Modeling potential equilibrium states of vegetation and terrestrial water cycle of Mesoamerica under climate change scenarios
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Imbach, I., Molina, L., Locatelli, B., Roupsard, O., Mahé, G., Neilson, R., Corrales, L., Scholze, M., Ciais, P. 2011. Modeling potential equilibrium states of vegetation and terrestrial water cycle of Mesoamerica under climate change scenarios . Journal of Hydrometeorology ISSN: 1525-755X.
Permanent link to this item: http://hdl.handle.net/10568/21006
We evaluated the likelihood and magnitude of the impacts of climate change on potential vegetation and the water cycle in Mesoamerica. Mesoamerica is a global biodiversity hotspot with highly diverse topographic and climatic conditions and is among the tropical regions with the highest expected changes in precipitation and temperature under future climate scenarios. We used the biogeographic soil-vegetation-atmosphere model MAPSS (Mapped Atmosphere Plant Soil System) for simulating the integrated changes in leaf area index (LAI), vegetation types (grass, shrubs and trees), evapotranspiration, and runoff at the end of the 21st century. We estimated the uncertainty as the likelihood of changes in vegetation and water cycle under three ensemble of model runs, one for each of the groups of greenhouse gas emission scenarios (low, intermediate and high emissions), for a total of 136 runs generated with 23 general circulation models (GCMs). LAI is likely to decrease over 77 – 89% of the region, depending on climate scenario groups, showing that potential vegetation will likely shift from humid to dry types. Accounting for potential effects of CO2 on water use efficiency significantly decreased impacts on LAI. Runoff will decrease across the region even in areas where precipitation increases (even under increased water use efficiency), as temperature change will increase evapotranspiration. Higher emission scenarios show lower uncertainty (higher likelihood) in modeled impacts. Although the projection spread is high for future precipitation, the impacts of climate change on vegetation and water cycle are predicted with relatively low uncertainty.
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