Feasibility Study for the Extraction of Wave Energy along the Coast of Ensenada, Baja California, Mexico

Mexico is one of the countries with the highest emissions of greenhouse gases. In order to reduce the emission of contaminants due to fossil fuels, the state of Baja California has recently launched several research projects for the optimization of facilities for the exploitation of renewable sources, and in particular wave energy. In this work a first-level feasibility study of energy extraction from wave motion is presented for the Ensenada coast, along a complex distance of more than 200 km. The methodology proposed provides good spatial and temporal resolution for wave heights and periods calculation and consequently for the wave power. The methodology is based on the application of the coupled Simulated Waves Nearshore and Advanced Circulation (SWAN + ADCIRC) model for generation, propagation and dissipation of waves. To take into account the meteorological variability within a 21-year dataset, the Typical Meteorological Year method was applied. Results show that overall, the most persistent energy potential during the year is >2 kW/m, with peaks of 5 and 10 kW/m during few months. Given the theoretical energy potential calculated, the Ensenada coast could produce hundreds of GWh per year. The proposed methodology can be applied for the exploration of other coasts with energy potential.

Developing a Modeling Framework to Simulate Compound Flooding: When Storm Surge Interacts With Riverine Flow

Compound flooding is a physical phenomenon that has become more destructive in recent years. Moreover, compound flooding is a broad term that envelops many different physical processes that can range from preconditioned, to multivariate, to temporally compounding, or spatially compounding. This research aims to analyze a specific case of compound flooding related to tropical cyclones where the compounding effect is on coastal flooding due to a combination of storm surge and river discharge. In recent years, such compound flood events have increased in frequency and magnitude, due to a number of factors such as sea-level rise from warming oceans. Therefore, the ability to model such events is of increasing urgency. At present, there is no holistic, integrated modeling system capable of simulating or forecasting compound flooding on a large regional or global scale, leading to the need to couple various existing models. More specifically, two more challenges in such a modeling effort are determining the primary model and accounting for the effect of adjacent watersheds that discharge to the same receiving water body in amplifying the impact of compound flooding from riverine discharge with storm surge when the scale of the model includes an entire coastal line. In this study, we investigated the possibility of using the Advanced Circulation (ADCIRC) model as the primary model to simulate the compounding effects of fluvial flooding and storm surge via loose one-way coupling with gage data through internal time-dependent flux boundary conditions. The performance of the ADCIRC model was compared with the Hydrologic Engineering Center- River Analysis System (HEC-RAS) model both at the watershed and global scales. Furthermore, the importance of including riverine discharges and the interactions among adjacent watersheds were quantified. Results showed that the ADCIRC model could reliably be used to model compound flooding on both a watershed scale and a regional scale. Moreover, accounting for the interaction of river discharge from multiple watersheds is critical in accurately predicting flood patterns when high amounts of riverine flow occur in conjunction with storm surge. Particularly, with storms such as Hurricane Harvey (2017), where river flows were near record levels, inundation patterns and water surface elevations were highly dependent on the incorporation of the discharge input from multiple watersheds. Such an effect caused extra and longer inundations in some areas during Hurricane Harvey. Comparisons with real gauge data show that adding internal flow boundary conditions into ADCIRC to account for river discharge from multiple watersheds significantly improves accuracy in predictions of water surface elevations during coastal flooding events.

A Comparison of Ensemble Kalman Filters for Storm Surge Assimilation

This study evaluates and compares the performances of several variants of the popular ensemble Kalman filter for the assimilation of storm surge data with the advanced circulation (ADCIRC) model. Using meteorological data from Hurricane Ike to force the ADCIRC model on a domain including the Gulf of Mexico coastline, the authors implement and compare the standard stochastic ensemble Kalman filter (EnKF) and three deterministic square root EnKFs: the singular evolutive interpolated Kalman (SEIK) filter, the ensemble transform Kalman filter (ETKF), and the ensemble adjustment Kalman filter (EAKF). Covariance inflation and localization are implemented in all of these filters. The results from twin experiments suggest that the square root ensemble filters could lead to very comparable performances with appropriate tuning of inflation and localization, suggesting that practical implementation details are at least as important as the choice of the square root ensemble filter itself. These filters also perform reasonably well with a relatively small ensemble size, whereas the stochastic EnKF requires larger ensemble sizes to provide similar accuracy for forecasts of storm surge.

WAVE MODELING WITH SWAN+ADCIRC FOR THE SOUTH CAROLINA COASTAL STORM SURGE STUDY

The South Carolina Surge Study (SCSS) used the tightly coupled SWAN+ADCIRC model to simulate tropical storm surge events. The tightly coupled model allowed calculation of wave-induced water level changes within the storm surge simulations. Inclusion of the wave-induced water level changes represents a more physics-based approach than previous methods that added wave setup after model simulations ended. Development of the SWAN+ADCIRC model included validation of water levels to local tidal forcing and for three historical hurricanes — Hazel (1954), Hugo (1989), and Ophelia (2005). The validation for waves did not include Hurricane Hazel because measured data was unavailable. Additional comparisons with WAM model results provided supplemental support to the SWAN model results. Model output applied in comparisons included contour plots of maximum wave parameters, time series of wave parameters at selected locations, and wave spectra.