Journal of Water Resources Planning and Management

The historical development of environmental stressors in the watershed of Onondaga Lake (N.Y.) and the adjoining Seneca River is outlined as a prelude to the presentation of a novel approach for accessing assimilative capacity in this water quality-limited system. Proposed efforts to reclaim lost uses in the lake through heroic treatment at a major metropolitan wastewater treatment plant with continued discharge to the lake have been called into question. One option, diversion of the treatment plant effluent to the Seneca River, is complicated by phenomena impacting receiving water oxygen resources. A dual discharge strategy, which takes advantage of seasonal variations in assimilative capacity and the differing response times of the lake–river systems, is proposed as an alternative. In this approach, the effluent is routed to the river except when oxygen standards may be compromised, in which case all or part of the effluent is routed to the lake. Mathematical models demonstrate the feasibility of this option to meet water quality goals for both the lake (phosphorus) and the river (oxygen).

This paper presents a stochastic groundwater management model explicitly considering land subsidence. Through the use of response matrix technique and one-dimensional consolidation equation, a deterministic management model is first developed. By Latin hypercube sampling technique, along with numerical subsurface flow simulation, statistical features of unit response coefficients due to random hydrogeologic parameters, including hydraulic conductivity $\left(K\right)$ and Lame constants ( $\mu$ and $\lambda$ ), are quantified. The first-order-variance-estimation method is adopted to analyze the uncertainties of drawdown and land subsidence based on which the concept of chance-constrained programming is applied to transfer the original deterministic management model into its stochastic form. The stochastic management model enables the determination of optimal total pumpage subject to the constraints that drawdown and land subsidence do not exceed the allowable values with a specified reliability. A hypothetical example is utilized to demonstrate the applicability of the stochastic model to five cases in which various levels of parameter uncertainty are considered. The results indicate that joint consideration of drawdown and land subsidence is essential, and the proposed stochastic management model can be generally applied for regional groundwater resources management in conjunction with controlling land subsidence.

Owing to the conflict encountered between the two objectives of fixed cost in reservoir installation and operating cost in time-varying water deficit, multiobjective planning of surface water resources is a difficult job. Instead of combining these two objectives into just one objective using the weighting factor approach, this investigation proposes a novel method by integrating a multiobjective genetic algorithm (MOGA) with constrained differential dynamic programming (CDDP). A MOGA is employed to generate the various combinations of reservoir capacity and estimate the noninferior solution set. However, applying this algorithm to solve the dynamics of the operating cost, the number of variables increasing with time will dramatically increase the use of computational resources. Consequently, the CDDP is herein adopted to distribute optimal releases among reservoirs to satisfy water demand as much as possible. Next, the effectiveness of the proposed methodology is verified by solving a multiobjective planning problem of surface water in southern Taiwan. This real application demonstrates that MOGA can be linked with CDDP to resolve a complex water resources problem. Additionally, the ability of MOGA on addressing multiple objectives simultaneously without converting to a weighted objective function provides the opportunity for significant advancement in multiobjective optimization. Finally, this investigation also proposes three suitable strategies of reservoir construction to decision makers with budget concerns through the analysis of all noninferior solutions.

Reservoirs play an important role within the water resources management framework. Among the available techniques for reservoir optimal operation, the most well known one is stochastic dynamic programming (SDP). In recent years, artificial intelligence techniques such as genetic algorithms (GA) and artificial neural networks have arisen as an alternative to overcome some of the limitations of traditional methods. Some of these limitations are related to the difficulty in combining SDP with other simulation and prediction models, the curse of dimensionality due to the increase in the number of decision and state variables, and the error resulting from the rough discretization of these variables. Here, we introduce a new approach for system optimization and operation, named stochastic fuzzy neural network (SFNN), which can be defined as a neuro-fuzzy system that is stochastically trained (optimized) by a GA model to represent the system operational strategy. Moreover, to deal with imprecision originated by the discretization of inflow intervals (events) in calculating the transition probabilities, we applied the method based on the conditional probability of a fuzzy event. To investigate the applicability and efficiency of the proposed method, the Barra Bonita Reservoir, Brazil, is stochastically optimized and operated. The results found by the SFNN method were compared to the results of other available dynamic programming models, showing success in developing and applying the proposed method to optimal reservoir operation.

