Journal of Water Resources Planning and Management

Analytical optimal hedging rules are derived for two-objective reservoir operation based on the characteristics of economic and multilevel environmental water demands. This approach is employed to balance current and future economic and environmental water demands using a two-period model. According to the marginal values of utilities, the derived three-step hedging rule curves (THRCs) consist of three steps and six stages. The Huaihe River basin of China is taken as an example to regress the THRCs from a two-objective optimization model. Results show that the derived THRCs can be regressed from the optimal release decision, thereby confirming the derivations and theoretical analysis. The shapes of the THRCs are sensitive to the weight parameters of the objectives and capture the core concern of multiobjective reservoir operation. The THRCs tend to widen and flatten the stages with increasing storage capacity, which implies more water storage and more consideration for future demands.

Water reuse provides a sustainable approach to balance water supply and demand in urban areas, and reclaimed water can be used for nonpotable applications to reduce demands on freshwater sources. Construction of a secondary network is required to distribute reclaimed water. Implementing water reuse projects on a wider scale is challenged by the need for communitywide public acceptance and adoption, which may be a dynamic and adaptive process. The adoption of new water infrastructure can drive hydraulic conditions in both the reclaimed network and an existing drinking water network. This research develops a dynamic modeling framework using a cellular automata (CA) approach to simulate consumer adoption of reclaimed water. The framework couples the CA model of consumer adoption with water distribution system models of the drinking and reclaimed water systems. Emergent distribution system hydraulic conditions are simulated, and the capacity utilization and system performance are evaluated as consumers adopt water reuse. The Town of Cary, North Carolina, is used as case study to develop and demonstrate the modeling framework. The CA parameters and rules are developed and validated using data about the addition of new accounts for the period 2001–2012. Projected water reuse adoption is simulated in Cary for the period 2012–2030, and impacts on the existing drinking water infrastructure and planned reclaimed water infrastructure are evaluated.

Air can be entrapped in water and wastewater systems under a variety of conditions, and if such air is not managed properly, it can impose considerable operational penalties. To avoid these problems and the dangers associated with air pockets, several air management strategies have been routinely suggested. However, the devastating consequences of air pockets frequently documented in the literature imply the need for further development in this area. Improving air management obviously requires a thorough understanding of the current strategies and their shortcomings. This paper overviews the sources of air in pipes and key consequences associated with its presence. Current measures for managing air in water pipes are reviewed and critiqued. Finally, knowledge gaps that limit the efficient application of current air management strategies to real world problems are identified, and suggestions for future development are presented.

This article presents the results of analyzing the use of dispersed storage tanks as a low impact development (LID) alternative to mitigate the flash flood risk in consolidated urban watersheds in Barranquilla, Colombia. Fieldwork, rainfall and flow rate monitoring, and numerical modeling were conducted to evaluate different scenarios combining storage tank capacity, two types of storage operations, and different locations by land use and watershed sectoring. These results showed that it is possible to reduce significantly the peak flow with small-capacity tanks distributed in the urban watershed with hydraulic structures that divide the discharge during the peak-flow time interval. By storing 3–17% of the runoff volume, equivalent to a dispersed storage capacity from 4 to 19 mm rainfall, it is possible to reduce the peak flow from 25 to 75%. These results demonstrate the advantages of using low impact development for stormwater management in consolidated cities with limited space, if regulatory policies encourage the massive use of LID in existing buildings.

In this paper, a novel field-oriented methodology to setup real-time control (RTC) for leakage reduction by pressure control valves in water distribution networks is presented. The paper introduces modalities to address the selection of proper RTC system architecture based on the network connectivity at the valve sites. Criteria for target node identification and RTC strategy selection in case of single-control (one valve–one target node) and multiple-control (multiple valve–one target node) architectures are developed. The impact on the control performance of controller calibration and communication protocol selection procedures, and of background noise in pressure signals is also explored. Then, developed criteria and procedures are applied to a Norwegian water distribution network in which a future field-pilot RTC system will be installed. Benefits in terms of pressure control effectiveness and water leakage reduction are evaluated by simulation under different control scenarios as a basic step to assess installation potentiality.

