Journal of Water Resources Planning and Management

This paper proposes a simple method for estimating the hydraulic behavior of combined sewer overflow (CSO) using only rainfall data. The method analyzes rainfall event time-series data to extract the critical rainfall intensity (the threshold for the occurrence of CSO) and uses the results to calculate quantities such as response time, duration, and runoff depth. Thresholds at 67 urban outfalls in Tokyo were identified from combined sewer simulation modeling using real-world rainfall data. CSO hydraulic behavior at these outfalls during 117 rainfall events, calculated using the simplified method, was strongly correlated with simulated results ($R^2$ value $>0.8$ in $>80\%$ of outfalls). The aggregated CSO depths from the whole 3,481 ha drainage area, determined for seven types of rainfall events using simulations and the simplified calculation method, differed by no more than 18%. These results indicate that event-based time-series rainfall data can yield accurate predictions of CSO behavior, and the simplicity and speed of calculation of the proposed method may make it an effective alternative to complex simulation models for stormwater management. Furthermore, the process of calculating CSO parameters reveals the underlying relationship between rainfall and CSO hydraulic behavior.

Stormwater utility managers have limited resources and funds to manage their pipe systems. To effectively make decisions on allocating limited resources and funds, a utility should be able to understand and assess the performance of its pipe system. There is no standard rating system or comprehensive list of performance parameters for stormwater pipe infrastructure. Previous research has identified performance parameters affecting stormwater pipes and developed a performance index using a weighted factor method. However, the weighted performance index model does not capture interdependencies between performance parameters. This paper develops a comprehensive list of parameters affecting stormwater pipe performance. This paper also develops a performance index using a fuzzy inference method to capture interdependencies among parameters. The performance index was evaluated and validated with the pipe ratings provided by one stormwater utility to document its effectiveness in real-world conditions.

Hydraulic models have been widely used to facilitate the design, management, and operation of water distribution systems (WDS). However, model parameters including pipe roughness coefficients (PRCs) and nodal demands (NDs) must be calibrated before practical applications, and the calibration accuracy and efficiency is important to enable their wide uptake in practice. This therefore motivates intensive studies to develop approaches for model calibration focusing on either optimization algorithms or numeric methods. Although these previous studies have advanced this research area, the calibration efficiency and effectiveness need to be further improved, especially when dealing with real-world problems. To attain this objective, this paper proposes an efficient numerical approach to simultaneously calibrate PRCs and NDs for WDS. Within this method, the calibration objective function is formulated as a least-square equation, followed by the derivation of iterative formulas according to a local sensitivity analysis. These formulas allow PRCs and NDs to be iteratively updated in an efficient manner until the given convergence criteria are met. To further improve the efficiency, the pipes and nodes are grouped according to their physical properties, which allows the calibration dimensions to be significantly reduced. Additionally, within the calibration process, values of PRCs and NDs are restricted into specified ranges to ensure the calibrated results are practically meaningful. Two case studies are used to demonstrate the utility of the proposed method, and results show that this approach is able to simultaneously calibrate PRCs and NDs efficiently, with the modeled hydraulic parameter values (pipe flows and nodal pressures) matching well with observations.

This paper presents a novel approach to model pressure- and flow-regulating devices in the context of the Newton-Raphson loop method for water distribution network simulation. The proposed approach uses a symmetric matrix for the underlying linear systems, which enables simpler implementation and faster solution, while producing iterations very close to the global gradient algorithm of EPANET. The structure of the matrix is kept unchanged regardless of the operational status of the valves. The paper presents results that validate its formulation, accuracy, and speed in various case studies.

The recent increase in frequency and severity of cyber-physical attacks on water treatment plants and distribution systems calls for the development of intrusion detection schemes that help protect these critical infrastructures. This paper contributes an algorithm specifically designed for detecting and localizing cyber attacks against water distribution systems. The algorithm builds on the idea of learning a data-driven model that reproduces the patterns of all hydraulic processes observed within a distribution system: the model is trained using data pertaining to normal operating conditions, in order to poorly reproduce anomalous patterns, such as those induced by cyber attacks. The modeling process is carried out using autoencoders, a deep learning neural network architecture capable of building a compressed and meaningful representation of high-dimensional input data patterns. The algorithm is developed and tested on three data sets devised for the Battle of the Attack Detection Algorithms—the only open-source data available, at this stage, for research in cyber security of water networks. Results show that the detection algorithm can identify all attacks featured in the data sets, including those compromising data integrity. The algorithm has two additional important features: it localizes the components under attack, and it is developed using only data pertaining to normal operating conditions, which are generally available to water utilities.