Fluid transients result in a substantial amount of data as pressure waves propagate throughout pipes. A new generation of leak detection and pipe roughness calibration techniques has arisen to exploit those data. Using the interactions of transient waves with leaks, the detection, location, and quantification of leakage using a combination of transient analysis and inverse mathematics is possible using inverse transient analysis (ITA). This paper presents further development of ITA and experimental observations for leak detection in a laboratory pipeline. The effects of data and model error on ITA results have been explored including strategies to minimize their effects using model error compensation techniques and ITA implementation approaches. The shape of the transient is important for successful application of ITA. A rapid input transient (which may be of small magnitude) contains maximum system response information, thus improving the uniqueness and quality of the ITA solution. The effect of using head measurements as boundary conditions for ITA has been shown to significantly reduce sensitivity, making both detection and quantification problematic. Model parsimony is used to limit the number of unknown leak candidates in ITA, thus reducing the minimization problem complexity. Experimental observations in a laboratory pipeline confirm the analysis and illustrate successful detection and quantification of both single and multiple leaks.

An iterative method based on the trade-off between reliability and cost is proposed for the strengthening and expansion of water distribution networks (WDNs) to obtain a level-one redundant network, i.e., a network that can sustain single pipe failure without affecting the consumer services either in part or in full. Reliability is assessed using the node and volume reliability factors which are evaluated considering the performance of WDNs under different conditions. Reliability of an existing network is improved at every iteration by either adding a parallel pipe of minimum size to one of the existing links, or increasing the size of one of the newly added (strengthened or expanded) pipes to the next higher size by considering the largest ratio of the marginal capacity factor to the marginal increase in cost. The method is initially illustrated with an example of a multisource network having time-varying demands. Then, a network in Washington, D.C. having multiple demand patterns is designed and the results are compared with those obtained by a linear-programming based algorithm.

The Philadelphia Water Department developed a unique, Web-based water quality forecasting system for the Schuylkill River called RiverCast. Based on real-time turbidity, flow, and rainfall data, it provides up-to-the-hour public service information on the estimated current fecal coliform concentrations in the river and the acceptable types of recreation based on those conditions. The system is designed to maximize accuracy while avoiding recommendations that suggest water quality is better than it is likely to be (avoidance of false positives). RiverCast can be reached on the Web at www.phillyrivercast.org.

A systems approach is used to model the urban water cycle. A model for analyzing the flows of water, energy, and chemicals and associated greenhouse gas emissions through the urban water infrastructure system is developed. A model is constructed to represent the City of Toronto urban water system from 2001 up to the year 2010. Scenarios are developed to assess the system-wide impacts of water distribution pipe renewal, sewer relining, demand management strategies, and energy recovery from anaerobically digested wastewater biosolids. Initiatives targeted at the early stages of the urban water cycle have greater positive downstream impacts on selected environmental indicators. Specifically, strategies aimed at reducing water demand produce more significant system-wide benefits. Demand management strategies aimed at reducing demand by 15% result in savings on the order of 12–18% for all environmental indicators. Demand management is also one of the most cost effective options.

An integrated planning activity was undertaken for the Amman-Zarqa Basin in Jordan to account for present and future uses of water resources including reclaimed water. Upgrades to existing wastewater treatment plants mean that new reclaimed water resources will be available in the future. To evaluate the impact of current and potential future uses on basin stakeholders, a spreadsheet-based model was developed to predict water quantity and quality at several key locations. The model consists of a flow component and a water quality component. The model was used to screen various water-use scenarios and to formulate feasible alternatives including the sequencing of future water demands. The use of a spreadsheet model allowed engineers and scientists within the Ministry of Water and Irrigation, and members of the consulting team who had limited modeling experience to be involved in the planning activity.