30 January 2016

Assessment of the importance of spatial scale in long-term land use modeling of the Midwestern United States

Summary

Models to help understand land use range from highly detailed local scenarios to massive, integrated systems that link energy, agriculture, and the economy over areas as large as the continent of Africa. But more sophisticated land-use modeling yields a complicated set of results when translated to regional or global analyses. Ways to analyze and visualize results must keep pace with the vast amount of data produced. To tackle this issue, researchers at Pacific Northwest National Laboratory developed a new analysis technique to improve land-use model results to simultaneously address the most important variables needed for regional and global analyses.

"Previous studies either highlighted a single land-use type or presented many figures that were difficult to assemble into a coherent whole," said PNNL modeling expert Page Kyle, who led the study. "Our work showcases a method that can assist with the interpretation of model output, particularly when comparing output across regions, land-cover types, and scenarios."

Contact
Page Kyle
Publications
Kyle, P, A Thomson, M Wise, and X Zhang.  2015.  "Assessment of the Importance of Spatial Scale in Long-Term Land Use Modeling of the Midwestern United States."  Environmental Modelling and Software 72: 261-271.  https://doi.org/10.1016/j.envsoft.2015.06.006.
Acknowledgments

This research was supported by the Office of Science of the U.S. Department of Energy through the Integrated Assessment Research Program as part of the Regional Integrated Assessment Modeling (RIAM) project (KP1703030). This research leveraged capabilities that were funded by the Platform for Regional Integrated Modeling and Analysis Initiative (PRIMA), which was conducted under the Laboratory Directed Research and Development Program at Pacific Northwest National Laboratory (PNNL). PNNL is operated for DOE by Battelle Memorial Institute under contract DE-AC05-76RL01830. GPK conducted GCAM simulations and NMDS statistical analysis; XZ provided the high resolution input dataset and interpretation; AMT and GPK conceived of the study; MAW provided interpretation of GCAM results; GPK, AMT and MAW wrote the paper. XZ's contributions were funded by the DOE Great Lakes Bioenergy Research Center (DOE BER Office of Science DE-FC02-07ER64494, DOE BER Office of Science KP1601050, DOE EERE OBP 20469-19145). The views and opinions expressed in this paper are those of the authors alone.