Water circulation and impact on water quality in the southwest of Efate Island, Vanuatu.

In Small Island Developing States (SIDS), water pollution is not monitored or assessed frequently enough to fully understand the processes, impacts of water quality issues and what solutions are available This study investigated flushing time in Erakor lagoon and Port Vila Bay, Vanuatu using a numerical model developed in Delft3D. Microbial contamination by Escherichia coli was detected in multiple locations in the lagoon system with counts exceeding thresholds related to human health concerns. Modelling demonstrated a poor flushing time overall with a further decrease as the influence of waves and wind increased, especially in Vila Bay. Sea level rise resulted in an increase in flushing time downstream of the lagoon near the open sea, while with a decrease upstream and in Vila Bay. Based on these results, we recommend long-term continuous monitoring and identification of higher risks areas to prioritise decisions around wastewater management.

Spatial Bayesian Network for predicting sea level rise induced coastal erosion in a small Pacific Island.

An integrated approach combining Bayesian Network with GIS was developed for making a probabilistic prediction of sea level rise induced coastal erosion and assessing the implications of adaptation measures. The Bayesian Network integrates extensive qualitative and quantitative information into a single probabilistic model while GIS explicitly deals with spatial data for inputting, storing, analysing and mapping. The integration of the Bayesian Network with GIS using a cell-by-cell comparison technique (aka map algebra) provides a new tool to perform the probabilistic spatial analysis. The spatial Bayesian Network was utilised for predicting coastal erosion scenarios at the case study location of Tanna Island, Vanuatu in the South Pacific. Based on the Bayesian Network model, a rate of the island shoreline change was predicted probabilistically for each shoreline segment, which was transferred into GIS for visualisation purposes. The spatial distribution of shoreline change prediction results for various sea level rise scenarios was mapped. The outcomes of this work support risk-based adaptation planning and will be further developed to enable the incorporation of high resolution coastal process models, thereby supporting localised land use planning decisions.