Journal of Water Resources Planning and Management

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.

Abstract

In the context of the Internet of Things, there is a growing demand for real-time monitoring of water distribution systems (WDS). Among the various leak detection methods, acoustic leak detection is considered to be a suitable method. However, existing methods are not very effective in environments with high daytime ambient noise. To address this issue, this paper conducted on-site data collection experiments and designed a monitoring system that combines traditional nighttime monitoring with daytime monitoring, combining water company pipeline inspections and repair work. A large number of daytime audio samples were collected. In this paper, the logarithmic spectrogram (log spectrogram) was used to represent the features of the leak signal. By comparing the features of the signal during day and night, noisy and quiet environments, and leak and normal signals, we identified the interfering frames that required noise reduction, and applied frame-level noise reduction processing to the signal. Based on this, a log PS-ResNet18 model was developed to identify leaks, and its performance was compared with other classification models [including traditional nighttime detection methods, random forests, XGBoost, and convolutional neural network (CNN)]. The results showed that the log PS-ResNet18 model had the best performance, with an all-day accuracy rate of 99.4% and a daytime accuracy rate of 99.3%. In addition, by conducting ablation experiments to explore the role and contribution of the log PS-ResNet18 and noise reduction methods in the model, the results showed that the log spectrogram and noise reduction methods increased the all-day accuracy rate by 18.8% and 23.2%, respectively, and by 24.7% when used together. In another practical application, the log PS-ResNet18 model achieved an all-day detection accuracy rate of 99.6%. This study demonstrated the applicability of the log spectrogram and CNN combination in daytime leak detection, overcoming research limitations in the field.

Practical Applications

This research presents the log PS-ResNet18 framework, which combines deep learning models and denoised logarithmic spectrograms to improve leak detection in water supply pipelines under daytime environmental noise. The research focuses on field data collection and analysis of cast iron pipes with different diameters in Hangzhou (HZ). The model was tested on cast iron pipes in Lishui (LS) and proved to be effective. The proposed method is highly versatile and can be applied to different regions and pipe materials after sufficient sample collection and model training validation. The research recommends a comprehensive leak monitoring solution that involves initial intelligent detection using front-end noise meters and secondary identification of suspicious audio signals using the log PS-ResNet18 model in the cloud. This enables water utility operators to respond quickly to pipeline leaks, leading to more efficient water resource conservation and improved water supply service quality.

Interbasin water transfer projects (IBWTPs) have become an important measure to alleviate regional water stresses by artificially regulating water resources between water-rich and water-scarce areas. However, there are many conflicting objectives and complex practical operational constraints that complicate the derivation of water allocation schemes for IBWTPs. Here, we propose a multiobjective optimization methodology for deriving water allocation schemes for IBWTPs, which includes three modules: (1) formulating a multiobjective optimal water allocation problem for IBWTPs based on practical operational constraints; (2) proposing a novel multiobjective cuckoo search (NMOCS) algorithm and combining it with a simulation-optimization approach to solve the water allocation problem; and (3) filtering the Pareto solutions using the analytic hierarchy process (AHP)-entropy method. Based on the multiobjective cuckoo search (MOCS) algorithm, the NMOCS employs four improvement strategies, including a population initialization strategy, flock search strategy, multistrategy external archive maintenance strategy, and adaptive parameters, to improve the convergence property and diversity of solutions. To test the performance of the NMOCS, we considered the MOCS and four widely used multiobjective evolutionary algorithms (MOEAs) as a comparison and employed these six MOEAs to solve 11 multiobjective mathematical benchmark problems as well as the water allocation problems of the Jiangsu Province, China, section of the South-to-North Water Diversion Project under normal, dry, and extremely dry hydrological conditions. The results show that the NMOCS outperformed other MOEAs in handling multiobjective mathematical benchmark problems, especially in ZDT1, ZDT2, ZDT6, DTLZ2, DTLZ4, and DTLZ7. Compared with other MOEAs, the NMOCS did not always capture the highest percentage of the reference water allocation schemes, but it provided more than 18% (greater than one-sixth) of effective water allocation schemes for all hydrologic conditions. Meanwhile, compared with optimal water allocation schemes derived from other MOEAs, the NMOCS effectively improved the operational performance under normal and drought hydrological conditions, especially in the total water pumping and the water withdrawn from the Yangtze River. This research can help to update our understanding of MOEAs, particularly the MOCS, and serve as a reference for better-allocating water resources in IBWTPs.

