Baird developed an empirical model for the prediction of existing and future extreme sea levels for a First Nations community on Chaleur Bay to address ongoing concerns of flooding and develop recommendations to be considered in the planning of future developments. Initially, a numerical model of storm surge in the Chaleur Bay region was developed. Large scale regional processes emanating from the Gulf of St. Lawrence caused modelling challenges within the Bay, yielding mixed success in simulating recorded events due to the lack of data at the model boundary. Baird used this model along with available data in the region to gain a deeper understanding of key processes at the site, which aided the development of the empirical model. From the cumulative distribution functions (CDFs) for predicted tide and sea level residuals, randomly sampled values were combined with randomly sampled storm surge events (wind and pressure set up) to simulate any number of random events. Uncertainty in future sea level rise (SLR) was considered by assigning a probability to selected representative concentration pathway scenarios to develop composite CDFs. By randomly selecting a date within a target time frame, SLR estimates are sampled from the composite CDFs and applied to the randomly generated extreme water level events. Numerous iterations of these random samples provided an indication of confidence levels with this sampling method. This is a valuable approach that can be used when numerical modelling scope is limited and when the nearby water level record is short (
