Robust Downscaling Approaches to Disaggregation of Data and Projections Under Uncertainties: Case of Land Cover and Land Use Change Systems

The interdependencies among land use systems at national and global levels motivate the development of advanced systems analysis approaches for integration of land use models operating at different weights. The paper develops novel general approaches based on cross entropy principle for downscaling...

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Published inCybernetics and systems analysis Vol. 53; no. 1; pp. 26 - 33
Main Authors Ermoliev, Y. M., Ermolieva, T. Y., Havlík, P., Mosnier, A., Leclere, D., Fritz, S., Obersteiner, M., Kyryzyuk, S. V., Borodina, O. M.
Format Journal Article
LanguageEnglish
Published New York Springer US 2017
Springer
Subjects
Artificial Intelligence
Case studies
Control
Forecasts and trends
Land use
Land use controls
Mathematics
Mathematics and Statistics
Methods
Processor Architectures
Software Engineering/Programming and Operating Systems
Sustainable development
Systems Theory
robust downscaling
sustainable development planning
local-global interdependencies
uncertainties
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Summary:The interdependencies among land use systems at national and global levels motivate the development of advanced systems analysis approaches for integration of land use models operating at different weights. The paper develops novel general approaches based on cross entropy principle for downscaling aggregate data and projections, which are robust with respect to feasible priors. Robust downscaling methods account for so-called non-Bayesian uncertainties, i.e., incomplete, unobservable, or erroneous information or data. In numerous case studies in China, African countries, Brazil, and Ukraine, the approaches allowed deriving local development projections of land use and land use change consistently with existing trends and expectations.
ISSN:1060-0396
1573-8337
DOI:10.1007/s10559-017-9904-z