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%.

Recent studies indicated that coastal green belts could not provide proper protection from extreme coastal flooding. Recent studies recommend employing a compound defense system of natural and artificial structures for extreme hazards. In this study, we introduce a new compound defense system consisting of coastal mangrove trees combined with reef ball modular structures. A series of laboratory experiments were conducted to investigate drag force reduction through the hybrid defense system. The hybrid defense system was subjected to a surge-type flow generated by a quickly lifting gate in a laboratory water tank. Within the experimental framework, the hydrodynamics of coastal flooding were described by the characteristics of the surge bore and the absorbed drag force. The obtained results show that the hybrid system effectively enhanced the absorbed bore drag forces and significantly improved the flow-damping performance.