Journal of Water Resources Planning and Management

As is well known, water systems grow gradually over long periods of time, and the life of piping tends to be much longer than the planning horizon used for pipe sizing. Furthermore, the uncertainty about future demands grows with the length of the time horizon. The design of water-distribution systems should therefore be performed in phases, to follow the gradual network growth, and taking account of the uncertainty connected with demand growth. The design approach proposed in this paper to consider these aspects is able to identify, on prefixed time steps or intervals, the necessary upgrades of the construction where each upgrade consists of installing pipes in new sites or in parallel to pipes that already exist, in order to render the network able to satisfy user demand with acceptable service pressure over the different phases of its life. Uncertainty in demand growth is considered by expressing the growth rate by means of a discrete random variable with assigned probability mass function. Optimization of phasing of construction is then performed by considering two objective functions: present-worth cost of the construction (to be minimized), and minimum-pressure surplus over time (to be maximized), which is represented as a discrete random variable with a derived probability distribution as a consequence of the assumption made on the water demand, which randomly grows from phase to phase of the construction. Within this framework, a specific criterion to rank discrete random variables is presented here. The application of the methodology to a case study shows that optimizing phasing of construction while accounting for uncertainty in demand growth leads to the network being sized more conservatively, so that the network construction obtained turns out to be more flexible to adapt itself to various conditions of demand growth over time.

Hydroelectric power plants are often assumed to serve a wide range of purposes, from renewable electricity production to flood control and new recreational opportunities. Planning documents and strategies often presume that hydroelectric power plants have additional recreational (use) values. However, there is some evidence of both alternatives in empirical studies which accentuate the benefits of additional recreational opportunities, as opposed to those studies that draw attention to the costs in terms of reduced recreational values. The objective of this paper is to clarify whether respondents in a survey value recreational opportunities along free-flowing sections of a river differently to those along dammed-up stretches. The novel discussion in this paper is on the results of a representative empirical survey of households in the River Mur Basin in the Federal Province of Styria in Austria. The respondents presented a detailed and differentiated picture of their recreational activities along the river. It turned out that all the activities, such as walking, hiking, and wildlife observation, are mostly pursued along the free-flowing sections of the river, contrary to some existing empirical papers emphasizing the great importance of dammed-up river stretches for recreation. In order to compute the recreational benefit of the free-flowing stretches of the river, recreational demand models (travel cost) were estimated econometrically for those recreational activities most often chosen by the respondents. While some prior hypotheses were supported (e.g., that the respondents make less frequent trips to the River Mur with higher travel costs, and with lower (perceived) water quality), the free-flowing character of the river adds a significant benefit. There is a close correlation between the free-flowing stretches of the river and the recreational benefits that range from 24 Euro to over 130 Euro per person and trip which are higher than those along the dammed-up sections depending in particular on the kind of recreational activity.

This paper describes a method for water resources optimization in the context of climate change. The method takes into account the midterm variability or seasonality of inflows as well as the uncertainty in the climate change and resulting flows. The objective of the optimization algorithm is to find a compromise between the long-term planning of water resources systems and the midterm operations for optimum hydropower production. The proposed algorithm consists of the midterm dynamic programming formulation coupled with the use of the expected value of the cost-to-go function between two consecutive long-term periods. Future climate projections and transition probabilities between projections represent the stochastic nature of inflows and the nonstationarity of climate. The performance of the method was evaluated through the simulation of inflow projections for the Manicouagan River basin in Quebec, Canada. The results showed that the algorithm was able to adapt the operating policy to the climate seasonality and climate change uncertainties in the optimization problem.

Water supply systems have to satisfy water needs in terms of quantity and quality. The constant changes in urban areas require the regular adaptation of the water supply infrastructure to meet new circumstances. However, decisions to design and operate water networks have to be taken under uncertainty. Flexibility is thus the key to more robust and confident decisions. An approach called real options (ROs) can be used in these situations. This approach makes it possible to use adaptive strategies in the decision process. Some decisions can be delayed pending future conditions. Water distribution systems are very costly and complex infrastructures. Once built, their operating structure cannot be changed significantly. This work presents an innovative ROs approach to define an objective function to cope with some future scenarios in a specific case study. The objective of our proposed model is to find a minimum cost solution for the first period of a planning horizon, while considering various possible future conditions that the network could have to cope with. The results of this work show that building flexibility into the decision strategy makes it possible to take an adaptive approach that can avert future lack of network capacity. In the case study, an adaptive design of the network incurs an extra initial cost, but this cost can easily be lower than the cost of reinforcing the network in a longer planning horizon. The real value of ROs is their ability to adapt systems to different possible future scenarios.

