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In this paper, a hierarchical temporal multi-layer decentralised model predictive control (HTML-DMPC) approach for drinking water networks (DWN) is proposed. The upper temporal layer works with a daily scale and is in charge of achieving the global objectives, which correspond with the optimal selection of the sources and the path to the reservoirs. On the other hand, the lower temporal layer is in charge of manipulating the set-point of the actuators to satisfy the local objectives, i.e., the minimisation of the energy needed for pumping water to the reservoirs. The system decomposition is based on graph partitioning theory, which considers the graph representation of the DWN topology. The obtained system decomposition allows to establish a hierarchical flow of information between the MPC controllers. Hence, the proposed DMPC strategy results in a hierarchical-like scheme. Results obtained when used selected simulation scenarios show the effectiveness of the proposed control strategy in terms of system modularity, reduced computational burden and, at the same time, the admissible loss of performance in contrast to a centralised MPC (CMPC) strategy.