Integrated forestry assessments for climate change impacts

Forests and the forest sector are sensitive to climate change at greatly varying scales. The complexity of the interactions among the physical environment, forest growth, the management and utilisation of forest resources, and market responses has stimulated efforts to model the impact of global changes on the forest sector by linking impact models developed from different disciplines. This paper reviews existing experiences in integrated forest sector impact assessments. Different ways of integrating cross-disciplinary impact assessments are classified as linking, coupling and integrated modelling. To date the most common method is a “one-way” linking, where results from one model are used as input to a different model. When different impact models are coupled, feedbacks can be analysed, e.g. between ecological and economic systems. Integrated modelling is described as a third step, where different sub-models are embedded into a common model framework. The concept of balance is introduced as a key to successful integration of different disciplines in integrated assessment (IA) studies. The review of existing experiences emphasises the problem of complexity and the need to simplify disciplinary approaches. It also illustrates how methodologies applied to forest sector IA studies have evolved over the last few years. Several scaling issues that are particularly important for IA modelling in forestry are discussed, including the consequences of heterogeneity in site conditions, the variable influence of extreme events on ecosystems and on the economic sector, and the differences in temporal and spatial scales over which key forest growth and renewal processes operate. Climate impact assessments include uncertainties. Some common sources of uncertainty in forest IA modelling are outlined, and methods that have been used to address this uncertainty are reviewed. We discuss the policy relevance of integrated impact assessments and stress the importance of stakeholder involvement in IA projects. The paper concludes with some recommendations for future developments in this relatively new field of research.

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