The water sector is facing emerging challenges, as cyber-physical threats target Supervisory Control and Data Acquisition (SCADA) systems of water utilities. A cyber-physical stress-testing platform is presented in this work, named RISKNOUGHT, which is able to model water distribution networks as cyber-physical systems, simulating the information flow of the cyber layer and the feedback interactions with the physical processes under control. RISKNOUGHT utilizes an EPANET-based solver for the physical process and a customizable network model for the SCADA system, capable of implementing complex control logic schemes within a simulation. The platform enables the development of composite cyber-physical attacks on various elements of the SCADA, including sensors, actuators, and PLCs, assessing the impact they have on the hydraulic response of the distribution network and the level of service. The platform is tested on a proof-of-concept benchmark network with promising results that demonstrate that the platform can form an innovative cyber-physical tool to support strategic planning and risk management.

Centralized urban water management currently faces multiple challenges, both at the supply side and the demand side. These challenges underpin the need to progress to the decentralization of urban water, where multiple distributed technologies (water-aware appliances, rainwater harvesting, greywater recycling, sustainable urban drainage) are applied in an integrated fashion and as a supplement to centralized systems to design more resilient neighborhoods. However, the methods and tools to assess the performance of these distributed solutions and provide management support for integrated projects are still few and mostly untested in real, combined cases. This study presents a simulation-based framework for the quantitative performance assessment of decentralized systems at a neighborhood scale, where different technologies can be linked together to provide beneficial effects across multiple urban water cycle domains. This framework links an urban water cycle model, which provides a scenario-based simulation testbed for the response of the whole system, with key performance indicators that evaluate the performance of integrated decentralized solutions at a neighborhood scale. The demonstrated framework is applied to provide an ex ante evaluation of SUPERLOCAL, a newly developed area in Limburg, the Netherlands, designed as a circular, water-wise neighborhood where multiple decentralized technologies are combined.

Contrary to the ‘make-use-dispose’ linearity of conventional management, circular economy design principles have been proposed as a resource management alternative that reduces waste and promotes efficiency. These principles also find use in water management, offering an alternative against centralized models. Despite the intrinsic links between circularity and resilience, few studies have advanced the identification and discussion of linkage beyond a theoretical or conceptual level. This study presents quantitative links between circularity and resilience, by demonstrating how different circular water management strategies lead to improved resilience performance for a regional urban-rural water system. A stress-testing framework based on a water cycle model is presented, where different circular interventions are evaluated in terms of their overall resilience against future uncertainty. The results demonstrate that circular water options lead to more resilient water systems. The more circular dimensions are addressed through interventions, the more robust resilience profiles become across different water cycle domains.

The planning and management of decentralized technologies in water systems is one of the promising, yet overlooked, domains where artificial intelligence (AI) can be successfully applied. In this study, we develop and deploy a reinforcement learning (RL)-based ‘smart planning agent’ capable of designing alternative decentralized water systems under demanding operational contexts. The agent's aim is to identify optimal water infrastructure configurations (i.e., proposed decisions on water management options and interventions) for different conditions with regard to climate, occupancy and water technology availability in a demanding, off-grid setting, i.e., a water system with high requirements of independence from centralized infrastructure. The agent is coupled with a source-to-tap water cycle simulation model capable of assessing and stress-testing the proposed configurations under different conditions. The approach is demonstrated in the case of a military camp deployed abroad for peacekeeping operations. The agent is tasked with selecting optimal interventions from an array of real-world camp water management technologies and evaluating their efficiency under highly variable, operational conditions explored through simulation. The results show that RL can be a useful addition to the arsenal of decision support systems (DSS) for distributed water system planning and management, especially under challenging, highly variable conditions.

Water systems are rapidly transforming into cyber-physical systems. Despite the benefits of remote control and monitoring, autonomous operation and connectivity, there is an expanded threat surface, which includes cyber-physical attacks. This study demonstrates a stress-testing methodology that focuses on assessing the performance of a contamination warning system, designed with alternative water quality (WQ) sensor placement strategies against cyber-physical attacks. The physical part of the attacks consists of backflow injection attacks with a contaminant, while the cyber part comprises cyber-attacks to the contamination warning system. The WQ sensor designs are generated with the Threat Ensemble Vulnerability Assessment and Sensor Placement Optimization Tool (TEVA-SPOT), based on optimizing various metrics. The coupled WDN and CPS operation, the deliberate contamination events, and the cyber-physical attacks, are simulated with the water system cyber-physical stress-testing platform RISKNOUGHT. Multidimensional resilience profile graphs are utilized to analyze performance, demonstrated in a benchmark case study. This type of assessment can be useful in risk assessment studies for water utilities as well as in WQ sensor placement optimization.