Population growth and a drying climate can push urban water supply across a tipping point into a new regime of water deficits. This research develops an analytical approach to characterize tipping points in water supply systems based on a loss of ecological resilience. A comprehensive sociotechnical modeling framework is developed to apply increasing stress gradients, representing climate change and population growth scenarios, to a water supply system and generate time series of deficits, which are analyzed to characterize tipping points. An agent-based modeling framework of water use, supply, and management simulates feedbacks and adaptations between human behavior and water infrastructure systems. Projections of climate change are generated using a stochastic reconstruction framework. A change point detection algorithm is applied to detect change points, and a tipping point rule selects a tipping point from a set of change points. The sociotechnical framework is applied for a case study to demonstrate tipping point analysis. A range of stress gradients are applied for a projected period, and tipping points are characterized for climate change scenarios and management strategies. The sociotechnical framework characterizes the resilience of water supply and can be applied to simulate, predict, and avert tipping points in water supply systems.

Urban water supply systems may be managed through supply-side and demand-side strategies, which focus on water source expansion and demand reductions, respectively. Supply-side strategies bear infrastructure and energy costs, while demand-side strategies bear costs of implementation and inconvenience to consumers. To evaluate the performance of demand-side strategies, the participation and water use adaptations of consumers should be simulated. In this study, a Complex Adaptive Systems (CAS) framework is developed to simulate consumer agents that change their consumption to affect the withdrawal from the water supply system, which, in turn influences operational policies and long-term resource planning. Agent-based models are encoded to represent consumers and a policy maker agent and are coupled with water resources system simulation models. The CAS framework is coupled with an evolutionary computation-based multi-objective methodology to explore tradeoffs in cost, inconvenience to consumers, and environmental impacts for both supply-side and demand-side strategies. Decisions are identified to specify storage levels in a reservoir that trigger: (1) increases in the volume of water pumped through inter-basin transfers from an external reservoir; and (2) drought stages, which restrict the volume of water that is allowed for residential outdoor uses. The proposed methodology is demonstrated for Arlington, Texas, water supply system to identify non-dominated strategies for an historic drought decade. Results demonstrate that pumping costs associated with maximizing environmental reliability exceed pumping costs associated with minimizing restrictions on consumer water use.

The availability of water resources in many urbanizing areas is the emergent property of the adaptive interactions among consumers, policy, and the hydrologic cycle. As water availability becomes more stressed, public officials often implement restrictions on water use, such as bans on outdoor watering. Consumers are influenced by policy and the choices of other consumers to select water-conservation technologies and practices, which aggregate as the demand on available water resources. Policy and behavior choices affect the availability of water for future use as reservoirs are depleted or filled. This research posited urban water supply as a complex adaptive system (CAS) by coupling a stochastic consumer demand model and a water supply model within an agent-based modeling (ABM) framework. Public officials were simulated as agents to choose water conservation strategies and interbasin transfer strategies, and consumers were simulated as agents, influenced by various conservation-based programs to select water conservation technologies and behaviors, and correspondingly update their individual demand models. A water supply reservoir was simulated to receive rainfall from the contributing watershed and to supply the demands of consumer agents. The ABM framework was applied to an illustrative urban case study. A set of scenarios was developed to represent moderate and strong water conservation strategies, and was simulated for a long-term precipitation record to evaluate the sustainability of water conservation practices.

Water supply systems' vulnerability toward physical, chemical/biological, and cyber threats has drawn significant attention to their potential failure. These systems are critical not only because they must provide sufficient water for everyday municipal users but they also must deliver significant flows and volumes of water with high reliability during emergencies such as pipe failures, power outage, and fires. In this study, a methodology is developed to determine the vulnerable components of a water distribution system with respect to a fire event by utilizing a risk based optimization procedure. Using this methodology, vulnerability analysis is performed for a water supply system under three scenarios: (1) accidental failure due to soil-pipe interaction, (2) accidental failure due to a seismic event, and (3) malevolent attack. Several mitigation strategies based on hardening of specific sets of water mains are evaluated through further simulation. Finally, the mitigation strategies are assessed with a benefit-cost analysis to complete the system risk analysis.

