Journal of Water Resources Planning and Management

Simulation-optimization frameworks, such as multiobjective evolutionary algorithms (MOEAs), are increasingly used for real-world water resources problems. Constraints in MOEA optimization commonly represent decision maker preference, which differs from their role in classical optimization. As a result, constraints are often considered an optional aspect of the problem formulation. However, the impact of including constraints on optimization search has not been rigorously examined. This study explores how constraints impact the effectiveness, efficiency, and consistency of MOEA optimization for two water resources problems. For each problem, algorithm performance metrics are compared for two cases: (1) with constraints included during search, eliminating solutions that do not meet preference requirements, and (2) with constraints applied a posteriori to filter the full set of solutions. Results show that constraints aid in the search process by favoring solutions that meet decision maker preferences, despite the increased difficulty of finding feasible solutions. This study highlights the importance of constraints in the problem formulation for simulation-optimization applications in water resources, balancing the performance of search algorithms with the decision relevance of the solution set.

Hydropower systems contribute to the welfare of a country by producing renewable and clean electricity and by generating revenue. Market liberalization and increasing share of new renewable energy sources, with associated increase in price volatility, are profoundly reshaping hydropower operations in many countries where these developments have already taken place. In this evolving context, some European countries are phasing out nuclear power plants and looking for alternatives to replace the lost production share. Hydropower is one of the candidates, particularly in water-abundant hydrological regions, where large storage hydropower systems contribute a big share of the production. Yet, shifting current revenue-oriented operations toward a production-maximizing strategy might come to the cost of a reduction in income for hydropower companies. In this paper, we specifically explore trade-off between maximizing hydropower electricity production and revenue in deregulated markets. We focus on a case study in the Swiss Alps that can be considered as paradigmatic of most hydropower systems in mountainous regions worldwide. We developed a stochastic biobjective optimal control problem, which allows the design alternative hydropower operating strategies differently balancing electricity production and revenue maximization, accounting for both the variability of reservoir inflow and electricity price. Our results show that a production-driven operation might have strong consequences on the hydropower company income, suggesting that there is a low rate of substitution between the two objectives in the current energy market situation. The proposed approach can support policy makers in analyzing the dualism production/revenue in any hydropower system under complex natural and socioeconomic constraints and represents a benchmark to test different types of mechanisms to promote favorable economic conditions to hydropower companies to increase electricity production.

The installation of hydropower plants equipped with pumps as turbines (PATs) in water supply systems has been demonstrated to be suitable and advantageous in order to both reduce pressure and recover energy. However, certain technical difficulties have delayed the widespread adoption of this innovative approach. The design of a hydropower plant within a water network should address three main problems, i.e., (1) the scarcity of information about PAT behavior; (2) the large and stochastic variability in the working conditions that can influence the efficiency and the reliability of the plant; and (3) the need to guarantee the correct operation of the water supply system. This paper presents a new regulation procedure based on the maximization of an upgraded version of the plant effectiveness, which takes into account the plant efficiency, reliability, and sustainability. Four plant regulation schemes are presented. A stochastic model is used to simulate the fluctuations of the hydraulic characteristics, while the PAT behavior has been calculated with a recently developed model that revises the turbomachinery affinity equations. A case study demonstrates the application of the new procedure with reference to a pressure reducing station in southern Italy and a semiaxial PAT.

The reliability of a water distribution system is highly dependent on the management of its pipeline network. A pipe or a portion of the network can be isolated for inspection, maintenance, and replacement by the installation of isolation valves along the pipelines. However, the presence of isolation valves may cause a large discrepancy in the hydraulic behavior between the real system and results from a simulation model if the statuses of some of the valves in the system are unknown. Possible problems related to these valves are missing valves in the model due to poor or nonexistent documentation, errors in data transfer, or valve mechanical failure. This paper introduces an innovative methodology for the identification of unknown partially/fully closed valves in a water distribution network. An optimization problem is formulated for the unknown valve issue and solved by application of three sequentially applied methods, which include a local sensitivity analysis, an application of genetic algorithms (GAs), and an application of the Levenberg-Marquardt algorithm. In the first method, the sensitivity of the flow rates and nodal heads at measurement locations with respect to the change in the minor losses of the valves is computed. This computation is used to identify the valves that are unable to be localized by the measurement data. The second method applies a genetic algorithm combined with an extended period simulation in order to preliminarily identify the locations of the partially/fully closed valves and their setting values, i.e., the degree of opening of the valve. Finally, the application of the Levenberg-Marquardt (LM) algorithm was implemented to correct the results from the GA model. Results and discussions from two case studies show that the proposed methodologies can solve real-world problems.

