  This paper presents a model based adaptive monitoring method for the
estimation of flow tracers, with application to mapping, prediction and
observation of oil spills in the immediate aftermath of an incident. Autonomous
agents are guided to optimal sensing locations via the solution of a PDE
constrained optimisation problem, obtained using the adjoint method. The
proposed method employs a dynamic model of the combined ocean and oil dynamics,
with states that are updated in real-time using a Kalman filter that fuses
agent-based measurements with a reduced-order model of the ocean circulation
dynamics. In turn, the updated predictions from the fluid model are used to
identify and update the reduced order model, in a process of continuous
feedback. The proposed method exhibits a 30% oil presence mapping and
prediction improvement compared to standard industrial oil observation sensor
guidance and model use.
