University of Cape Town
Regional CO₂ flux estimates for South Africa through inverse modelling.
Degree: PhD, Statistical Sciences, 2018, University of Cape Town
Bayesian inverse modelling provides a top-down technique of verifying emissions and uptake of carbon dioxide (CO₂) from both natural and anthropogenic sources. It relies on accurate measurements of CO₂ concentrations at appropriately placed sites and "best-guess" initial estimates of the biogenic and anthropogenic emissions, together with uncertainty estimates. The Bayesian framework improves current estimates of CO₂ fluxes based on independent measurements of CO₂ concentrations while being constrained by the initial estimates of these fluxes. Monitoring, reporting and verification (MRV) is critical for establishing whether emission reducing activities to mitigate the effects of climate change are being effective, and the Bayesian inverse modelling approach of correcting CO₂ flux estimates provides one of the tools regulators and researchers can use to refine these emission estimates. South Africa is known to be the largest emitter of CO₂ on the African continent. The first major objective of this research project was to carry out such an optimal network design for South Africa. This study used fossil fuel emission estimates from a satellite product based on observations of night-time lights and locations of power stations (Fossil Fuel Data Assimilations System (FFDAS)), and biogenic productivity estimates from a carbon assessment carried out for South Africa to provide the initial CO₂ flux estimates and their uncertainties. Sensitivity analyses considered changes to the covariance matrix and spatial scale of the inversion, as well as different optimisation algorithms, to assess the impact of these specifications on the optimal network solution. This question was addressed in Chapters 2 and 3. The second major objective of this project was to use the Bayesian inverse modelling approach to obtain estimates of CO₂ fluxes over Cape Town and surrounding area. I collected measurements of atmospheric CO₂ concentrations from March 2012 until July 2013 at Robben Island and Hangklip lighthouses. CABLE (Community Atmosphere Biosphere Land Exchange), a land-atmosphere exchange model, provided the biogenic estimates of CO₂ fluxes and their uncertainties. Fossil fuel estimates and uncertainties were obtained by means of an inventory analysis for Cape Town. As an inventory analysis was not available for Cape Town, this exercise formed an additional objective of the project, presented in Chapter 4. A spatially and temporally explicit, high resolution surface of fossil fuel emission estimates was derived from road vehicle, aviation and shipping vessel count data, population census data, and industrial fuel use statistics, making use of well-established emission factors. The city-scale inversion for Cape Town solved for weekly fluxes of CO₂ emissions on a 1 km × 1 km grid, keeping fossil fuel and biogenic emissions as separate sources. I present these results for the Cape Town inversion under the proposed best available configuration of the Bayesian inversion framework in Chapter 5. Due to the large number of CO₂ sources at this…
Advisors/Committee Members: Rayner, Peter (advisor), Scholes, Bob (advisor), Erni, Birgit (advisor), Underhill, Leslie G (advisor).
Subjects/Keywords: Bayesian inverse modelling
to Zotero / EndNote / Reference
APA (6th Edition):
Nickless, A. (2018). Regional CO₂ flux estimates for South Africa through inverse modelling. (Doctoral Dissertation). University of Cape Town. Retrieved from http://hdl.handle.net/11427/29703
Chicago Manual of Style (16th Edition):
Nickless, Alecia. “Regional CO₂ flux estimates for South Africa through inverse modelling.” 2018. Doctoral Dissertation, University of Cape Town. Accessed November 23, 2020.
MLA Handbook (7th Edition):
Nickless, Alecia. “Regional CO₂ flux estimates for South Africa through inverse modelling.” 2018. Web. 23 Nov 2020.
Nickless A. Regional CO₂ flux estimates for South Africa through inverse modelling. [Internet] [Doctoral dissertation]. University of Cape Town; 2018. [cited 2020 Nov 23].
Available from: http://hdl.handle.net/11427/29703.
Council of Science Editors:
Nickless A. Regional CO₂ flux estimates for South Africa through inverse modelling. [Doctoral Dissertation]. University of Cape Town; 2018. Available from: http://hdl.handle.net/11427/29703