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A hierarchical and decentralised model predictive control (DMPC) strategy for drinking water networks (DWN) is proposed. The DWN is partitioned into a set of subnetworks using a partitioning algorithm that makes use of the topology of the network, historic information about the actuator usage and heuristics. A suboptimal DMPC strategy was derived, which consists in a set of MPC controllers, whose prediction model is a plant partition, where each element solves its control problem in a hierarchical order. A comparative simulation study between centralised MPC (CMPC) and DMPC approaches is developed using a case study, which consists in an aggregate version of the Barcelona DWN. Results have shown the effectiveness of the proposed DMPC approach in terms of the scalability of computations with an acceptable admissible loss of performance in all the considered scenarios.