Journal of Water Resources Planning and Management

This paper reviews the principles of scenario-based planning and illustrates an application of this approach in a study of a regional water supply and demand in northeastern Illinois. The scenario-based planning has been adopted over the traditional approaches because the scenario approach facilitates an explicit consideration of uncertainty in both supply and demand. An important component of the study was the development of scenario-based forecasts of water demand for major user sectors and geographical subareas within the 11-county region which, includes the city of Chicago and 250 municipalities. The study revealed that the increase in total withdrawals in 2050, under baseline scenario, would be 530 mgd (millions of gallons per day) (36 %) and could reach as high as 949 mgd (64 %) under high demand growth scenario. Thus, meeting these additional demands would require large capital outlays for water infrastructure and would likely have significant impacts on some of the regional sources of water supply, especially groundwater aquifers and local rivers. The experience and results of the study indicate that a scenario-based approach is suitable for supporting stakeholder inputs into long-term management and planning of water supply and demand.

California’s Sacramento-San Joaquin Delta is the major hub of California’s water supply system and is central to the ecosystem of many native threatened and endangered species. Conflicts over the Delta have evolved over more than a century. This paper traces changes in this conflict in game-theoretic terms, with its implications for the region’s physical and ecological decline and governance. The Delta is not a zero-sum problem and win-win resolutions may exist if stakeholders cooperate. Game theory provides some insights on the potential for win-win solutions. The Delta problem has had a Prisoner’s Dilemma structure, in which stakeholder self-interest makes cooperation unlikely within a reasonable time frame. However, the core of the Delta conflict is changing as the unsustainable future becomes more widely understood. Today’s Delta problem has characteristics of a Chicken game, where cooperation is in everyone’s interest, but it is unlikely because parties deviating from the status quo are likely to bear more of the costs of a long-term solution. The State of California may become the victim (or chicken) of the Delta game, bearing the greatest costs, if it continues to rely on leaving parties to develop voluntary cooperative solutions without a sufficient mechanism for enforcing cooperation.

The objective of this paper is to assess the impact of proposed Canadian climate change mitigation policies (discounting and carbon pricing) on cost, energy use, and greenhouse gas (GHG) emissions in the single-objective design/expansion optimization of the Amherstview water distribution system in Amherstview, Ontario, Canada. The single-objective optimization problem is solved with the elitist genetic algorithm (EGA). The optimization approach is used in a parametric analysis to examine the impact of discounting and carbon pricing on GHG reductions for cement-mortar ductile iron and polyvinyl chloride pipe materials. Preliminary results indicate that the discount rate and carbon prices investigated had no significant influence on energy use and GHG mass in the Amherstview system and did not meet the emission-reduction targets set by the Canadian government. This result was attributed to a number of factors, including adequately installed hydraulic capacity in the Amherstview system, the use of a time-declining GHG emission intensity factor, and the scope of the expansion problem.

The stormwater investment strategy evaluation (StormWISE) model identifies the mix of characteristics that storm-water retrofit projects should have in an impaired watershed to maximize multiple water quality benefits subject to constraints on financial resources. The model can assist watershed managers faced with the daunting task of selecting cost-effective sites to implement best management practices (BMPs) and low-impact development (LID) technologies in response to watershed assessments that identify stream impairments. A methodology is provided to bridge the large gap between the general recommendations, typically found in watershed-level studies, and the ultimate site-specific decisions required at the land parcel level. A multiobjective optimization formulation is used to maximize water quality benefits expressed as functions of decision variables that set target levels for investment in projects according to two aggregated land parcel attributes, land use category, and watershed drainage zone, and according to the BMP/LID technologies deployed. Results take the form of constellations of Pareto-optimal solutions that guide the search for specific project sites and BMP/LID technologies to be implemented. This screening level analysis will usually be followed by identification of sites that possess high-priority attributes determined by the screening model for major investments for which the corresponding high-priority BMP/LID technologies can be deployed. If possible, candidate projects are subjected to further analysis through detailed watershed-level simulations. The theory underlying the model is presented, including development of water quality benefit functions and mathematical formulation of the optimization model as a piecewise linear program. A case study of the model’s application is included for the impaired Little Crum Creek watershed in suburban Philadelphia, where StormWISE has been used to develop an action plan for a partnership of four municipalities having storm sewers that discharge into the stream.

