Robust Downscaling Approaches to Disaggregation of Data and Projections Under Uncertainties: Case of Land Cover and Land Use Change Systems*
| Autor: | O. M. Borodina, Steffen Fritz, S. Kyryzyuk, Michael Obersteiner, Tatiana Ermolieva, Petr Havlik, Yu. M. Ermoliev, David Leclère, Aline Mosnier |
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| Rok vydání: | 2017 |
| Předmět: |
0106 biological sciences
021103 operations research General Computer Science Land use Computer science 0211 other engineering and technologies 02 engineering and technology Land cover 010603 evolutionary biology 01 natural sciences Unobservable Cross entropy Systems analysis Econometrics Aggregate data Land use land-use change and forestry Downscaling |
| Zdroj: | Cybernetics and Systems Analysis. 53:26-33 |
| ISSN: | 1573-8337 1060-0396 |
| DOI: | 10.1007/s10559-017-9904-z |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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