Demand-side management (DSM) programs are essential to accommodate larger shares of distributed, variable, renewable energy generation. These programs can be used by water utilities to foster a more sustainable operational management. In this work, an integrated DSM strategy is adopted for the optimal control of pumping loads to reduce energy costs in a water supply system (WSS). For this purpose, the participation in demand response programs, namely, using time-of-use (TOU) and real-time pricing (RTP), as well as on-site renewable energy generation, are explored. A gradient-based method was used to optimize the pump scheduling based on a nonlinear programming model integrating hydraulic simulation with optimization tools. The main original contribution of this work is the development of a thorough optimization model integrating available supply and demand energy resources into WSS operational management, as well as presenting a novel approach to formulate the pump operation problem. The Anytown WSS benchmark is used to validate the proposed model. The results show a decrease of 11%–52% in the cost of WSS operation when considering an integrated optimization of the available energy resources in a time-differentiated tariffs context.

Flood bypasses are used in regional flood management to reduce flood risk and damage by routing substantial flood flows to a supplementary river channel to lower flood stages in the usual river mainstem. Design, operations, and modifications to flood bypasses commonly employ hydraulic analysis and evaluation. Economics of flood bypasses is usually considered only in terms of feasibility, and bypass design is rarely optimized economically. The literature lacks formal integration of economics, engineering, and hydrology for flood bypasses. Economically analyzed flood bypass systems better incorporate bypasses into flood management, reflecting and balancing the range of costs, impacts, and opportunities of bypasses. This paper presents a method to integrate economics, hydrology, and engineering for flood bypass planning. An economic optimization model is developed based on a cost–benefit analysis. The optimization suggests and explores a preferred economic flood bypass capacity. A preliminary analysis is developed, with a base case that includes benefits from flood risk reduction only. Costs include for a levee setback, weir widening, and land use. The optimization model is applied preliminarily to California’s Yolo Bypass. With the simplification of unlimited land availability, the model results suggest an optimal Yolo Bypass capacity expansion of approximately $\mathrm{5,800}{\mathrm{m}}^3/\mathrm{s}$. The expansion suggested is close to the Department of Water Resources (DWR) stated objective in their 2017 Central Valley Flood Protection Plan and part of the Basin-Wide Feasibility Studies Sacramento River Basin, part of the Central Valley Flood Management Program of 2017. The model is also applied to the Mississippi River Basin’s, New Madrid Birds Point Bypass and Morganza Floodway. In all cases, actual and theoretically optimal bypass capacities agree in their general behavior. This paper provides a framework for decision analysis. This method can better inform policymakers and stakeholders on bypass design and structural modifications and long-term flood management strategy.

Water demand continuously increases in urban zones, and water scarcity is frequently associated with a lack or a reduction of water availability at water sources and maintenance problems, such as leakage and pipe aging. These facts inevitably lead to challenging water distribution system (WDS) management. In this scenario, intermittent operation emerges as an alternative to system operation. This is not the most desirable solution from a social perspective because many consumers cannot be supplied as desired for days. To tackle problems like this, many works have investigated how to help decision makers improve water system efficiency during the last decades. Nevertheless, few works have considered combining several structural interventions, such as pipe replacement, installation of new pump stations, fixing leaks, and installing and controlling pump stations and valves. One reason is that alternatives for recovering the hydraulic capacity in decision-making processes are computationally burdensome, mathematically complex, and, sometimes, even physically incompatible. Considering the problem stated by the Battle of Intermittent Water Supply, this work proposes a methodology for optimal operation and recovery of a WDS. The Battle problem is presented in two stages organized during different years: Year 0 and the following 5 years. For Year 0, only operational optimization is allowed. Consequently, optimal operation of pumps and valves is proposed for this initial year to maximize the number of nodes being supplied. For the rest of the years, because implementing structural changes is allowed within a defined budget, the proposal suggests applying a search space reduction process based on a cost-benefit trade-off and the hydraulic relevance of each structural alternative evaluated individually in terms of the nine indicators proposed in the Battle statement. Those alternatives that better improve the indicators are then considered in a multiobjective optimization setting. For every year, a set of structural changes is selected, followed by related changes in the operational setup. The alternatives are selected year by year and evaluated considering the past selected alternatives to assess the effects during the five evaluation years. This is done in a dynamic programming process, ensuring that a near optimal is achieved by the end of the last, fifth, year.

