Journal of Water Resources Planning and Management

Properly designing contamination warning systems requires an understanding of potential public health impacts for a range of contaminated water systems and a wide range of contaminants. To address this need, we determined potential impacts for 12 actual systems serving populations ranging from $\sim {10}^4$ to over ${10}^6$ persons by simulating contamination events for the systems. We found several consistent patterns in the estimated impacts (defined as the size of the population receiving an ingestion dose above a given level). Significant impacts, those similar to worst-case impacts, result from injections of contaminants at only a minority of nodes. For contaminants with high thresholds for adverse effects, significant exposures are concentrated near the injection location, and impacts are not sensitive to population served. However, for contaminants with low thresholds, significant exposures are present over a significant fraction of the system, and impacts are sensitive to population. When exposures are concentrated near the injection node, the area affected tends to decrease with increasing population density. Accounting for all possible exposures and events may complicate the design of contamination warning systems.

To protect drinking water systems, a contamination warning system can use in-line sensors to indicate possible accidental and deliberate contamination. Currently, reporting of an incident occurs when data from a single station detects an anomaly. This paper proposes an approach for combining data from multiple stations to reduce false background alarms. By considering the location and time of individual detections as points resulting from a random space-time point process, Kulldorff’s scan test can find statistically significant clusters of detections. Using EPANET to simulate contaminant plumes of varying sizes moving through a water network with varying amounts of sensing nodes, it is shown that the scan test can detect significant clusters of events. Also, these significant clusters can reduce the false alarms resulting from background noise and the clusters can help indicate the time and source location of the contaminant. Fusion of monitoring station results within a moderately sized network show false alarm errors are reduced by three orders of magnitude using the scan test.

To provide more accurate estimates and account for associated uncertainties, a parameter estimation methodology for water distribution systems (WDSs) that combines demand and parameter estimation processes is proposed. A two-step sequential method for dual estimation of demand and roughness coefficient is presented based on a weighted least-squares scheme using field measurements of pipe flow rates and nodal pressure heads under multiple demand conditions. The uncertainties in the estimated variables and resulting nodal pressure predictions are quantified in terms of confidence limits using the first-order second moment method. The algorithm is applied to two network systems including a midsized real WDS. The two-step sequential model provides accurate and precise estimates while joint estimation provides poor estimates.

This paper presents an optimization methodology to calibrate the nodal demand in the model of water supply distribution systems. The matrix analysis has been carried out to get the sensitive coefficients matrix for the model of water distribution systems. Singular value decomposition is applied to calculate search director of optimization by solving the sensitive coefficients matrix, which is more efficient than the finite-difference method. Two cases are used to show the performance of this algorithm. The first case is a hypothetical water distribution network showing the calculation of the optimization vector. The second case is the application of real-life water distribution system in Hangzhou, China, which shows the determination of the cutoff singular value and the search step size in one-dimensional search. The calibrated model has been also validated using the measured data. The results have shown that the proposed algorithm is reliable and effective in real-time system.

Models used for future based scenarios should be calibrated with historical water supply and use data. Historical water records in Australia are discontinuous, incomplete, and often incongruently disaggregated. We present a systematic method to produce a coherent reconstruction of the historical provision and consumption of water in catchments of the State of Victoria. This is demonstrated using water account system (WAS): an accounting and simulation tool that tracks the stocks and flows of physical quantities relating to the water system. The WAS is part of, and informed by, an integrated framework of stocks and flows calculators for simulating long-term interactions between other sectors of the physical economy. Both the WAS and related frameworks consider a wide scope of inputs regarding population, land use, energy, and water. The physical history of the water sector is reconstructed by integrating water data with these information sources using a data modeling process that resolves conflicts and deduces missing information. The WAS outputs demonstrate the water and energy implications of the treatment, delivery, and end use of water cognizant of historical records.

A new methodology is developed to analyze existing monitoring networks. This methodology incorporates different aspects of monitoring, including vulnerability/probability assessment, environmental health risk, the value of information, and redundancy reduction. A conceptual framework for groundwater quality monitoring is formulated to represent the methodology’s context. Relevance vector machine (RVM) plays a basic role in this conceptual framework, and is employed to reduce redundancy and to create probability map of contaminant distribution, and accordingly to estimate the expected value of sample information. Disability adjusted life years approach of the global burden of disease is used for quantifying the health risk consequences. This is demonstrated through a case study application to nitrate contamination monitoring in the West Bank, Palestine. The results obtained from the RVM analysis showed that an overlap error of less than 30% were obtained based on using around 30% of the monitoring sites (170 relevance vectors). This reflects the importance of the RVM as a useful model for improving the efficiency of monitoring systems, both in terms of reducing redundancy and increasing the information content of the collected data. However, in this application, the results of health risk assessment and the evaluation of monitoring investments were less encouraging due to the minimal elasticity of the nitrate health effect with respect to monitoring information and uncertainty.

