Journal of Water Resources Planning and Management

Forum papers are thought-provoking opinion pieces or essays founded in fact, sometimes containing speculation, on a civil engineering topic of general interest and relevance to the readership of the journal. The views expressed in this Forum article do not necessarily reflect the views of ASCE or the Editorial Board of the journal.

Short streamflow records make it difficult to determine the extent to which discharge changes in excess of ecological thresholds are due to dam operations or natural variability. Unnecessary changes to reservoir operating rules can reduce off-stream benefits, whereas no changes to rules when thresholds are exceeded can degrade downstream riverine ecosystems. We introduce a Bayesian decision tree approach to a hypothetical hydropower–ecosystem decision problem that compares expected in-stream and off-stream losses resulting from incorrect decisions. Expected losses are computed using loss probabilities derived using Bayes’ theorem, type I and II errors, and prior probabilities of alteration. Decision-tree recommendations compared with those from deterministic and null hypothesis significance testing under a variety of conditions illuminate the benefits of including valuations of hydropower and ecological losses as well as type II error probabilities in reservoir operation decisions. This is the first study to both introduce and demonstrate the value of Bayesian decision trees for addressing tradeoffs between hydropower and ecosystem benefits and losses.

Stress on California’s salmon fisheries drives a need for effective temperature management in California’s Sacramento River. Shasta Dam has a temperature control device that enables managers to provide cold discharge temperatures to the river that are required for downstream Chinook salmon spawning and rearing. An added stress to water temperature management is prolonged drought and associated high air temperatures. California experienced its worst drought in a century from 2012 to 2015, and these conditions may provide insight into future reservoir conditions, as climate change predictions suggest that droughts will be more frequent and intense. In this study, a two-dimensional hydrodynamic model of Shasta Reservoir was used to simulate: (1) alternative reservoir operations during 2015 aimed at sustaining the volume of cold water in the reservoir to provide downstream cold water habitat during the critical time for endangered winter-run Chinook salmon egg incubation; and (2) reservoir temperature conditions under potential climate change scenarios. Results suggest that, with climate change, water managers at Shasta Reservoir may have increasing difficulty maintaining adequate cold pool volume in the reservoir through November, and even extreme reductions in reservoir outflows may not be enough to mitigate this result.

To satisfy growing energy demand, the hydropower industry of China is experiencing unprecedented development, and the total power generation and installed capacity of hydropower in China rank first in the world. The system scale and rate of development have posed computational modeling challenges, because the computational burden in hydropower optimization modeling using classical dynamic programming methods grows exponentially as the number of reservoirs increases. One method designed to reduce this burden, the progressive optimality algorithm (POA), still suffers from the dimensionality problem and the need for iterative computations to address large-scale hydropower systems. To enhance the performance of POA, this work develops a new method referred to as the simplex progressive optimality algorithm (SPOA). In SPOA, the complex multistage problem is divided into several easy-to-solve two-stage subproblems, and then the Nelder–Mead simplex direct search method is adopted to search for the improved solution to each subproblem, enhancing the solution’s quality via iterative computation. Experimental results indicate that the proposed SPOA method can significantly reduce execution time and memory usage under different cases, demonstrating its applicability for large-scale hydropower system operation problems.

Optimal operation of water resource systems requires understanding the interaction between the surface water and groundwater in the region of interest. Furthermore, model parameters as well as effective surface and underground factors should be estimated and calculated. This research focuses on linking the nondominated sorting genetic algorithm II with a coupled surface water–groundwater model in an area of southwestern Iran. The advantage of this structure is achieving and sustaining a balance between surface water and groundwater withdrawal by considering various constraints. The optimized withdrawal from surface water and groundwater in wet and dry areas is obtained by linking the optimization algorithm with the coupled model. Application of this method in the study area can first, to some extent, enhance the demand supply reliability and contribute to reducing the groundwater drawdown at the end of the operational period. In addition, in the proposed coupled model, all the components involved in the surface water and groundwater models are linked, with data and results flowing back and forth between the two models for each computational time step. Thus, the model structure can allow decision makers to simulate surface water–groundwater interaction in the region for computing simultaneous depletions in river flow and groundwater, particularly during dry years. This structure is suggested for sustainable water resources management, particularly in low-flow areas.

