Journal of Water Resources Planning and Management

Forum papers are thought-provoking opinion pieces or essays founded in fact, sometimes containing speculation, on a civil engineering topic of general interest and relevance to the readership of the journal. The views expressed in this Forum article do not necessarily reflect the views of ASCE or the Editorial Board of the journal.

The provision of flows for the environment, e-flows, is a means to restore the benefits of naturally flowing rivers. Despite the development of numerous methodologies to determine e-flows and optimize dam releases, actual implementation is relatively limited. Examples of successful e-flows implementation through dam reoperation exist in scientific literature; however, there is a missing narrative on cases where dam reoperation has been attempted but not successfully implemented. This study explores this impasse narrative and presents four hypotheses for further research on this subject: (1) Scientists are important stakeholders in the process of dam reoperation, but should play a supportive role rather than drive the process; (2) In undertaking scientific studies for determination of e-flows, a consensus on the priorities, knowledge gap, and solutions must be reached together with local stakeholders; (3) Local-level legislation and policy on e-flows provide the enabling environment for dam reoperation for e-flows; and (4) Genuine, carefully designed consultations of, and negotiations between, stakeholders can overcome hurdles encountered in the process of dam reoperation for e-flows implementation.

Basin-wide cooperation is an approach to resolve water disputes and improve the resilience of transboundary river basins to extreme droughts under climate change. However, the effectiveness of cooperation among various stakeholders has disputes in moving from theoretical research to practical application. This paper aims at developing basin-wide water resources management strategies to improve cooperation effectiveness in the Lancang-Mekong River Basin (LMB). An interdisciplinary physically based integrated hydroeconomic water system model is developed for strategy evaluation, and is shown to have good performance in reproducing historical events. First of all, we find that agricultural benefits in downstream countries are primarily affected by hydrological conditions, without significant impacts from reservoir construction. Second, the cooperative action—three-phase emergency water supply—by upstream reservoirs discharge can alleviate the agricultural drought of downstream countries, and it is further improved by better spatial and temporal water reallocation among different stakeholders. Last but not least, we characterize the cooperation effectiveness under different hydrological conditions by two indicators (i.e., incremental benefit and cooperation efficiency). They show two opposite trends: from extremely high-flow years to extremely low-flow years, the incremental benefit brought by basin-wide cooperation increases by nearly six times, whereas the cooperation efficiency (measured by how many times benefits are gained by downstream from upstream unit loss) decreases by 80 percent. This study provides guidance and support for transboundary basin cooperation and sustainable management.

Sewer pipe deterioration is driven by a finite number of root causes and processes. Thus, it should be both feasible and advantageous to have a uniform classification system that can be universally applied in sewer deterioration modeling and asset management. However, a literature review of existing classification systems revealed several problems and inconsistencies, and no widely adopted system. This work proposes a uniform classification system that can be used for different purposes in the fields of gravity pipe deterioration and asset management. This paper focuses on separated sewer systems, but the proposed system can be easily adapted for combined sewer and stormwater systems. The proposed system is based on three top-level categories of failures, defects, and factors with subcategories based on functional considerations. Each category is unambiguously defined and a classification flow diagram is proposed. This work demonstrates how existing parameters can be consistently classified, discusses the interactions between different defects and failures, and illustrates the application of the proposed system to the processes causing overflow failures due to sedimentation.

The installation of hydropower pressure reduction (HPPR) technology in water supply networks (WSN) has recently been encouraged to limit water losses through leakage. Traditionally, pressure reducing valves (PRVs) were positioned at strategic nodes in a water network and were operated continuously to dissipate excess energy and minimize pipe failure. The alternative adoption of low-cost and highly reliable HPPR technology—Pump As Turbine (PAT)—can recover energy to improve the sustainable management of water networks and increase the energy, economic, and environmental benefits derived from the provision of water resources. To encourage the exploitation of HPPR, indicators and metrics were developed and applied for a case study (olive crop production with an irrigation network in Andalusia, Spain). The case study incorporated (1) a number of WSN scenarios based on water supply conditions, location of the HPPR within the network, and pipe age; (2) the expected performance of the PAT in the market; and (3) the expected optimal performance of an HPPR design based on a variable operation strategy (VOS). Independent life cycle indicators and nexus metrics were created to enhance the evaluation of the performance of HPPR technology as part of the water-energy-food nexus. In this case study, the new metrics expressed the impact of HPPR technology on the water-energy food nexus in terms of climate change and in the context of olive production in an irrigation network. The methodology for assessing combined energy, economic, and environmental metrics (3EM) can be used to account for the value of HPPR deployment in any WSN. These metrics can inform local decision makers and national policy makers, by providing relative and/or absolute evidence on the environmental impacts of HPPR technology in the water sector.