The water planning and management policies of the Rio Grande basin no longer respond to the sustainable needs of water users, environment, and international commitments of this transboundary basin between Mexico and the United States. This paper describes how groundwater banking through an in lieu method is one approach leading to better water management in this basin. In lieu groundwater banking is a conjunctive water allocation policy applicable to water users supplied from surface water and groundwater sources. A basin simulation model of the Rio Grande basin, built in the water evaluation and planning system (WEAP) software, was used to evaluate the groundwater banking policy. Two scenarios are discussed: a baseline scenario without new water allocation policies implemented and a groundwater banking scenario considering the in lieu groundwater banking method implemented in the Meoqui aquifer. Results show that groundwater banking can significantly improve water management in the basin, increasing system storage, improving water supply for users in the basin, and enhancing compliance with the treaty obligations.

This paper investigates the value of ensemble streamflow predictions and energy price forecasts as aid to decision makers in scheduling the quantity and timing of reservoir releases for daily, weekly, and seasonal operations while meeting regulatory constraints. A decision support system (DSS) is described as it incorporates two integrated models of system operation: a simulation model that replicates general operating rules for the hydropower system and an optimization model that refines operations based upon forecasts of state variables. The DSS provides a series of recommendations for the quantity and timing of reservoir releases to optimize the economic value of the electrical energy produced, while balancing requirements and concerns related to flood control, environmental flows, and water supply. The DSS generates a range of optimal reservoir releases using an ensemble streamflow forecast and identifies robust operational solutions. The results indicate the value of the forecasts in improving system operation.

Water quality management is subject to large uncertainties due to inherent randomness in the natural system and vagueness in the decision-making process. For water quality management optimization models, this means that some model coefficients can be represented by probability distributions, while others can be expressed only by ranges. Interval linear programming (ILP) and risk explicit interval linear programming (REILP) models for optimal load reduction at the watershed scale are developed for the management of Lake Qionghai Watershed, China. The optimal solution space of an ILP model is represented using intervals corresponding to the lower and upper bounds of each decision variable. The REILP model extends the ILP model through introducing a risk function and aspiration levels $\left({\lambda }_{\text{pre}}\right)$ into the model formulation. The REILP model is able to generate practical solutions and trade-offs through solving a series of submodels, minimizing the risk function under different aspiration levels. This is illustrated in the present study by solving 11 submodels corresponding to different aspiration levels. The results show that the ILP model suffers severe limitations in practical decision support, while the REILP model can generate solutions explicitly relating system performance to risk level. Weighing the optimal solutions and corresponding risk factors, decision makers can develop an efficient and practical implementation plan based directly on the REILP solution.

This paper examines the effect on economic welfare of alternative surface water allocations in the Alberta portion of the South Saskatchewan River Basin in Canada. Growing demands for urban and instream water uses add to significant irrigation withdrawals, all contributing to relative water scarcity. A computational model optimizes the annual value derived from spatially diverse water withdrawals, fully respecting their hydrologic and economic linkages. The model simulates current allocation practices and defines optimal allocations under alternative demand and supply scenarios. The use of the publicly developed Aquarius modeling software illustrates its comparative strengths relative to other models that optimize explicitly with respect to economic values. The model’s numerical estimates support the conclusion that there are sufficient surface water resources for users to adapt to a range of alterations in water supply or water demand, but that substantial short-term reallocations of water among users might be required to achieve the greatest benefit.

