Computer model predicts future deforestation in Congo Basin rainforest

November 21st, 2009 - 2:54 pm ICT by ANI  

London, November 21 (ANI): Scientists have come up with a computer model that predicts future changes in the world’s forests, which has forecasted deforestation in the Congo Basin rainforest in the future.

Forest management is expected to be a key point of discussion at the United Nations climate summit in Copenhagen in December.

Countries will negotiate on how to reward rainforest nations for protecting their forests, a mechanism dubbed REDD for ‘reducing emissions from deforestation and forest degradation’.

Deforestation rates in the Congo Basin rainforest - the second-largest rainforest on Earth - have hovered at around 0.15 percent per year for the past 15 years.

But, according to a report in Nature News, preliminary results from the model predict that forest cutting in the region will increase to 0.3-0.5 percent per year by 2020-30.

“There are strong indications that Central African forests are at a critical turning point for the future,” said Carlos de Wasseige, the coordinator of an EU-funded project called Forests of Central Africa, which hopes to set up a regional forest monitoring centre.

“Most proposals for (REDD) suggest that history is the best predictor of tomorrow,” said Michael Obersteiner, who is leading the development of the forestry model at the International Institute for Applied Systems Analysis, based in Laxenburg, Austria.

“But for Central African countries, the forward-looking projections will be more reliable,” he added.

The model, which has a resolution of 10-50 km2, is a combination of three global land-use models called GLOBIOM, G4M and EPIC.

Its predictions are based on key global drivers of deforestation, including population growth and gross-domestic-product growth, as well as global demand and production of biofuel, timber and agricultural crops.

The model works by calculating the profitability of forest clearance in certain areas on the basis of topography, soil composition and climate. (ANI)

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