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Eberhard Karls Universität Tübingen (2016)

Quantification of soil CO2 emissions under the influence of climate change on the Qinghai-Tibet Plateau based on freely accessible data

Bosch, Anna

Titre : Quantification of soil CO2 emissions under the influence of climate change on the Qinghai-Tibet Plateau based on freely accessible data

Auteur : Bosch, Anna

Université de soutenance : Eberhard Karls Universität Tübingen

Grade : Doktors der Naturwissenschaften (Dr. rer. nat.) 2016

Soil CO2 emissions are of important significance for the global carbon cycle and, thus, for climate change. Soils function as main source of atmospheric CO2 from terrestrial ecosystems. Even small changes in soil CO2 emissions can accelerate global warming. Reciprocally, climate change influences soil CO2 emissions. Against this background, it is highly essential to quantify potential soil CO2 emissions in order to be able to project future developments of global warming. In this context, the permafrost region of the Qinghai-Tibet Plateau is a key region for soil CO2 emissions. Permafrost soils are considered as a CO2 source with high potential. In consequence of thawing processes, large quantities of carbon stored in these soils become subject to microbial decomposition and are emitted as CO2. Because of its large area (1.050 × 106 km2) and high sensitivity to climate together with increasing permafrost degradation, the Qinghai-Tibet Plateau attains global significance. Empirical models still represent the commonly used type of model, being highly advantageous especially for large and remote areas with a high data scarcity as e.g. the Qinghai-Tibetan Plateau. Due to the large area difficult to access, field measurements are very costly and time consuming. Thus, they are strongly limited on the Qinghai-Tibetan Plateau. Consequently, area-explicit data sets mainly exhibit a low spatial resolution, are not comprehensive or freely accessible. However, freely available global datasets of a high resolution ( 1 km) enable an application of empirical models to predict soil CO2 emissions on the Qinghai-Tibet Plateau area explicitly. This thesis provides an approach to quantify CO2 emissions from permafrost soils efficiently. Belowground biomass on the Qinghai-Tibet Plateau was calculated using empirical models since it represents a not yet area-explicitly quantified key input factor in empirical models for soil CO2 emissions on the Qinghai-Tibet Plateau. Based on a comparison of different regression models for quantifying current soil CO2 emissions on the Qinghai-Tibet Plateau, the one closest representing field measurements throughout various vegetation zones was identified. Applying this model, which incorporates mean annual precipitation as input factor, future soil CO2 emissions were predicted. Consequently, scenarios of climate change for mean annual precipitation underlie the predictions of potential soil CO2 emissions for 2050 and 2070. To account for the high importance of permafrost in the study area, thawing-induced CO2 emissions from those soils were calculated additionally using experimental data on carbon losses from permafrost soils that were taken from the literature. To quantify those CO2 emissions, area-explicit carbon stocks were calculated for the Qinghai-Tibet Plateau. This thesis highlights the quantitative dimension of CO2 from permafrost soils on the Qinghai-Tibet Plateau for global warming, with 0.15 Pg C year-1 fitting the order of magnitude of results of comparable studies. The thesis further demonstrates the impact of climate change especially on thawing-induced CO2 emissions from permafrost soils. Their order of magnitude, approximately 4% of the annual average atmospheric increase of CO2-C, justifies strategies for climate protection in particular. By comparing the modeled results to data from field measurements, this thesis further indicates that empirical models represent suitable tools to adequately model and predict belowground biomass and soil CO2 emissions. Using exclusively freely accessible data sets, this thesis further exemplifies a highly efficient quantification of complex phenomena on a regional scale at a high resolution. Data-scarce areas of global relevance potentially profit most.


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