Journal of Water Resources Planning and Management

Pacific Northwest temperatures have warmed by 0.8°C since 1920 and are predicted to increase in the 21st century. Streamflow timing shifts associated with climate change would degrade the water resources system performance for climate change scenarios using existing system operation policies for the Columbia River Basin. To mitigate the hydrologic impacts of anticipated climate change on this complex water resource system, optimized flood control operating rule curves were developed at a monthly time step in a previous study and were evaluated with a monthly time-step simulation model. Here, a daily time-step simulation model is used over a smaller portion of the domain to evaluate and refine the optimized flood-control curves derived from monthly time-step analysis. Daily time-step simulations demonstrate that maximum evacuation targets for flood control derived from the monthly analysis were remarkably robust. However, the evacuation schedules for Libby and Duncan Dams from February to April conflicted with Kootenay Lake level requirements specified in the 1938 International Joint Commission Order on Kootenay Lake. We refined the flood rule curves derived from monthly analysis by creating a gradual evacuation schedule, keeping the timing and magnitude of maximum evacuation the same as in the monthly analysis. After these refinements, the performance at monthly timescales reported in our previous study proved robust at daily timescales. Owing to a decrease in July storage deficits, additional benefits such as more revenue from hydropower generation and more July and August outflow for fish augmentation were observed when the optimized flood-control curves were used for a climate-change scenario.

There is evidence that long-term climate change has altered streamflow patterns in the Puget Sound region, particularly during spring and summer months, and forecasts suggests these changes will continue. This research explored the ability of regional water supply systems in the Puget Sound, including the cities of Seattle, Everett, Tacoma, and Bellevue, to meet future demands given such changes. To evaluate regional water supply, three sets of climate impacted streamflows were created for 75-year, daily sequences associated with decades centered on the years 2000, 2025, 2050, and 2075 using three general circulation models and two emission scenarios. The performance of each water supply system, characterized by firm yield and reliability metrics, was determined. Alternative operating procedures were then examined for the systems. This research also evaluated the efficacy of municipal and industrial (M&I) demand curtailments as an emergency mitigation strategy to alleviate future shortfall events. The research determined that climate change will decrease system safe yield in the future and that alternative operating policies or other system changes are necessary to meet projected demands beyond the year 2055.

This paper describes an approach for establishing overhaul repair schedules for clear water pump stations that balance the goal of minimizing the total life-cycle cost of the pumps with the goal of minimizing the ${\mathrm{CO}}_2$ emissions associated with the generation of the electricity used to operate the pumps. The specific focus of this study is on pump stations with a parallel pump configuration in which clear water is pumped from a clear water tank at a groundwater treatment plant (GWTP) to a service reservoir. The approach uses models for the operation and deterioration of such pumps, and forms the basis for a model-based decision support tool (DSS) that has been developed to assist in budgeting and planning decisions associated with pump overhaul repairs.

To achieve the triple bottom line (TBL) of sustainability, total water management efforts must analyze alternatives to address the potentially conflicting goals of economics (financial), environmental, and social issues. Traditionally, decisions were heavily influenced by economics. The alternative with the best cost-benefit ratio was generally considered to be superior. As the need to make sustainable decisions becomes increasingly important, a similar need surfaces for a methodology to balance sustainability objectives in a realistic manner. Goal programming is a technique that uses optimization methods to provide a means to solve a problem by striving towards multiple objectives simultaneously. This research applies an optimization framework to the integrated water supply planning process. The research demonstrates a methodology that can successfully generate a feasible set of alternative solutions while balancing all three goals of the TBL. Furthermore, the research showed that the goal programming methodology could use existing data (utility master plans) and widely available tools (Excel) to execute the model and develop trade-offs between the various aspects of the TBL. Demonstration data from a California utility’s publicly available water supply master plan were used to define the trade-offs between the relative costs for enhanced environmental and social goal achievement. The methodology explicitly details the societal and environmental values for each water management alternative, including desalination, dam, water rights exchanges, and drilling of new wells. The decision analysis of water supply strategies is enhanced through the incorporation of environmental and social sustainability in a balanced manner.

