{"@context": "https://schema.org", "@type": "Dataset", "@id": "https://doi.org/10.5878/3j5c-yh37", "identifier": [{"@type": "PropertyValue", "unitCode": "DOI", "value": "https://doi.org/10.5878/3j5c-yh37"}, {"@type": "PropertyValue", "unitCode": "https://www.rikstermbanken.se/termposter/1848/diarienummer", "unitText": "principalRegistrationNumber", "value": "SLU.mark.2023.4.4.I\u00c4-1"}, {"@type": "PropertyValue", "unitCode": "DOI", "value": "https://doi.org/10.5878/r8jz-pc11"}], "version": "1", "name": [{"@value": "Modelling best management practices for reducing nutrient losses from agricultural catchments under different climate trajectories.", "@language": "en"}, {"@value": "Modellera b\u00e4sta f\u00f6rvaltningspraxis f\u00f6r att minska n\u00e4ringsf\u00f6rlusterna fr\u00e5n jordbrukets avrinningsomr\u00e5den under olika klimatbanor.", "@language": "sv"}], "description": [{"@value": "This dataset contains all the geospatial information and HYPE inputs and outputs related to the publication of \"How to achieve a 50% reduction in nutrient losses from agricultural catchments under different climate trajectories?\". \n\nIn this study, we build high-resolution geospatial data to build a semi-distributed water quantity and water quality model for two Swedish Agricultural Catchments in Hydrological Predictions of the Environment (HYPE). We calibrated and validated the model using discharge and water quality monitoring data from the streams in our study sites. \nWe subsequently used the calibrated model to forecast the impacts of climate change on nutrient (Inorganic Nitrogen and Total Phosphorus) loads under three relative concentration pathways (RCP2.6, RCP4.5, and RCP 8.5) and three periods (2022-2035, 2050-2065, and 2085-2100). Finally, we backcasted a 50% reduction in nutrient loads using  catchment mitigation scenarios (20% reduction in fertilisation, increasing in floodplain area, implementation of cover crops). This dataset contains all the monitoring data, model inputs (including parameterisation), and the model outputs. Moreover, it contains the R scripts with summary statistics and plotting and the summarised outputs of all model runs in csv files.\n\nThe dataset contains three folders. \n1. The Geopatial Information folder contains all the geospatial data for both study catchments. These include land cover, soil, DEM, and finally the Soil Land Cover maps, which were used to build the HYPE models. The coding of the geospatial shapefiles and raster files can be found in the Readme document. \n2. The HYPE_model folder contains all of the HYPE model building blocks necessary to run the calibrated model for Hestadb\u00e4cken and Tullstorp\u00e5n in seperate folders. It also contains the goodness-of-fit outcomes for both the calibrated model and the validation period. This folder also contains the future climate forecasts and the different mitigation scenario testing outcomes. \n3. The outputs_and_data_analysis folder contains csv files with all of the model outcomes for IN, TP, and Q in both catchments for all combinations of RCP, period, and climate models. It also contains R scripts used to calculate trends, summary statistics, t-tests, and plot the figures. Moreover, it contains the outcomes of the percentages of change, correlation tests, and t-tests.", "@language": "en"}, {"@value": "Denna dataupps\u00e4ttning inneh\u00e5ller all geospatial information och HYPE-ing\u00e5ngar och utdata relaterade till publiceringen av \"Hur uppn\u00e5r man en 50 % minskning av n\u00e4ringsbelastningar fr\u00e5n jordbrukets avrinningsomr\u00e5den under olika klimatbanor?\".\n\nI den h\u00e4r studien bygger vi h\u00f6guppl\u00f6sta geospatiala data f\u00f6r att bygga en semidistribuerad vattenm\u00e4ngds- och vattenkvalitetsmodell f\u00f6r tv\u00e5 svenska jordbruksupptagningsomr\u00e5den i hydrologiska f\u00f6ruts\u00e4gelser av milj\u00f6n (HYPE). Vi kalibrerade och validerade modellen med hj\u00e4lp av utsl\u00e4pps- och vattenkvalitets\u00f6vervakningsdata fr\u00e5n b\u00e4ckarna p\u00e5 v\u00e5ra studieplatser.\nVi anv\u00e4nde d\u00e4refter den kalibrerade modellen f\u00f6r att f\u00f6ruts\u00e4ga effekterna av klimatf\u00f6r\u00e4ndringar p\u00e5 belastningar av n\u00e4rings\u00e4mnen (oorganiskt kv\u00e4ve och totalt fosfor) under tre relativa koncentrationsv\u00e4gar (RCP2.6, RCP4.5 och RCP 8.5) och tre perioder (2022-2035, 2050-2065 och 2085-2100). Slutligen backade vi en 50 % minskning av n\u00e4ringsbelastningen genom att anv\u00e4nda scenarier f\u00f6r begr\u00e4nsning av avrinningsomr\u00e5den (20 % minskning av g\u00f6dsling, \u00f6kning av \u00f6versv\u00e4mningsomr\u00e5det, implementering av t\u00e4ckgr\u00f6dor). Denna dataupps\u00e4ttning inneh\u00e5ller alla \u00f6vervakningsdata, modelling\u00e5ngar (inklusive parametrering) och modellutg\u00e5ngarna. Dessutom inneh\u00e5ller den R-skripten med sammanfattande statistik och plottning och sammanfattade utdata fr\u00e5n alla modellk\u00f6rningar i csv-filer.