期刊
GEOPHYSICAL RESEARCH LETTERS
卷 50, 期 15, 页码 -出版社
AMER GEOPHYSICAL UNION
DOI: 10.1029/2023GL103932
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This study develop data-driven models to predict woody cover in Africa and find that woody cover can be accurately modeled using Random Forest. The simulations based on CMIP6 precipitation data project an overall increase in woody cover at the continental scale by 2100. However, this increase is mainly observed in regions with annual precipitation less than 1,600 mm, while higher rainfall areas are predicted to experience a decrease in woody cover. These results suggest that climate change may lead to changes in the functioning of dryland ecosystems and a loss of carbon stocks in humid areas.
Projection of future woody cover is essential to understand potential changes in structure and functioning of terrestrial ecosystems. Previous studies mapped woody cover during historical periods observed by satellites, however, it remains unclear how woody cover is expected to change in response to future climate change. Here, we develop data-driven models to predict woody cover in Africa using multiple environmental predictors and show that woody cover can be accurately modeled using Random Forest. Empirically-based simulations forced by precipitation from CMIP6 project an overall increase in woody cover at the continental scale by 2100. However, increases are mainly occurring in regions with annual precipitation less than & SIM;1,600 mm y(-1), whereas woody cover is predicted to decrease in areas of higher rainfall. Our results suggest that climate change may alter the functioning of dryland ecosystems by continued woody encroachment and cause a loss of carbon stocks in humid areas.
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