Groundwater basins are important sources of water supply and storage for many cities. Groundwater exchange pools offer additional opportunities for utilizing these common pool resources, but their potential role in urban water management is not clear, and modeling such exchanges can be challenging. This paper presents an analysis of the potential for groundwater basin exchange pools to contribute to urban water supply sustainability. Building on an existing model of urban water management in Los Angeles, the analysis assesses the potential for groundwater exchange pools to reduce scarcity and demonstrates a method for modeling two-way (undirected) flows within a directed-network model using linear programming. Results indicate that exchange pools can help alleviate shortages from operational changes (reduced imported water) in Los Angeles, but providing more parties with access to storage improves their effectiveness. Exchange pools could potentially provide 6–12% of total supplies and reduce shortages as much as 86%. Considerations for organizing exchange pools are discussed to explore policy implications for managing common pool resources. The analytical method for embedding undirected network flows within a larger directed-network model has wide applicability for water resource systems analysis applications, including modeling water markets and interbasin transfers.

This study investigates annual river basin–level water criticality (ratio of available water to withdrawals) considering effects of climate change on supply and of future population change on demand. A steady-state water balance model was developed to estimate the water mass budget and disaggregate the internal and external water supply sources at a river basin level. Future precipitation and evapotranspiration were dynamically downscaled under a moderate greenhouse gas emission scenario to 4-km horizontal resolution using a regional climate model for a decade centered on 2090. The climate data were also statistically downscaled via the bias correction spatial disaggregation (BCSD) method applied to the CMIP5 (the fifth phase of the Coupled Model Intercomparison Project) archives for four emission scenarios for decades centered on 2040 and 2090. Bootstrapping and $k$-nearest-neighbor ($k$-NN) algorithms were applied to simulate future water demand and external basin supply with uncertainty. Water stress is classified into four levels: (i) “very high-water stress” when the water criticality ratio $<1.25$, (ii) “high water stress” when criticality ranges from 1.25 to 2.5, (iii) “moderate water stress” when criticality ranges from 2.5 to 10, and (iii) “no water stress” when criticality $>10$. A basin with a criticality ratio of 1 indicates that the basin demand has been exactly met by the available supply sources. Most river basins have current water criticality less than 2 and are dependent on inflow from other basins (i.e., are not self sustaining). Future projections indicate modest increases in net available water for Utah through the end of the current century from climate change, with increasing vulnerability largely driven by population growth. Out of 11 basins, 4 achieve a high and 3 achieve a very high water stress status by the 2040s. Four basins achieve a very high water stress status by the 2090s compared to only two in a very high water stress status in the 2010s.

Optimal operation of a large water distribution system (WDS) has always been a tedious task, especially when combined with determination of district metered areas (DMAs). This paper presents a novel framework based on graph theory and optimization models to design DMA configuration and identify optimal operation of large WDSs for both dry and rainy seasons. The methodology comprises three main phases: preliminary analysis, DMA configuration, and optimal operation. The preliminary analysis assists in identifying key features and potential bottlenecks in the WDS, narrowing down the large number of decision variables. The second phase employs a graph theory approach to specify DMAs and adjust their configuration based on similarity of total water demands and pressure uniformity in DMAs. The third phase uses several consecutive, single-objective and multiobjective optimization models. The decision variables are pipe rehabilitation, tank upgrade, location of valves and pipe closures, and valve settings for each DMA. The objective functions are to minimize total annual cost of rehabilitation, water age, and pressure uniformity. The proposed methodology is demonstrated through its application to the large, real-world WDS of E-Town. The results show that the proposed methodology can determine a desirable DMA configuration mainly supplied directly by trunk mains.

Using a system to promptly detect anomalous water quality levels in a water distribution system (WDS) is a critical task to ensure security of a public water supply. Using continuous monitoring stations placed at strategic locations throughout a WDS has shown to be an effective method to detect potential contamination or low water quality; however, the performance of these monitoring stations is highly sensitive to the specific locations at which they are placed throughout the network. As a result, a large amount of research has explored how to determine the locations at which to place monitoring stations in a WDS, which may be composed of tens of thousands of junctions and pipes. These studies have typically used explicit simulations of network hydraulics, and contamination events imposed on a water distribution system, to compare how effectively a network of monitoring stations detects simulated contamination events. Building off these previous studies, the work herein proposes a framework to place fixed monitoring stations and input inline mobile sensors to best detect contamination events under uncertain water quality conditions. An adaptive-noisy-multiobjective-messy genetic algorithm is used to efficiently determine the locations at which to place monitoring stations in two sample water distribution systems for minimum cost. Results show that monitoring stations and sensor networks designed within a demand uncertain framework outperform the solutions designed in a deterministic demand framework when evaluated under more realistic demand uncertain conditions.

