A model estimating forest growth, stock, harvest amount and comparing incomes from forestry and alternative land uses on a global scale. 

The IIASA Global Forest Model (G4M) estimates the productivity of five forest types (evergreen needleleaf, evergreen broadleaf, deciduous needleleaf, deciduous broadleaf and woody savannas) in four ecoregions (Tropical, Subtropical, Temperate and Boreal) based on dynamic site characteristics (monthly Temperature, Precipitation, Radiation, CO2), semi dynamic (Water holding capacity, sold depth, Nitrogen, Phosphorus, Salinity, pH-Value) and static (air pressure). Combining productivity with a management regime (e.g. keep current stock, maximize harvests, maximize stock, no harvests) which could be enabled or disabled to change the current species to better adapted to the potentially changed site characteristics, it is possible to show the development of increment (carbon sequestration), stock (stored carbon) and harvests (potential substitutes for nonrenewable products and in those also stored carbon). 

It can compare the income derived from forests with the income that could be derived from an alternative use of the same land, for example to grow grain for food or biofuel. 

To do this, G4M estimates the amount of net income currently being derived from forests by calculating the amount and value of wood produced minus the harvesting costs (i.e. logging and timber extraction). It also assesses the potential income represented by carbon storage in forests (sequestration). 

Taking these values into account, G4M demonstrates whether it would be more profitable to grow agricultural crops or bio-fuels at the location, or whether forestry is the economically best option for the land. This allows us to identify regions with high deforestation pressure and regions which could be afforested. By estimating the time needed to make this land use changes it's possible to also show forest cover development over time. 

G4M thus provides an economically sound basis for decision making, either independently, or in conjunction with other models. 

About G4M

G4M was developed at IIASA in the mid-2000s for modeling afforestation in Latin America under the name of DIMA. From there, it evolved to global forestry scenario analysis, and is now used as a basis for decision making in forest management. 

As well as demonstrating the pros and cons of different land uses, it can compute optimal forest rotation times to optimize biomass stocking and harvesting rates and can also help to rationalize other important aspects of forest management. 

G4M gives information at a resolution that depends on the input data provided. We currently use maps typical with a resolution roughly corresponding to a 1 x 1 km grid. G4M can use data from individual forest stands or aggregated larger regions. It could be used globally but also for a small region. 


FAST FACTS

  • G4M belongs to IIASA's Integrated Modeling Cluster, a holistic tool designed to study complex problems of integrated land and ecosystems management with an emphasis on forests and their sustainable management. 
  • G4M is part of the model cluster being used to calculate Reduced Emissions from Deforestation and Forest Degradation (REDD), an important question to be resolved under the UNFCCC. 
  • G4M can be used to model parameters based on a country's own statistics, for example, forest cover, species composition, age class distribution, and live biomass, to check their accuracy. 
  • In 2010 G4M was successfully used to estimate and validate forest growth for regions of Germany and Austria. 

Fig. G4M output example: Potential net primary productivity map © IIASA
Fig. G4M output example: Potential net primary productivity map

How G4M works

G4M is a versatile model that can be integrated with other models to gain greater clarification of land use potential. 

For instance, forest biomass in European forests under sustainable forest management is first modeled using G4M, then with IIASA's BEWHERE model to discover the optimal size and location of bio-energy plants in Europe. 

To model forest growth, the G4M can estimate the net primary productivity (NPP) by its own but can also take it as an input from other models. This was done e.g. for a simulation in Europe where three regional models provided yearly NPP estimates to G4M. G4M also uses information from other models or databases to produce forecasts of land-use change, carbon sequestration and/or emissions in forests, the impacts of carbon incentives (e.g. avoided deforestation), and supply of biomass for bio-energy and timber. 

The model can incorporate many factors or needs, for instance, the need to provide food security, to understand future urbanization patterns, and to gauge how wildfires or insects might affect forest productivity.

Model applications

G4M provides valuable information to policymakers at the national, regional, and international levels. G4M, linked to GLOBIOM and MESSAGE, was used for estimating land use, land use change and forestry emissions for a set of SSP/RCP scenarios. Together with GLOBIOM, GAINS, and a number of models from outside of IIASA, G4M was contributing to development of the 2013, 2016 and 2020 reference scenarios for the European Union. Flexibility of G4M allows simulating different forest management options, for example, estimate an impact of various assumptions on the forest reference level. 

Future deforestation projections using IIASA models G4M, GLOBIOM, and EPIC are helping Central African nations prepare for negotiating at the climate talks on how to reward rainforest nations for protecting their forests by reducing emissions from deforestation and forest degradation (REDD). 

In the Toyota Asian Ozone project, G4M provided estimates of mitigation potentials in the land use and land use change sector to show how the energy needs for economic growth could be delivered while addressing environmental issues such as ozone pollution. 

Verifying and updating the model

As a core model of the IIASA integrated modeling cluster for Biodiversity and Natural Resources, G4M is constantly updated and verified. 

Most recently, forest growth for the regions of Germany and Austria was successfully estimated and validated against field observations, and new data on several tree species groups were added for modeling on a global scale. 

Currently, G4M is being used to determine optimal CO2 prices for reducing deforestation and increasing afforestation. A prototype for a universal forest harvest cost model was also recently introduced into G4M and a linkage was developed to the main European forest stand-level models to provide yield estimates. 

It is planned to integrate a soil dynamics model, showing changes in soil carbon, nitrogen and phosphorus. It is planned to allow using G4M from the software environment for statistical computing R. Also it is planned to update and publish all used input data and results, like the forest biomass map, at the Global Forest Database