This paper describes and demonstrates an efficient method for online hydraulic state estimation in urban water networks. The proposed method employs an online predictor-corrector (PC) procedure for forecasting future water demands. A statistical data-driven algorithm (M5 Model-Trees algorithm) is applied to estimate future water demands, and an evolutionary optimization technique (genetic algorithms) is used to correct these predictions with online monitoring data. The calibration problem is solved using a modified least-squares (LS) fit method (Huber function) in which the objective function is the minimization of the residuals between predicted and measured pressure at several system locations, with the decision variables being the hourly variations in water demands. To meet the computational efficiency requirements of real-time hydraulic state estimation for prototype urban networks that typically comprise tens of thousands of links and nodes, a reduced model is introduced using a water system–aggregation technique. The reduced model achieves a high-fidelity representation for the hydraulic performance of the complete network, but greatly simplifies the computation of the PC loop and facilitates the implementation of the online model. The proposed methodology is demonstrated on a prototypical municipal water-distribution system.

In this paper, based on the combination of the back-propagation network (BPN) and the self-organizing map (SOM), a groundwater level forecasting model is proposed, named improved multisite SOM-BPN model. In the proposed model, the SOM is used to determine the number of hidden layer neurons, and the autoregressive integrated moving-average (ARIMA) model and semivariogram are used to determine the number of input neurons. To evaluate the forecast accuracy of the proposed model, the improved multisite SOM-BPN model as well as five other models (ARIMA model, single-site BPN model, single-site SOM-BPN model, multisite BPN model, and multisite SOM-BPN model) are applied to actual groundwater level data in southern Taiwan. According to the results, it is found that the single-site and multisite BPN models can forecast more precisely than the ARIMA model. Moreover, the results show that the multisite model is more competent in forecasting groundwater level as compared to the single-site model. Finally, among the six models, the improved multisite SOM-BPN model has the highest accuracy. The improved multisite SOM-BPN model is recommended as an alternative to groundwater level forecasting because it cannot only produce reasonable forecasts, but also objectively determine the suitable number of hidden layer neurons of BPN.

Object-oriented Bayesian networks (OOBNs) have recently been introduced to model water systems that can be represented as repetitive patterns. This paper shows the way in which OOBNs can be used as a groundwater management decision support system in two Spanish case studies. The two areas, in the southern and eastern parts of inland Spain, are characterized by a semiarid climate, water scarcity, and frequent droughts; consequently, the agrarian economy in both cases depends on the provision of irrigation from groundwater sources. Both case studies are illustrative examples of conflict among various water actors, complexity, and uncertainty about the consequences of water management actions. Each study is approached from a different viewpoint: one from an agroeconomic and the other from a hydrogeological perspective. The sites display different degrees of aquifer overexploitation and agrarian profitability. This indicates that, in each case, the effects generated by water management interventions and the time needed for recovery to natural conditions are different. The processes governing both systems can be represented as a series of repeating patterns, which makes them good candidates for an OOBN analysis. The OOBNs developed have been constructed with the participation of stakeholders to help minimize conflicts and make management decisions more understandable and acceptable for all users. The innovative nature of this research lies with the implementation of OOBNs for groundwater management. Results of the study demonstrate that the OOBN tool is a powerful decision support system that can help managers make decisions in cases for which the consequences of alternative interventions are unknown. It provides the probabilities of obtaining certain outcomes from alternative management actions for the economy and for the state of the environment. OOBNs meet the requirements of the European Water Framework Directive as a simple, participative, and integrative tool. Finally, this research represents a starting point for additional applications to support the integrated groundwater management of other complex water resources systems.

Although phenomenon-mimicking or metaheuristic algorithms, such as genetic algorithm, simulated annealing, tabu search, shuffled frog-leaping algorithm, ant colony optimization algorithm, harmony search, cross entropy, scatter search, hybrid particle swarm optimization, and honeybee mating optimization, have overcome the disadvantages of gradient-based algorithms when optimally designing water distribution networks, those algorithms require the extra tedious task of algorithm parameter setting. This study proposes a novel parameter-setting-free technique for two major algorithm parameters (HMCR and PAR) and combines it with the harmony search algorithm. When the proposed model is applied to the optimal design of a popular benchmark problem, it reaches the global optimum with good results. Thus, the technique is expected to be used in the real-world design process under a more user-friendly environment.