A real-life water distribution system (WDS) contains uncertainty in numerous stages. This makes the optimal management and design of a WDS a complex problem. Water quality has also become a significant factor in the design and management of a WDS. Our objective was to incorporate water quality uncertainty in the WDS design problem. The mixing level was assumed to be uncertain and used to design the WDS such that the design was immune to the level of mixing. This method aimed to yield designs that satisfied the nodal concentration constraints irrespective of the mixing level in the junctions. Two optimization methodologies, robust optimization and info-gap decision theory combined with a cuckoo search optimization algorithm, were proposed to solve this problem. An illustrative example $4\times 4$ grid network was used to understand nonuniform mixing and explain the design methodology using both methodologies. Then these methodologies were applied to solve a similar treatment plant problem on a modified Fossolo network. The results also exhibited a significant variation in cost between complete mixing and nonuniform mixing. The WDS designs obtained from both methods were evaluated through Monte Carlo simulations.

A reliable short-term forecasting model is fundamental to managing a water distribution system properly. This study addresses the problem of the efficient development of a deep neural network model for short-term forecasting of water consumption in small-scale water supply systems. These aqueducts experience significant fluctuations in their consumption due to a small number of users, making them a challenging task. To deal with this issue, this study proposes a procedure to develop an ensemble neural network model. To reinforce the ensemble model to successfully deal with the weekly and yearly seasonality which affect these data, two different time-varying correction modules are proposed. To constitute the ensemble model, the simple recurrent neural network, the long short-term memory, the gated recurrent unit, and the feedforward architectures are analyzed in two case studies. The results show that the proposed ensemble model can achieve a robust and reliable prediction for all four of the architectures adopted. In addition, the results highlight that the proposed correction modules can significantly improve the predictions.

One strategy to deal with unauthorized groundwater withdrawals by farmers is to impose penalties. Because farmers’ over-exploitation is subject to social, economic, cultural, and agricultural characteristics of the region, feedback assessments of a penalty policy using socio-economic simulation is indispensable. This study presents an agent-based framework to study the interaction among agricultural, environmental, and regulator agents within an agricultural system. The behavior of the agricultural sector was simulated at two levels: agricultural sub-agents, and agricultural group-agents. For agricultural sub-agents, individual benefit was maximized under behavioral and physical constraints simulated by a fuzzy inference system (FIS) and mathematical programing. A coupled linear optimization and nondominated sorting genetic algorithm (NSGA-II) were adopted to identify the best solution, from the agricultural sector’s viewpoint. The proposed framework was implemented in the Najaf Abad hydrological unit, situated in Isfahan province, central Iran, considering fixed and stepwise penalty approaches. Sensitivity analysis of the amount of base penalty in the fixed penalty scenario indicated that, under a $0.12 per cubic meter penalty, the annual over-exploitation declined from the current 79 million cubic meters (MCM) to zero. Furthermore, the results indicated that if the width of the step increased from 1 to $9\mathrm{MCM}/\mathrm{year}$, the over-exploitation increased by about $18\mathrm{MCM}/\mathrm{year}$.

The detection and localization of leaks in water distribution networks (WDNs) is one of the major concerns of water utilities, due to the necessity of an efficient operation that satisfies the worldwide growing demand for water. There exists a wide range of methods, from equipment-based techniques that rely only on hardware devices to software-based methods that exploit models and algorithms as well. Model-based approaches provide an effective performance but rely on the availability of an hydraulic model of the WDN, while data-driven techniques only require measurements from the network operation but may produce less accurate results. This paper proposes two methodologies: a model-based approach that uses the hydraulic model of the network, as well as pressure and demand information; and a fully data-driven method based on graph interpolation and a new candidate selection criteria. Their complementary application was successfully applied to the Battle of the Leakage Detection and Isolation Methods (BattLeDIM) 2020 challenge, and the achieved results are presented in this paper to demonstrate the suitability of the methods.

Water distribution is an important service due to its economic, social, and health purpose. Therefore, a continuous supply is required to keep consumers satisfied. However, emergency scenarios of water scarcity, caused by recurrent or exceptional droughts, can impair supply, forcing intermittent operation. This operation has clear disadvantages, such as the increased risk of pathogen intrusion and pipe bursts during filling. However, a reduced number of operation hours, mainly during high-pressure periods, can significantly reduce leakage. It is helpful to preserve the quantity and quality of the main water source during a longer period to maintain its multiple purposes. Considering this new operation, pump stations must be adapted, and energy consumption can significantly increase if an optimal schedule is not determined. Accordingly, this paper studies different levels of intermittency as a strategy to operate under severe water scarcity conditions and identify the gains that can be made in leakage reduction, combined with optimal pump operation, to maintain operational costs at the same level, or better, as continuous operation.

