Carbon fluxes of Kobresia pygmaea pastures on the Tibetan Plateau
2 Institute of Ecology, University of Innsbruck, Innsbruck, Austria
3 Senckenberg Museum for Natural History, Botany, Görlitz, Germany
4 Soil Science of Temperate Ecosystems, Georg-August University Göttingen, Göttingen, Germany
5 Soil Science, Leibnitz University Hannover, Hannover, Germany
6 Geography, Philipps University Marburg, Marburg, Germany
7 Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China
8 Micrometeorology, University of Bayreuth, Bayreuth, GermanySciences, Beijing, China
P 2.5 in Research in its Prime: First Results of Ongoing Research
The Cyperaceae Kobresia pygmaea (Kp) forms on the Tibetan Plateau (TP) one of the earth’s largest alpine ecosystems. It grows between 3000 to 6000 m a.s.l. and is especially adapted to grazing pressure, growing 2-6 cm tall while building up a stable turf layer from roots and protecting soil from erosion.
Grazing exclosure sites have been installed in different altitudes on the TP to investigate the impact of human induced land use change on Kp pastures, especially plant communities, soil carbon stocks and turnover, and energy and matter exchange at the border between atmosphere and ecosystem.
Experiments in 3300-3600 m a.s.l showed differences in vegetation composition and soil carbon stocks and turnover on 7 year grazing exclosures. Overall fodder quality and C-storage within soil was reduced, suggesting that exclusion of grazing livestock might not be the best choice for management of Kp meadows. In 2010 and 2012 multidisciplinary experiments were conducted within the centre of the major distribution of Kp in 4400 m a.s.l.. C-turnover within the plant-soil-atmosphere continuum was observed by combining NEE measurements (eddy covariance method) with relative C-fluxes (13C-labelling). Additionally chamber based flux measurements over different surface types, which make up the heterogeneity of the pastures, were conducted. The derived knowledge will be used to setup and validate land surface models which can resolve the special characteristics of this ecosystem.