Uncertainty about the timing and the magnitude of natural disasters (such as floods, droughts, earthquakes) affects water resources planning and management in terms of the supply of safe drinking water and access to sanitation infrastructure. This in turn has a profound effect on human health. Drinking contaminated water often results in the outbreak of diarrheal infections (such as cholera, Shigella, and so on). Infectious pathogens (such as Vibrio cholerae) can survive in aquatic environments under appropriate hydroclimatic conditions. Therefore, the challenge is to estimate the risk of an outbreak of disease after a natural disaster occurs. Using cholera as a signature diarrheal disease and employing the weighted raster overlay method, a framework is presented for assessing the role of water resources, particularly water, sanitation and hygiene (WASH), in determining the likelihood of an outbreak of a disease in the human population. Results indicate that there were favorable hydroclimatic conditions for the survival of pathogenic cholera bacteria in natural water systems in the aftermath of the earthquake in Nepal in 2015. However, few cholera patients were reported in the country, indicating that the prevailing resilient WASH infrastructure played a pivotal role in deterring a disease outbreak.

In this paper, a particle swarm optimization (PSO) algorithm augmented by fly-back and harmony memory features—referred to as the heuristic particle swarm optimization (HPSO) algorithm—is used for solving the sewer network optimization problem. The fly-back and harmony memory mechanisms are meant to avoid ineffective particle flights and to increase the efficiency and computational stability of the PSO algorithm. Problem constraints are checked and observed at two levels through a mechanism that enhances the convergence of the PSO algorithm as compared with those of conventional penalizing methods used in other evolutionary methods. The HPSO algorithm is then combined with dynamic programming (DP) to yield a hybrid algorithm called dynamic programming with heuristic particle swarm optimization (DPHPSO). Eliminating the inadequacies associated with either component method, this hybrid algorithm does not rely on the discretization of elevations, thereby reducing the complexity of the problem and the time required for solving it when compared with the rival DP method. Moreover, compared with the situation in which an evolutionary algorithm is used alone, the DP partitioning employed in HPSO leads to a reduced number of decision variables in the metaheuristic algorithm and also decreases the changes in ultimate objective function. The proposed methods are validated by applying them to three benchmark sewer network problems. Comparison of the results with those obtained from other optimization methods indicates the superiority of these algorithms over those reported in the literature.

This paper introduces a generic multireservoir planning testbed called the Eldorado Utility Planning Model (EUPM). The purpose of the EUPM is to facilitate experimentation with advanced water supply planning tools and techniques using a complex system that is not subject to the sensitivities or paradigms of real-world case study models. The model centers on a hypothetical Colorado utility and incorporates the regional context of the western United States, e.g., competition between water users and the spatial and temporal complexities that impact water availability. The benefits of the EUPM are demonstrated through an experimental multiobjective evolutionary algorithm (MOEA) optimization problem formulation designed to generate informative tradeoffs without the use of uncertain estimates of monetary costs. Incorporating two performance measures—minimization of new storage and of new supply—into the set of long-term planning objectives produces a suite of planning alternatives that can facilitate the application of human judgment to broader planning policy discussions, including the implications that a portfolio has on total plan cost.

This study aims at identifying suitable sites for underground or subsurface dams using a spatial multicriteria evaluation (SMCE) for water supplies in an arid watershed in southwestern Iran. Specific constraints and three groups of criteria were used in developing a spatial decision model. Constraints included slope, fault, land use, and geology. Site characteristics were classified based on slope, geomorphology, infiltration, precipitation, geology, and distances from roads, from rock materials sites, from faults and springs, from rivers and streams, and from various land uses. A criteria tree was designed and standardized using Boolean logic and fuzzy membership on factors. Factors were weighed by both ranking and direct methods. Finally, the composite index map (CIM) was used to identify three best sites. With valley shape consideration, Site2 was the most suitable for construction of a subsurface dam. Interpretation of valley profile, optimum decisions on subsurface dams, and multicriteria for site selection can be used in water resource planning.

