Understanding the dynamics of bodies of water and their impact on the global environment requires sensing information over the full volume of water. We develop a gradient-based decentralized controller that dynamically adjusts the depth of a network of underwater sensors to optimize sensing for computing maximally detailed volumetric models. We prove that the controller converges to a local minimum. We implement the controller on an underwater sensor network capable of adjusting their depths. Through simulations and experiments, we verify the functionality and performance of the system and algorithm.