Climate change brings intense hurricanes and storm surges to the US Atlantic coast. These disruptive meteorological events, combined with sea level rise (SLR), inundate coastal areas and adversely impact infrastructure and environmental assets. Thus, storm surge projection and associated risk quantification are needed in coastal adaptation planning and emergency management. However, the projections can have large uncertainties depending on the planning time horizon. Excessive uncertainties arise from inadequately quantified ocean-climatic processes that control hurricane formation, storm track, and SLR in time of climate change. For this challenge, we propose an objective-based analytical-statistical approach using the National Oceanic and Atmospheric Administration's (NOAA)'s Sea, Lake, and Overland Surge from Hurricanes (SLOSH) model in scenario analysis of the storm surge impacts. In this approach, synthetic hurricanes (wind profile and track direction) are simulated to yield the likely range of the maximum envelope of water (MEOW), the maximum of the maximum (MOM), local wind speed, and directions. The surge height and time progression at a location are analyzed using a validated SLOSH model for a given adaptation or planning objective with a set of uncertainty tolerance. We further illustrate the approach in three case studies at Mattapoisett (MA), Bridgeport (CT), and Lower Chesapeake Bay along the US Atlantic coast. Simulated MOMs as the worst-case surge scenarios defined the long-term climate risk to the shoreside wastewater plants in Bridgeport and environmental assets in the Lower Chesapeake Bay. The wind-surge probability envelopes in simulated MEOWs provide location-specific estimates of the storm surge probability for local adaptation analysis at four locations in Lower Chesapeake Bay and at Mattapoisett of the southeastern Massachusetts coast. Using the constraints of local bathymetry and topography, the wind-surge probability curves and time progression also provide quantitative probability estimates for emergency response planning, as illustrated in the Mattapoisett case study.

Tools used to predict hydraulics and water quality within premise plumbing systems have gained recent interest. An open-source Python-based tool-PPMtools-for modeling and analyzing premise plumbing systems with WNTR or EPANET is presented. A relative water age-the time water has spent in a home-study using three real-world single-family homes was used to demonstrate PPMtools. Results showed that increased use-more people or higher flow fixtures-led to a general decrease in relative water ages. However, even with more use, one user could still experience water for a drinking activity with a relative water age equal to, or longer than, the duration of the longest stagnant period (sleeping or absence from home). Simulations also showed that the general relative water ages increased if the homes were plumbed with larger diameter piping [19.1 mm (3/4 in.) versus 12.7 mm (1/2 in.)]. Hot water heaters were found to have the largest impact on relative water age. Smaller volume uses generally had more variability in relative water ages, while larger volume uses (e.g., showering) resulted in generally low relative water ages with less variability because larger uses fully replaced water in the home with water from the main. This study highlights the potential for using PPMtools to explore more complex water quality modeling within premise plumbing systems.

Drinking water utilities are vulnerable to both human-caused and natural disasters that can impact the system infrastructure and the delivery of potable water to consumers. Analyzing system performance and resilience can help utilities identify areas of high risk or concern, understand the impacts on consumers, and evaluate response actions during disasters. In this case study, the Water Network Tool for Resilience (WNTR) was used to investigate the performance and resilience of a drinking water system in New York during increased demands due to firefighting, pipe damage, and loss of the source water emergencies. This case study introduced a new combined performance index (CPI) resilience metric, which served to quantify system resilience as a ratio of system performance during an emergency to normal operations. The results revealed that this drinking water system was able to maintain service to most of the consumers during these emergencies due to high redundancy within the system, and conservation efforts extended water service for an additional 20 h. The analysis in this paper can be used by other drinking water utilities to understand their vulnerabilities and evaluate resilience-improving actions in similar disaster scenarios.

