Journal of Water Resources Planning and Management

Integrated Water Resources Management (IWRM) is increasingly important due to water scarcity, population growth, climate change, and deterioration of resource quality. IWRM requires analytical tools such as automated models and interactive systems, which may be difficult to implement, particularly in developing countries. This paper reviews existing basin-level models and decision support systems (DSSs) for public water allocation, provides application examples, and systematically reviews the literature on concepts arising from the definition of IWRM. Two environmental concepts considered in this review, water quantity-quality management and sustainability, were the most frequently used in revised allocation models, most of these in spatial decision support systems (SDSS). These automated systems presented, in most cases, three advantages: a well-developed friendly interface enabling application of more than one model, allowance for different decision criteria and restrictions together with the analysis of scenarios/sensitivity, and tight coupling and connection to spatial databases. This greatly facilitates and enhances their use by decision makers and stakeholders, favoring an environmentally sustainable management of water resources. Findings showed few models that combined the requirement of sustainable management maximizing economic well-being together with equality. Overall, most of the tools developed are prescriptive (optimization), nonlinear and deterministic models, which were not available through any DSS. In addition, allocation modeling that considers aspects of quantity-quality combined with economic optimization was almost entirely developed in the last decade of the analyzed period. Implementation of IWRM needs a process with participation of all the stakeholders involved, supported by hydrological and economic models integrated and available through DSS.

This paper presents a state-of-the-art review of modeling the mixing phenomenon of junctions in water distribution systems (WDSs). Firstly, the basic concepts and governing equations of the mixing phenomenon are presented. The complete mixing simplifying assumption used in the existing tools of WDSs modeling and management (EPANET, WaterCAD) is described. The paper goes on to show that the complete mixing simplifying assumption can lead to erroneous results. Then, the most frequent incomplete mixing models are described, including AZRED, EPANET-BAM, EPANET-BAM-WRAP, and two analytical models. Also, a summary of all the existing mixing models is provided, trying to determine which mixing model is better to use for a specific urban water distribution network. Because this question can only be partially answered, the paper ends by proposing potential future developments in modeling the mixing in junctions: taking into account the effect of the chemical reaction on the mixing phenomenon in junctions, and considering real-world pipe sizes and pressure to study the mixing phenomenon.

The devastating impacts of frequent coastal storms, such as Hurricane Katrina in New Orleans in 2005 or Superstorm Sandy in New York City in 2012, in major low-lying metropolitan areas have had repercussions far beyond previously experienced disasters. Two key findings that describe these repercussions related to these events were stated as follows: in the case of Katrina, there was a lack of holistic or system-based thinking, and in the case of Sandy, there was an insufficient integration of critical infrastructure. Among different remediation actions, inland placement of best management practices (BMPs) in order to protect coastal cities from storm surges has received a lot of attention. However, nearshore BMPs as a way of source (wave height) reduction have not yet been thoroughly investigated. Nearshore practices are especially attractive in coastal areas that experience significant wave heights as a total water level component. In this paper, the effects of bathymetric shallowing through an apron on reducing the wave height and water level during storms are investigated in coastal areas. Southern Brooklyn in New York City is selected as the case study. A hydrodynamic model is utilized to generate wave height and storm tide. A water-level hydrograph in ungauged areas of the case study is constructed, which has not been realized before in similar coastal flood studies. Water level variations are investigated to include generated storm surge and wave characteristics. The results show that shallowing can reduce the peak of wave height and water level by 47% and 10%, respectively, in open coastal areas. In a more contained bay area, the reduction of a water level was more than 13%. These figures show the potential of nearshore BMP applications that could significantly improve our flood preparedness plans. The proposed methodology can be applied to other coastal geographical settings.

This paper investigates the problem of optimal placement and operation of valves and chlorine boosters in water networks. The objective is to minimize average zone pressure while penalizing deviations from target chlorine concentrations. The problem formulation includes nonconvex quadratic terms within constraints representing the energy conservation law for each pipe, and discretized differential equations modeling advective transport of chlorine concentrations. Moreover, binary variables model the placement of valves and chlorine boosters. The resulting optimization problem is a nonconvex mixed integer nonlinear program, which is difficult to solve, especially when large water networks are considered. We develop a new convex heuristic to optimally place and operate valves and chlorine boosters in water networks, while estimating the optimality gaps for the computed solutions. We evaluate the proposed heuristic using case studies with varying sizes and levels of connectivity and complexity, including two large operational water networks. The convex heuristic is shown to generate good-quality feasible solutions in all problem instances with bounds on the optimality gap comparable to the level of uncertainty inherent in hydraulic and water quality models.

