Current and future global distribution of potential biomes under climate change scenarios ...
Probability and uncertainty maps showing the potential current and future natural vegetation on a global scale under three different climate change scenarios (RCP 2.6, RCP 4.5 and RCP 8.5) predicted using ensemble machine learning. Current (2022 - 2023) conditions are calculated on historical long t...
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Online Access: | https://dx.doi.org/10.5281/zenodo.7520813 https://zenodo.org/record/7520813 |
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ftdatacite:10.5281/zenodo.7520813 2023-06-11T04:07:09+02:00 Current and future global distribution of potential biomes under climate change scenarios ... Bonannella, Carmelo Hengl, Tomislav Leal Parente, Leandro de Bruin, Sytze 2023 https://dx.doi.org/10.5281/zenodo.7520813 https://zenodo.org/record/7520813 en eng Zenodo https://zenodo.org/communities/oemc-project https://dx.doi.org/10.5281/zenodo.7520814 https://dx.doi.org/10.5281/zenodo.7822868 https://zenodo.org/communities/oemc-project Open Access Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 info:eu-repo/semantics/openAccess ensemble machine learning climate change scenario rcp biomes global Dataset dataset 2023 ftdatacite https://doi.org/10.5281/zenodo.752081310.5281/zenodo.752081410.5281/zenodo.7822868 2023-06-01T11:24:40Z Probability and uncertainty maps showing the potential current and future natural vegetation on a global scale under three different climate change scenarios (RCP 2.6, RCP 4.5 and RCP 8.5) predicted using ensemble machine learning. Current (2022 - 2023) conditions are calculated on historical long term averages (1979 - 2013), while future projections cover two different epochs: 2040 - 2060 and 2061 - 2080. Files are named according to the following naming convention, e.g.: biomes_graminoid.and.forb.tundra.rcp85_p_1km_a_20610101_20801231_go_epsg.4326_v20230410 with the following fields: generic theme: biomes , variable name: graminoid.and.forb.tundra.rcp85 , variable type, e.g. probability (" p "), hard class (" c "), model deviation (" md ") spatial resolution: 1km , depth reference, e.g. below (" b "), above (" a ") ground or at surface (" s "), begin time (YYYYMMDD): 20610101 , end time: 20801231 , bounding box, e.g. global land without Antarctica (" go "), EPSG code: epsg.4326 , version code, e.g. ... Dataset Antarc* Antarctica DataCite Metadata Store (German National Library of Science and Technology) |
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Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
English |
topic |
ensemble machine learning climate change scenario rcp biomes global |
spellingShingle |
ensemble machine learning climate change scenario rcp biomes global Bonannella, Carmelo Hengl, Tomislav Leal Parente, Leandro de Bruin, Sytze Current and future global distribution of potential biomes under climate change scenarios ... |
topic_facet |
ensemble machine learning climate change scenario rcp biomes global |
description |
Probability and uncertainty maps showing the potential current and future natural vegetation on a global scale under three different climate change scenarios (RCP 2.6, RCP 4.5 and RCP 8.5) predicted using ensemble machine learning. Current (2022 - 2023) conditions are calculated on historical long term averages (1979 - 2013), while future projections cover two different epochs: 2040 - 2060 and 2061 - 2080. Files are named according to the following naming convention, e.g.: biomes_graminoid.and.forb.tundra.rcp85_p_1km_a_20610101_20801231_go_epsg.4326_v20230410 with the following fields: generic theme: biomes , variable name: graminoid.and.forb.tundra.rcp85 , variable type, e.g. probability (" p "), hard class (" c "), model deviation (" md ") spatial resolution: 1km , depth reference, e.g. below (" b "), above (" a ") ground or at surface (" s "), begin time (YYYYMMDD): 20610101 , end time: 20801231 , bounding box, e.g. global land without Antarctica (" go "), EPSG code: epsg.4326 , version code, e.g. ... |
format |
Dataset |
author |
Bonannella, Carmelo Hengl, Tomislav Leal Parente, Leandro de Bruin, Sytze |
author_facet |
Bonannella, Carmelo Hengl, Tomislav Leal Parente, Leandro de Bruin, Sytze |
author_sort |
Bonannella, Carmelo |
title |
Current and future global distribution of potential biomes under climate change scenarios ... |
title_short |
Current and future global distribution of potential biomes under climate change scenarios ... |
title_full |
Current and future global distribution of potential biomes under climate change scenarios ... |
title_fullStr |
Current and future global distribution of potential biomes under climate change scenarios ... |
title_full_unstemmed |
Current and future global distribution of potential biomes under climate change scenarios ... |
title_sort |
current and future global distribution of potential biomes under climate change scenarios ... |
publisher |
Zenodo |
publishDate |
2023 |
url |
https://dx.doi.org/10.5281/zenodo.7520813 https://zenodo.org/record/7520813 |
genre |
Antarc* Antarctica |
genre_facet |
Antarc* Antarctica |
op_relation |
https://zenodo.org/communities/oemc-project https://dx.doi.org/10.5281/zenodo.7520814 https://dx.doi.org/10.5281/zenodo.7822868 https://zenodo.org/communities/oemc-project |
op_rights |
Open Access Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.5281/zenodo.752081310.5281/zenodo.752081410.5281/zenodo.7822868 |
_version_ |
1768379904301203456 |