, Volume 9, Issue 2, pp 121-130
Date: 07 Oct 2008

Quantifying disturbance effects on vegetation carbon pools in mountain forests based on historical data

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Although the terrestrial carbon budget is of key importance for atmospheric CO2 concentrations, little is known on the effects of management and natural disturbances on historical carbon stocks at the regional scale. We reconstruct the dynamics of vegetation carbon stocks and flows in forests across the past 100 years for a valley in the eastern Swiss Prealps using quantitative and qualitative information from forest management plans. The excellent quality of the historical information makes it possible to link dynamics in growing stocks with high-resolution time series for natural and anthropogenic disturbances. The results of the historical reconstruction are compared with modelled potential natural vegetation. Forest carbon stock at the beginning of the twentieth century was substantially reduced compared to natural conditions as a result of large scale clearcutting lasting until the late nineteenth century. Recovery of the forests from this unsustainable exploitation and systematic forest management were the main drivers of a strong carbon accumulation during almost the entire twentieth century. In the 1990s two major storm events and subsequent bark beetle infestations significantly reduced stocks back to the levels of the mid-twentieth century. The future potential for further carbon accumulation was found to be strongly limited, as the potential for further forest expansion in this valley is low and forest properties seem to approach equilibrium with the natural disturbance regime. We conclude that consistent long-term observations of carbon stocks and their changes provide rich information on the historical range of variability of forest ecosystems. Such historical information improves our ability to assess future changes in carbon stocks. Further, the information is vital for better parameterization and initialization of dynamic regional scale vegetation models and it provides important background for appropriate management decisions.