Forward contract is a useful tool for hydropower producers to hedge revenue uncertainty sourced from varied spot prices. However, few studies have been explored to jointly derive the optimal forward contract and operating rules to guide hydropower operations. To address this issue, the following steps for cascade reservoirs are proposed: (1) a two-layer nested approach is used to derive optimal contract targets coordinated with operation trajectory (saying reservoir water level time series) by inputting deterministic inflows, (2) the forms of the operating rules are then identified based on the optimal operation trajectory from the first step, and (3) a simulation-based optimization approach is used to jointly refine the optimal contract and operating rules with the forms identified in the second step. With China’s Hubei Province as the market boundary, the Qing River cascade reservoir system was selected as the case study. The results show that (1) the two-layer nested approach could find the optimal deterministic operation together with contract targets; (2) the total cascade output is related to both available energy and spot price, with the coefficients of determination for each month mostly greater than 0.5; and (3) the refined operating rules and contract effectively improved the annual hydropower revenue by 3.65% ($14.15 million) compared to that of the conventional operating rules. The proposed method to jointly derive forward contract and operating rules is helpful for gaining more revenue under long-term hydropower operations.

Changes in surface or groundwater management influence water use patterns as well as the economic value and sustainability of all water uses. In water-scarce regions, programs that establish environmental flows usually involve reallocating water from another productive use. Few peer-reviewed papers to date have investigated impacts on system-wide economic performance resulting from environmental flow regimes. This work presents an original approach to address that gap by developing and applying a basin-scale hydroeconomic optimization model of North America’s Middle Rio Grande Basin to explore impacts of environmental pulse flows on the region’s economy and water stocks. The model accounts for surface and groundwater storage, irrigation, urban, recreational, and environmental demands; surface water inflows under various climate scenarios; groundwater pumping and recharge; substitute water prices; crop water use; evaporation; as well as institutional constraints governing water use. Results show that climate change, in the form of highly variable inflows, has an impact on the total and marginal cost of implementing environmental pulse flows, amplified by the conjunctive nature of the system.

Accurate prediction of water mains condition is critical for effective rehabilitation planning. Advances in machine learning techniques can improve condition predictions. This paper compares the capabilities of various data mining techniques in predicting the condition of water mains. Predictive models investigated include generalized linear model, deep learning, decision tree, random forest, XGBoost, AdaBoost, and support vector machines. Models are first constructed leveraging a portion of the City of Waterloo, Canada, database. Genetic algorithm and cross-validation are then employed to optimize the hyperparameter tuning process. Several performance metrics and statistical tests are employed to compare the performance of the developed models utilizing a new set of data not previously used. The XGBoost model yielded the most promising results, with a mean relative error of 1.29%. Water main conditions are numerically represented on a scale from 0 to 10, with 10 indicating the highest condition. Extensive sensitivity analysis is conducted to obtain deeper insights into the most critical attributes for condition prediction. The developed model may help city managers develop optimal rehabilitation and renewal plans, considering the current and expected condition of their pipe inventory.

The real-time control of water distribution networks for firefighting is an important but little-studied problem. Water utilities in England and Wales reduce hydraulic pressure to a minimum regulatory threshold in order to reduce leakage and avoid financial penalties. However, utilities are not legally bound to guarantee specific flow rates from fire hydrants, which poses a risk for firefighting. This study presents an adaptive nonlinear model predictive control scheme for water distribution networks integrating two modes of control: (1) normal control, when a network operates under normal conditions and the objective is to minimize energy costs and average zonal pressure, and (2) fire control, when fire flows need to be delivered from hydrants without exceeding maximum pressure and customer demand is maintained to the best extent possible. The proposed scheme is applied to an operational network. It demonstrates the combined benefits of reducing costs and leakage with increased fire flows.

