Journal of Water Resources Planning and Management

Past studies of commercial, industrial, and institutional (CII) water use efficiency have been limited to simple normalization benchmarking measures. Simple normalization ratios (e.g., water use per employee) account for only one driver of water use and are often misleading because of the predominantly skewed nature of water use intensities. In order to improve on benchmarking systems to assess the water use efficiency of CII customers, this paper explores two popular methods used in the energy efficiency field: ordinary least squares (OLS) and data envelopment analysis (DEA). Parcel-level data from Austin, Texas, is used to demonstrate the methods, where a data-driven approach is employed to include average water use, building area, building value, year built, parcel area, and number of employees as measures of input, and annual sales as the economic measure of output.

One of the best known hydraulic water distribution modeling toolkits that is most used by both researchers and practitioners is EPANET 2. Initially the authors aimed to speed up such simulations by utilizing modern multicore processors in the code implementation. The 30-year-old linear solver was replaced in steps by seven different modern multicore capable solvers. Subsequently, speedup tests were carried out with small- (up to $1.6\times {10}^3$ nodes), medium- (up to $6.3\times {10}^4$ nodes) and large-sized (up to $6.3\times {10}^5$ nodes) test cases. None of the tested solvers was found to perform faster than the original solver for networks with a real-world character, although two solvers showed a speedup on medium and large water distribution networks. This is an example that strategies to reduce computation time can produce promising results in a theoretic research environment, but fail in practical engineering applications. Likewise, this paper highlights again the importance of considering realistic test cases during the implementation phase. Further, the authors point out why the different tested strategies in this work have not succeeded. Although two other issues (implemented hash table and node reordering) could be identified for potential improvement of the code, it was concluded that the original solver is still the fastest for practical system configurations.

This paper applies an approach for determining water resources vulnerability caused by climate change to the New York City water supply system (NYCWSS). The results provide potential responses of the system to changes in climate and guidance that can inform short-term and long-term planning decisions. This research includes models of the hydrology and operations of the NYCWSS and includes a statistical model of turbidity loading and a zero-dimentional model of turbidity concentration in the Ashokan Reservoir. Using a stochastic weather generator, incremental changes are made to precipitation and temperature and these are used to drive a coupled hydrology-simulation model. The results illustrate the sensitivity of the system, and in particular Ashokan Reservoir turbidity, to changes in climate.

This paper presents a methodology to forecast short-term water demands either offline or online by combining seasonal autoregressive integrated moving average (SARIMA) models with data assimilation. In offline mode, the method frequently reestimates the models using the latest historical data. In online mode, the method applies a Kalman filter to optimally and efficiently update the models using a real-time feed of data. The tailoring process consists of identifying, estimating, and validating the models, along with exploring how the length of demand history used in fitting can improve forecast performance. A suite of models are obtained that are adequate for 15-min, hourly, and daily demands having daily and weekly periodicities. The model output is analyzed across temporal resolutions, periodicities, and forecasting modes. The study finds that the normalized forecast deviations range from approximately 4.2 to 1.3%, in correspondence to a decrease in temporal granularity. Models of the weekly-seasonal type are found to more efficiently remove the autocorrelations with respect to models of the daily-seasonal type. In terms of the forecasting mode, the online implementation is shown to produce a higher performance specially for models with higher temporal resolution. Finally, a case study is conducted where forecasts are compared to the actual water production volumes of the local water utility. The results indicate that a significant improvement may be obtained in estimating the production of water based on the model output.

The purpose of this study is to develop an optimization model for the selection and placement of best management practice (BMP) at the watershed level in order to control water quantity and quality. Linear programming (LP) was selected from among several mathematical optimization methods, with the objective function being to minimize the total cost of BMPs within the constraints of guaranteeing a free-surface flow for the whole network during heavy rainfall and limiting the peak flow generated at the outlet of the watershed at the interceptor capacity for frequent rainfalls. The model was used on two scenarios considering a 10-year return rainfall period for the water quantity control, and 25 mm rain for the water quality control. The impact of climate change and urbanization on the combined sewer performances is also discussed for each scenario. The results obtained from the model effectively demonstrate the viability of implementing BMPs in the goal of improving sewer performances and show that an increase of 32% in the overall quantity-control cost is sufficient to achieve also the quality-control objective.

The position of the low salinity zone in the San Francisco Bay Delta—given its correlation with the abundance of several estuarine species—is used for water management in a system that supplies water to more than 20 million people and contains one of the most diverse ecosystems on the Pacific coast. This work consolidates legacy and modern salinity data to develop a reasonably complete daily record spanning nine decades. The position of the low salinity zone, which is effectively characterized by an empirical model that was developed to support data cleaning and filling, reveals statistically significant trends consistent with increasing water demands and introduction of upstream reservoirs, e.g., increasing salinity trends in wet months and decreasing salinity trends in dry months. Reservoir effects are particularly apparent in drier years, with greater seasonal variability in the early part of the record before major reservoirs operated in the watershed. These data provide a basis for further analysis of how and why the position of the estuary’s low salinity zone has changed over time.

Water distribution systems throughout the world are deteriorating in part due to corrosion of cast iron pipes. Developing a deeper understanding of the operational association with failure may lead to operational improvements extending asset life. Operations engineers can be expected to develop credible theories of pipe failure based on their experience dealing with failures in the field. However, these failure theories can be more rigorously tested by a systemwide investigation of a large number of pipe failures. A forensic framework was developed to investigate how system operations are associated with large-diameter trunk-main failures. The framework utilizes a pipe failure database and historical supervisory control and data acquisition (SCADA) data to analyze trunk-main failures. Using the framework, about 141 large-diameter ($\ge 300\mathrm{mm}$) trunk-main failures were investigated in the Lake Zone in Newcastle, Australia. A predominant system failure mode called pump off peak (POP) was identified that accounted for approximately 38% of the failures. POP was characterized by an increasing static pressure in low demand periods with limited pressure relief from downstream reservoirs.

Utilizing economic instruments, especially water pricing, is a useful strategy in the management of water demand. In this study, water pricing policies along with incentive strategies through subsidies to farmers are implemented and simulated for improving the efficient use of water. To determine the crop pattern configuration and water allocation schemes, a genetic algorithm (GA) based optimization model is developed to obtain the potential maximum net benefit. The economic value of water is obtained using a combination of mathematical programming and residual methods within an optimal framework. In this model, water prices increase at the same time that subsidies are given to farmers to encourage them to use efficient irrigation technologies. Results indicate that implementation of this scenario increases the net benefit to US$249.3 million per year, which is 40% higher than the current historical value. Because of the efficiency improvements in the use of water, the economic value of water increases from $\mathrm{US}\$0.133/{\mathrm{m}}^3$ to US $0.178/{\mathrm{m}}^3$. The results demonstrate the significant effects of water pricing policies on persuading consumers to reduce their water consumption.