Stochasticity in urban water demand arises due to the unpredictability and randomness of consumer behavior, which is influenced by population growth, climatic conditions, and conservation programs. Most urban water demand estimation methodologies are based on end-use models or stochastic models. End-use models describe the uses of water by households at the appliance level and require extensive and detailed data about water activities and water appliances. Stochastic models, however, predict water use using empirical relationships based on predictors, such as population size and water pricing. Integration of mechanistic end-use modeling with stochastic modeling can aid in better understanding of consumers' water use behavior and, therefore, can aid in better estimation of water availability in planning and management of urban water resources. A novel mechanistic-stochastic water demand model is developed here through the integration of an end-use model and a stochastic model. The model is developed using residential customer billing records from two water utilities. The historical water billing records are fitted to a gamma distribution based on the Akaike Information Criterion (AIC) values compared to exponential, extreme value, and log-normal distribution. Consumers are categorized into different groups from the distribution of water billing records and aggregated demand is estimated for the water system. To validate the modeled customer categories, housing survey data is collected and analyzed. Integration of mechanistic and stochastic modeling along with linkage of multiple data sources through this methodology can provide a powerful tool for efficient and sustainable water resources management.

One of the critical public safety roles for water distribution systems is suppression of urban fire events, and fire safety is a major concern for infrastructure and emergency response planners, managers, and regulators. Due to the interdependency of water systems and emergency services, water utility managers should design and manage a water distribution system carefully to mitigate consequences associated with urban fire events, through actions and decisions that may reduce threats to property and public health. One approach to mitigate urban conflagration may be through redesigning water distribution infrastructure to improve fireflows, and potential designs for new pipelines can be selected based on the costs of redesign and the reduction of threats to the water distribution system. A set of important dynamics and interactions between water distribution systems and fire response systems influence the performance of fire suppression strategies and should be considered in planning and management for both systems. Most existing fire spread and suppression models for disaster planning and management consider firefighting demands as static parameters. While a few have modeled the interconnection between water system and fire response, those, however, have not addressed the dynamic linking between the two infrastructures. Additionally, existing fire spread models may oversimplify the realistic fire spread process. A novel framework that dynamically interconnects water distribution and urban fire spread models can aid decision makers in developing disaster management strategies. A cellular automata (CA)-based fire spread model is developed in this research to simulate the gradual spread of fire in large urban areas. The CA-based fire spread model will be coupled with a water distribution system's model to capture the dynamics of urban conflagration, emergency response, and water distribution hydraulics. This framework will be used to generate strategies to improve water distribution systems' fire fighting capabilities through dynamically opening hydrants and enlarging pipe sizes. This methodology will be applied to a hypothetical case study of an urban water distribution system to explore possible fire mitigation strategies.

One of the critical public safety roles for water distribution systems (WDS) is suppression of urban fire events. Previous studies have investigated WDS rehabilitation with a major focus on improving reliability by pipe enlargement. However, pipe enlargement can cause water quality problems and place public health at risk during normal operational periods. Thus, a novel approach is required to effectively address the conflicting goals of the WDS: reliable delivery of water during normal and emergency conditions, meeting water quality standards, and finding cost-effective design and rehabilitation options. In this study an evolutionary computation-based multiobjective optimization-simulation framework is developed to design effective mitigation strategies for urban fire events for water distribution systems with three objectives: (1) minimizing potential fire damages, (2) minimizing water quality deficiencies, and (3) minimizing the cost of mitigation. An elitist nondominated sorting genetic algorithm (NSGA-II) is modified for an evolution strategy (ES)-based implementation to address difficulties for heuristic algorithms posed by WDS problems. Implementation of this methodology generates Pareto-optimal solution surfaces that express the trade-off relationship between fire flow, water quality, and mitigation cost objectives. The method provides decision-makers with the flexibility to choose a mitigation plan for urban fire events best suited for their circumstances. Each Pareto-optimal solution comprises a set of pipes to be enlarged to achieve increased fire flow and the corresponding diameters of these pipes. The algorithm is illustrated with several non WDS test functions. The Micropolis virtual city is then used to demonstrate the application of the proposed methodology to a complex WDS. DOI: 10.1061/(ASCE)WR.1943-5452.0000156. (C) 2012 American Society of Civil Engineers.

