A Coupled Hydrodynamic–Optimization Framework for Flood Prediction and Green Gray Stormwater Management: Multi Objective Trade Offs in a Tropical Catchment
Abstract
Tropical coastal cities in Southeast Asia are highly vulnerable to pluvial and fluvial flooding, which is exacerbated by rapid upland land use change and a reduction in natural water retention. This study develops and applies a novel decision support tool that couples a JAX accelerated diffusive wave solver for shallow water equation (SWE) with a multi objective optimization framework inspired by the Non dominated Sorting Genetic Algorithm II (NSGA II). The model is designed to identify the optimal location and sizing of upland retention basins, balancing two competing objectives: minimizing peak flood depth and maximizing groundwater recharge. Applied to a synthetically realistic representation of Roxas City, Philippines, the framework reveals a clear “mitigation ceiling” under extreme storm events. For the 150 mm/h 1 in 50 year event, the peak flood depth stabilizes at 0.589 m, irrespective of the specific Pareto optimal configuration, indicating that the current basin network has reached its volumetric limit for peak reduction. Nevertheless, the “Max Recharge” strategy achieves the same flood safety as the “Min Depth” strategy while capturing an additional ~1,000 m³ of water (~20% more), demonstrating that it is the superior all round solution for both flood mitigation and water security. The proposed methodology known as SHOMS has been piloted as a project known as Hydrointelligence System (HIS) in Roxas City; where a transferable, physics based, multi objective planning tool that supports local government units in the Philippines in designing nature based stormwater management interventions is being implemented and such can be replicated across the world.