Journal of Water Resources Planning and Management

Water demand is a highly important variable for operational control and decision making. Therefore, the development of accurate forecasts is a valuable field of research to further improve the efficiency of water utilities. Focusing on probabilistic multi-step-ahead forecasting, a time series model is introduced to capture typical autoregressive, calendar, and seasonal effects; to account for time-varying variance; and to quantify the uncertainty and path-dependency of the water demand process. To deal with the high complexity of the water demand process, a high-dimensional feature space is applied, which is efficiently tuned by an automatic shrinkage and selection operator (lasso). It allows to obtain an accurate, simple interpretable and fast computable forecasting model, which is well suited for real-time applications. The complete probabilistic forecasting framework allows not only for simulating the mean and the marginal properties, but also the correlation structure between hours within the forecasting horizon. For practitioners, complete probabilistic multi-step-ahead forecasts are of considerable relevance as they provide additional information about the expected aggregated or cumulative water demand, so that a statement can be made about the probability with which a water storage capacity can guarantee the supply over a certain period of time. This information allows to better control storage capacities and to better ensure the smooth operation of pumps. To appropriately evaluate the forecasting performance of the considered models, the energy score (ES) as a strictly proper multidimensional evaluation criterion, is introduced. The methodology is applied to the hourly water demand data of a German water supplier.

The basic objective of long-term optimization of large-scale hydropower system operations (LOLHSO) is to increase water resource use efficiency and construct a clean and low-carbon-intensity energy system. However, limited by rather stochastic inflows, large-scale hydropower systems, and complex objectives and constraints, LOLHSO is characterized by uncertainty, high dimensionality, nonlinearity, and nonconvexity, which pose great challenges in modeling. This paper presents an improved hybrid decomposition-coordination and discrete differential dynamic programming model (IDC-DDDP) for solving the LOLHSO problem. In IDC-DDDP, a decomposition strategy considering the adjustment potential of hydropower plants is designed to reduce the system size. Meanwhile, owing to the sensitivity of DDDP to initial trajectories, a data mining–based strategy is developed as a means of generating superior initial trajectories. A corridor generation strategy is presented to determine the discrete steps and enhance global search abilities. Case studies in a hydropower system in southwestwern China demonstrate the practicability and robustness of the proposed model.

Massive investments in hydropower are transforming several river basins across the world—especially in developing countries, where hydroelectricity is often seen as a means to sustain economic growth. While the environmental alterations caused by these projects have been well addressed in the scientific literature, less is known about their actual performance, which could be affected by water availability or the uncoordinated planning of the power generation and transmission facilities. To fill in this gap, we developed a novel water-energy modeling framework that relies on a macroscale semidistributed hydrologic model and a unit commitment model that schedules and dispatches electricity from hydropower reservoirs and other energy sources. The framework is applied to Laos, which has recently attracted large investments in the hydropower sector, making it the so-called Battery of Asia. Simulation results over a 10-year period show that the energy system heavily relies on thermal power during the pre-monsoon months; a condition that is exacerbated by the dry spells caused by El Niño events. During the driest years, the annual generation costs and carbon dioxide emissions could increase by more than 20%. On the other hand, monsoon rainfalls largely increase the available hydropower, whose dispatch is severely limited by the capacity of the high-voltage transmission facilities. In the latter part of the analysis, we discuss the opportunities to improve system performance, and show how the capacity expansion of just a few transmission lines could ease the redistribution of hydroelectricity during peak-production periods.

Water main breaks in city areas can cause significant economic loss, interrupt traffic, and result in negative publicity for utilities. The South Australian Water Corporation (SA Water) has taken a proactive approach to reduce water main breaks in the Adelaide, Australia, central business district (CBD) by detecting developing pipe cracks/leaks using distributed acoustic sensors (accelerometers) with customized analytics and internet of things (IoT) technologies. This paper reports the findings and the outcomes of this initiative 24 months after the first commissioning of the continuous leak detection system. The acoustic data analytics team (comprising the authors) has successfully interpreted acoustic data from numerous cracked pipes in the Adelaide CBD, with approximately 55% of all developing leaks associated with cracked pipes being able to be detected before uncontrolled failure (i.e., before unplanned interruption of customers and/or third party economic damage). Furthermore, the localization and repair of specific cracks (sometimes through sections of corroded cast iron pipe walls) can help justify extending the service life of the water main as points of weakness (faults) are specifically rectified. However, only 45% of the developing cracks have been able to be repaired, after detection, under controlled conditions (i.e., before the planned interruption of customers) due to practical limitations affecting operational responses. The results have confirmed the additional insight that can be obtained by continuously collecting acoustic data in terms of distinguishing environmental and crack noises as well as circumferential from longitudinal cracks.

Reevaluation of the reliability of reservoirs in meeting operating objectives is needed as the landscape and operational priorities change. This study examined the individual and collective changes in reservoir performance at the 13 flood regulation projects within the Willamette River Basin (WRB) in Oregon. By applying a scenario-based analysis, derived as part of a broader collaborative modeling project, operational performance was evaluated in response to climate and operational change scenarios. Results indicated that, for the climate scenarios analyzed, primary operating objectives of flood risk reduction and meeting summer biological opinion (BiOp) flow targets were unlikely to be affected. Modest reductions in the ability to fully refill the reservoirs and to meet spring BiOp flow targets were found, particularly under the more severe warming scenario. However, refilling the reservoirs 2 weeks earlier was effective in reducing the storage deficit introduced by climate change without compromising the ability to meet other operational objectives. Drawdown scenarios for maintenance of aging infrastructure had minimal effect at the system scale. While the model was limited by the number of climate scenarios represented and in its ability to represent the full range of variability of this large basin, the results cumulatively suggested that the hydrologic changes associated with the range of climate change examined were not likely to substantially reduce the reliability of the Willamette Project in meeting operational objectives. Where potential deficiencies emerged, they could largely be mitigated through simple operational changes. Instead, it is likely that changes in the sociopolitical landscape (e.g., water rights allocations) and management of water quality (e.g., temperature, harmful algal blooms) will be as or more important than hydrologic changes in reducing reservoir performance in meeting operational objectives.