Assessment of climate change impacts on wind characteristics is crucial for the design, operation, and maintenance of coastal and offshore infrastructures. In the present study, the Model Output Statistics (MOS) method was used to downscale a Coupled Model Intercomparison Project Phase 5 (CMIP5) with General Circulation Model (GCM) results for a case study in the North Atlantic Ocean, and a supervised machine learning method (M5’ Decision Tree model) was developed for the first time to establish a statistical relationship between predicator and predicant. To do so, the GCM simulation results and altimeter remote sensing data were employed to examine the capabilities of the M5’DT model in predicting future wind speed and identifying spatiotemporal trends in wind characteristics. For this purpose, three classes of M5′ models were developed to study the annual, seasonal, and monthly variations of wind characteristics. The developed decision tree (DT) models were employed to statistically downscale the Beijing Normal University Earth System Model (BNU-ESM) global climate model output. The M5′ models are calibrated and successfully validated against the GCM simulation results and altimeter remote sensing data. All the proposed models showed firm outputs in the training section. Predictions from the monthly model with a 70/30 training to test ratio demonstrated the best model performance. The monthly prediction model highlighted the decreasing trend in wind speed relative to the control period in 2030 to 2040 for the case study location and across all three future climate change scenarios tested within this study. This reduction in wind speed reduces wind energy by 13% to 19%.

A Lagrangian particle-based two-phase flow model is developed to simulate the scouring process induced by standing wave in front of the trunk section of a vertical breakwater. Given the two-dimensional nature of the scouring problem at the trunk of vertical wall, the fluid phase is simulated with two-dimensional Navier–Stokes equations based on weakly compressible smoothed particle hydrodynamics (WCSPH) formulation in conjunction with sub-particle scale (SPS) turbulence closure model. The sediment phase is simulated using the Discrete Element Method (DEM). The effects of interparticle and particle-wall collisions are computed by activating a spring-dashpot system. The WCSPH fluid-phase and DEM sediment-phase are coupled through a weakly one-way coupling procedure using wave orbital velocity. The numerical model is successfully validated against experimental data. The maximum scour depth predicted by WCSPH-DEM model is closely approximate the experimental data. This study, for the first time, demonstrated an extra recirculating sediment transport mechanism in front of the vertical breakwater, similar to steady streaming recirculating cells in the fluid phase, which has a direct impact on the formation of scour hole and maximum scour depth at the breakwater trunk. The scenario modeling conducted in this study show that by increasing the steady streaming velocity, the deposition rate and the depth of scour hole were increased.