Journal of Water Resources Planning and Management

Recent advances in long-lead climate forecasting have made it possible to produce useful streamflow forecasts for the Columbia River basin roughly six months earlier than current forecasts that rely on snowpack measurements. The resulting increase in forecast lead time facilitates considerable improvements in system operating performance, especially in years of expected above average flows. In the current reservoir operating system, the so called “critical” and “assured refill” rule curves that restrict releases for hydropower generation in the period from August to December are based on the critical (most severe low flow) and third lowest flow sequences of record, respectively. These rule curves provide appropriate protection of energy capacity and reservoir refill in extreme low flow conditions, but are restrictive in normal and high flow years until midwinter when operational streamflow forecasts based on observed snowpack become available, and the climatological constraints are relaxed to account for expected summer streamflows. The use of long lead time streamflow forecasts allows current operating constraints to be relaxed in years when there is a high likelihood of ample streamflow. In these years, more spot market energy sales can be made in the late summer and fall/early winter because of increased available water for releases, and spill from reservoirs in wet years is also reduced. Reservoir model simulations using alternative reservoir rule curves based upon retrospective long-lead streamflow forecasts from water years 1931 to 1987 show that the proposed alternative operating system based on climate forecasts can increase nonfirm energy production from the major Columbia River hydropower dams by as much as 5.5 million MW/h/year, resulting in an average increase in annual revenue of approximately $153 million per year in comparison with the status quo. Other uses of the Columbia River are largely unaffected by the proposed changes in the operating system. In particular, firm energy, the reliability of storage reservoir refill, and the frequency of meeting of streamflow targets for salmon protection would be essentially unchanged in comparison with the status quo. The increased hydropower revenue is therefore directly attributed to use of long-lead forecast information and does not represent a trade-off among other management objectives.

This paper develops a generalized network flow optimization model for long-term supply-demand analysis for basin-wide water resources planning. A set of nodes and arcs are used to form the network, and the decision variables are reservoir storage and water supply for public and agricultural uses. The objective function to be minimized is formed by summing the products of the decision variables multiplied by their corresponding cost coefficients. The constraints of the model include continuity equations, reservoir operation rule curves, reduced water supply due to water shortage, and evaporation losses from reservoirs. The formulated network model has losses in the transshipment and is solved by an efficient embedded generalized network solver (EMNET). The developed model is applied to a river basin located in the northern part of Taiwan. To analyze the characteristics of the model, the results obtained from the optimization model are compared with those obtained from a well-calibrated simulation model. The comparison shows that the water shortage computed by the optimization model is smaller than that computed by the simulation model. The developed model is then used to analyze future water supply-demand conditions for the area.

Desalination of sea and brackish water is gaining interest as a viable water source in several arid regions of the world where limited freshwater resources are under stress from increasing demands and deteriorating water quality. Although a few large desalination plants exist and are planned for future implementation, most desalinization facilities are constructed on a small scale to satisfy domestic requirements for individual cities and resort areas. As the cost of desalination continues to decline with improved technologies, integrated methodologies are needed for optimally locating and sizing desalinization facilities over regional scales. The problem is complicated by multiple, noncommensurate objectives related to economic, social, and political criteria and complex spatial analysis in locating facilities. A methodology is presented that integrates geographic information systems with multicriteria decision analysis for regionally locating and sizing desalinization facilities for domestic water supply. Application to the northwestern coast of Egypt, a region remote from freshwater sources in the Nile Valley and Delta, prioritizes cities to be served and optimally locates and sizes desalination facilities over the region.

This paper describes the development of the knowledge base expert system denoted as Sewer Cataloging, Retrieval and Prioritization System. This computer support system prioritizes sewer pipeline inspections used to target critical areas within a sewer drainage system. This system addresses a growing need of municipalities. The sewer infrastructure of many cities is in a state of disrepair due to budgetary constraints, a history of neglect and, often most importantly, a lack of critical information about the aging and complex system of sewers that convey wastewater for 75% of the population. The knowledge base was assembled with input from a national group of experts from both the public and private sectors. Input from the experts assesses the overall need to inspect based on both the line’s consequence and likelihood of failure. In turn, consequence and likelihood of failure are based on six mechanisms describing failure and two mechanisms predicting the impact of failure. Prioritization is accomplished using a Bayesian belief network that allows the uncertainty of the experts’ beliefs to be propagated through the decision process. The knowledge base is evaluated with a series of case studies and is shown to be effective at mimicking the knowledge of experts.

Mathematical modeling of a water distribution system allows comparisons between design and operation alternatives. However, to make meaningful comparisons, the true field system must be represented within the model. Water distribution systems contain a large number of pipes with unknown roughness coefficients. Even with an extensive data collection effort, pipe roughnesses for all links cannot be determined exactly. Therefore, the system is simplified by assuming sets of pipes have the same roughness coefficient. The impacts of such simplification have not been examined in a quantitative manner. This work develops a methodology to quantify impacts introduced by system simplification and identify the best number of pipe groupings for a network.

Reliability/availability analyses are performed on two large-scale municipal water distribution networks in the Tucson Metropolitan Area in Arizona. Reliability is defined as the probability of satisfying nodal demands and pressure heads for various possible pipe failures (breaks) in the water distribution system at any given time. An existing reliability model based on a minimum cut-set method is linked to a steady-state simulation model that implicitly solves the continuity and energy equations. The results from the simulation model are used in the reliability model to define minimum cut sets and determine the values of system and nodal reliability. Availability is the proportion of time the system adequately satisfies the demands upon it. This more practical measure combines hydraulic and mechanical availability in a form that defines the proportion of the time that the system will satisfactorily fulfill its function. In addition to a simple discrete failure relationship with absolute failure if pressure heads fall below a prescribed minimum, hydraulic availability is modeled with a continuous increasing acceptability with higher pressures. Comparisons of results illustrate the similarities and differences in the design of each system under varying operating conditions. Design alternatives to improve reliability are discussed.

A major objective of drinking water treatment is to provide water that is both microbiologically and chemically safe for human consumption. Drinking water chlorination, therefore, poses a dilemma. Chemical disinfection reduces the risk of infectious disease, but the interaction between chemical disinfectants and precursor materials in source water may result in potentially harmful by-products. Chlorine consumption results in the formation of by-products, and the loss of chlorine residual reduces protection against potentially pathogenic bacteria. Therefore, much effort has been invested in characterizing the loss of chlorine residuals in raw and treated water. This paper presents a mathematical model based on the use of two second-order terms for predicting this loss or decay.