Journal of Water Resources Planning and Management

Water distribution networks (WDN) are traditionally designed without integrating tanks in the optimization process, despite the numerous benefits tanks bring to network operation. Since 2004, a number of efforts have been made to integrate tanks, and especially consideration of tank sizing, in optimization models for WDN, but important limitations in these models have impeded their adoption. This paper reviews different approaches to including tank consideration in WDN optimization, and examines the gap between proposed models and current engineering practice. It then analyzes the strengths and weaknesses of these models and suggests a procedure for selecting the most suitable approach to real-world WDN optimization projects. Further research is proposed to improve the validity and applicability of models to decision makers and policymakers.

This paper reviews the literature on urban water demand forecasting published from 2000 to 2010 to identify methods and models useful for specific water utility decision making problems. Results show that although a wide variety of methods and models have attracted attention, applications of these models differ, depending on the forecast variable, its periodicity and the forecast horizon. Whereas artificial neural networks are more likely to be used for short-term forecasting, econometric models, coupled with simulation or scenario-based forecasting, tend to be used for long-term strategic decisions. Much more attention needs to be given to probabilistic forecasting methods if utilities are to make decisions that reflect the level of uncertainty in future demand forecasts.

When designing a water distribution system (WDS), it is imperative that the reliability of the network is taken into consideration. It is possible to directly evaluate the reliability of a WDS, although the calculation processes involved are computationally intensive and thus undesirable for some state-of-the-art, iterative design approaches (such as optimization). Consequently, interest has recently grown in the use of reliability indicators, which are simpler and faster to evaluate than direct reliability methods. In this study, two existing reliability indicators, the Todini resilience index and entropy for WDS, are examined by analyzing their relationships with different subcategories of reliability, namely the mechanical (network tolerance to pipe failure) and hydraulic reliability (network tolerance to demand change). The analysis is performed by generating comparable solutions through multiobjective optimization of cost against the chosen indicators using the well known Anytown WDS benchmark as a case study. It is found that WDS solutions with high entropy have increased mechanical reliability, yet are expensive and have poor hydraulic operation and water quality. In contrast, high resilience index networks are relatively cheaper and present reasonable hydraulic operational performance, yet have limited improvement in mechanical reliability. Both indicators appear to be correlated to hydraulic reliability, but each has its own associated disadvantages. Including minimum surplus head as an additional objective in the optimization of the reliability indicators appeared to improve the performance. When optimized together, a trade-off between the two indicators is identified, implying that significantly increasing both simultaneously is not possible, and thus a new indicator is recommended to account for both the mechanical and hydraulic reliability while ensuring reasonable standards of hydraulic operation.

Water distribution system (WDS) models may improve system control when applied using real-time data, and in doing so, help meet consumer and regulatory demands. Such real-time modeling often overlooks the multiple sources of system uncertainty that cascade into model forecasts and affect the identification of robust operational solutions. This paper considers key uncertainties in WDS modeling and reviews promising approaches for uncertainty quantification and reduction in the modeling cascade from calibration, through data assimilation, to model forecasting. An uncertainty framework exemplifying how such methods may be applied to propagate uncertainty through the real-time control process is outlined. Innovative methods to constrain uncertainty when the time-horizon and data availability limit such thorough analysis are also discussed, alongside challenges that need to be addressed to incorporate uncertain information into the control decision. Further work evaluating the value of these methods in light of computational resources, and the nature of model errors in real WDS, is required. Such work is necessary to demonstrate the benefits of considering model and data uncertainty, leading to robust control decisions.

This work describes a multiobjective model for trading-off pumping cost and water quality for water distribution systems operation. Constraints are imposed on flows and pressures, on periodical tanks operation, and on tanks storage. The methodology links the multiobjective SPEA2 algorithm with EPANET, and is applied on two example applications of increasing complexity, under extended period simulation conditions and variable energy tariffs. The proposed approach enables decision makers to take full advantage of the obtained information on a multiobjective scale for trading-off, cost, water quality, and storage-reliability requirements. Verification of the model outcomes through engineering judgment on all runs for both example applications confirmed the model suitability as a decision tool. Limitations of the proposed model reside in using variable speed pumps with assumed constant efficiency as representing an entire pumping station operation, the storage reliability constraint as an u-priori set parameter, and in the computational intensity required to obtain solutions for real sized systems.

In the United States, there are approximately 84,000 dams including approximately 14,000 dams that are classified as high hazard. Approximately 50% of high hazard dams do not have an emergency action plan (EAP), a document describing potential emergency conditions and potential areas at risk of flooding. A critical data set required for identifying flood risk regions through modeling and simulation is digital elevation models (DEM). These data have become increasingly available at high resolution. The difficulty in utilizing the higher resolution data is that model computation time is increased drastically and becomes, in the case of wide-area (regional) analyses, infeasible to use. The tendency for modelers, therefore, is to use lower resolution data for these model applications. It is clear that when using the lower resolution data that topographic features are not represented as well, but it is not as clear what impact this has on two-dimensional modeling and flood risk estimation. Additionally, there is no rule of thumb as to which resolution should be used. This paper evaluates the impact grid resolution has on estimating the flood risk area resulting from dam failures using two-dimensional models. Results indicate that while flood extent, depths, and flood wave timing are sensitive to grid resolution, socioeconomic metrics such as population at risk and economic loss are less sensitive to simulation grid resolution. This observed socioeconomic insensitivity validates the potential of using coarse resolution simulation as a flood screening tool or in emergency response situations.

