Journal of Water Resources Planning and Management

Steady state analysis of flows and pressures in a water distribution network is a major concern for hydraulic engineers involved in the monitoring and control of water distribution systems. Most of the hydraulic network solution strategies currently available are meant for obtaining flows/pressures given a minimum number of measurements. If more measurements than the minimum required are available, then the redundant measurements can be exploited to obtain a better estimate of the state of the network, taking into account noise in these measurements. In this paper, a state estimation technique for well instrumented water distribution networks is described. The proposed method uses graph-theoretic concepts to reduce the dimensionality of the problem and thereby achieves significant computational efficiency. Applicability of the proposed state estimation procedure is demonstrated on realistic urban water distribution networks. It is also shown that the proposed method can be used for state estimation even when some of the demands are not measured.

Groundwater management models are often applied to problems in which the aquifer state is a mildly nonlinear function of managed stresses. The use of the successive linear programming algorithm to solve such problems is examined. The algorithm solves a series of linear programs, each assembled using a response matrix. At each iteration perturbation from the most recent value of the managed stresses is used to estimate response coefficients. Iterations continue until a convergence criterion is met. The algorithm is tested on a water supply problem in Antelope Valley, Calif. where large volumes of water are injected and extracted each year producing a significant nonlinear response in the unconfined aquifer. The algorithm is shown to perform well under a variety of settings.

Reliability of a “preassumed” optimal remediation design using a pump and treat method is evaluated. A “true” hydraulic conductivity field with a certain spatial correlation is supposed. Hydraulic conductivity values are sampled at several locations from the true field. The preassumed remediation design is optimized in the hydraulic conductivity field composed by interpolation of the sampled data. This design is applied to random conductivity fields generated by using conditional simulations in which the conductivity values sampled from the true field are used as the conditioning points. The remediation costs for each generated conductivity field are computed when a preassumed optimal remediation design is applied. The cumulative distribution functions (CDFs) of the required remediation costs are obtained. The CDFs of the remediation cost show that the density of sampling and concentrated sampling in a highly contaminated zone have a great influence on the reliability and uncertainty of the preassumed optimal remediation design.

Powerful simulation/optimization (S/O) models exist for designing groundwater well systems and pumping strategies. However, it can be challenging to use S/O modeling effectively for large, complex, and computationally intensive problems within project time and cost constraints. Here, we present a generic two-stage optimization procedure for making S/O modeling more practical. Application is illustrated for developing optimal transient 30-year pump-and-treat designs for Blaine Naval Ammunition Depot (NAD), Nebraska, and using an innovative hybrid advanced genetic algorithm with tabu search features (AGT). AGT includes standard genetic algorithm and tabu search features plus healing, elitism, threshold acceptance, and a new subset/subspace decomposition optimization. The screening stage simplifies the optimization problem, and selects desirable remediation wells from among many candidates. During this stage, computational effort is lessened by reducing the number of state variables needing evaluation, and the solution space dimensionality (including temporal dimensions). Subset/subspace decomposition optimization of steady flow rates is used to identify desirable sets of candidate wells. The transient optimization stage develops mathematically optimal time-varying pumping rates for well subsets identified by the screening stage. It also includes reoptimization using the original objective function plus goal programming to increase strategy robustness. Initializing the AGT with feasible solutions reduces computational effort. Within a short period the procedure developed optimal pump and treat system designs for NAD. The procedure yields better objective function values than trial and error. Because optimization causes tight constraints, the computed strategy is sensitive to changes in model parameters. Increasing strategy robustness via AGT and goal programming degrades the value of the initial objective function.

Retention basins are an effective and common means of flood control. Differing from detention, retention fully contains a particular storm event, using infiltration as the primary means of disposal. For a complex watershed with multiple subwatersheds; however, requiring basins to retain only flow from their contributing subwatershed may result in a suboptimal solution both in terms of cost and overall flood control. This paper introduces a design procedure for considering location and sizing of a network of retention basins within a watershed. It utilizes the discrete dynamic programming technique to determine the ideal locations and geometry for each basin. A design example demonstrates that this method may allow significant cost savings over traditional methods.

