, Volume 26, Issue 6, pp 1713-1721
Date: 21 Apr 2012

Flux-based ozone risk assessment for adult beech forests

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Abstract

Tropospheric ozone (O3) is a critical threat to forest ecosystems. A stomatal flux-based risk evaluation methodology at the leaf level was established recently in the context of the Convention on Long-Range Transboundary Air Pollution (LRTAP). This study demonstrates improvement and validation of the stomatal flux-effect approach for adult beech with results from the 8-year free-air O3 enrichment experiment at “Kranzberger Forst” (Germany). The risk assessment module of the SVAT model FO3REST, being under development for local scale O3-risk assessment of adult beech stands, was parameterized according to the LRTAP Convention’s Mapping Manual. Mean maximum stomatal conductance for water vapour of 245 mmol H2O m−2 PLA s−1, as suggested in the LRTAP Convention’s Mapping Manual for beech, was affirmed by assessment at “Kranzberger Forst”, resulting in 162 mmol O3 m−2 PLA s−1 upon recommended adjustment of the O3/water vapour diffusivity ratio to 0.663. Based on this ratio, a provisional corrected flux-effect function was deduced. Modelled Phytotoxic O3 Doses (POD 1) and potential O3-caused losses in biomass formation estimated with a site-specific stomatal conductance algorithm differed slightly only from estimates by the original LRTAP parameterisation. Analysis-derived POD 1 target value within the meaning of Article 2 of the European Council Directive 2008/50/EC of 10 mmol O3 m−2 corresponded to potential loss in biomass formation of about 10 % in ambient air relative to “pre-industrial” conditions. However, exceedance occurred by about a factor of two during the study period, indicating high risk at “Kranzberger Forst” under ambient air. Assessment for doubled O3 exposure indicated potential underestimation even of the O3 risk because modelled losses in biomass formation are in the lower range of the standard deviation of the observed ones.

Communicated by F. Mohren.
Special topic: Integrating Modeling and Experiment.