Geology topics

Alba Ricondo

Publications and source records attributed to Alba Ricondo.

3 recordsLinked to original sources

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↗

USGS2023

HyWaves: Hybrid downscaling of multimodal wave spectra to nearshore areas

Long-term and accurate wave hindcast databases are often required in different coastal engineering projects. The assessment of the nearshore wave climate is often accomplished by using downscaling techniques to translate offshore waves to coastal areas. However, dynamical downscaling approaches may incur huge computational cost. Additionally, the common use of bulk parameterizations are often not accurate for multidimensional waves. To overcome these limitations, we present a hybrid downscaling approach that combines mathematical algorithms (statistical downscaling) and numerical modeling (dynamical downscaling) over the individual spectral partitions. Every wave partition is downscaled and aggregated afterward by using principles of wave linear theory. By assuming linearity in the propagation of the wave celerity, the application of the method is limited from offshore to intermediate water depths. In addition, the method proposed uses a technique to simplify the spectral boundary conditions in complex domains. The methodology has been applied and validated in the island states of Samoa, American Samoa, Majuro, and Kwajalein, showing good skill at reproducing the spectral hourly time series of significant wave height, peak period, and peak direction. Moreover, an accurate representation of the observed energy spectrum was achieved. This study provides insight into the numerical approximation of the combined sea-swell states while improving the quality of fast spectral forecasting and early warning systems.

Ocean Modeling↗