Autonomous surface vehicles (ASVs) are becoming more widely used in environmental monitoring applications. Due to the limited duration of these vehicles, algorithms need to be developed to save energy and maximize monitoring efficiency. This paper compares receding horizon path planning models for their effectiveness at collecting usable data in an aquatic environment. An adaptive receding horizon approach is used to plan ASV paths to collect data. A problem that often troubles conventional receding horizon algorithms is the path planner becoming trapped at local optima. Our proposed Jumping Horizon (J-Horizon) algorithm planner improves on the conventional receding horizon algorithm by jumping out of local optima. We demonstrate that the J-Horizon algorithm collects data more efficiently than commonly used lawnmower patterns, and we provide a proof-of-concept field implementation on an ASV with a temperature monitoring task in a lake.