The main objective here is to collate all the relevant datasets that are required for the project execution. Datasets already exist for a few regional sites, and these will require assessment to assure adequate quality control and improved assurance of project outputs. The selection of other datasets will be based on availability, suitability, and viability. Where data gaps exist, they will be identified and addressed using physical modelling or short-term primary data collection, as is feasible within the project scope. Some specific activities will include: retrieving and collating any existing primary and secondary data; performing data quality checks on retrieved datasets; processing data to assess spatial and temporal extent; performing preliminary correlations within datasets; identifying data gaps; filling these gaps to produce coherent datasets; planning any short-term primary data collection campaigns; performing any physical modelling; and collating all processed and summarized datasets in a project-team-only data repository. This research task ensures the quality of the input to other research activities and, as such, dictates the quality of the output.

The main objective here is to disseminate the output from the project to a wider regional audience. Because it is essential to reach a diverse audience, a range of mechanisms will be employed. The team will be expected to prepare and submit open-access publications in reputable journals, collate & disseminate any raw or summarized data online and on request, and attend international conferences. To reach a wider stakeholder group, we also intend to hold online and in-person stakeholder meetings. To engage younger audiences, we hope to host an art competition and to launch a 5-part animated series that sensitizes young persons across the Caribbean to the ways they can shape the flooding hazard, while teaching some foundational concepts in a fun and engaging manner. 

The main objective of the numerical modelling to develop and compare deterministic and probabilistic modelling frameworks that estimate likely flood extents. Both modelling approaches will be executed at a field site, and the results contrasted to illustrate the advantages and disadvantages of each technique. Some activities will include: deterministic modelling of flood extent using process-based tools such as MIKE21, including the calibration and validation of these models; the development of a probabilistic technique, most likely building on previous work by the lead investigator in which a Monte Carlo simulation is used to yield possible outcomes of a flooding event given a range of input parameters; the subsequent comparison of the outputs and methods of each modelling technique; and finally, summarizing all the output.

The main objective of this research task is to use the collected data to derive formulations or parametric expressions that link response variables to independent variables. Response variables may include flooded area, wave height, or water velocity, and independent variables will encompass parameters such as wind speed, offshore waves, and ecosystem characteristics. Formulations will be derived through multi-variate analyses on datasets that may be augmented with field-scale modelling or CFD analyses. Field-scale models will also serve to calibrate and validate these formulations, as well as to demonstrate their relevance. Some activities will include: augmenting datasets using numerical modelling; developing generalized formulations from the patterns and trends in the data; calibrating and validating the generated expressions; applying them to a test field site; summarizing the output from these analyses, including limitations and recommendations; and collating all processed and summarized datasets.