Journal of Water Resources Planning and Management

Pressure management in water supply pipe networks using conventional pressure reducing valves (PRVs) results in the dissipated energy being lost. In pursuit of more energy efficient solutions in the sector, researchers are exploring the technological and economic feasibility of replacing PRVs with low-cost hydroelectric converters such as pump-as-turbines (PATs). The present study addresses two problems: (1) is it possible to accurately estimate the energy recovery potential of a PRV site using only the statistics of its recorded operating conditions? and (2) can we define a bound representing minimal average operating conditions at PRVs for which their upgrade to PAT is expected to be economically viable? The first problem was addressed by calculating descriptive statistics of yearly recordings of the operating conditions at 38 real-world PRVs from Ireland and Spain, and carrying out linear least-squares regression analysis in which the statistics were used as predictor variables. The results imply that global plant efficiency can deviate up to 33.33% depending on the level of variation of the operating conditions, and that a realistic value of the expected global plant efficiency is around 0.4277 rather than 0.65 that was used in some of the previous studies. To address the second problem, the study first identifies the range of variability of the operating conditions and total installation costs for 340 hypothetical PRV sites whose average operating points (OPs) were evenly distributed across the investigated domain. The optimal net present value (NPV) was assessed for all hypothetical sites and their variability scenarios. The results suggest that there is a high probability that sites with the gross hydraulic power of 12 kW or larger will be economically viable for the upgrade.

This paper used the fusion of an active-set method (ASM) and an interior point method (IPM), with a Newton method, to solve for the steady-state flows, heads, and outflows of a pressure-dependent water distribution system. The outflow constraints which arise from the pressure dependency are handled by an ASM, and the linkflow constraints are handled by an IPM. The authors believe that this is the first time an ASM and an IPM have been used together in this way to solve a real-world optimization problem. Including flow constraints in a network model allows a variety of flow control devices (flow control valves, check valves, pumps) to be modeled efficiently. The new method does not require damping. The separate treatment methods for the two constraint sets mean that the linear inequality constraint qualification condition cannot be violated during iteration, unlike the case in which all the constraints are handled by an ASM. The method was shown to converge quickly on nine case study networks, the largest of which had more than 157,000 links, 150,000 nodes, and 6,000 linkflow constraints. When tested on those same nine networks, an interior point optimizer software package took about 2–4 times longer than the new method for five of the networks, 7 and 9 times as long for two of the networks, and about an equal amount of time for the remaining two networks. For the largest network, the software package took 34 min, whereas the method presented here took 4 min. The generality of the method makes it applicable to network design and management, capacity analysis, and self-cleaning networks.

Modern cities have water supply systems (WSS) that reliably meet the water demand of every individual household. If inefficiently managed, these energy-consuming systems can become very costly. To produce the most cost-efficient operational strategies, many techniques using emerging computer power, simulation technologies, and sensing devices have been developed. Some of these techniques rely on water consumption predictions to provide optimal pumping strategies. However, predicted consumptions contain errors, and these techniques do not automatically adjust to sudden changes in demand. To solve this problem, a cost-adaptive finite-horizon controller to efficiently update the pumping operation strategy based on monitored deviations against the predicted consumption is proposed. This methodology takes into account the tariffs of electricity over time and an optimized reference to continuously make the most cost-efficient updates in the pumping strategy. To validate the cost-adaptive controller, two case studies were used: (1) a single pump-tank network; and (2) the Richmond benchmark network. Both evaluations delivered positive results achieving the desired control reliability while improving cost efficiency. The combination of water consumption predictions and adaptive control methodologies provides a reliable and cost-efficient solution for the automatic operation of WSS.

In this study, a cooperative game theory framework is proposed to investigate how the cooperation of groundwater consumers influences the reduction in environmental damages and benefit the stakeholders in coastal regions. To this end, an evolutionary simulation-optimization model coupled with a hydroeconomic model and different cooperation scenarios are considered. The proposed methodology is applied on a coastal groundwater system in Nassau County, Long Island, New York. The results indicate that the highest sum of net benefits achieved under the full-cooperation condition both with and without considering environmental damages. The increase in the sum of net benefits is about 7% using the full-cooperation condition compared with the noncooperation condition, whereas the groundwater withdrawal increases by about 2.5%. The risk values are higher under the full-cooperation condition compared with the baseline condition in the decrease of (1) groundwater level, (2) saltwater intrusion, and (3) filling ratio, but are lower in the submarine groundwater discharge indicator. The proposed framework can be used to assess and manage other coastal groundwater resources.

A stochastic approach for modeling and analysing the transients due to the users’ water consumptions in a real water distribution system is presented. The analysis is based on field measurements of water consumption at each user and pressure at three nodes, acquired at 1 min and 0.01 s time step, respectively. The hydraulic numerical model used is based on the method of characteristics and includes the unsteady friction. Several scenarios of water consumptions at 1-s time step are generated starting from those observed. The corresponding pressure variation scenarios are given by the numerical model and stochastically compared with the measured values. The analysis of the results shows that the approach is capable of stochastically reproducing the dynamic behavior of the system. Specifically, the generated water consumption scenarios with random maneuvering times allow properly reproducing the main statistics (mean, variance, and minimum and maximum values) of the observed pressures. Finally, the average cumulative distribution of the simulated pressure viably simulates the cumulative distribution of the observed ones from a stochastic point of view.