Accurate and reliable weekly and monthly water demand forecasting is important for effective and sustainable planning and use of urban water supply infrastructure. This study explored a hybrid wavelet–bootstrap–artificial neural network (WBANN) modeling approach for weekly (one-week) and monthly (one- and two-month) urban water demand forecasting in situations with limited data availability. The performance of WBANN models was also compared with that of standard artificial neural networks (ANN), bootstrap-based ANN (BANN), and wavelet-based ANN (WANN) models. The proposed WBANN method is aimed at improving the accuracy and reliability of water demand forecasting by incorporating the capability of wavelet transformation and bootstrap analysis using artificial neural networks. Daily and monthly maximum temperature, total precipitation, and water demand data for almost three years obtained from the city of Calgary, Alberta, Canada were used in this study. For weekly and monthly lead-time forecasting, the hybrid WBANN and WANN models were determined to be more accurate compared with the ANN and BANN methods. The WANN and WBANN models simulated peak water demand very effectively. The better performance of the WANN and WBANN models for weekly and monthly water demand forecasts indicated that wavelet analysis significantly improved the model’s performance, whereas the bootstrap technique improved the reliability of water demand forecasts by producing ensemble forecasts. WBANN models were also found to be effective in assessing the uncertainty associated with water demand forecasts in terms of confidence bands, which is helpful in operational water demand forecasting. This study was conducted with a very short length of available data, indicating the effectiveness of WANN and WBANN modeling approaches in situations with limited data availability.

Control of agricultural nonpoint sources of pollution is achievable through implementation of conservation practices at the farm or field level. There are several approaches to achieve a targeted implementation of conservation practices at the watershed scale. Recent studies have shown that optimization methods hold great promise for optimal allocation of nonpoint source pollution control measures. However, the use of optimization is a computationally intensive task and ultimately depends upon availability of automated optimization tools and expertise to analyze the results. In this study, a novel multicriteria decision analysis (MCDA) framework is proposed to identify a near-optimal suite of conservation practices at the watershed scale using a priori knowledge about the system. The proposed framework requires: (1) selecting a set of criteria, depending upon the objectives of the study, that should be considered in ranking the alternatives; (2) constructing an evaluation matrix; and (3) using a computational MCDA method to aggregate the scores based upon the various criteria and rank the alternatives. The framework was used to identify optimal placement of four types of conservation practices for nutrient and pesticide load control at minimum cost in the Eagle Creek Watershed, Indiana, United States. Results were compared with optimal solutions obtained from an optimization framework coupled with the soil and water assessment tool (SWAT). The results of this study showed that the proposed framework can be an effective and efficient approach in identifying near-optimal solutions for nonpoint source pollution control. The MCDA framework outperformed the optimization method by identifying similar solutions with more diversity without any need for iterative search algorithms. For highly complex problems or for a poorly established evaluation matrix, the MCDA framework may fail to identify near-optimal solutions; however, the results can effectively serve as a good initial population in a hybrid MCDA and optimization framework. A hybrid framework substantially improved efficiency of the search algorithm, optimality of the Pareto-front, and diversity of the solutions. This study also highlighted importance of the defining proper decision variables and accurate scoring of the conservation practices for successful implementation of conservation plans at the watershed scale.

The average loss of snowpack storage from a warmer versus current climate in the Owens River basin of the Eastern Sierra Nevada was estimated using 9 years of gridded, daily fractional snow-covered-area data from satellite, plus ground-based reconstructions of daily potential snowmelt from snow pillow sites in the basin. This basin provides water supply for the City of Los Angeles, and snowpack is the main surface storage in the system. The snowpack occurs mainly above 1,500-m elevation and increases in depth up to the Sierra crest above 3,900 m. Snowmelt or seasonal snowpack storage currently averages about $0.42+0.15\mathrm{m}$ per year in the Owens River basin. Considering only the direct effect of temperature changes on snow, this amount is projected to decrease about 0.036 m for each 1°C of warming, equivalent to a 50% loss of snowmelt with a 6°C warming. For a current average snowmelt of about 1,460 $\pm 480\times 106{\mathrm{m}}^3$ for the Owens headwaters, this represents a loss of snowpack storage of about $120\times 106{\mathrm{m}}^3$ per °C of warming. Snowcover depletion is projected to occur about 8–12 days earlier for each 1°C of warming.