Hybrid surrogate modeling of compound flood events using SFINCS-LSG

Compound floods arise from interacting drivers such as rainfall, river discharge, and coastal surge, posing challenges for risk assessment due to their stochastic nature. Traditional hydrodynamic models, while accurate, are computationally expensive for ensemble forecasting and probabilistic analysis. Surrogate models offer a promising alternative, but most existing approaches focus on single drivers or static flood conditions, limiting their applicability to compound events. The hybrid SFINCS–LSG surrogate model addresses these gaps by integrating low-resolution SFINCS simulations with Empirical Orthogonal Function (EOF) decomposition and Sparse Gaussian Process learning to emulate high-resolution flood dynamics. Two case studies, Charleston, USA, and Brisbane, Australia, were selected to evaluate model performance under diverse flood conditions. Training datasets were generated by scaling historical events decomposed to individual flood drivers to ensure coverage of diverse flood conditions. Model skill was assessed against high-fidelity SFINCS simulations using Critical Success Index (CSI) for flood extent and Root Mean Square Error (RMSE) for flood depth. Our results showed that SFINCS–LSG achieved speed-ups of 50–150× compared to high-fidelity SFINCS simulations with good accuracy. The median RMSE for flood depth was 0.06 m for the Charleston and a CSI of 0.96 and 0.91 respectively. However, performance varied by flood type due to large variability in extent between coastal and compound or pluvial-fluvial events. The compression of spatial information through EOF analysis introduced noise, which constrained the model’s ability to reproduce dominant flood driver zones. Despite these limitations, the approach demonstrates potential for real-time forecasting and probabilistic risk analysis where many simulations are required. This research advances state-of-the art surrogate models by capturing dynamic spatiotemporal flood evolution under multi-driver conditions rather than static peak inundation. Overall, the SFINCS–LSG framework offers a scalable solution for accelerating compound flood modelling at very limited loss of accuracy.