This work presents a set-membership state estimator for autonomous surface vehicles, based on an augmented state including lumped disturbances. The position and orientation are assumed to be measured subject to bounded noises. A novel dynamical decomposition decouples the estimation problem into two simpler subproblems, for the rotational and positional dynamics. Then, under physically motivated assumptions about the vessel maximum velocities and acceleration rates, the estimator computes sets enclosing the positions, velocities, and lumped generalised disturbances gathering several kinds of modelling uncertainties. The sets are described by zonotopes. A set-based estimation of the lumped generalised disturbances paves the way toward an enhanced motion control scheme, where low-level controllers could compensate them, depending on the estimation accuracy. Several simulations with a well-known test-bed craft compare the performance of the proposed algorithm with a previous one from the literature under realistic environmental conditions.