Urban water management specifies supply-side infrastructure and demand-side policies to balance water supply and demands for social and environmental systems. As the sustainability of water resources depends on the dynamic interactions among the environmental, technological, and social characteristics of the water system and local population, an adaptive water management approach can be used to update utility decisions based on the feedback among these systems and may enable a more efficient use of resources. Adaptive demand-side management strategies, such as regulating water for outdoor use, can be designed with increasing restrictions corresponding to the depletion of reservoirs. Adaptive supply-side strategies supplement supply by increasing the volume of water that is transferred among basins when reservoirs levels drop. Frequent water use restrictions, however, can have adverse effects on property values, due to prolonged periods without lawn watering, while pumping water from external basins carries a high cost due to energy requirements. A trade-off exists between the management costs to the water utility and the number of day when outdoor water use is restricted. In this study, a Complex Adaptive Systems (CAS) framework is used to simulate the adaptive behaviors of consumers, the adaptive decisions of the water utility, and an engineering model of the water supply infrastructure. The CAS framework is coupled with a multi-objective optimization methodology to evaluate a combination of supply-side and demand-side adaptive water management strategies in achieving the conflicting goals of minimizing management costs and minimizing the number of days with outdoor water use restrictions. An evolutionary computation-based methodology, Hypervolume Maximizing Multi-objective Evolutionary Algorithm (HM2EA), is applied to an illustrative case study of an urban water supply system to explore Pareto-optimal solution sets of adaptive water management strategies.

Sustainable basin-scale management of water resources is challenged by the rapid increase in population and the expected changes in future climatic conditions. Population and economic growth has caused increases in water demands which, along with climate variability, may reduce the availability of water resources. Management strategies often employ supply-side management for water scarcity through the design and implementation of large infrastructure improvements. The stresses on water system could alternatively be reduced by conservation-based policy developments, including outdoor water use restrictions and household-level adoption of water smart technologies. An adaptive management approach may achieve high levels of water conservation most effectively. For example, policy officials may campaign for water conservation measures during drought conditions only, or inter-basin transfers may be implemented by anticipating the timing of inflows to the reservoirs. This research will explore adaptive basin-scale management strategies by modeling an urban water resources system as a Complex Adaptive System. Agent-based modeling is used to represent policy and consumer water use model, and are coupled with a reservoir system model. Policy officials can select pumping and inter-basin transfer operations and or water use restrictions, and consumers can select water use behaviors based on policy developments and pricing information. The proposed adaptive modeling framework will be applied to a case study, and optimal management strategies will be developed to minimize energy costs for different climatic conditions.

The availability of water resources in many urbanizing areas is the emergent property of the adaptive interactions among consumers, policy, and the hydrologic cycle. As water availability becomes more stressed, public officials often implement restrictions on water use, such as bans on outdoor watering. Consumers are influenced by policy and the choices of other consumers to select water conservation technologies and practices, which aggregate as the demand on available water resources. Policy and behavior choices impact the availability of water for future use as reservoirs are depleted or filled. This research posits urban water supply as a Complex Adaptive System (CAS) by coupling a consumer end use model and a water supply model within an agent-based modeling (ABM) framework. Public officials are simulated as agents to choose water pricing structures, and consumers are simulated as agents, influenced by water prices and the choices of other consumers, to select water conservation technologies and behaviors, and correspondingly update their individual end use models. A water supply reservoir is simulated to receive rainfall from the contributing watershed and supply the demands of consumer agents. The ABM framework is applied to an illustrative urban case study. A set of water pricing structures are developed to represent risky and risk-averse policies and are simulated for a long-term precipitation record to evaluate the sustainability of water conservation practices.

