Bayesian generalised additive models for quantifying sea-level change: Methods and Software

• Main Author: Upton, Maeve
• Format: Thesis
• Language: English
• Published: 2023
• Subjects: North Atlantic
• Online Access: https://mural.maynoothuniversity.ie/18319/; https://mural.maynoothuniversity.ie/18319/1/Thesis_MaeveUpton.pdf

Rising sea levels pose significant risks to coastal regions worldwide, and the 2021 Intergovernmental Panel on Climate Change AR6 report emphasised that rates of sea-level rise are the fastest in at least the last 3000 years. To understand historical sea-level trends at regional and local scales, it is crucial to analyse the drivers of sea-level change and their potential impacts. The influence of these different drivers interact at a range of spatial (global, regional, local level) and temporal (annual to millennia) scales. The development of a statistical model that seeks to estimate a number of these characteristics would be of immeasurable value to the sea level and climate impact communities. These characteristics would include: exhibiting flexibility in time and space; having the capability to examine the separate drivers; and taking account of uncertainty. The aim of our project is to develop statistical models to examine historic sea-level changes for North America’s Atlantic coast and extend to the North Atlantic region, incorporating Ireland’s coastline. For our models, we utilise sea-level proxies and tide gauge data which provide relative sea level estimates with uncertainty. Proxy data can reconstruct sea-level variations over the late Holocene, spanning the last 2000 years, providing a valuable pre-anthropogenic context for understanding historical relative sea-level changes. We study a range of statistical models used to examine relative sea-level data accounting for uncertainty and varying in space and time. The statistical approaches employed range from simple linear regressions to advanced Bayesian Generalised Additive Models (GAMs), which allow separate components of sea-level change to be modelled individually and efficiently and for smooth rates of change to be calculated. Our most advanced models are built in a Bayesian framework which allows for external prior information to constrain the evolution of sea-level change over space and time. To investigate the drivers of sea-level change, we ...