We present simple models of forest net primary production (NPP) in Germany that show increasing productivity, especially in mountainous areas, under warming unless water becomes a limiting factor. They can be used for spatially explicit, rapid climate impact assessment.
Climate impact studies largely rely on process-based forest models generally requiring detailed input data which are not everywhere available. This study aims to derive simple models with low data requirements which allow calculation of NPP and analysis of climate impacts using many climate scenarios at a large amount of sites. We fitted regression functions to the output of simulation experiments conducted with the process-based forest model 4C at 2342 climate stations in Germany for four main tree species on four different soil types and two time periods, 1951–2006 and 2031–2060.The regression functions showed a reasonable fit to measured NPP datasets. Temperature increase of up to 3 K leads to positive effects on NPP. In water-limited regions, this positive effect is dependent on the length of drought periods. The highest NPP increase occurs in mountainous regions. Rapid analyses, using reduced models as presented here, can complement more detailed analyses with process-based models. Especially for dry sites, we recommend further study of climate impacts with process-based models or detailed measurements.
altmetric doi=”10.1007/s13595-015-0532-3″ float=”right”]
Gutsch M, Lasch-Born P, Suckow F, Reyer CO 2015. Evaluating the productivity of four main tree species in Germany under climate change with static reduced models. Ann. For. Sci.: 1-10. 10.1007/s13595-015-0532-3.
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