Título:	Projection of climate change impacts on net primary productivity of the legal Amazon - Brazil.
Autoria:	SILVA, L. A. P. da SOUZA, C. M. P. de SILVA, C. R. da BOLFE, E. L. ROCHA, A. M.
Afiliação:	LUCAS AUGUSTO PEREIRA DA SILVA, UNIVERSIDADE FEDERAL DE UBERLÂNDIA; CRISTIANO MARCELO PEREIRA DE SOUZA, UNIVERSIDADE ESTADUAL DE MONTES CLAROS; CLAUDIONOR RIBEIRO DA SILVA, UNIVERSIDADE FEDERAL DE UBERLÂNDIA; EDSON LUIS BOLFE, CNPTIA, UNIVERSIDADE ESTADUAL DE CAMPINAS; ANDRE MEDEIROS ROCHA, UNIVERSIDADE DE SÃO PAULO.
Ano de publicação:	2023
Referência:	Caderno de Geografia, v. 33, n. 72, p. 110-130, jan./mar. 2023.
Conteúdo:	Abstract. The Amazon Rainforest is one of the main carbon sinks (CO2) on Earth. However, recently, owing to anthropogenic activities and climate change, it has lost its stability in CO2 absorption. Therefore, understanding the dynamics of future climate change scenarios is essential. We assessed the influence of future climate change scenarios on NPP (biomass) levels in the Amazon Forest using ML models. The tested models were Bayesian, linear, and random forest models. The current scenario was evaluated using 19 bioclimatic covariates (WorldClim dataset). Future scenarios were based on RCPs 2.6 and 8.5 (based on the MIROC5 and HadGEM2-ES models). Random Forest had the best performance statistics (R² = 0.71 in training and 0.68 in the holdout-test). These climate change scenarios imply an increase in the average NPP for the Amazon forest, especially with the greater intensification in RCP 2.6 (10 and 12 % for the HadGEM2-ES and MIROC5 models, respectively). Forests (evergreen broadleaf forest areas) will have a greater carbon fixation capacity. In general, the Amazon forest will have an increased carbon fixation capacity by the end of the century.
NAL Thesaurus:	Climate change
Palavras-chave:	Aprendizado de máquina Floresta Amazônica Floresta aleatória Sumidouro de carbono Mudanças climáticas Random Forest Machine Learning Carbon sink Amazon Forest
Digital Object Identifier:	10.5752/p.2318-2962.2023v33n.72p.110
Tipo do material:	Artigo de periódico
Acesso:	openAccess
Aparece nas coleções:	Artigo em periódico indexado (CNPTIA)