Real-time reservoir operation models carried out under short operating horizons have no information about the seasonal changes in streamflow. The overall operation may improve provided that long-term information can be incorporated into the short-term process. This paper proposes a procedure that attempts to do such incorporation by means of stochastic and deterministic optimization. Long-term (monthly) and short-term (daily) reservoir inflow forecasts are assumed to be uncertain and reliable, respectively. The idea is to first solve a monthly operation model by an explicit stochastic programming approach and then use its information to guide the daily operation, which is solved by deterministic optimization. The monthly information will decide whether hedging should start being implemented during the daily operation in order to mitigate a potential future shortage. This stochastic-deterministic procedure is applied to a case study in Japan and the results show that it provides operating rules relatively similar to the ones obtained when perfect forecasts are used for the monthly inflows.

The quantity of a contaminant ingested by individuals using tap water drawn from a water distribution system during a contamination event depends on the concentration of the contaminant in the water and the volume of water ingested. If the concentration varies with time, the actual time of exposure affects the quantity ingested. The influence of the timing of exposure and of individual variability in the volume of water ingested on estimated impacts for a contamination event has received limited attention. We examine the significance of ingestion timing and variability in the volume of water ingested by using a number of models for ingestion timing and volume. Contaminant concentrations were obtained from simulations of an actual distribution system for cases involving contaminant injections lasting from 1 to $24\mathrm{h}$ . We find that assumptions about exposure can significantly influence estimated impacts, especially when injection durations are short and impact thresholds are high. The influence of ingestion timing and volume should be considered when assessing impacts for contamination events.

This paper introduces a simple topological approach to systematize the isolation of contaminated areas within the pressure zones of drinking water distribution systems (DWDSs). Assuming optimal location of contaminant detectors and known flow conditions, a heuristic procedure delineates the area to be isolated and identifies the valves to be closed by response teams sent in the field, taking into account a response delay from the time of first detection. As a first step leading to the development of a more comprehensive algorithmic application, the approach was elaborated and validated from a pragmatic perspective using two real-world DWDSs. Depending on each network’s design, configuration, and assumed flow conditions, application of the isolation procedure will result in different isolation strategies (extent of isolated areas, number of required isolation valves). The current approach is based on a set of simplifying assumptions, and needs to be further validated with other networks. The proposed methodology can be used to assess the required emergency response capabilities and even possible network design improvements.

Management of transboundary water resources has been the focus of many studies due to the observed water stress all over the globe combined with the continually increasing water demand. States sharing water resources often proceed to negotiations regarding water allocation issues and other managerial issues in order to settle their disputes. During such negotiations the use of appropriate tools, which will provide assistance to the opponent parties in terms of scenarios quantification and simulation processes, is a vital component toward conflict resolution. The present paper implements game theoretical concepts in the case study of Greek–Bulgarian negotiations on Nestos/Mesta transboundary river. The quantification of consequences caused by water flow decrease for different scenarios provides the background for estimations of compromising solutions that could be acceptable to both countries. Moreover, implementing interconnected games widens the countries’ available options and contributes to the avoidance of unreasonable outcomes while balancing uneven “power” potentials. The different utilities of the calculated payoffs to each country are leveled by the incorporation of socioeconomic factors reflecting the differences observed in the development level of the two countries.

To improve water quality and enhance habitat for fish and wildlife, a restoration project was initiated on the Truckee River at McCarran Ranch, Nevada. A water quality model was parameterized using Hydrologic Simulation Program–Fortran to represent the river’s restored characteristics to test the hypothesis that restoration of the Truckee River improves the river’s nitrogen (N) assimilative capacity. This model was calibrated to data collected postrestoration, and modified to represent prerestoration conditions and additional potential restoration scenarios. Restoration scenarios involved changes in benthic algae habitat that affect in-stream N and dissolved oxygen (DO) concentrations. Assimilative capacity was assessed by comparing N loads for restoration scenarios that would result in meeting or exceeding prerestoration minimum DO concentration simulated under 2004 conditions. Modeled algal dynamics affected model results, which indicated that there is a certain amount of increased habitat that is beneficial to N uptake while maintaining acceptable DO concentrations, and that the amount and species of N assimilated depends on the type and extent of restoration. However, evaluation of the modeling approach highlights some critical model limitations. Thus, interpretation of model results should be used with caution.