Addressing global challenges such as coastal hazards and climate change requires innovative tools capable of analyzing complex environmental drivers, including waves, storm surges, and cyclones, across varying scales. These tools are vital for predicting floods, assessing risks, and planning adaptive responses. BlueMath-Hub has been developed as a global collaborative initiative to provide accessible, customizable solutions for both researchers and practitioners. It aims to simplify the use of advanced statistical and numerical models, fostering creative and scalable approaches in coastal science and engineering. BlueMath, the core of this platform, is an open-source repository of Python tools accessible via a cloud-based Jupyter Hub environment. It integrates statistical methods and numerical model wrappers within a modular framework. The system includes: (a) BlueMath-Toolkit, providing tools for data mining, interpolation, and model integration; (b) BlueMath-Statistical Downscaling, focusing on extreme events and generalized models; (c) BlueMath-Hybrid Downscaling, combining statistical and numerical approaches for optimized solutions; and (d) BlueMath-Climate Services, supporting integrated applications such as compound flooding assessments. BlueMath is continuously evolving, with its tools already applied in research, publications, and training. By lowering barriers to entry and enabling collaborative workflows, BlueMath-Hub supports the development of innovative solutions to mitigate the impacts of a changing climate.

The assessment of coastal flood risk on a reef-lined coastline presents several challenges. From the probabilistic side, we need to consider all possible events that could occur in the system, taking into account the different contributions of waves, storm surges, and tides that contribute to the total water level. To estimate reliable flood extents, we need to accurately model the complex wave processes that occur across the reef. To explore the multivariate nature of coastal flooding, we rely on a climate emulator that accounts for climate variability and simulates time series of all the variables involved. Due to the computational constraints to numerically simulate thousands of events, we explore the feasibility of using a recently developed tool, the HyCreWW (Hybrid Coral Reef Wave and Water level) meta-model to estimate wave run-up and flooding extents. Limitation of a 1D assessment are analyzed and results compared with 2D modeling.