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This literature review was developed for the Ministry of Foreign Affairs and Trade (MFAT) to provide background for the project Probabilistic Assessment of Coastal Inundation in the Pacific. Projected sea-level rise greatly differs depending on future emissions scenarios and ice sheet modelling uncertainties. When considering the impacts of sea-level rise, it is compounding factors, such as high tides, waves generated from tropical cyclones and storm surges, that pose the most significant threat to low-lying communities. However, consideration of compound events is often neglected in planning for sea-level rise. We take Tarawa, Kiribati, as a case study to examine how sea-level rise will likely impact low-lying atoll island communities and provide an overview of the strategies in place for mitigation and adaptation. Although there is a national strategy to manage future sea-level rise, resources for coastal assessment studies and maintaining and improving of coastal defence infrastructure remains limited. We then review the use of Bayesian network models and suggest some advantages of these models for application to this project (The author)

Predictive understanding of precipitation δ2H and δ18O in New Zealand faces unique challenges, including high spatial variability in precipitation amounts, alternation between subtropical and sub-Antarctic precipitation sources, and a compressed latitudinal range of 34 to 47 °S. To map the precipitation isotope ratios across New Zealand, three years of integrated monthly precipitation samples were acquired from >50 stations. Conventional mean-annual precipitation δ2H and δ18O maps were produced by regressions using geographic and annual climate variables. Incomplete data and short-term variation in climate and precipitation sources limited the utility of this approach. We overcome these difficulties by calculating precipitation-weighted monthly climate parameters using national 5-km-gridded daily climate data. This data plus geographic variables were regressed to predict δ2H, δ18O, and d-excess at all sites. The procedure yields statistically-valid predictions of the isotope composition of precipitation (long-term average root mean square error (RMSE) for δ18O = 0.6 ‰; δ2H = 5.5 ‰); and monthly RMSE δ18O = 1.9 ‰, δ2H = 16 ‰. This approach has substantial benefits for studies that require the isotope composition of precipitation during specific time intervals, and may be further improved by comparison to daily and event-based precipitation samples as well as the use of back-trajectory calculations.