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Soil respiration across scales: the importance of a model-data integration framework for data interpretation

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons62524

Reichstein,  M.
Research Group Biogeochemical Model-data Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons62333

Beer,  C.
Research Group Biogeochemical Model-data Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Zitation

Reichstein, M., & Beer, C. (2008). Soil respiration across scales: the importance of a model-data integration framework for data interpretation. Journal of Plant Nutrition and Soil Science, 171(3), 344-354. doi:10.1002/jpln.200700075.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000E-D741-0
Zusammenfassung
Soil respiration represents the second largest CO2 flux of the terrestrial biosphere and amounts 10 times higher than the current rate of fossil-fuel combustion. Thus, even a small change in soil respiration could significantly intensity-or mitigate-current atmospheric increases of CO2, with potential feedbacks to climate change. Consequently, to understand future dynamics of the earth system, it is mandatory to precisely understand the response of soil respiration to changing environmental factors. Among those changing factors, temperature is the one with clearest and most certain future trend. The relationship of soil respiration to environmental factors and particular temperature has been tackled at a variety of scales-from laboratory via field to global scale, with each of the approaches having its particular problems, where results from different scales are sometimes contradictory. Here, we give an overview of modeling approaches to soil respiration, discuss the dependencies of soil respiration on various environmental factors, and summarize the most important pitfalls to consider when analyzing soil respiration at the respective scale. We then introduce a model-data synthesis framework that should be able to reconcile and finally integrate apparently contradictory results from the various scales.