Wave-propelled unmanned surface vehicles (USVs) rely on the forces exerted by the environment to navigate as intended. The unpredictability of the environment and the low speed and limited maneuverability that characterizes such platforms motivates the need of a continuous monitoring of both static and dynamic obstacles over a long time horizon. In this manuscript we present an automatic anti-collision and anti-grounding system that integrates digital charts and automatic identification system (AIS) messages received onboard to enhance the situational awareness perceived by a wave-propelled USV, and enable evasive maneuvers to avoid grounding and collisions with static and dynamic obstacles. The acquired information is used in a scenario-based model predictive control (SB-MPC) algorithm that computes optimal behaviors that minimize the risk of collisions, grounding and damage. Additionally, the proposed system integrates environmental factors (wind, waves and sea currents) in the SB-MPC optimization process, in order to command the safest behavior when high sea states increase the risk of drifting and grounding. The information contained in Electronic Navigational Charts (ENCs) is represented with point clouds, enabling fast software manipulation of large areas and therefore increasing the USV responsiveness when scenarios with static obstacles turn into dynamic collision avoidance scenarios. The proposed anti-collision and anti-grounding system is tested in simulations and field experiments.