The BATtle of the Attack Detection ALgorithms (BATADAL) is the most recent competition on planning and management of water networks undertaken within the Water Distribution Systems Analysis Symposium. The goal of the battle was to compare the performance of algorithms for the detection of cyber-physical attacks, whose frequency has increased in the last few years along with the adoption of smart water technologies. The design challenge was set for the C-Town network, a real-world, medium-sized water distribution system operated through programmable logic controllers and a supervisory control and data acquisition (SCADA) system. Participants were provided with data sets containing (simulated) SCADA observations, and challenged to design an attack detection algorithm. The effectiveness of all submitted algorithms was evaluated in terms of time-to-detection and classification accuracy. Seven teams participated in the battle and proposed a variety of successful approaches leveraging data analysis, model-based detection mechanisms, and rule checking. Results were presented at the Water Distribution Systems Analysis Symposium (World Environmental and Water Resources Congress) in Sacramento, California on May 21–25, 2017. This paper summarizes the BATADAL problem, proposed algorithms, results, and future research directions.

Droughts are a significant stressor for water supply systems and freshwater ecosystems. Studies on drought risks and impacts have focused on the frequency, duration, and severity of droughts. While these metrics may be appropriate for understanding the risks to consumptive water supply because they directly relate to deficits in catchment yields, they are insufficient to represent risks to ecosystems whose conditions are highly dependent on the sequencing of particular flow events. This paper explores the importance of differing sequences of wet and dry spells preceding and within a recorded drought period to water management objectives. A number of hydrological scenarios were developed by systematically making modest adjustments to the input hydrological sequences on an annual basis. A case study of the Murray Region (Australia) shows that sequencing of wet and dry spells both preceding and within a drought can have substantial influences on ecological outcomes during the drought period. This has implications for developing scenarios for assessing climate change risks, highlighting the importance of both traditional drought indices and the natural variability of hydrological sequences.

Changes in climate and land use are two primary drivers of hydrologic adjustment. This study analyzes 40 years of water resources data for 10 watersheds in the Washington, DC metropolitan area to quantify the impact of climate change and urbanization on water yield. The watersheds investigated have experienced varying degrees of land-use change, from relatively little change to rapid and extensive urbanization. Comparing the data trends for different watersheds allows the separation of effects that are due largely to climate change from those due to land-use change. Predominantly rural watersheds show a steady decline in annual water yield, whereas predominantly urban watersheds do not show any similar trend with time. Separating the year into growing versus nongrowing seasons reveals that limited evapotranspiration from urban surfaces during the growing season or the general effects of a leaking water distribution network may mask the reductions in water yield in urban watersheds from changing climate. These analyses provide hydrological evidence for generally enhanced evapotranspiration and complex interactions between concurrent climate change and urbanization within the study area.

The concept of integrated water resource management (IWRM) attempts to integrate all elements of water resources. Different tools are developed to assist in developing sound IWRM plans. One such tool is multiobjective analysis using an integrated hydro-economic model (IHEM). However, IHEM mainly deals with the optimization of river flow (blue water) in a river basin. This paper linked a distributed model of green water (landscape water uses) in the upper catchment with mainly blue water uses in the lower catchment of the Pangani Basin. The results show that agricultural water use has the highest water productivity and competes with all other objective functions in the catchment. The generation of firm energy competes with the downstream ecosystem requirements. The integrated study shows that improving rainfed cropping through supplementary irrigation has comparable marginal water values to full-scale irrigation but are much higher compared with hydropower. However, hydropower has more benefits if used in conjunction with the environment. The methodological approach has increased the understanding of trade-offs between green and blue water uses that are highly interdependent in African landscapes.

A harvested stormwater and managed aquifer recharge system has been analyzed through both simulation sensitivity analysis and optimization to reduce operational pumping costs and increase the volume of water harvested. The simulation sensitivity analysis explored increasing the size of a storage tank, replacing the three harvesting pumps, and using wider tank trigger levels in the system operation. In the optimization, trigger levels and irrigation schedules were considered as decision variables. Various streamflow (input) series have been considered in the optimization by finding the optimal controls for each individual series or by finding the controls that best perform under a range of different conditions. Optimal controls for the current system were compared with those found for the system with new replacement pumps. The newly sized pumps were found to provide significant benefits by reducing pump operating costs by 50% and increasing the volume of water able to be harvested. Using wider tank trigger levels and altering the irrigation schedule so that the irrigation pump operated at a more efficient point also resulted in a small reduction in cost for the current system.