When decentralized strategies are considered for managing wastewater, natural systems for wastewater treatment (NSWT) (constructed wetlands) seem to be strongly preferred in comparison with the conventional activated sludge wastewater treatment systems. However, because of their high land requirements, land-use suitability analysis should be conducted to specify adequate areas for their accommodation. Multicriteria spatial decision support systems have emerged as the technology that takes into account both decision criteria and constraints in complex land-use planning problems. This study presents a multiattribute decision analysis approach called the spatial fuzzy Analytic Hierarchy Process (SFAHP) to support raster-based land-use suitability studies for the implementation of NSWT. The SFAHP enables decision makers to circumvent vagueness when they perform evaluations using linguistic variables. Its application in a region of Northeastern Greece using a two-level criteria hierarchical model demonstrates its potential use for suitability analyses.

This paper presents the development of a mathematical model to optimize the management and operation of the Brazilian hydrothermal system. The system consists of a large set of individual hydropower plants and a set of aggregated thermal plants. The energy generated in the system is interconnected by a transmission network so it can be transmitted to centers of consumption throughout the country. The optimization model offered is capable of handling different types of constraints, such as interbasin water transfers, water supply for various purposes, and environmental requirements. Its overall objective is to produce energy to meet the country’s demand at a minimum cost. Called HIDROTERM, the model integrates a database with basic hydrological and technical information to run the optimization model, and provides an interface to manage the input and output data. The optimization model uses the General Algebraic Modeling System (GAMS) package and can invoke different linear as well as nonlinear programming solvers. The optimization model was applied to the Brazilian hydrothermal system, one of the largest in the world. The system is divided into four subsystems with 127 active hydropower plants. Preliminary results under different scenarios of inflow, demand, and installed capacity demonstrate the efficiency and utility of the model. From this and other case studies in Brazil, the results indicate that the methodology developed is suitable to different applications, such as planning operation, capacity expansion, and operational rule studies, and trade-off analysis among multiple water users.

One of the critical public safety roles for water distribution systems (WDS) is suppression of urban fire events. Previous studies have investigated WDS rehabilitation with a major focus on improving reliability by pipe enlargement. However, pipe enlargement can cause water quality problems and place public health at risk during normal operational periods. Thus, a novel approach is required to effectively address the conflicting goals of the WDS: reliable delivery of water during normal and emergency conditions, meeting water quality standards, and finding cost-effective design and rehabilitation options. In this study an evolutionary computation-based multiobjective optimization-simulation framework is developed to design effective mitigation strategies for urban fire events for water distribution systems with three objectives: (1) minimizing potential fire damages, (2) minimizing water quality deficiencies, and (3) minimizing the cost of mitigation. An elitist nondominated sorting genetic algorithm (NSGA-II) is modified for an evolution strategy (ES)-based implementation to address difficulties for heuristic algorithms posed by WDS problems. Implementation of this methodology generates Pareto-optimal solution surfaces that express the trade-off relationship between fire flow, water quality, and mitigation cost objectives. The method provides decision-makers with the flexibility to choose a mitigation plan for urban fire events best suited for their circumstances. Each Pareto-optimal solution comprises a set of pipes to be enlarged to achieve increased fire flow and the corresponding diameters of these pipes. The algorithm is illustrated with several non WDS test functions. The Micropolis virtual city is then used to demonstrate the application of the proposed methodology to a complex WDS.

A water distribution system, represented as a spatially organized graph, is a complex network of multiple interconnected nodes and links. The overall robustness of such a system, in addition to the reliability of individual components, depends on the underlying network structure. This paper presents a deterministic network-based approach to study the relationship between the structure and function of water distribution systems and to critically review the application of structural measurements in the analysis of vulnerability and robustness of such systems. Benchmark water supply networks are studied, and their level of resistance to random failures and targeted attacks on their bridges and cut-sets are explored. Qualitative concepts such as redundancy, optimal connectivity, and structural robustness are quantified. Among other measurements, two metrics of meshedness coefficient and algebraic connectivity are found of great use toward quantifying redundancy and optimal connectivity, respectively. A brief discussion on the usefulness, scope, and limitations of the proposed approach in the analysis of real-world water distribution networks is presented.