Increases in greenhouse gases emissions have encouraged the replacement of fossil fuels with renewable energy sources. This paper investigates the potential of wind energy as a renewable resource for producing agricultural water in Eghlid city, Iran. The purpose of the optimization model herein considered is to maximize the net benefit from crop production by selecting an optimal cropping pattern. This paper’s results demonstrate that wind energy can be efficiently applied to provide irrigation water and optimize cropping patterns. Specifically, the application of wind energy to withdraw irrigation water increases agricultural production benefits in the amount of 1,254 million Rials (US$45,000).

While significant emphasis has been placed on contamination warning system design, event detection, and source identification, relatively little emphasis has been placed on characterizing contaminant spread and confirmatory sampling. This study developed algorithms that utilize contamination source probabilities to forecast contaminant spread, which is then used to identify confirmatory sampling locations to maximize contaminant spread information based on entropy concepts. The algorithms were applied to simulated contamination scenarios using one small network with five-sensor locations, and one large network with 5-, 10-, 20-, or 50-sensor locations. The first step in the forecasting process was to identify the past contamination probability using existing sensor information and a probabilistic contamination source identification algorithm. In general, the past contamination status of the nodes was either correctly identified or did not have enough information to classify; incorrect classifications were typically less than 3%. The past contamination status was then used to forecast contaminant spread 4 h into the future with the classification performance for the correct (32–53%) and incorrect (6–17%) identification, and unable to classify (30–57%) categories directly dependent on the accuracy of past contamination characterization. In general, forecasting accuracy increased with the number of sensor locations and decreased with longer forecasting time horizons. Confirmatory sampling locations were identified using the forecasted spread information and compared with locations selected using actual (i.e., perfectly known) spread for comparison. Both the forecasted and actual approaches generated similar confirmatory sampling locations. In general, the confirmatory sampling locations identified with the estimated forecasts created new information (i.e., reduced the number of nodes with unknown status), while using the actual forecasts reinforced existing information. As a result of the new information generated, the confirmatory sampling locations identified with the estimated forecasts tended to better characterize the contamination event than the locations identified with the actual sensor information known up to the future time of sampling.

Adaptive approaches are required to counteract the mounting threats to water security. Demand management will feature centrally in such adaptations. The increase in the use of smart-meter technology offers an improved way for utilities to gauge consumer demand and to supply consumers with consumption feedback in (near) real time. Such feedback can decrease the discrepancies between perceived and actual water usage. In contrast to the energy sector, however, where the advantages associated with smart-meter consumption feedback are extensively documented, few studies have focused on the usefulness of such feedback when it comes to managing water consumption. This review assesses the evidence base for the effectiveness of water-usage feedback technology in encouraging water conservation. The review highlights the potential value of high-granularity smart-meter feedback technology in managing domestic water consumption. Findings from the papers covered in this review ($N=21$) indicate that feedback was associated with decreases in water use of between 2.5 and 28.6%, with an average of 12.15% ($\mathrm{SD}=8.75$). A single paper reported a 16% increase in consumption associated with smart-meter feedback. The benefits for water utilities are highlighted, but the costs for utilities need to be considered further. Overall, more work is needed to conclusively pinpoint the most effective type of feedback in terms of information content and granularity, frequency of delivery and medium, and how water consumption is linked to energy consumption. This information is needed to make concrete recommendations to the water industry about the costs and benefits of investment in smart metering and consumer feedback.

Application of a developed methodology for characterization of distributed contaminant sources in a contaminated mine site aquifer involving complex geochemical processes is presented. Linked simulation-optimization models are widely used as efficient tools for identifying unknown groundwater pollution sources. This is an essential first step in determining effective and reliable groundwater management and remediation strategies for a polluted aquifer. However, linking robust numerical models to simulate the transport processes of reactive chemical contaminant species in aquifers involving complex and highly nonlinear physical and geochemical process increases the computational burden extensively, and may affect the feasibility and efficiency of the methodology. To overcome this computational limitation and improve the computational feasibility by avoiding the necessity of repeated numerical simulations, genetic programming-based trained surrogate models are developed to approximately simulate such complex transport processes. Simulation model solution results are then approximated by training and testing genetic programming (GP)-based surrogate models. Performance evaluation of the GP models as surrogate models for the reactive species transport in groundwater demonstrates the feasibility of its use and the associated computational advantages. Some of the hydrogeologic parameter uncertainties are also incorporated in the surrogate models by using training samples obtained by random perturbation of the parameters. The linked simulation-optimization based methodology is evaluated for a contaminated former mining site in Queensland, Australia, as a limited case study.

