Abstract
Land-use change (LUC) is a complex process that is difficult to project. Model collaboration, an aggregate term for model harmonization, comparison and/or coupling, intends to combine the strengths of different models to improve LUC projections. Several model collaborations have been performed, but to the authors’ knowledge, the effect of coupling has not been evaluated quantitatively. Therefore, for a case study of Brazil, we harmonized and coupled the partial equilibrium model GLOBIOM-Brazil and the demand-driven spatially explicit model PLUC, and then compared the coupled-model projections with those by GLOBIOM-Brazil individually. The largest differences between projections occurred in Mato Grosso and Pará, frontiers of agricultural expansion. In addition, we validated both projections for Mato Grosso using land-use maps from remote sensing images. The coupled model clearly outperformed GLOBIOM-Brazil. Reductions in the root mean squared error (RMSE) for LUC dynamics ranged from 31% to 80% and for total land use, from 10% to 57%. Only for pasture, the coupled model performed worse in total land use (RMSE 9% higher). Reasons for a better performance of the coupled model were considered to be, inter alia, the initial map, more spatially explicit information about drivers, and the path-dependence effect in the allocation through the cellular-automata approach of PLUC.
Highlights
Land-use change (LUC) is an important direct form of human impact on the environment [1] and a key factor contributing to anthropogenic greenhouse gas (GHG) emissions [2,3]
Reductions in the root mean squared error (RMSE) for LUC dynamics ranged from 31% to 80% and for total land use, from 10% to 57%
We aimed to answer the following research questions: (1) What are the differences between land-use patterns produced by the coupled model and GLOBIOM-Brazil and what do these differences tell us about the models? (2) For which land use classes, if any, does the coupled model produce better results than GLOBIOM-Brazil individually when being validated against independent observational data?
Summary
Land-use change (LUC) is an important direct form of human impact on the environment [1] and a key factor contributing to anthropogenic greenhouse gas (GHG) emissions [2,3]. LUC directly affects ecological, biophysical and biochemical processes and system states such as biodiversity, freshwater storage and flow regimes [4]. It indirectly influences climate by changing characteristics of the earth’s surface such as soil moisture and albedo [5,6]. Different models have been developed for projecting LUC. Each of these models has its strengths and weaknesses, due to, for example, spatial scale (reflecting a specific decision-making level of actors, such as individual farmers, a group of land owners, or spatial planners), thematic application
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