Modeling Past and Present Land Use and Land Cover Change
There is significant uncertainty in how land use and land cover will evolve in the future. Some of this uncertainty arises from uncertainty in how the drivers of land-use change (e.g., population, income, and diet) will evolve, but some of the uncertainty is in how landowners make decisions. Researchers at the U.S. Department of Energy’s Pacific Northwest National Laboratory (PNNL) developed a software tool to model land use and land cover change both in the past and in the future. This tool allows users to test alternative decision-making frameworks, comparing the results to observations in the past and projecting the implications into the future.
PNNL researchers isolated the land allocation mechanism within the Global Change Analysis Model (GCAM) from the rest of the model, providing a computationally efficient, easy-to-use tool to model land use and land cover change. By isolating this mechanism, this tool allows researchers to examine uncertainty in land-use change in a way that was not previously possible. The tool can be used to understand historical land-use decisions, quantify uncertainty in future projections, or link to other models.
gcamland v1.0 is an open-source R package that was built to allocate land across a variety of uses based on changes in agricultural yield and commodity price. The land allocation algorithm is based on the one included in GCAM. gcamland v1.0 includes the ability to run in a historical mode, enabling model validation and parameter estimation, or in a future mode, simulating changes in land use and land cover in the future. For both modes, gcamland v1.0 can run a single simulation or a large ensemble of simulations with different parameters. When ensembles are generated in the historical mode, gcamland v1.0 calculates the likelihood of a given parameter set by comparing to observational data. gcamland v1.0 is publicly available via GitHub and can be adjusted to represent alternative scenarios or configured to different regions and land types.