Effective flood risk management requires consideration of a range of different mitigation measures. Depending on the location, these could include structural or nonstructural measures as well as maintenance regimes for existing levee systems. Risk analysis models are used to quantify the benefits, in terms of risk reduction, when introducing different measures; further investigation is required to identify the most appropriate solution to implement. Effective flood risk management decision making requires consideration of a range of performance criteria. Determining the better performing strategies, according to multiple criteria, can be a challenge. This article describes the development of a decision support system that couples a multiobjective optimization algorithm with a flood risk analysis model and an automated cost model. The system has the ability to generate potential mitigation measures that are implemented at different points in time. It then optimizes the performance of the mitigation measures against multiple criteria. The decision support system is applied to an area of the Thames Estuary and the results obtained demonstrate the benefits multiobjective optimization can bring to flood risk management.

Hydroreservoirs usually serve two main purposes: hydropower production and water consumption. The great flexibility, low operating costs, and low carbon impact of hydroturbines turns them into a desirable technology in the generator mix of power systems. In addition, sustainability and environmental concerns support their use in current power systems, along with other renewable energy sources like wind and solar energy. However, the stochastic nature of river inflows hinders their long-term use and hints at the need to use planning tools. Furthermore, it also requires the use of planning tools in order to balance present and future requirements. This work presents a simulation tool that is employed at Iberdrola to help in the preparation of medium-term hydroelectric production schedules. The main objective of the simulation is to follow the production guidelines given by a long-term hydrothermal problem, while avoiding spillages and failures to fulfill water release agreements. In order to achieve this, the simulation algorithm is structured around several phases that aim at coordinating the operation of all the elements in the basin. This way, the simulation tool provides the operator a way to evaluate the outcome from forecasts of either water inflows or future operation situations. Some of the potential applications of this simulation tool are shown in this work: general simulation in order to know in advance the consequences of possible inflow forecasts, and how to assess several maintenance schedules and different upgrade plans.

As one of the three key components of state of Georgia’s state water plan, a surface water availability resource assessment (SWRA) involves three sequential steps, as follows: (1) synthesizing unimpaired flow (UIF) data by removing anthropogenic effects from streamflow observations, (2) developing instream flow protection thresholds based on the UIF data, and (3) assessing possible resource gaps based on UIF data, flow protection thresholds, and water demands. Uncertainties are embedded in the observed input data and development processes, and could be propagated through these steps. A legitimate question pertains to the reliability of the SWRA results since they may affect regional water development plans. In this study, the uncertainties in all input data and development processes of SWRA were identified and quantified through Monte Carlo simulations. Results at two typical nodes indicate that uncertainties associated with the derived UIF data, flow thresholds, and SWRA results are very limited. The largest uncertainty came from the flow data-filling process. The most efficient way to reduce the uncertainty in future resource assessments is to maintain relatively complete flow and water use records.

This study proposes an interval semiinfinite De Novo programming (ISIDP) method for the planning of water resource management systems under uncertainty. The ISIDP problem is settled by dividing it into two interactive linear programming subproblems and solving it by using a conventional simplex method. To evaluate the applicability of the proposed model, the method is applied to a case study of the Yuecheng Reservoir in Zhangweinan River Basin, China. The results indicate that the strategies generated through ISIDP would not increase the complexity in decision-making processes. Compared with the conventional optimization method, ISIDP has the advantages of (1) better reflecting the association of the system benefits with water price, (2) generating more reliable solutions with a lower risk of system failure as a result of the possible violation of constraints, and (3) providing more flexible management planning because the availability of budgets can be adjusted with the variations in water price.

Through a simulation modeling approach, this paper seeks to modify the design of detention ponds to preserve the natural ecological flows while satisfying the requisite regulatory flow requirements. This paper will utilize an innovative ecological flow paradigm: the ecoflow statistics. The ecoflow statistics consist of nine hydrological flow statistics that have been shown to be particularly relevant to ecological quality. The statistics include annual and seasonal ecodeficits and ecosurplus, calculated using median annual and seasonal flow duration curves, and the total seasonal ecochange. A new metric called the ecodifference is defined as the weighted sum of the nine ecoflow statistics and represents the hydrologic alteration in the stream. The ecodifference in a receiving stream can be calculated using the outflow hydrograph from a detention pond hydrologic simulator. A design approach using a hydrologic model, detention pond model, and the ecodifference metric will be used to design a series of flow controls in a detention pond outlet control structure that reduces the ecological impact to the stream caused by development while meeting current design regulations. For a case study site, trial designs have demonstrated that improvements in ecological flows can be achieved while meeting design regulations. By introducing this approach for ecological detention ponds and then demonstrating its performance, this paper has potential to impact stormwater management design practice.

This paper presents a case study on the application of a dynamic framework for the intelligent control of flooding in the Boise River system in Idaho. This framework couples a robust and numerically efficient hydraulic routing approach with the popular multi-objective nondominated sorting genetic algorithm II (NSGA-II). The novelty of this framework is that it allows for controlled flooding when the conveyance capacity of the river system is exceeded or is about to exceed. Controlled flooding is based on weight factors assigned to each reach of the system, depending on the amount of damage that would occur, should a flood occur. For example, an urban setting would receive a higher weight factor than a rural or agricultural area. The weight factor for a reach does not need to be constant as it can be made a function of the flooding volume (or water stage) in the reach. The optimization algorithm minimizes flood damage by favoring low-weighted floodplain areas (e.g., rural areas) rather than high-weighted areas (e.g., urban areas) for the overbank flows. The proposed framework has the potential to improve water management and use of flood-prone areas in river systems, especially of those systems subjected to frequent flooding. This work is part of a long-term project that aims to develop a reservoir operation model that combines short-term objectives (e.g., flooding) and long-term objectives (e.g., hydropower, irrigation, water supply). The scope of this first paper is limited to the application of the proposed framework to flood control. Results for the Boise River system show a promising outcome in the application of this framework for flood control.