An iterative heuristic is proposed to improve the distribution of isolation valves within an existing water distribution network to decrease the magnitude of service interruptions while using the minimum number of valves required to isolate any individual segment within the system. The method takes advantage of graph theory concepts to create a valve augmentation scheme that provides gradual upgrades using the minimum number of new isolation valves at each step. The developed algorithm seeks to provide a tradeoff between an increase in the number of isolation valves and a reduction in water shortages resulting from disconnected pipe segments. The approach is applied to an actual water distribution network with known existing valve locations. The results demonstrate the feasibility and utility of the procedure for multiple operational constraints (i.e., maximum number of valves per segment, or maximum allowable water shortage evaluated over all segments). By use of an incremental performance target set by the user, the algorithm can prove beneficial even for utilities with limited financial resources.

This study introduces a novel hybrid leak detection method based on machine learning (ML) and hydraulic transient modeling for pipe networks. First, the transient hydraulic simulation model is developed in the time domain. Then, the optimum measurement sites for sampling the network’s hydraulic responses are determined using a graph-based method. The model exploits the network’s high-frequency transient responses at measurement sites to generate data sets. The generated samples are transformed into the frequency domain using the fast Fourier transform (FFT). The neighborhood component analysis (NCA) is used for feature selection and the optimum classifier is selected by comparing the performance of different classification algorithms. The model is finally applied to two case studies: an experimental reservoir-pipe-valve (RPV) system and a complex water distribution network (WDN). The accuracy of leak detection is evaluated considering fast and slow transient excitations concerning various levels of uncertainty in the system parameters. The results indicated that the model could detect leaks accurately and is stable and reliable against high uncertainties in pipe friction factors and nodal demands.

Apparent water losses can be problematic to water companies’ revenues. This type of loss is very difficult to detect and quantify and is often associated with water meter anomalies. This study was motivated by a water company’s challenge that links a decrease in water consumption to water meters’ malfunction. The aim is to develop a strategy to detect decreasing water usage patterns, contributing to meter performance assessment. The basis of the approach is a combination of statistical methods. First, the time series of billed water consumption is decomposed using Seasonal-Trend decomposition based on Loess. Next, breakpoint analysis is performed on the seasonally adjusted time series. After that, the Mann–Kendall test and Sen’s slope estimator are used to analyze periods of progressive decrease changes in water consumption, defined by breakpoints. A quantitative indicator of this change is proposed. The strategy was successfully applied to eight-time series of water consumption from the Algarve, Portugal.

Sediment accumulation in reservoirs across the world diminishes reservoir benefits, increases maintenance costs, and damages the environment. United States regulatory policy requires State and Federal permits for discharging sediment into waterways, which has tended to discourage alternatives that pass sediment downstream of the dam. The cost of methods that remove the sediment from the fluvial system is usually prohibitive and is a major factor precluding sustainable sediment management in the United States and other countries with similar sediment reduction policies. This paper challenges the notion that US policy requires sediment removal and terrestrial storage by highlighting seven case studies in the United States where reservoir sediment management included downstream sediment discharge. Where technically feasible, these types of solutions can lead to greater economic and environmental sustainability of US reservoirs.

Climate change impacts on hydrological processes can affect reservoir operational performance. Hence, the reservoir operation model, based on historical climate conditions, may not guarantee sustainable water resources management in the future. To enable stakeholders to design reliable adaptation strategies, this study aims to propose a cascading framework to quantify the impacts of climate change on the operational performance and sustainability of a multipurpose reservoir. The Danjiangkou Reservoir (DJKR), which serves as the water source for the middle route of the South-to-North Water Diversion Project in China, was selected as a case study. To achieve the aforementioned aims, bias-corrected simulations from 13 global climate models (GCMs) were first input into five hydrological models [i.e., one data-driven [deep belief network (DBN)], three conceptual [SIMHYD, HBV, and Xin’anjiang (XAJ)], and one physically-based [variable infiltration capacity (VIC)]. The simulated reservoir inflows were then fed into a 10-day reservoir simulation model where DJKR operation followed the designed operating rules to evaluate reservoir operational performance. Finally, a data envelopment analysis (DEA) model was proposed to assess reservoir sustainability under both historical (1976–2005) and future (2021–2050) climate conditions. The results show that the combination of the GCM ensembles and the SIMHYD, HBV, XAJ, and VIC models exhibit similar growth patterns in the reservoir inflow and operational benefits for the future period. However, the DBN model produces consistent decreases in most cases, which may be attributed to its inability to generate accurate estimates of extreme events. The results indicate that hydrological models may be extensively utilized in decision making with greater confidence, and the data-driven model should be interpreted with caution when used in hydrological climate change impact studies. The efficiency metrics suggest that decision makers should focus more on increasing operational benefits, which can subsequently enhance reservoir sustainability. Overall, the framework proposed in this study provides a foundation for evaluating the reservoir sustainability and adaptability to climate change from water managers’ perspective.