Comprehensive assessment of hydrological alteration due to anthropogenic interventions is a prerequisite for sustaining the native biological profile and integrity of river ecosystems. This paper proposes a novel strategy to measurably evaluate how the dams have modified the natural flow regime at a downstream site. The Bhagirathi Basin, a part of the Himalayan ranges of the Ganga River Basin in India, is studied to comprehend the impact of multiple dams on natural river flow at Devprayag. The methodology for estimating the River Flow Health Index (RFHI) consists of four steps: (1) segregation of the flow data based on preimpact and postimpact periods, (2) identification of important hydrological parameters, (3) assessment of the alterations, and (4) development of an index indicating the health of the river flow during the altered period on a 0–1 scale. Results show that the different components of the flow regime have changed due to construction of dams across the river, and the hydrological alterations could be seen at the downstream site, Devprayag. The RFHI increased after subsequent addition of dams, i.e., 0.328 after commissioning of Maneri Bhali I hydro-electric projects (HEP), 0.374 after Maneri Bhali I HEP, Maneri Bhali II HEP, and Tehri HEP were commissioned, and 0.411 after Koteshwar HEP was commissioned in 2011. Our results may help upgrade the design and implementation of reservoir operation policies that consider downstream hydrological alterations. Further studies can be made to consider the coordination of hydrological alterations with different river ecosystem services important for the sustainable management of water assets.

Water-resources planners use regional water management models (WMMs) to identify vulnerabilities to climate change. Frequently, dynamically downscaled climate inputs are used in conjunction with land-surface models (LSMs) to provide hydrologic streamflow projections, which serve as critical inputs for WMMs. Here, we show how even modest projection errors can strongly affect assessments of water availability and financial stability for irrigation districts in California. Specifically, our results highlight that LSM errors in projections of flood and drought extremes are highly interactive across timescales, path-dependent, and can be amplified when modeling infrastructure systems (e.g., misrepresenting banked groundwater). Common strategies for reducing errors in deterministic LSM hydrologic projections (e.g., bias correction) can themselves strongly distort projected climate vulnerabilities and misrepresent their inferred financial consequences. Overall, our results indicate a need to move beyond standard deterministic climate projection and error management frameworks that are dependent on single simulated climate change scenario outcomes.

In this paper, a pragmatic approach for leakage detection and localization is presented to solve the problem introduced within the framework of the Battle of the Leakage Detection and Isolation Methods (BattLeDIM). The method is based on the application of the hydraulic model of the water distribution network and the comparison of simulated pressures against the corresponding values observed in field. In particular, it consists of two phases: (1) calibration of the hydraulic model of the network; and (2) detection and localization of the leakages affecting the water distribution network through the application of engineering judgment and the adoption of an enumerative procedure. The method was applied to the case-study network of L-Town, enabling 16 of 23 leakages to be efficiently detected and 11 of these also accurately localized. The proposed method is simple and transparent and can aid water utilities in water leakage management.

This study investigates factors contributing to the unexpected loss of a recreational Chinook salmon fishery in a reach of the Rangitata River in Canterbury, New Zealand. The Rangitata is one of only 14 rivers nationwide in which flows are protected by water conservation orders to provide for instream values in the face of competition from extractive uses. However, the river has experienced significant water abstraction or takes for irrigation and hydroelectricity generation, even recently. In this study, local fishers determined flow requirements and conditions for successful fishing based on historical catch data and gauged flows. Relationships between preferred and historical flows were examined using scenario analyses to identify the cumulative impacts of water takes on the conditions required for fishing. An irrigation take of $19.6{\mathrm{m}}^3/\mathrm{s}$ beginning in 2013 was found to have severely reduced the duration of flows in a $30{\mathrm{m}}^3/\mathrm{s}$ flow range that previously supported fishing success. A recently proposed further take of $10{\mathrm{m}}^3/\mathrm{s}$ will completely remove any remaining flows from this preferred flow range. These results show that cumulative takes of water from river systems may have profound and unintended consequences when the flow regimes that support recreational uses are not well understood and retained or protected. Impact assessments that address both cumulative takes and flow-specific values are needed to reliably detect and manage adverse effects. In this case, additional controls are required to maintain the values specifically recognized by the water conservation order.

This research integrated bias-corrected results from regional climate models, a watershed hydrology model, and water footprint analysis to measure the impact of climate change on future water scarcity in Lesotho, a water-rich yet data-poor country. Simulations from three climate models with two different Coupled Model Intercomparison Project Representative Concentration Pathways, 4.5 and 8.5, were bias corrected. Streamflows were simulated using the Soil Water Assessment Tool in the Senqu basin, which covers approximately two-thirds of Lesotho. This provided the future blue water availability of this river until 2100. Three scenarios were adopted to analyze the water scarcity of Lesotho. Scenario 1 used the national blue water footprint in the water scarcity calculation to investigate the worst-case scenario. Scenario 2 used the modified blue water footprint based on the population living within the Senqu River basin. Scenario 3 used a modified blue water footprint that accounted for the projected population growth of Lesotho. The results of Scenario 1 indicated 4 years of moderate water scarcity, and the results of Scenario 3 indicated 2 years of severe water scarcity. The modeling framework from this study can be applied to other remote places where limited data are available.

The Santo Antônio is the fifth largest hydro plant in Brazil. Due to the environmental constraints, the plant follows a run-of-river operation and, to handle inflow variability, possesses the 50 most oversized bulb generating units (GUs) in the world. These issues render a challenging short-term hydro scheduling problem (STHS), where the critical aspect is modeling the hydropower function (HPF). In this context, this article proposes a two-step strategy based on mixed-integer linear programming (MILP) to solve the STHS, that: (1) explores the symmetry related to the identical GUs by applying a binary expansion to find which units will be active, and (2) obtain the optimal load distribution by linearizing a series of nonlinear problems. In both steps, the HPF is linearized using the logarithmic aggregated convex combination (LACC) model. The symmetry exploration based on the binary expansion is new in the context of the STHS. The experiments show that the proposed strategy yields equal solutions to those obtained by an individual-LACC approach while providing, on average, a 74% reduction in computational time.