Urban water distribution systems are lifeline infrastructure, playing a crucial role on community resilience, safety, and governance. They are vulnerable to various threats, including extreme hazard events, inevitable infrastructure deterioration, disruptions from upstream dependencies, and climate change. Facing these evolving challenges, it is urgent for water utilities to incorporate resilience management into their daily operations and long-term strategic planning. In the past two decades, existing research on the resilience management of water distribution systems primarily focuses on resilience assessment. Besides quantifying resilience, an important question for water utilities is what proactive measures can be taken before disruptions to improve system resilience.

This dissertation aims to develop algorithms and tools to guide water utilities’ long-term resilience planning. First, we identify and establish useful performance measures for the resilience planning of water utilities. Second, we develop novel approaches for guaranteed deterministic and probabilistic vulnerability analysis of water distribution to inform mitigation strategy development. For deterministic vulnerability diagnosis, we develop a guaranteed N-k contingency analysis method, which builds on a novel search algorithm that integrates enumeration and a guaranteed-error sampling scheme. For probabilistic vulnerability quantification, we establish a probabilistic functionality analysis scheme that incorporates the guaranteed-error sampling scheme for principled estimations. Third, we introduce advanced measures and tools to facilitate heuristic and optimal mitigation strategy development. Regarding decision modeling, we suggest using a superquantile measure as a probabilistic decision criterion to account for risk averseness and deep uncertainty. We propose network augmentation as an alternative mitigation strategy that goes beyond traditional component hardening. For optimal mitigation, we introduce the Cross Entropy method, a promising optimization analysis tool for combinatorial stochastic optimization problems. Finally, we develop generic system modeling and policy search methodologies for water distribution reconfiguration, exploring opportunities to alter the layout of water distribution systems. Specifically, we develop a meso-scale water distribution system representation model that approximates large-scale water systems using reduced backbone networks to enable policy search. We establish a multi-objective optimization model that explores trade-offs across alternative distributed water system configurations.

We demonstrate and test our approaches and tools on hypothetical and practical water systems. We find that the internal vulnerabilities within a water distribution system are determined by the complex interactions among its network topology, physical characteristics, and demand patterns. There are usually a few critical components whose failure can cause significant performance losses to water distribution systems, while the rest of the system components have a uniformly small impact on system functioning. Informed hardening and network augmentation strategies can yield efficient vulnerability reduction, improving distribution resilience. Additionally, reconfiguring centralized water systems to distributed alternatives presents an opportunity to enhance supply resilience.

This dissertation aims to provide water utilities with the knowledge and tools they need to navigate alternative resilience-improving strategies, ranging from component hardening to network augmentation to system reconfiguration. The algorithms and tools we develop can be implemented on resilience-oriented planning platforms, such as the Interdependent Networked Community Resilience Modeling Environment (IN-CORE) and the Computational Modeling and Simulation Center (SimCenter) to guide practical resilience planning for water distribution systems.