TY - JOUR

T1 - An ensemble of spatially explicit land-cover model projections: prospects and challenges to retrospectively evaluate deforestation policy

AU - Bradley, Andrew V.

AU - Rosa, Isabel M. D.

AU - Brandao, Amintas, Jr.

AU - Crema, Stefano

AU - Dobler, Carlos

AU - Moulds, Simon

AU - Ahmed, Sadia E.

AU - Carneiro, Tiago

AU - Smith, Matthew J.

AU - Ewers, Robert M.

PY - 2017/12/1

Y1 - 2017/12/1

N2 - 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 km2. 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 km2 and a prevented forest loss estimate of 50%. Using all 21 projections with diverse calibration periods improved these errors to ±179.26 km2 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.

AB - 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 km2. 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 km2 and a prevented forest loss estimate of 50%. Using all 21 projections with diverse calibration periods improved these errors to ±179.26 km2 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.

KW - Land-cover modeling

KW - Deforestation

KW - Environmental policy

KW - SimiVal

KW - Brazil

KW - Green Municipality

U2 - 10.1007/s40808-017-0376-y

DO - 10.1007/s40808-017-0376-y

M3 - Article

VL - 3

SP - 1215

EP - 1228

JO - Modeling Earth Systems and Environment

JF - Modeling Earth Systems and Environment

SN - 2363-6203

IS - 4

ER -