Nexus thinking is critical to jointly address growing water, energy, and food security challenges. This paper evaluates the water, energy, and food nexus (WEFN) in the Indus River of Pakistan using the Indus Basin Model Revised—Multi Year, a hydro-agro-economic model extended with an agricultural energy use module. Impacts of a range of climate change scenarios on the WEFN in the Indus Basin were modeled and then the potential of different alternative water allocation mechanisms and water infrastructure developments to address growing water, energy and food security concerns in the country were assessed. Results show growing water and energy use under hotter and wetter climate conditions. While more flexible surface water allocation policies can mitigate negative climate change impacts on agricultural water and energy use allowing for larger crop and hydropower production, such policies might also increase the inter-annual variability of resource use. Moreover, a more flexible surface water allocation policy would increase surface water use in the basin, while groundwater and energy use would be lower. Study results can inform the WEFN in areas with similar hydro-climatic environments, such as California and Central Asia. Further integration of a groundwater model and an energy market model and explicitly addressing changes in food and energy demand as a result of demographic dynamics are three areas for future study.

The need for more efficient water use has increased in importance with growing water scarcity and increasing competition among water users. Measuring the economic efficiency of water use has become a useful indicator for water resources management at all levels. This study proposes a multilevel factorial fractional programming model to support water resources management under uncertainty. Linear fractional programming is introduced to provide a practical way for taking into account the ratio of economic benefit to water consumption in the modeling process. This approach allows water allocation plans to be developed on the basis of the optimal economic efficiency of water use rather than economic incentives. A multilevel factorial analysis technique is integrated within linear fractional programming framework to deal with data uncertainty. This technique can quantify the individual and interactive effects of uncertain parameters on system performance and help decision makers gain improved insight into a changing system. To demonstrate its applicability, the model is applied to Xingshan County in China. A set of decision alternatives with respect to the optimal economic efficiency of water use are provided. The effects of uncertain parameters are quantified through a detailed uncertainty analysis, and the most influential parameters are identified.

This work presents a methodology for extracting optimal operational rules for selective reservoir water withdrawal by considering fixed levels of reservoir water outlets for thermal control. The outlet water temperature of the Karkheh reservoir, Iran, is simulated with the CE-QUAL-W2 model. A data-mining model (the LIBSVM model) is applied as a surrogate model of the CE-QUAL-W2 model and coupled with a genetic algorithm (GA), resulting in the LIBSVM-GA algorithm. The selective withdrawal approach considered four fixed reservoir outlets, located at 120, 140, 163, and 181 m above sea level, to account for reservoir thermal stratification. This paper’s methods are evaluated with nonselective and selective withdrawal operations through different scenarios in which single outlet, fixed withdrawal proportions, fixed monthly variable proportions, continually variable (10-day) proportions using total monthly LIBSVM input data, and continually variable (10-day) proportions using separated monthly LIBSVM input data are considered. The highest outlet (at 181 m) was found to be the best level for the nonselective withdrawal scenario. The best selective withdrawal operations scenario was the continually variable (10-day) proportions using separated monthly LIBSVM input data, which minimize the root-mean-square deviation (RMSD) between upstream and downstream temperatures during the operating period.

Stochastic dual dynamic programming (SDDP) is a widely used technique for operation optimization of large-scale hydropower systems in which reservoir inflow uncertainty is modeled with discrete scenarios produced by statistical time series models, such as the family of periodic auto-regressive (PAR) models. It is a common practice in statistical modeling of hydrologic time series to fit a well-known probability distribution (usually normal distribution) to the data by applying proper transformation. Box-Cox transformation is a commonly used transformation in the case of normal distribution fitting. The convexity requirement of SDDP means that nonlinearly transformed time series cannot be used for statistical inflow model calibration. In this paper, a linear approximation is proposed to estimate the expected value of the next stage inflow. In the proposed approach, next-stage inflows are estimated by a model that uses transformed time series. Furthermore, using the proposed linear approximation, it is shown that it is possible to utilize the time series transformed by Box-Cox transformation for scenario generation in SDDP. The Karoon multireservoir system in Iran has been used as a case study in order to show the effectiveness of the proposed method. Some concluding remarks have also been provided by comparing the results of the two SDDP models, with and without the proposed linear approximation.

