Datasets:
language:
- en
tags:
- climate
pretty_name: Project Resilience Emissions from Land-Use Change Dataset
size_categories:
- 10M<n<100M
Project Resilience Emissions from Land-Use Change Dataset
Project Resilience
To contribute to this project see Project Resilience (Github Repo)
Land Use Change data is provided by the Land Use Harmonization Project, providing land-use changes from 850-2100
Emissions from Land-Use Change (ELUC) data is provided by the Global Carbon Budget 2023 Bookkeeping of Land-Use Emissions (BLUE) model.
Data was used in Discovering Effective Policies for Land-Use Planning at NeurIPS 2023 Workshop: Tackling Climate Change with Machine Learning
Land Use Types
Primary: Vegetation that is untouched by humans
- primf: Primary forest
- primn: Primary nonforest vegetation
Secondary: Vegetation that has been touched by humans
- secdf: Secondary forest
- secdn: Secondary nonforest vegetation
Urban
Crop
- c3ann: Annual C3 crops (e.g. wheat)
- c4ann: Annual C4 crops (e.g. maize)
- c3per: Perennial C3 crops (e.g. banana)
- c4per: Perennial C4 crops (e.g. sugarcane)
- c3nfx: Nitrogen fixing C3 crops (e.g. soybean)
Pasture
- pastr: Managed pasture land
- range: Natural grassland/savannah/desert/etc.
Dataset
The dataset is indexed by latitude, longitude, and time, with each row consisting of the land use of a given year, the land-use change from year to year+1, and the committed ELUC at the end of year in tons of carbon per hectare (tC/ha).
Committed ELUC means the sum of all simulated future emissions due to a land-use change.
In addition, the cell area of the cell in hectares and the name of the country the cell is located in are provided.
A crop and crop_diff column consisting of the sums of all the crop types and crop type diffs is provided as well as the BLUE model treats all crop types the same.
Raw data files are provided as: merged_aggregated_dataset_1850_2022.zarr.zip
and BLUE_LUH2-GCB2022_ELUC-committed_gridded_net_1850-2021.nc
, which are the land-use changes and the committed emissions respectively.