With the water sector moving towards a circular and digital future, there is a need for a systemic approach to tackle multi-disciplinary and multi-objective challenges that the future brings. Developing holistic and large scale digital twins (e.g., for cities or industrial zones) provides a valuable tool for the stakeholders to improve their decision-making process. The heterogeneous and fragmented nature of the data and computational models in different sub-domains of the water system makes this development a challenging task which calls for an interoperable and transparent framework. My research aims at developing methodologies for interoperable decision support systems by using ontologies and knowledge graphs to create a dynamic digital twin that can provide a great opportunity for automated process optimization at a holistic scale.