黑料吃瓜网

Atmospheric concentrations of greenhouse gases (GHGas) play an extremely important role in regulating Earth鈥檚 climate system. Researchers need to understand how GHGs are produced at a process-level in order to predict what might happen under future climate scenarios. A great deal of work has gone into understanding the fundamental processes that control GHG production and consumption, but many questions remain. To date, much of this research has focused on the biology of the soil system but there are also many physical processes that control the transport of decomposable substrate, nutrient supply, the local-environment (e.g. temperature and moisture) as well as the eventual emission of GHGs to the atmosphere (i.e. diffusion and advection). Some recent soil respiration studies suggest that the physical aspects of the soil have an equal or greater influence on the measurement and interpretation of soil respiration data.

Here a combination of numerical and analytical models, laboratory experiment and field studies are used to help understand the effect that soil physics has on the measurement and interpretation of soil respiration data. These analyses focus mainly on high-resolution and istopologue techniques for understanding soil respiration, and how considerations including gas diffusion and thermal conduction affect results obtained using these methods. The interpretation of soil respiration data is also carefully considered, again with a focus on how physical drivers can explain patterns in field measurements, and how physical and biological processes might be disentangled in GHG investigations. The results presented here show clearly how gaseous diffusion, thermal conduction and poor methodological assumptions can bias the measurement and interpretation of GHG emissions data. These biases and misinterpretations can often be resolved through application of physical principles and mathematical modelling The physical and mathematical approaches presented here from a basis for making robust measurements of GHG emissions and also for forming processes-based models that can be more universally applicable across space and time.

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Pages: 238
Supervisor: David Risk