Underwater environments may vary gradually even when the occurrence of events is detected. Sensory data may follow a certain trend and are predictable during certain time durations. Taking these into consideration, a simple but practical data prediction mechanism is adopted for estimating sensory data and the geographical location of sensor nodes at sink nodes, and these data are synchronized with those sensed by underwater sensor nodes only when their variation is beyond a prespecified threshold. Leveraging these predicted data, the coverage and sources of potential events are identified by the sink node, and the evolution of these events is determined accordingly. Evaluation results show the applicability and energy-efficiency of this approach, especially when the variation of network environments follows certain and simple patterns