Current and future global distribution of potential biomes under climate change scenarios

Dataset

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. creation date: v20230410. We provide probability and hard class layers using a revised classification system of the BIOME 6000 project explained in the work of Hengl et al. (2018). The 20 classes from this classification system have then been aggregated in 6 biome classes following the IUCN Global Ecosystem Typology classification system. For probability layers, the uncertainty (model deviation: md) is calculated as the standard deviation of the predicted values of the base learners of the ensemble model. The higher the standard deviation the more uncertain the model is regarding the right value to assign to the pixel. For hard class layers the uncertainty is calculated using the margin of victory (Calderón-Loor et al., 2021) defined as the difference between the first and the second highest class probability value in a given pixel. High values would be measures of low uncertainty, while low values would indicate a high uncertainty. It is highly recommended to use the md layers to properly interpret the results of the map. Styling files are provided in both .SLD and .QML format; two different styling files are provided for the uncertainty of the probability layers and the hard classes due to the different interpretation of the chosen uncertainty metrics. A publication describing, in detail, all processing steps, accuracy assessment and general analysis of the biome maps is under preparation The R scripts and a tutorial will be uploaded to the PNVmaps Github repository, where previous versions of the biomes maps from Hengl et al. (2018) is currently hosted. In the meantime, to cite the maps and the methodology, it is possible to refer to the preprint: Bonannella, C., Hengl, T., Parente, L., and de Bruin, S. (2023). "Biomes of the world under climate change scenarios: increasing aridity and higher temperatures lead to significant shifts in natural vegetation". Preprint (Version 1) available on Research Square [https://doi.org/10.21203/rs.3.rs-2471847/v1]
Date made available10 Apr 2023

Keywords

  • ensemble machine learning
  • biomes
  • global
  • rcp
  • climate change scenario

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