Quantifying uncertainty in the temporal disposition of groundwater inundation under sea level rise projections

Over the next century, coastal regions are under threat from projected rising sea levels and the potential emergence of groundwater at the land surface (groundwater inundation). The potential economic and social damages of this largely unseen, and often poorly characterised natural hazard are substantial. To support risk-based decision making in response to this emerging hazard, we present a Bayesian modelling framework (or workflow), which maps the spatial distribution of groundwater level uncertainty and inundation under Intergovernmental Panel on Climate Change (IPCC) projections of Sea Level Rise (SLR). Such probabilistic mapping assessments, which explicitly acknowledge the spatial uncertainty of groundwater flow model predictions, and the deep uncertainty of the IPCC-SLR projections themselves, remains challenging for coastal groundwater systems. Our study, therefore, presents a generalisable workflow to support decision makers, that we demonstrate for a case study of a low-lying coastal region in Aotearoa New Zealand. Our results provide posterior predictive distributions of groundwater levels to map susceptibility to the groundwater inundation hazard, according to exceedance of specified model top elevations. We also explore the value of history matching (model calibration) in the context of reducing predictive uncertainty, and the benefits of predicting changes (rather than absolute values) in relation to a decision threshold. The latter may have profound implications for the many at-risk coastal communities and ecosystems, which are typically data poor. We conclude that history matching can indeed increase the spatial confidence of posterior groundwater inundation predictions for the 2030-2050 timeframe.