Abstract

In many countries, the drinking water provision in rural areas is in the charge of the users themselves, who constitute rural water supply services (RWS) to operate and maintain the public infrastructure provided by the State. However, in practice, the condition of the components of the implied infrastructure managed by RWS varies considerably, which has important implications for delivering high-quality service. This case study explores the nexus among infrastructure conditions, performance outcomes, and organization characteristics using the case of Chile. Using representatives’ assessments from 406 RWS regarding the need for the replacement of several components of their system’s infrastructure, an index of infrastructure conditions was constructed and subjected to a cluster analysis that identified three dissimilar groups of RWS. The top condition cluster represents a reference group (benchmark) that exhibits the highest scores in water quality, quantity, and delivery reliability, which highlights the association between infrastructure conditions and performance outcomes. In addition, a comparison of RWS attributes of these three clusters allowed us to characterize them in terms of structural, organizational, management, and environmental variables. This study sheds light on the role of RWS infrastructure in enabling these organizations to deliver high-quality service, and the findings serve to guide policy actions and tailored planning. The methodology presented here can be applied in other regions beyond that of the case study because it represents a low-cost tool to measure the infrastructure condition of RWS based on representatives’ assessments and is an effective and practical way to distinguish RWS most in need of support.

Practical Applications

In many countries worldwide, the provision of drinking water in rural areas is in charge of the users themselves using public infrastructure provided by the government. This case study, in the context of Chilean rural water supply services (RWS), empirically verified that adequate infrastructure condition and a high quality of service are correlated. If the infrastructure components become damaged or obsolete, a series of events that threaten the quality of service can occur. These issues may encompass distribution and treatment problems and, even worse, service interruptions. Such challenges can have adverse impacts on the health and well-being of rural communities, so it is imperative to identify typologies of RWS infrastructure conditions to formulate targeted strategies and prioritize public investments. The results of this case study, based on a large sample of 406 RWS, offer an insightful classification of organizations into three distinct groups in terms of their infrastructure conditions. The findings underscore the close relationship between infrastructure conditions and the quality of the service delivered. Additionally, the study characterizes these groups in terms of structural, organizational, operation and management, and environmental aspects, providing valuable insights for informed decision-making.

Collective action arrangements have been successful in allocating scarce resources in small communities around the world. As these arrangements are being used to address broader resource allocation issues, it is critical to understand the context and factors impacting outcomes in larger institutional settings. South Carolina historically has benefited from abundant water supplies, but disputes between various water interests have become more common due to increasing demand for water resources. To address projected demand, the South Carolina Department of Natural Resources (SCDNR) initiated a process of river basin water management planning, facilitated through formal institutions referred to as River Basin Councils (RBCs), for the eight major river basins of South Carolina. RBCs are tasked with reviewing surface and ground water availability and formulating an implementable consensus-based river basin plan and identifying possible policy recommendations for an updated state water plan. Thirteen of 22 representative stakeholders of the Edisto RBC, the first RBC to start the planning process, participated in semistructured interviews. We found that as stakeholders transitioned to the Edisto RBC, stakeholder goals were informed largely by beliefs about water problems and solutions that originate from large subsystem coalition groups. Furthermore, we found that stakeholders are motivated to compromise with opposing coalition members in an effort to minimize stakeholder sector losses and/or maximize coalition group policy gains.