Agricultural activities are the main contributors of nonpoint source water pollution within agricultural systems. In this study, a possibilistic stochastic water management (PSWM) model is developed and applied to a case study of water quality management within an agricultural system in China. This study is a first application of hybrid possibilistic chance-constrained programming approach to nonpoint source water quality management problems within an agricultural system. Hybrid uncertainties with the synergy of fuzzy and stochastic implications are effectively characterized by the PSWM model with the following advantages: (1) it improves upon the existing possibilistic and chance-constrained programming methods through direct incorporation of fuzziness and randomness within a general optimization framework; (2) it will not lead to more complicated intermediate models and thus have lower computational requirements; (3) its solutions offer flexibility in interpreting the results and reflect the interactional effects of uncertain parameters on system conditions variations; and (4) it can help examine the risk of violating system constraints and the associated consequences. The results of the case study show useful information for feasible decision schemes of agricultural activities, including the trade-offs between economic and environmental considerations. Moreover, a strong desire to acquire high agricultural income will run into the risk of potentially violating the related water quality standards, while willingness to accept low agricultural income will increase the risk of potential system failure (violating system constraints). The results suggest that the developed approach is also applicable to many practical problems where hybrid uncertainties exist.

This study develops procedures that calculate optimal water release curtailments during droughts using a future value function derived with a sampling stochastic dynamic programming model. Triggers that switch between a normal operating policy and an emergency operating policy (EOP) are based on initial reservoir storage values representing a 95% water supply reliability and an aggregate drought index that employs 6-month cumulative rainfall and 4-month cumulative streamflow. To verify the effectiveness of the method, a cross-validation scheme (using 2,100 combination sets) is employed to simulate the Geum River basin system in Korea. The simulation results demonstrate that the EOP approach: (1) reduces the maximum water shortage; (2) is most valuable when the initial storages of the drawdown period are low; and (3) is superior to other approaches when explicitly considering forecast uncertainty.

The California Department of Water Resources (DWR) and U.S. Bureau of Reclamation Mid-Pacific Region (Reclamation) have developed a rapid, interactive screening model, named CalLite, for evaluating various Central Valley water management options. The CalLite model is being used as a computer-aided negotiations tool in a variety of stakeholder’s processes for improved understanding of water system operations and consensus-based decision making. CalLite was developed using the GoldSim system dynamics software that enables simulation of complex process through a buildup of simple object relationships, incorporates Monte Carlo stochastic methods, and includes dynamic, interactive user interfaces. The screening tool models much of the Central Valley of California that contributes flow to the Delta. The geographical coverage includes the Upper Trinity River, the Sacramento River Valley, the San Joaquin River Valley, the Sacramento–San Joaquin Delta, and the Central Valley Project and the State Water Project service areas. The model allows interactive modification of a variety of water management actions including alternative conveyance options, off-stream storage reservoirs, groundwater management programs, demand management, and Delta channel flow and salinity targets. While CalLite maintains the hydrologic, operational, and institution integrity as represented in the companion model CalSim, very detailed simulation model of the California Water Resources system, also developed and maintained by DWR and Reclamation. The CalLite is easy to use and reduces run time significantly. The simulation results obtained from a typical CalLite run are within 5% of a corresponding CalSim run while run time is less than 5 min compared to 30 min for a corresponding CalSim run.

The aim of the paper is to present two optimization methodologies applied to a real water distribution system. The first one involves model calibration, while the second addresses the problem of optimal leakage management. Model calibration is based on a single objective, real-coded genetic algorithm, which provides optimal values of pipe friction factors and a leakage-dependent coefficient. Leakage management is accomplished through the introduction and regulation of pressure reducing valves. To this end, the writers adopted an approach based on multiobjective genetic algorithms, searching for the optimal placement and regulation of such valves. In particular, the first criterion is represented by the minimization of the number of valves, while the second is the minimization of the total leakage in the system. The algorithm provided several trade-off alternatives between leakage reduction and installation costs: the water utility managing the system chose to place and regulate the first four valves identified by the algorithm, resulting in a water loss reduction (as evidenced by the continuous monitoring of the system after valve installation) and in a decrease in pumping costs.

Calculating the optimum replacement period of meters has always been a major concern for water utility managers. Its determination is time-consuming and requires multiple calculations. This note presents a graphical method to obtain, in a simple but accurate manner, the optimum replacement period of installed meters. For this purpose, a chart has been produced, in which the most influencing variables are considered. These variables include the degradation rate of the weighted error of the meters, the selling price of water, the acquisition and installation cost of the meters, the volume consumed by the users and the discount rate. The chart also allows for a quick sensitivity analysis of different options. For example, by plotting straight lines it is possible to determine by how much the optimum replacement frequency of a meter would change if it degrades at a different rate than expected or if the selling price of water increases.