The two Category-5 hurricanes that impacted the United States Virgin Islands in 2017 exposed critical infrastructure vulnerabilities that must be addressed. While the drinking water utility has first-hand knowledge about how the hurricanes affected their systems, the use of modeling and simulation tools can provide additional insight to aid investment planning and preparedness. This paper provides a case study on resilience analysis for the island's potable water systems subject to long term power outages. Power outage scenarios help quantify differences in water delivery, water quality, and water quantity during and after the disruption. The analysis helps illustrate important differences in system operations and recovery time across the islands. Results from this case study can be used to better understand how the system might behave during future disruptions, provide justification for investment, and provide recommendations to increase resilience of the system. The analysis framework can also be used by other utilities to explore vulnerability to long term power outages.

Drinking water systems commonly use manual or grab sampling to monitor water quality, identify or confirm issues, and verify that corrective or emergency response actions have been effective. In this paper, the effectiveness of regulatory sampling locations for emergency response is explored. An optimization formulation based on the literature was used to identify manual sampling locations to maximize overall nodal coverage of the system. Results showed that sampling locations could be effective in confirming incidents for which they were not designed. When evaluating sampling locations optimized for emergency response against regulatory scenarios, the average performance was reduced by 3%-4%, while using optimized regulatory sampling locations for emergency response reduced performance by 7%-10%. Secondary constraints were also included in the formulation to ensure geographical and water age diversity with minimal impact on the performance. This work highlighted that regulatory sampling locations provide value in responding to an emergency for these networks.

A Lagrangian method to simulate the advection, dispersion, and reaction of a single chemical, biological, or physical constituent within drinking water pipe networks is presented. This Lagrangian approach removes the need for fixed computational grids typically required in Eulerian and Eulerian-Lagrangian methods and allows for nonuniform computational segments. This makes the method fully compatible with the advection-reaction water quality engine currently used in EPANET. An operator splitting approach is used, in which the advection-reaction process is modeled before the dispersion process for each water quality step. The dispersion equation is discretized using a segment-centered finite-difference scheme, and flux continuity boundary conditions are applied at network junctions. A staged approach is implemented to solve the dispersion equation for interconnected pipe networks. First, a linear relationship between the boundary and internal concentrations is established for every pipe. Second, a symmetric and positive definite linear system of equations is constructed to calculate the concentrations at network junctions. Last, pipe internal concentrations are updated based on the junction concentrations. The solution generates exact results when the analytical solutions are available and leads to more accurate water quality simulations than advection-reaction-only water quality models, especially in the areas where dispersion dominates advection.

Uncertainty in the impacts of climate change and development on freshwater resources pose significant challenges for water resources management. Integrated and adaptive approaches to water resources management are a promising means of addressing uncertainty that afford flexibility in balancing multiple stakeholder objectives. However, guidance on designing such plans is lacking. In this study, we use multi-objective optimization to strategically incorporate green infrastructure (GI) into water resources management plans that maximize reductions in nutrient loads, minimize stormwater runoff, and minimize costs. Robust decision-making methods are applied to the resulting plan options to evaluate how optimized GI implementation varies under different possible future climates and to determine which solutions would be robust under a range of plausible future conditions. We demonstrate these coupled methods using a case study in southern Massachusetts, to address water quality issues related to point and nonpoint source nutrients in a rapidly developing watershed.

The lead contamination of drinking water in homes and buildings remains an important public health concern. In order to assess strategies to measure and reduce exposure to lead from drinking water, models are needed that incorporate the multiple factors affecting lead concentrations in premise plumbing systems (PPS). In this study, the use of EPANET, a commonly used hydraulic and water quality model for water distribution systems, was assessed for its ability to predict lead concentrations in PPS. The model was calibrated and validated against data collected from multiple experiments in the EPA's Home Plumbing Simulator that contained a lead service line and other lead sources. The EPANET's first-order saturation kinetics model was used to simulate the dissolution of lead in the lead service line. A version of EPANET was developed to include one-dimensional mass dispersion. Modeling results were compared to experimental data, and recommendations were made to improve the EPANET-based modeling framework for predicting lead concentrations in PPS.

