Forest Forward.

Climate data for the forestry sector.

Plans are underway to plant millions, if not billions of trees in the coming years. But trees grow slowly and our climate is changing quickly. So, if we want these trees to survive and thrive over the next 30, 50 or 70 years, which species should we plant and where? 

In the absence of a crystal ball that can predict the future, we have to turn to scientific methods. Together with Tecnalia, we explored the potential of using Copernicus climate data combined with species observations to predict the future distribution of trees under different climate scenarios. The results of this approach can be seen in Forest Forward, a market study and web app demo we completed for the European Centre for Medium-Range Weather Forecasts (ECMWF). In this project, we mapped the modelled distribution of commercially important tree species over the next 90 years under two climate change scenarios. It is intended to be an example of a different approach to long-term planning for the forestry sector.

This initiative has been organised within the framework of a Copernicus Climate Change Service (C3S) contract; C3S is one of the six services of the EU’s Copernicus Programme and is implemented by the European Centre for Medium-Range Weather Forecast (ECMWF) on behalf of the European Commission.

Species Distribution Models.

The models we made for Forest Forward are predictions of what might happen in the future. Our models combined climate data from the Copernicus Climate Change Service (C3S) including temperature, precipitation and extreme climatic conditions with species location data from the Global Biodiversity Information Facility. With this data, we are able to predict which locations might be best suited for the tree species we want to plant.


Another potential application of species distribution models is in the restoration of degraded lands. Efforts to restore natural landscapes and reintroduce species — both flora and fauna — are building momentum. By including climate data in the decision-making process, we can increase the chances of long-term success.

Identify future suitable areas for forestry.

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