62.4 -19.3, 62.4 26.2, 133 26.2, 133 -19.3, 62.4 -19.3))"],"orcid":["https://orcid.org/0000-0002-2081-2693","https://orcid.org/0000-0002-1587-4996","https://orcid.org/0000-0002-8949-4785","https://orcid.org/0000-0003-1813-056X"],"organisation":["National Oceanography Centre","Plymouth Marine Laboratory"],"recordType":"Dataset","resourceIdentifier":["https://data.inms.international/id/36bff017-345e-43c8-a470-6d605e15c1dc"],"resourceStatus":"Available","resourceType":"Dataset","state":"published","title":"Concentrations of Nitrate (1998-2007, 2010, 2005-2015 and 2045-2055) and Chlorophyll-a (1998-2007 and 2010) for South Asia Seas","version":1.0,"view":["public","ccaporusso@gmx.com","slwa@noc.ac.uk"],"shortenedDescription":"Model concentrations of July-to-September mean nitrate and August-to-October mean chlorophyll-a for South Asia seas (62.4°E to 98.3°E, 2.0°N to 26.2°N) derived from a hindcast of the Nucleus for a European Model of the Ocean (NEMO) coupled to the European Regional Seas..."}],"facets":[{"fieldName":"recordType","displayName":"Record type","hierarchical":false,"results":[{"name":"Dataset","url":"http://data.inms.international:443/inms/documents/?facet=recordType%7CDataset","active":false,"count":12},{"name":"Model (UKCEH)","url":"http://data.inms.international:443/inms/documents/?facet=recordType%7CModel+%28UKCEH%29","active":false,"count":15},{"name":"Third-party dataset","url":"http://data.inms.international:443/inms/documents/?facet=recordType%7CThird-party+dataset","active":false,"count":2}]},{"fieldName":"impScale","displayName":"Scale","hierarchical":false,"results":[{"name":"Basin","url":"http://data.inms.international:443/inms/documents/?facet=impScale%7CBasin","active":false,"count":1},{"name":"Global","url":"http://data.inms.international:443/inms/documents/?facet=impScale%7CGlobal","active":false,"count":9}]},{"fieldName":"impTopic","displayName":"Topic","hierarchical":false,"results":[{"name":"Agricultural Practices","url":"http://data.inms.international:443/inms/documents/?facet=impTopic%7CAgricultural+Practices","active":false,"count":1},{"name":"Air Pollution","url":"http://data.inms.international:443/inms/documents/?facet=impTopic%7CAir+Pollution","active":false,"count":1},{"name":"Biodiversity","url":"http://data.inms.international:443/inms/documents/?facet=impTopic%7CBiodiversity","active":false,"count":1},{"name":"Greenhouse Gases","url":"http://data.inms.international:443/inms/documents/?facet=impTopic%7CGreenhouse+Gases","active":false,"count":2},{"name":"Hydrological","url":"http://data.inms.international:443/inms/documents/?facet=impTopic%7CHydrological","active":false,"count":1},{"name":"Land","url":"http://data.inms.international:443/inms/documents/?facet=impTopic%7CLand","active":false,"count":1},{"name":"Soil","url":"http://data.inms.international:443/inms/documents/?facet=impTopic%7CSoil","active":false,"count":1},{"name":"Water","url":"http://data.inms.international:443/inms/documents/?facet=impTopic%7CWater","active":false,"count":1}]},{"fieldName":"impWaterPollutant","displayName":"Pollutant","hierarchical":false,"results":[{"name":"Nitrogen","url":"http://data.inms.international:443/inms/documents/?facet=impWaterPollutant%7CNitrogen","active":false,"count":2},{"name":"Phosphorous","url":"http://data.inms.international:443/inms/documents/?facet=impWaterPollutant%7CPhosphorous","active":false,"count":1}]},{"fieldName":"modelType","displayName":"Model Type","hierarchical":false,"results":[{"name":"Scenario Model","url":"http://data.inms.international:443/inms/documents/?facet=modelType%7CScenario+Model","active":false,"count":1}]},{"fieldName":"inmsDemonstrationRegion","displayName":"Demonstration Region","hierarchical":false},{"fieldName":"inmsProject","displayName":"Project","hierarchical":false,"results":[{"name":"INMS","url":"http://data.inms.international:443/inms/documents/?facet=inmsProject%7CINMS","active":false,"count":29}]}]}