How will our forests look like in 20, 50 or even 80 years? Nobody knows for sure, but we forest researchers try to imagine how they will, could or should look like. This forecasting has been done for hundreds of years by forest practitioners with different methods to secure sustainable wood use. Today, in light of the climate crisis, we have to ask ourselves about the extent of climate change impacts on our forests. And we are investigating how we can adapt forests and their management to reduce these impacts and what role they can play in the global effort to mitigate climate change. In this regard, forest models help us understand the dynamics that are happening and project them into the future.
We summarize our knowledge, data, and empirical findings about forests to create these simplified abstractions of reality (= models). Did you know that models of other real systems are present in our everyday life? Examples are maps. They depict different things depending on the objective of the map. City maps show streets, residential areas and parks while retaining an identical scale over the whole extent of the map. Public transport maps on the other hand only show stations and the connections between them via lines. In this case a consistent scale across the map can be ignored: stations being close together in the urban periphery on the map may be far apart in reality, while stations being far apart in the dense city center on the map are only a few minutes apart from a geographical distance point of view.
Depending on the objective of the forest model, different approaches are applied as well – from more empirical models to more mechanistic ones. The former described primarily statistical relationships between different quantities, while the latter focus at the level of processes (e.g. photosynthesis, carbon allocation, phenology, mortality, regeneration) happening in the forest, forming forest dynamics over time stimulated by the interplay of the different processes. Mechanistic process-based models are most often used for climate change related studies because they rely to a lesser extent on correlative relationships of the past that may change in the future. But to make projections into the future, forest models alone are not sufficient – we also need to make assumptions of plausible future conditions that might affect forests.
Drivers, scenarios, and uncertainties
Scenarios are our way to describe plausible “futures” considering certain drivers important for forests. These drivers influence the forest in one or the other way. Examples are forest management, climate, or the natural disturbance regime. These drivers can continue in the way they act on the forest, i.e., a business-as-usual scenario. However, drivers can also change due to changing external conditions, like climate change and disturbance regimes or shifts in management in response to both.
As you can imagine, these scenarios are all associated with uncertainties since we cannot precisely forecast which of the many plausible “futures” will be realized. There are many reasons why it is impossible to precisely predict the future. But when investigating forests under climate change, two are most relevant: Firstly, we do not yet understand the systems well enough to make precise projections what happens if, for example what happens to the frequency and intensity of storms under an alternative European climate. Secondly, an outcome depends on decisions taken and actions made at the global political level. Those are not predictable either, like the efforts to mitigate climate change by curbing the green-house gas emissions that result in the degree of climate change in the future.
This uncertainty about which of the plausible futures will be realized propagates through the forest models and is further exacerbated by the uncertainty of the forest model itself, since forest models summarize our incomplete knowledge of the forest. The projections made with these models based on the scenarios are therefore uncertain. Hence, we need to be careful not to mix up the modelled world with reality but to draw conclusions through the lens of the model. Nevertheless, these projections are exceptionally useful to understand what could be if and how the extreme edges of potential future developments look like.
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