\n\nDataupps\u00e4ttningen inneh\u00e5ller fyra mappar.\n1. Mappen Geopatial Information inneh\u00e5ller alla geospatiala data f\u00f6r b\u00e5da studieavrinningsomr\u00e5dena. Dessa inkluderar landt\u00e4cke, jord, DEM och slutligen Soil Land Cover kartorna, som anv\u00e4ndes f\u00f6r att bygga HYPE-modellerna. Kodningen av de geospatiala formfilerna och rasterfilerna finns i Readme-dokumentet.\n2. Mappen HYPE_model som best\u00e5r av:\n-Mappen HYPE_Hestadb\u00e4cken som inneh\u00e5ller alla HYPE-modellbyggstenar som beh\u00f6vs f\u00f6r att k\u00f6ra den kalibrerade modellen f\u00f6r Hestadb\u00e4cken. Den inneh\u00e5ller ocks\u00e5 godhet-of-fit-resultaten f\u00f6r b\u00e5de den kalibrerade modellen och valideringsperioden. Den h\u00e4r foldern inneh\u00e5ller ocks\u00e5 framtida klimatprognoser och testresultaten f\u00f6r olika begr\u00e4nsningsscenarier. Informationen om scenarionumreringen finns i dokumentationsfilerna.\n-Mappen HYPE_Tullstorp\u00e5n som inneh\u00e5ller alla HYPE-modellbyggstenar som beh\u00f6vs f\u00f6r att k\u00f6ra den kalibrerade modellen f\u00f6r Tullstorp\u00e5ns avrinningsomr\u00e5de. Den inneh\u00e5ller ocks\u00e5 godhet-of-fit-resultaten f\u00f6r b\u00e5de den kalibrerade modellen och valideringsperioden. Den h\u00e4r foldern inneh\u00e5ller ocks\u00e5 framtida klimatprognoser och testresultaten f\u00f6r olika begr\u00e4nsningsscenarier. Informationen om scenarionumreringen finns i dokumentationsfilerna.\n3. Mappen f\u00f6r utdata och dataanalys inneh\u00e5ller R-skript som anv\u00e4nds f\u00f6r att ber\u00e4kna trender, sammanfattande statistik, t-tester och plotta siffrorna. Slutligen inneh\u00e5ller den h\u00e4r mappen ocks\u00e5 utfallen av f\u00f6r\u00e4ndringsprocenten och de \u00e5rliga belastningsutfallen f\u00f6r alla kombinationer av RCP, period och modell, kombinerade i csv-filer.", "@language": "sv"}], "creator": [{"@type": "Person", "@id": "https://orcid.org/0000-0002-5367-7619", "sameAs": "https://orcid.org/0000-0002-5367-7619", "name": "Maarten Wynants", "email": "maarten.wynants@slu.se", "affiliation": [{"@type": "Organization", "name": [{"@value": "Swedish University of Agricultural Sciences, Department of Soil and Environment", "@language": "en"}, {"@value": "Sveriges lantbruksuniversitet, Institutionen f\u00f6r mark och milj\u00f6", "@language": "sv"}]}]}], "contributor": [{"@type": "Person", "@id": "https://orcid.org/0000-0002-0404-4145", "sameAs": "https://orcid.org/0000-0002-0404-4145", "name": "Lukas Hallberg", "email": "Lukas.Hallberg@slu.se", "affiliation": [{"@type": "Organization", "name": [{"@value": "Swedish University of Agricultural Sciences, Department of Soil and Environment", "@language": "en"}, {"@value": "Sveriges Lantbruksuniversitet, Institutionen f\u00f6r mark och milj\u00f6", "@language": "sv"}]}]}, {"@type": "Person", "@id": "https://orcid.org/0000-0002-3520-4375", "sameAs": "https://orcid.org/0000-0002-3520-4375", "name": "Magdalena Bieroza", "email": "magdalena.bieroza@slu.se", "affiliation": [{"@type": "Organization", "name": [{"@value": "Swedish University of Agricultural Sciences, Department of Soil and Environment", "@language": "en"}, {"@value": "Sveriges Lantbruksuniversitet", "@language": "sv"}]}]}, {"@type": "Person", "@id": "https://orcid.org/0000-0002-8016-814X", "sameAs": "https://orcid.org/0000-0002-8016-814X", "name": "John Livsey", "email": "john.livsey@slu.se", "affiliation": [{"@type": "Organization", "name": [{"@value": "Swedish University of Agricultural Sciences, Department of Soil and Environment", "@language": "en"}, {"@value": "Sveriges Lantbruksuniversitet, Institutionen f\u00f6r mark och milj\u00f6", "@language": "sv"}]}]}], "includedInDataCatalog": {"@type": "DataCatalog", "name": [{"@value": "Swedish National Data Service", "@language": "en"}, {"@value": "Svensk Nationell Datatj\u00e4nst", "@language": "sv"}], "url": "https://snd.se/catalogue/"}, "license": "CC-BY-NC-4.0", "citation": [{"@type": "CreativeWork", "name": [{"@value": "Maarten Wynants, Johan Str\u00f6mqvist, Lukas Hallberg, John Livsey, G\u00f6ran Lindstr\u00f6m, Magdalena Bieroza. 2023. 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