Disinfectants are typically used in water distribution networks (WDNs) to maintain the microbiological quality of potable water throughout the network. Disinfectant residual levels in such large distribution networks need to be maintained within the prescribed bounds to address two major problems: (1) preventing microbial regrowth, and (2) minimizing harmful disinfection by-product (DBP) formation resulting from high levels of the disinfectant itself. These requirements pose a two-point constrained control problem of disinfectant residual levels. Another important aspect of consideration is that such WDNs exhibit spatial and temporal variations in water quality at different points in the network. Therefore, conventional systems engineering tools such as modeling, optimization, and control need to be adapted to accommodate these variations for water quality management in distribution networks. In this paper, we propose a novel zone control based approach to address the two-point quality control problem. Further, to combat the spatial complexity, we combine the zone control approach with a decentralized optimization strategy, namely, the model coordination method, which has been widely proposed to solve large-scale optimization problems. The resulting novel formulation is proposed here to address the aforementioned problems in operational optimization of booster chlorination water distribution systems by using a realistic multispecies model developed from a previous study. We demonstrate the efficacy of this approach on a real-scale distribution network of Cherry Hill Brushy Plains (CH/BP) (EPANET Example network 2).

California’s 5-year drought has ended, even as its aftermath lingers. From 2012–2016 much or all of California was under severe drought conditions, with greatly diminished precipitation, snowpack, and streamflow and higher temperatures. Water shortages to forests, aquatic ecosystems, hydroelectric power plants, rural drinking water supplies, agriculture, and cities caused billions of dollars in economic losses, killed millions of forest trees, brought several fish species closer to extinction, and caused inconvenience and some expense to millions of households and businesses. The drought also brought innovations and improvements in water management, some of which will better prepare California for future droughts. This paper summarizes the magnitude and impacts of the 2012–2016 California drought. The paper then reviews innovations arising from the drought in the larger historical context of water management in California. Lessons for California and for modern drought management are then discussed. Droughts in modern, well-managed water systems serving globalized economies need not be economically catastrophic, but will always have impacts and challenges, particularly for native ecosystems. In California and every other water system, droughts usefully expose weaknesses and inadequate preparation in water management. In this regard for California, managers of ecosystems and small rural water supplies had the most to learn.

Urban growth usually aggravates flooding problems, disrupting city life and requiring engineering works to ensure safety conditions to lower-lying city areas. Traditionally, canalization works were the preferred choice for flood mitigation. However, increased city growth limits the effectiveness of this approach due to the lack of free spaces. In addition, local canalization practices tend to transfer flooding downstream in a nonsustainable way. In the last 80 years, the town of Cittanova, in the province of Reggio Calabria, Italy, has been subjected to several flooding events related to river overflows or storm drain failures causing significant damages to mobility, households, and even to public safety. This paper focuses on the Forio Creek, which is a tributary of the Vacale River and runs through the city. The study was carried out using mathematical tools within a conceptual framework with the aim of mapping floods to prevent damage to the city and avoid future losses, thus reducing risks. MODCEL was the hydrodynamic model used to map urban floods and to support the use of the Flood Risk Index (FRI), which is a quantitative multicriteria tool relating flood levels to socioeconomic losses. Both the MODCEL and the FRI were previously available, but their combined use and the final integrated analyses carried in this paper introduce a simple and quantifiable flood risk procedure to identify present flood risks and foresee future scenarios while supporting alternative design and prioritizing flood control choices. This framework was applied to Cittanova and validated by practical observations.

In this study, the authors developed a multi-timescale optimal dispatch model to balance both the environmental flow requirement of the downstream dewatered river channel and the power output of the cascade hydropower stations. The operation characteristics of turbine units, reservoir regulation capacity, inseparable hydraulic connections, and the mass balance equation of the cascade reservoirs and water head loss characteristics of the tunnels have been considered to constrain the model. The daily average flow solution set of the monthly timescale model was converted to a daily flow process solution set of the daily timescale model to decouple the two-hierarchy model. The application of the model with two hierarchies shows that the model can reduce the dimension of the decision variables and derive optimal decisions more quickly to simultaneously meet the two objectives. Considering the environmental flow demand of the dewatered river channel, this study puts forward an operation strategy for Jinping cascade hydropower stations. The research results can provide decision support for other cascade reservoirs that have a dewatered river channel.

Because of the growing demand for energy in recent years, multireservoir system operation with a power deficit aspect is becoming an increasingly important problem in electrical power systems. To satisfy this practical requirement, a min-max linear programming (LP) model is developed to determine the optimal generation of all the hydroplants so as to equally distribute electricity shortage in the scheduling horizon. The objective of the LP model is to minimize the maximum, rather than the traditional variance function, of residual load series that is obtained by subtracting the total outputs of all the hydroplants from the original load curve. Also, in the modeling process, the LP model takes a set of necessary operation constraints into account. The proposed model is applied to a classical multireservoir system with 10 coupled reservoirs. The results indicate that the proposed LP model outperforms several existing methods in smoothing the power deficit. Thus, a new perspective is provided for the operation of hydropower systems in the cases where the water head can be assumed to be a constant.