The aim of this paper is to evaluate water resources use efficiency (WRUE), which focuses on the entire water consumption process by utilizing data envelopment analysis (DEA) and the Malmquist index (MI). A comprehensive analysis of WRUE both on the spatial and temporal scale has been conducted. For the spatial scale, the difference of WRUEs among cities is presented, and the negative factors by analyzing and decomposing relative water resources use efficiency (RWRUE) are examined. Similarly, the dynamic changes of WRUE over time is presented to analyze the temporal scale. Moreover, a real-world case study in Guansu Province, China, from 2003 to 2013 is employed to evaluate the performance of the proposed WRUE analysis. Results show that the decision makers can determine the comprehensive and accurate WRUE and negative factors of WRUE in Gansu. Furthermore, the results provide alternative recommendations to decision makers to plan for efficient use of water resources in different cities.

Large-scale water-resource planning and allocation models with several interconnected components can have a significant number of unknown return-flow coefficients. This study presents an optimization-based approach for automatic calibration of a complex water planning and allocation model of the Gilan Province water-resource system (GWRS), in the heart of Iran’s rice-production area. The frequent natural cycling of water among supply and demand sites is conceptualized by using flow links and their return-flow coefficients, which need to be optimally determined. A control module, consisting of a model-developer unit linked to optimization solvers in a software program solves the nonlinear program of the calibration problem by minimizing the differences between observed and predicted discharges. The simulated calibration results correspond well with observed records of river outflows at hydrometric stations, as well as with historical water allocations. The optimized parameter values are used subsequently to evaluate the GWRS performance against a scenario involving a reduced inflow to the Sefidrud Reservoir, Gilan, Iran. The derived systemwide operation policies for the reservoir and other components show that the GWRS has the potential to adapt to the planned 30% future reduction in water availability.

Using treated wastewater effluent (reclaimed water) for beneficial purposes can be a sustainable practice that reduces demand on potable networks. However, implementing reclaimed water networks can have unintended effects, specifically unintended increases in energy consumption. This case study employs multiperiod scenario analysis to examine energy consumption associated with the potable and reclaimed water systems for the Town of Cary, North Carolina. Using hydraulic planning models of both systems provided by the design engineers, the conveyance and additional treatment energy is tabulated. This method considers uncertainty in reclaimed water demand by varying the expected demand for each build out of the reclaimed water network. Differential electricity consumption is calculated as the difference between the electricity consumed to deliver reclaimed water through a secondary network compared to the electricity consumed to deliver the same volume through the potable water network. Demand uncertainty, in conjunction with spatial growth, is found to have large impacts on differential electricity consumption. Because of the high quality of wastewater effluent, no additional energy is required for treatment, causing the reclaimed water network to consume less energy than the business-as-usual scenario, where the demands are supplied via the potable network. The differential electricity consumption decreases with network expansion because the reclaimed water system becomes less energy efficient per unit volume with increasing flow rate, while the potable water system energy efficiency remains fairly constant. Understanding the trade-offs between water and energy when planning reclaimed water networks is important for sustainable resource management within the built environment.

In this paper, an alternative uncertainty treatment for the traditional unconstrained weighted-least-squares (WLS) method is presented. This treatment enables hydraulic constraints (i.e., null demands at transit nodes or null flows at closed pipes, pumps, or valves, etc.), high-precision measurements, and upper and lower variable bounds (i.e., head levels at tanks) to be included within the state estimation (SE) problem for water distribution systems. With this approach, there is no need to choose appropriate weights associated with these types of measurements in order to correctly assess uncertainty for the SE problem. The method set out herein tackles these as constraints and works with the linear system of equations derived from imposing first-order optimality conditions for the constrained SE problem. This approach enables general quantification of the SE uncertainty for all the hydraulic variables within the water system by applying the first-order second-moment (FOSM) method. Moreover, it enables standard computation of the covariance residual matrix associated with it, which is necessary to detect erroneous measurements. An illustrative example and a case study are shown to bring out the fact that the SE uncertainty results are more accurate and to show how the numerical conditioning of the system is affected, which may be crucial when dealing with large-scale water networks.