<p>It is important to quantify multimodal uncertainties, associated with the monitoring and the management of natural resources e.g., ocean wave and wind energy. Current case the study offers a state-of-the-art methodology for multidimensional environmental/structural systems damage risk and natural hazard prognostics. Proposed reliability approach has been specifically designed for the analysis of quasi-stationary, multi-dimensional engineering systems (both environmental and structural), that were either been simulated numerically over a representative period, or were physically monitored. Presented case study shows that relatively accurate forecasts of the system’s hazard or failure probability/risk are attainable even given a limited underlying dataset. Due to nonstationary and nonlinear correlations between system’s essential elements (or dimensions), high dimensional environmental/structural systems are not easily treated by to existing reliability and risk assessment techniques. Risk assessment being important design issue for marine, naval and offshore structures, operating in particular ocean regions of interest, occasionally encountering adverse weather conditions. Advocated multimodal risk evaluation methodology makes it possible to forecast natural hazards for nonlinear high-dimensional dynamic environmental and structural systems robustly and effectively. Ability to assess risks for spatiotemporal environmental systems, possessing number of interconnected components higher than two, i.e., beyond bivariate systems, being the primary advantage of the advocated novel Gaidai hazard/risk evaluation methodology. Artificial intelligence pattern recognition features of the underlying windspeed dataset are briefly discussed.</p>