Classical dendrochronology involves using standard statistical methods, such as correlation coefficients and t-values to crossmatch undated tree-ring width sequences to dated 'master' chronologies. This crossmatching process aims to identify the 'best' offset between the dated and undated sequences with a view to providing a calendar date estimate for the undated trees. Motivated by the successful and routine use of Bayesian statistical methods to provide a fully probabilistic approach to radiocarbon dating, this thesis investigates the practicality of using a process-based forward model known as 'VSLite' at the core of Bayesian dendrochronology. The mechanistic VSLite model has the potential to capture key characteristics of the complex system that links climate to tree-ring formation. It allows simulated, dated tree-ring chronologies to be generated at any geographical location where historical climate records exist. Embedding VSLite within a Bayesian approach to tree-ring dating allows combination of both ring-width data and any available prior information. Additionally, instead of identifying the `best' calendar date estimate, the Bayesian approach allows provision of probabilistic statements about a collection of possible dates, each with a specific (posterior) probability. The impact of uncertainty in the VSLite input parameters on the model output has been systematically investigated in this thesis, and the VSLite-based approach to Bayesian tree-ring dating has been explored using both simulated and real data. Results of implementing the new VSLite-based approach are compared with those using current classical and Bayesian approaches. An option for reducing the need for preprocessing data is also investigated via a data-adaptive rescaling approach. Having established the effectiveness of using the mechanistic forward model as the core for Bayesian dendrochronology, the practicality of adopting it to aid in dating in the absence of suitable local master chronologies is also explored.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:698834 |
| Date | January 2016 |
| Creators | Haasan, Masoud |
| Contributors | Buck, Caitlin |
| Publisher | University of Sheffield |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://etheses.whiterose.ac.uk/15746/ |
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