Emerging trends of resilient and reliable water infrastructure advocate for the development of efficient state estimation (SE) techniques in water distribution systems (WDSs). SE refers to estimating the flows and heads in the WDS at unmonitored locations based on measurements collected from limited monitoring locations. Current physics-based SE methods typically require more exhaustive than readily available information about the WDS and are computationally demanding to attain real-time SE fully. Using neural networks for SE is a promising avenue because neural networks are more adaptable to the availability of sensory data and can shift most of the computation efforts to the offline training phase. Once trained, the inference is more computationally efficient compared to the physics-based SE methods. This work proposes a graph neural network (GNN) model for SE in WDSs. Unlike traditional neural networks, GNNs are more suitable for the SE problem for two main reasons: (1) given a limited number of monitoring locations, the SE problem inherently requires a semisupervised learning method, and (2) GNNs enable learning from the graph structure of a WDS, thus providing a mechanism to incorporate the functional relationships between the monitored and unmonitored locations and incorporate the physical laws during the training process. To evaluate the performance of GNNs for SE, we tested supervised and semisupervised approaches, investigated the impact of GNN architecture choices on its performance, and examined model performance under different levels of noise in the training data. The results demonstrate that GNNs are promising for SE for their ability to learn from graph structure with a limited amount of information while exhibiting robustness to noise. This study contributes toward advancing real-time GNN-based SE in WDSs. Future research is needed to incorporate various hydraulic devices and investigate the scalability of GNNs to large-scale WDSs.

Due to the increasing deployment of sensors in water distribution networks (WDNs) for continuous monitoring, hydrodynamic data are readily available for engineers to improve the daily operations of WDNs. In collaboration with Public Utility Board (PUB), Singapore’s National Water Agency, an alternative model calibration approach using continuously monitored data is proposed to facilitate PUB’s smart water grid (SWG). The generic approach was developed as an integrated solution methodology for practical engineers to conduct a series of systematic analyses daily, namely, (1) estimating the system’s daily nonrevenue water (NRW) volume and NRW time series; (2) adjusting the available pumps’ operational curves and control statuses, followed by calibrating the system’s net demand pattern to fulfill the flow balance accounting for the actual consumption; (3) identifying and rectifying possible offsets in the monitored pressure head values for each sensor station; (4) performing model calibration with anomaly localization analysis when the system’s total NRW volume exceeds its assumed background NRW volume; and (5) calibrating other physical properties to fulfill the system’s energy balance, especially for the peak demand hours. The effectiveness of our proposed approach was then tested and verified using a real-world WDN having total pipe length of $>650\mathrm{km}$ with available monitoring data. Key findings from the case study analysis include (1) anomaly events localized including, but not limited to, five out of six reported leaks for the selected week to within 400 m with lead time of 1–2 days; (2) the system’s initial flow imbalance addressed by estimating the daily NRW volume and localizing the possible anomaly events; and (3) pipe roughness values calibrated to further improve the energy balance in the system, especially during the peak demand hours, by attaining an average mean absolute percentage error (MAPE) score of 2.5%.

Quantifying the relationship between reservoir flood capacity and peak flow reduction can identify reservoirs with good flood control effect and be important for reasonably allocating flood control capacity for larger peak flow reduction. In this study, an optimization-clustering-fitting framework consisting of the Dynamic Programming-Progressive Optimality Algorithm, K-means method, and multiple linear regression, is proposed and applied to a multireservoir system in the middle and lower reaches of the Ganjiang River, China. The results reveal the law of diminishing marginal benefit in reservoir flood control capacity, which means that as reservoir capacity increases, the peak flow reduction increase is larger in low water levels than that in high ones. In this multireservoir flood control system, the Wan’an reservoir has greater flood control potential and can reduce more peak flow of the downstream flood control points than the Xiajiang reservoir when the same capacity of both reservoirs is used for flood control.

The conflicting demands for water used for irrigating crops, generating power, and supplying humans and livestock have placed many river basins around the world under stress. The competition for scarce water resources will be exacerbated by the impact of climate change. This article uses a residual imputation and net rents approach to obtain economic values and water demand curves of irrigators in Brazil’s Sao Marcos River Basin (SMRB). We estimate that current water prices must increase by over 230 times in the dry season to reduce the water consumption in the basin to the level that is required to accommodate its use to generate electricity as required by the water regulator. These estimated water prices are constrained efficient in the sense that they promote the efficient reduction in water consumption from the agricultural sector to satisfy the existing water permit granted for electricity generation.