Research about southern Oahu

USGS2026

Advancing compound coastal flood modeling on aouthern O’ahu, Hawai’i: A hybrid stochastic approach

Sea-level rise and changing patterns of storminess associated with climate change are expected to increase the frequency and severity of compound coastal flooding events, posing significant challenges to coastal communities in enhancing preparedness and adaptation strategies. This work presents a hybrid stochastic approach for the probabilistic assessment of compound coastal flooding. The stochastic climate emulator TESLA is employed to generate synthetic time series of oceanographic and hydrologic conditions, incorporating meteorologic-oceanographic drivers such as sea-level anomalies, tropical cyclones, storm surge, tides, wind-waves, and precipitation. These time series are downscaled using a hybrid statistical-numerical framework that integrates surrogate models of high-fidelity hydrodynamic simulators (e.g., Delft3D, SWAN, SWASH, SFINCS). The framework is applied to southern O’ahu, Hawai’i, to simulate flood exposure under present climate as well as various sea-level rise scenarios and climate projections. The framework is designed to support decision-making processes by facilitating the participatory development of dynamic adaptation pathways. By allowing for rapid evaluation of flood risks, the approach provides valuable insights for building resilient adaptation strategies for vulnerable coastal communities.

Hawaii↗

USGS2026

HyFlood: A surrogate-model-based framework for compound coastal flooding

Compound coastal flooding is a major threat to low-lying coastal regions and is expected to intensify under future climate change projections. However, modeling the joint interaction of waves, storm surge, tides, and rainfall remains computationally demanding, limiting the development of fast and reliable forecast tools. Here we present HyFlood, a hybrid statistical-numerical downscaling framework capable of computing and mapping high-resolution compound flood hazards while substantially reducing the computational cost compared with fully process-based hydrodynamic modeling. HyFlood combines statistical sampling and selection algorithms with a cascade of reduced-complexity surrogate models that emulate nearshore wave transformation, surf-zone hydrodynamics, and coastal, fluvial, and pluvial flooding. The surrogate models employ machine-learning and regression algorithms applied to a low-dimensional representation of the flooding outputs, obtained through statistical dimensionality reduction. The framework is demonstrated in southern O'ahu, Hawai'i, a region exposed to elevated sea levels driven by tides, waves, and storm surge along with frequent precipitation-driven flash flooding. Validation of the surrogates against the physics-based model outputs demonstrates that HyFlood accurately reproduces daily maxima of spatially distributed flooding depths. This hybrid approach offers a scalable and efficient tool to better quantify how changes in flooding drivers translate into hazard and impact assessments, and to support compound-flood risk assessments and climate-change adaptation planning.

Hawaii↗