An ensemble of spatially explicit land-cover model projections: prospects and challenges to retrospectively evaluate deforestation policy

• Published in: Modeling earth systems and environment Vol. 3; no. 4; pp. 1215 - 1228
• Main Authors: Bradley, Andrew V., Rosa, Isabel M. D., Brandão, Amintas, Crema, Stefano, Dobler, Carlos, Moulds, Simon, Ahmed, Sadia E., Carneiro, Tiago, Smith, Matthew J., Ewers, Robert M.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.12.2017; Springer Nature B.V
• Subjects: Calibration; Chemistry and Earth Sciences; Computer Science; Computer simulation; Deforestation; Earth and Environmental Science; Earth Sciences; Earth System Sciences; Ecosystems; Environment; Exploitation; Forest conservation; Forest management; Land cover; Math. Appl. in Environmental Science; Mathematical Applications in the Physical Sciences; Methods; Modelling; Municipalities; Original Article; Physics; Policies; Rainforests; Statistics for Engineering; Tropical climate; Tropical forests; Uncertainty; Deforestation; Brazil; SimiVal; Green Municipality; Environmental policy; Land-cover modeling
• ISSN: 2363-6203; 2363-6211
• DOI: 10.1007/s40808-017-0376-y

Ensemble techniques, common in many disciplines, have yet to be fully exploited with spatially explicit projections from land-change models. We trial a land-change model ensemble to assess the impact of policies designed to conserve tropical rainforest at the municipality scale in Brazil, noting the achievements made and challenges ahead. Four spatial model frameworks that were calibrated with the same predictor variables produced 21 counterfactual simulations of the actual landscape. Individual projections with a uniform calibration period gave estimates that between 29 and 68% of the simulated deforestation was saved, but lacked an uncertainty estimate, whilst batch projections from two different model frameworks provided a more dependable mean estimate that 38 and 49% deforestation was prevented with an uncertainty range of 1900 and 1000 km 2 . The consensus ensembles used agreement between the projections and found that the seven examples with a uniform calibration period produced an error margin of ±435.94 km 2 and a prevented forest loss estimate of 50%. Using all 21 projections with diverse calibration periods improved these errors to ±179.26 km 2 with a 53% estimate of prevented forest loss. Whilst we achieved a method of combining projections of different frameworks to reduce uncertainty of individual modelling frameworks, demonstrating a control model and accounting for non-linear conditions are challenges that will provide better confidence in this method as an operational tool. Such retrospective evidence could be used to make timely rewards for efforts of governments and municipalities to support tropical forest conservation and help mitigate deforestation.