One of the critical public safety roles for water distribution systems (WDS) is suppression of urban fire events. Previous studies have investigated WDS rehabilitation for mitigation of potential fire events with a major focus on improving fire flows by pipe enlargement. However, pipe enlargement can cause water quality problems and place public health at risk during normal operational periods. Thus a novel approach is required to effectively address the conflicting goals of the WDS: reliable delivery of water during normal as well as emergency conditions, meeting water quality standards, and finding cost-effective design and rehabilitation options. In this study an evolutionary computation-based multi-objective optimization-simulation framework is developed to design effective mitigation strategies for urban fire events for water distribution systems with three objectives: (1) minimizing fire damages, (2) minimizing water quality deficiencies, and (3) minimizing the cost of mitigation. An elitist non-dominated sorting genetic algorithm (NSGA-II) is modified by incorporating an evolution strategy (ES) to address difficulties for heuristic algorithms posed by WDS problems. Implementation of this methodology generates Pareto-optimal solution surfaces that express the trade-off relationship between fire damage, water quality, and least cost objectives. Thus, the method provides decision makers with the flexibility to choose a mitigation plan for urban fire events best suited for their circumstances. Each Pareto-optimal solution comprises a set of pipes to be enlarged to achieve increased fire flow and the corresponding diameters of these pipes. The algorithm is illustrated with several test functions. The Micropolis virtual city is then used to demonstrate the application of the proposed methodology to a complex WDS.

Water distribution systems and the other critical infrastructures with which they are interdependent are vulnerable to multi-mode attacks and failures (MMAFs). Of particular concern is urban fire protection reliant on interdependent water distribution and emergency response systems. MMAFs involve simultaneous attack/failure events with cascading consequences affecting more than one interdependent infrastructure. A methodology is presented for conducting a vulnerability analysis of the most serious multi-mode attack and failure scenarios and developing effective mitigation strategies to increase water utilities' resistance to complex disasters. A cost-benefit analysis of various mitigation options is presented based on simulations of multi-mode attacks and failures using a coupled water distribution system/urban fire spread and suppression model. The role for optimization techniques in vulnerability analysis is also explored. The methodology incorporates geographic information system (GIS) information, allowing it to be applied for specific urban area characteristics. The potential for future refinement of these techniques is discussed.

In a society concerned over the possibility of terrorism, secrecy , and security of infrastructure data is crucial. However, research on infrastructure security is difficult in this environment since experiments on real systems can not be publicized. “Virtual cities” are one potential answer to this problem, and a library of these virtual cities is now under development. “Micropolis” is a virtual city of 5000 resi dents fully described in both GIS and EPANet hydraulic model frameworks. To simulate realism of infrastructure, a developmental timeline spanning 130 years was included. This timeline is manifested in items such as pipe material, diameter, and topology. An example of using the virtual city for simulation of fire protection is presented. The data files describing Micropolis are avail able from the authors for other s’ use. A larger city, “Mesopolis,” is currently under development and will incorporate add itional critical infrastructure dependencies such as electrical power grids and communications. This will supplement the development of further models to account for risks and probability of electrical power failure due to hurricane events. It is hoped t hat Micropolis, Mesopolis , and additional virtual cities will serve as a “hub” for the development of further research models.

Water systems' vulnerability to natural disaster and terrorist attack has prompted extensive study of the probability and consequences of their potential failure. These systems are vital in their own right because they provide water for drinking, but they are also indispensable components of many cities' fire response plans. Water systems are also dependent on — or key inputs to — many of the other infrastructures that support daily life and efficient emergency response. Any risk assessment of water systems must therefore consider the importance of water systems' interdependence with fire response and other affected infrastructures. More investigation is needed on the effects of infrastructure interdependence on water systems' vulnerability. To highlight the importance of infrastructure interdependencies, a coupled model of water system performance and urban fire spread and suppression will be presented. The system's ability to suppress an urban fire under moderate damage to the water system will be modeled, and the consequences of this multi-mode failure — disabled water system and urban fire spread — will be discussed. This coupled modeling approach allows diagnosis of water systems' potential vulnerabilities to multi-mode attacks or failures (MMAFs) and development of possible mitigation strategies.