School of Earth Sciences - Theses
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ItemInverse modeling of regional CO2 surface fluxes in the Latrobe Valley/ Otway basin( 2019)Carbon dioxide (CO2) is the most important greenhouse gas (GHG) attributable to human activity. Many natural components are responsible for the carbon emissions in the atmosphere. Therefore, large uncertainties exist in the current carbon budget. Using the atmospheric transport model and inverse techniques, we can estimate the carbon sources and sinks from the atmospheric CO2 measurements. The current global network is quite efficient in constraining the global carbon emissions. However, to obtain detailed knowledge about the controlling processes and better understanding of the carbon cycle, estimates of regional fluxes are important. To constrain the regional fluxes, it is important to capture the right signals at the right time. Therefore, continuous monitoring of atmospheric CO2 has been an essential part of the project. An optimal network design indicates the best location for the atmospheric monitoring units to be placed in the given domain. For our network design study, we have used the Lagrangian Particle Dispersion Model (LPDM), driven by mean wind velocity, turbulent kinetic energy and potential temperature. The necessary fields to drive LPDM are taken from Weather Research Forecasting (WRF) Model. Since, the input fields for LPDM are dependant on the output of the WRF model. On that account, it is important to choose an appropriate planetary boundary layer (PBL) scheme, which can produce all the required fields to drive LPDM. Scarcity of measurements and inaccurate representation of vertical transport within the planetary boundary layer in atmospheric transport can also lead to large uncertainties. Therefore, selection of an appropriate planetary boundary layer scheme to represent realistic atmospheric transport is indispensable. This study covers three main aspects: 1. Comparison between different planetary boundary layer schemes available in the Weather Research Forecasting (WRF) model. 2. Obtain an optimal network to constrain the greenhouse gas emissions in the Latrobe Valley. 3. Constraining the carbon sources and sinks in the Otway basin. We have used Bayesian synthesis inversion and incremental optimization to obtain an optimal network. The stations towards the central Latrobe Valley and east of the given domain would be able to constrain emissions from the power plants and major towns in the Latrobe Valley. As a proof of concept, we demomstrate the methodology using observations. We constrain emissions in the Otway Basin. For inversion, we adapt Bayesian synthesis approach. We further assessed the inverted fluxes with the flux tower data. Once we have data for the Latrobe Valley, we can use the same approach to estimate carbon fluxes.