Water distribution systems are vulnerable to hazards that threaten water delivery, water quality, and physical and cybernetic infrastructure. Water utilities and managers are responsible for assessing and preparing for these hazards, and researchers have developed a range of computational frameworks to explore and identify strategies for what-if scenarios. This manuscript conducts a review of the literature to report on the state of the art in modeling methodologies that have been developed to support the security of water distribution systems. First, the major activities outlined in the emergency management framework are reviewed; the activities include risk assessment, mitigation, emergency preparedness, response, and recovery. Simulation approaches and prototype software tools are reviewed that have been developed by government agencies and researchers for assessing and mitigating four threat modes, including contamination events, physical destruction, interconnected infrastructure cascading failures, and cybernetic attacks. Modeling tools are mapped to emergency management activities, and an analysis of the research is conducted to group studies based on methodologies that are used and developed to support emergency management activities. Recommendations are made for research needs that will contribute to the enhancement of the security of water distribution systems.

Sampling of drinking water distribution systems is performed to ensure good water quality and protect public health. Sampling also satisfies regulatory requirements and is done to respond to customer complaints or emergency situations. Water distribution system modeling techniques can be used to plan and inform sampling strategies. However, a high degree of accuracy and confidence in the hydraulic and water quality models is required to support real-time response. One source of error in these models is related to uncertainty in model input parameters. Effective characterization of these uncertainties and their effect on contaminant transport during a contamination incident is critical for providing confidence estimates in model-based design and evaluation of different sampling strategies. In this paper, the effects of uncertainty in customer demand, isolation valve status, bulk reaction rate coefficient, contaminant injection location, start time, duration and rate on the size and location of the contaminant plume are quantified for two example water distribution systems. Results show that the most important parameter was the injection location. The size of the plume was also affected by the reaction rate coefficient, injection rate and the injection duration, while the the exact location of the plume was additionally affected by the isolation valve status. Uncertainty quantification provides a more complete picture of how contaminants move within a water distribution system and provides more information when using modeling results to select sampling locations.

Distribution network modeling is often used to investigate and manage water quality variations in a distribution network. It relies on pipe network simplification through skeletonization and uses water demand patterns that are often generalized or derived historical monthly water usage records. As automatic water meter reading and supervisory control and data acquisition (SCADA) technologies are widely used, it is possible now to explore the hydraulic complexity in the network. Processes such as stochastic and pulse water demand on solute transport characteristics can be investigated. Fidelity and appropriateness of network modeling by network simplification can be quantified. In this paper, these two questions are assessed by using real-time water demand measurements and comparative network simulations for an independent segment of a large water utility in the U.S. An all-pipe all-demand (APAD) model and an hourly demand variation curves (HDVC) demand model are simulated for the same network operations. The results show the prevalence of intermittent and pulse water demand particularly in network perimeters and dead-end branches. The results also highlight different node hydraulic properties such as , water age, and flow oscillation when water demand in APAD model is replaced by HDVC-based time-continuous generalized demand patterns. The degrees of such difference varies specific to the distribution network configurations such as H-loop, branches and dead-ends. These additional insights provide further understanding of the varying flow properties and their impacts on the movement of water parcels in pipe configurations. It is suggested that APAD network simulation be used for accuracy-demanding water quality simulation.

Real-time water distribution system (WDS) hydraulic models are used in water utilities to facilitate the planning and operation of the water distribution system. As a critical model input, spatiotemporally varying nodal water demands significantly affect the performance and applicability of such WDS models. Thus, real-time nodal demands must be calibrated for reliability before their use. The main difficulty for real-time calibration is the lack of observed data sufficient to determine thousands of nodal demands accurately in a network. To address the difficulty, this study proposes a formal Bayesian approach to determine nodal demands in WDS hydraulic modeling by explicitly taking prior water demand information into account and coupling more information to constrain the nodal water demand modeling. Application of the approach on a simple hypothetical network and a field network in a city of eastern Zhejiang Province, China demonstrates that by adding prior information, the nodal demand can be uniquely determined in real time. The approach limits uncertainty propagation and improves the robustness of the real-time model calibration and analysis.