Journal of Water Resources Planning and Management

Selective withdrawal systems can take advantage of thermal stratification in reservoirs to manage downstream temperatures. Selective withdrawal might also help adapt operations to environmental changes, such as increased stream temperatures expected with climate change. This exploratory study develops a linear programming model to release water from different thermal pools in reservoirs to minimize deviations from target downstream temperatures. The model is applied with representative thermal dynamics to Lake Spaulding, a multipurpose reservoir on the South Fork Yuba River in California with climate warming represented by uniform increases in air temperature. Optimization results for thermal pool management with selective withdrawal are compared to a single, low-level outlet release model. Optimal selective withdrawal hedges the winter release of cold water to decrease summer stream temperatures. With climate warming, selective withdrawal can help lessen stream warming in the summer but at a cost of warmer stream temperatures in winter. As numerous assumptions are made, particularly regarding representation of thermodynamics, modeling improvements are needed to further develop selective withdrawal optimization models; several improvements are discussed.

Attributes of an effective infrastructure adaptation planning process as well as methods for choosing among adaptation strategies are described. The major attributes include: (1) a vulnerability assessment, (2) proactive adaptation strategies that are implemented over time and space, (3) climate change scenario analysis including climate surprises to handle the uncertainty of the future climate, (4) actions that are robust and/or flexible and adjustable, (5) a planned, progressive approach that ties implementation to critical thresholds of actual climate changes and preserves options for future actions, (6) evaluation with multiple social, economic and environmental criteria, and (7) integration of local stakeholders into the planning process. Multiple methods can be used to generate and evaluate adaptation strategies. A subset of the key attributes is then used in a case study of urban drainage management, which was designed and implemented to illustrate these attributes. It is shown that multicriteria scenario analysis can be effectively used to generate and evaluate alternative adaptation strategies. The identification of when critical thresholds are reached under conditions of climate variability and change is a major research need.

Water utilities have large amounts of data at their disposal, which are seldom being used to their full potential. Integrating water billing records with land-use and demographic data organizes information and makes inherent correlations easier to understand, facilitating communication to stakeholders. This data was integrated for three Ontario (Canada) municipalities, Barrie, Guelph, and London. A summary tool was created, with proposed metrics and charts, that facilitates comparisons between cities, definition of benchmarks, and identification of targets for conservation. More than 60% of consumption in these cities is residential, and mostly lies below the Ontario average of $267\mathrm{L}/\mathrm{cap}·\mathrm{day}$. Water user clusters were created through self-organizing maps, K-means, and hierarchical clustering, and selected according to their pseudo-F and Rand statistics. Users within the same or similar property codes were found to cluster together. The application of data-mining methods provides actionable information for utilities seeking to reduce demands and increase system sustainability.

Many countries are considering policy instruments such as a carbon tax and economic discounting to reduce greenhouse gas (GHG) emissions in key sectors like the water sector. This paper examines the impact of economic discounting and a carbon tax on the optimization of water main rehabilitation. A new pipe rehabilitation optimization algorithm that accounts for GHGs was developed and applied to the Fairfield water distribution system in Amherstview, Ontario, Canada. GHG intensity factors for the provinces of Ontario (low-carbon) and Alberta (high-carbon) were applied to the Fairfield network. In both cases, adopting a low discount rate and levying a carbon tax had a weak effect in reducing energy use, GHG emissions, pipe breaks, and leakage. Further, a low discount rate and a carbon tax encouraged the search process to invest in rehabilitation early in the planning period to reduce continuing leakage, pipe repair, energy, and GHG costs.

In order to address the issue of water demand uncertainty due to the effect of climate change and urbanization in the design and management of water distribution systems (WDSs), a flexible methodology that combines sampling techniques (Monte Carlo—MC or Latin Hypercube—LH simulations), decision tree analysis, and genetic algorithm optimization is presented. The methodology gives flexible and optimal decisions as future water demand unfolds. The problem of optimal WDS design under uncertain future water demand is formulated here as a multiobjective optimization problem. The two objectives are as follows: (1) minimisation of total intervention cost; and (2) maximization of WDS end resilience. The decision variables are the conventional design interventions (e.g., pipe duplication and/or replacement of existing pipes with new ones, addition of tanks and pumps, etc.) and the water demand threshold values. The output from the nondominated sorting genetic algorithm (NSGA2) optimisation process is the Pareto front containing staged (developmental) design solutions represented in a decision tree form with an optimal water demand threshold value that results due to the trade-off in terms of the two objectives analyzed over the planning horizon. This methodology was applied on the New York Tunnels and the Anytown network problems. The results show that there is value achieved by building flexibility in design when compared to the deterministic approach in the long-term planning of WDSs under uncertainty.

Many water resources systems include multiple, independent, and distributed decision makers representing different and conflicting interests. In much of the water resources literature, the operation of these systems is studied assuming a fully cooperative attitude by the parties involved and maximizing the global efficiency at the system-level. However, assuming the presence of a social planner might be questionable when multiple institutions are involved, particularly in transboundary systems. At the other extreme, totally uncoordinated strategies among institutionally independent decision makers, acting according to the principle of individual-rationality, are more often experienced in these contexts, yielding a decrease in the system-level performance. In this paper, a novel approach is proposed based on multiagent systems to support the design of regulatory mechanisms, which drive the originally fully independent decision makers towards a more coordinated and system-wide efficient situation. The agent-based model is coupled with tools and algorithms based on distributed constraint reasoning to represent the interactions between the decision makers. The approach is demonstrated on a hypothetical water allocation problem, involving several active human agents and passive ecological agents. Different regulatory mechanisms are explored in three different scenarios of water availability to quantitatively support the discussion about the efficiency-acceptability trade-off. Numerical results show that the proposed approach has a great potential to support the design of distributed solutions balancing system-level efficiency and individual-level acceptability.

Cyanobacteria blooming in surface waters have become a major concern worldwide, as they are unsightly, and cause a variety of toxins, undesirable tastes, and odors. Approaches of mathematical process-based (deterministic), statistically based, rule-based (heuristic), and artificial neural networks have been the subject of extensive research for cyanobacteria forecasting. This study suggests a new framework of linking an evolutionary computational method (a genetic algorithm) with a data driven modeling engine (model trees) for external loading, physical, chemical, and biological parameters selection, all coupled with their associated time lags as decision variables for cyanobacteria prediction in surface waters. The methodology is demonstrated through trial runs and sensitivity analyses on Lake Kinneret (the Sea of Galilee), Israel. Model trials produced good matching as depicted through the results correlation coefficient on verification data sets. Temperature was reconfirmed as a predominant parameter for cyanobacteria prediction. Model optimal input variables and forecast horizons differed in various solutions. Those in turn raised the problem of best variables selection, pointing towards the need of a multiobjective optimization model in future extensions of the proposed methodology.