Rapid urban growth has stressed limited supplies of high-quality water sources and water shortage has become a major issue in worldwide. Today, sustainable water supply to meet growing demand is a significant concern for water utilities. Developing additional high-quality sources is not necessarily available; it may be costly to convey water from sources that are usually located far from users or there may be legal battles over water rights. A well-proven source that does not have significant conveyance costs and that does not pose water right conflicts is wastewater reclamation for nonpotable uses. A few studies have been completed for the design of reclaimed water distribution through a parallel pipe system. In earlier works, however, attention has mainly focused on the recycling of effluent for large users, such as golf courses, parks, and schools with the objective of minimizing economic cost. As reclaimed water takes on a larger role in many communities’ water supply, the questions of how and what water to deliver to consumers must be addressed. To that end, this study presents a multi-objective optimization algorithm for designing dual-water distribution systems that recyce effluents for local residential nonpotable uses. Triple-bottom-line objectives are posed, as well as economic costs for pipe and pump installation and operation, environmental costs represented by greenhouse gas production, and system reliability against mechanical failure representing social cost. Under the premise that the systems will be constructed in a new community, thorough comparisons are made between a conventional single system and three alternative dual systems. The resulting problems are mixed integer problems and solved using a genetic algorithm (GA) linked to a hydraulic simulation model. Applications begin to demonstrate water deliver trade-offs and can guide policy decisions on appropriate system types taking a fuller picture of system costs and effects.

In this paper, the optimal pump operation of reservoirs or tank system is considered. The pump operation optimization problem is formulated as a Boolean integer nonlinear programming (BINLP) problem to minimize the cost of electrical energy pump on/off operations. The convex optimization model is solved by stochastic perturbation of reduced gradient method (SPRGM) through penalty function method to overcome the difficulty of solving the BINLP problem. The overall solution approach has merits for practical application to real system of municipal water reservoir operation.

Increased awareness of climate change has shifted the focus of water distribution system (WDS) optimization research from cost minimization only to the incorporation of energy or associated greenhouse gas (GHG) minimization. In this study, a sensitivity analysis is conducted to investigate the effect of electricity tariff and generation (emission factors) on the results of multiobjective WDS optimization accounting for both total economic cost (both capital and operating costs) and GHGs. A multiobjective genetic algorithm-based optimization approach is used to conduct the analysis. The results show that electricity tariff has a significant effect on the total economic cost of WDSs and the selection of optimal solutions. In contrast, the changes of emission factors in the future have a significant effect on the total GHGs from WDSs. However, it does not alter the final solutions on the Pareto-optimal front.

It is a generally adopted policy, albeit unofficially, to sample flow and pressure data at a 15-min interval for water distribution system hydraulic measurements. Further, for flow, this is usually averaged, whereas pressure is instantaneous. This paper sets out the findings of studies into the potential benefits of a higher sampling rate and averaging for flow and pressure measurements in water distribution systems. A data set comprising sampling at 5 s (in the case of pressure), 1 min, 5 min, and 15 min, both instantaneous and averaged, for a set of flow and pressure sensors deployed within two DMAs has been used. Engineered events conducted by opening fire hydrants/wash outs were used to form a controlled baseline detection comparison with known event start times. A data analysis system using support vector regression (SVR) was used to analyze the flow and pressure time series data from the deployed sensors and hence, detect these abnormal events. Results are analyzed over different sensors and events. The overall trend in the results is that a faster sampling rate leads to earlier event detection. However, it is concluded that a sampling interval of 1 or 5 min does not significantly improve detection to the point at which it is worth the added increase in power, communications, and data management requirements with current technologies. It was discovered that averaging pressure data can result in more rapid detection when compared with using the same instantaneous sampling rate. Averaging of pressure data is also likely to provide better regulatory compliance and provide improved data for EPS hydraulic modelling. This improvement can be achieved without any additional overheads on communications by a simple firmware alteration and hence, is potentially a very low cost upgrade with significant gains.