The sustainable management of water infrastructure in low-income communities is a development objective that cuts across several global development goals, including poverty alleviation and environmental sustainability. The monitoring of water infrastructure in developing communities is essential to ensuring the reliability of services and is a requisite for long-term sustainability. Development organizations, government agencies, and communities, however, lack tools to measure reliability and evaluate performance characteristics. The aim of this paper is to demonstrate an innovative approach to performance monitoring and show evidence that water quantity performance is linked to water management. This study included the monitoring of 17 piped water systems in Madagascar and Nicaragua, wherein the reliability and availability of water was evaluated. A strength of management analysis reveals that good management improves both reliability and the availability of water. The conclusions from this study show scientific evidence that good management prevents system failure and that development agencies should focus efforts to improve local capacity. Recommendations associated with this study support the need for remote monitoring and better evaluation tools to ensure the sustainable management of water infrastructure.

A permanent “leak-before-break” type acoustic monitoring system has been deployed by the South Australian Water Corporation across the Adelaide City Central Business District (CBD) since early 2017. Several analytic approaches applied to the collected acoustic data and results, in terms of reducing the number of uncontrolled pipe main breaks, have been reported. New statistical approaches to the analysis of the data are being continually developed with a statistical method based on the rate of change of the acoustic data within measured spectrums reported in this paper. The method involves the determination of short and long-term noise power level benchmarks, using noise power levels in an empirically determined number of windows with different frequency bin widths, to determine the rate of change of power levels across the frequency spectrum. The rate of change is quantified via the determination of normalized power level changes over weekly and monthly periods and the calculation of noise ratios relative to earlier weekly and monthly periods. Thresholds are applied to determine whether a weekly or monthly change alert should be raised for the investigation of potential cracks.

Local governments in the US routinely provide water services drinking water delivery and wastewater treatment. After the Great Depression, the federal government shared the financial responsibility for water services. However, over the last 3 decades, federal support has declined, exposing local governments to greater financial risk. We analyzed the US municipal bond market and the bond offerings of 25 Pennsylvania water utilities over a 30-year period to understand how financial conditions and risk have evolved for local government water utilities. The financial health of water utilities in the US is affected by monetary policy, federal tax policy, the broader financial (bond) market, regional corporate/industrial activity, and increased pressure to maintain credit quality. Utilities in our case study are persisting with a delicate balance of low interest rates for municipal bond issuances, moderate credit downgrades, and raising water rates to maintain credit quality. If one or more of these forces slips to a greater extent (e.g., higher interest rates, larger credit downgrades, or rising affordability crises), utilities could be left with few options to sustain the fiscal solvency needed to ensure safe and reliable water services. Water managers and engineers can use these findings to understand better how federal funding and policy, central bank decisions, and market sentiment affect municipal utilities and the population that they serve.

The use of assessment methods is essential for proper planning, improvement of the water system, and water economy in municipal water management. Thus, statistical model approaches can be used to evaluate the factors influencing water administration, understand their implications, forecast future use, and develop these systems accurately. The main purposes of this study are to reveal the current status of municipal water management in Turkey, assess the system with alternative regression models (with comparisons), and propose suggestions to managers as a decision support approach. Missing data imputation methods are practiced in order to improve the data set and model quality. According to the results, water extraction, income, and water treatment facilities are the most important issues in municipal water management. It is also seen from the modeling results that dams and treatment plants have a negative impact on municipal water use. In order to establish an adaptive municipal water management structure in Turkey, a number of management suggestions are proposed and assessed according to the models, which have intrinsic variable selection feature. Consequently, the least absolute shrinkage and selection operator (LASSO) may be practiced in short-term and cost-effective management planning. In long-term planning (with better model performance and high costs), elastic net and sparse least trimmed squares (LTS) may be preferred.