Land use and land cover are important determinants of outcomes in MultiSector Dynamics models and global climate models. The Moirai land data system was designed to produce historical land data inputs for different modeling platforms including the agriculture and land use module of the Global Change Analysis Model (GCAM). Moirai’s primary function is to amalgamate a suite of spatial and tabular products to generate easily interpretable regional summaries of land use metrics.

We now present a new spatial output that provides a detailed accounting of historical land use and land cover at a 5-arcminute (0.08333-degree) resolution globally. The hierarchical dataset presents four broad types of land use (cropland, pastures, urban land and unmanaged land) which have nested categories that distinguish potential vegetation/land cover classes from the SAGE dataset (The vegetation cover is calculated using downscaled ISAM data in combination with the potential vegetation data from SAGE) , land use designation from the HYDE dataset, and harvested or arable designation for crop types FAO harvest data resulting in a total of 36 available land classes (4 land use categories, 22 vegetation types and 10 types of harvested crops) commonly used by Earth System Models. The result is a harmonized product that can generate the land use and land cover change data for any year from 1800-2016, thus providing significant temporal coverage that is ideal for use in MultiSector Dynamics and climate models. Future developments in the dataset will include more detail to forest management (forest harvest, forest age) and protection (land suitability and protection).