Pipeline leak detection is critical for targeted maintenance and water loss reduction within water distribution systems. This paper proposes a hydraulic transient, impulse response function (IRF)–based, signal analysis approach for leak detection in water pipelines and networks. In the proposed approach, continuous pressure signals are sent into the pipeline, where pressure responses are measured by two transducers (separated by a distance) located close to the generator. Given this setup, a signal analysis methodology was theoretically derived to extract the major components of the deconvolution between these two measured pressure traces. The result shows that the deconvolution consists of a pair of IRFs of the pipeline with opposite signs and a time shift associated with the distance between the transducers. Hereon, they are referred to as a paired IRF. A leak is shown to induce a pair of pulses on the paired-IRF trace. With the paired IRF obtained, the leak can be localized by analyzing the occurrence times of the leak-induced paired pulses. Numerical verification was undertaken in both a single pipe and a pipe network using the pipeline pressure responses simulated by the method of characteristics. The leaks in the pipelines were successfully detected using the new approach. Experimental verification was conducted on a laboratory copper pipeline with a leak simulated by a discharge orifice. The proposed method was found to accurately localize the leak even with the pressure waves contaminated by realistic background pressure fluctuations and noise. The numerical and experimental cases demonstrate that the novel paired-IRF method is applicable to pipe networks, robust to system interference, and able to deal with realistic background pressure fluctuations and noise.

This paper addresses a knowledge gap that exists for citywide or neighborhood-wide applications of onsite water-conserving technologies, such as rainwater and graywater systems. A framework is developed for evaluating policies requiring onsite rainwater and graywater systems in residential units. This framework incorporates housing stock dynamics, fixture retrofitting, and water demand models. It assesses costs and benefits of policy implementation strategies for three urban neighborhoods selected according to their built environment and socioeconomic characteristics. Evaluation results identify a potential 5.4%–37.2% reduction in future neighborhoods’ water demand. With the most cost-efficient water-conserving technologies, a household is expected to save $160–$393 from their annual water bills. The cost-benefit analyses indicate substantial variation in water-saving potential and cost-efficiency of onsite water-conserving technologies across neighborhoods. The findings indicate that to maximize effectiveness, the specific choice of water-conserving technology and implementation strategy needs to account for local conditions of land-use characteristics, household structure, and water-fixture conditions.

Pumping stations used in water distribution networks (WDNs) consume a significant portion of the energy required to deliver municipal drinking water. Smart management strategies such as optimal pump scheduling (OPS) have gained the attention of water companies and managing authorities because they help reduce both energy costs and detrimental consequences for the environment. Genetic algorithms (GAs) are frequently used to approximate the solution of OPS problems, although many researchers have resorted to hybrid models to improve computational performance. This paper shows that despite the lack of support in the literature, a well-designed GA is capable of tackling OPS problems effortlessly. In addition, a new decision-variable representation is proposed, specifically suited to parallel pump systems, which is able to further improve the performance of a GA. Finally, the outperforming capabilities of the new variable representation are demonstrated with two case studies from recent literature.

It is challenging to obtain optimal operation rules for a system of reservoirs in parallel. This study derives generic optimal operation rules from a multistage nonlinear programming (NLP) model established for a system of reservoirs in parallel with a single demand. The optimal operation depends on the system’s capability in coordinating individual reservoir storages to regulate inflows to those reservoirs and it is interfered by the full-empty cycles of reservoirs due to the variability of the inflows and/or the limitation of storage capacities. In general, small reservoirs are often made empty to leave space for future inflow or full to reserve water for the future, while large reservoirs are operated within their capacities. Based on the derived optimality conditions, a computationally efficient algorithm is developed for the NLP solution, which determines the system level releases and identifies all full and empty states and stages with no release for individual reservoirs in the system. The algorithm is demonstrated through a case study, in which the solution accuracy and computation time are compared with dynamic programming. This paper and subsequent studies on developing algorithms for optimal reservoir operation can potentially relieve the stress on water supply when new emerging sectors such as biomass and biofuel production increase water use.

Concerns about the collapse of dams due to overtopping are increasing owing to heavy rainfall and frequent typhoons associated with climate change. As a result, there has been a spike in anxiety among residents living near the downstream areas of dams. Although the dangers associated with dam safety are increasing, there is still a lack of comprehensive dam rehabilitation and implementation frameworks that consider climate change. A thorough review of existing dam facilities is needed in light of climate change and the deteriorating conditions of aging dams. In this study, we performed a hydrological safety assessment and developed a dam rehabilitation implementation framework to proactively diagnose and resolve dam safety issues caused by climate change and aging water infrastructure. The dam rehabilitation assessment framework has three main facets: appropriateness of evaluation components, importance of weighted analysis, and comprehensive dam rehabilitation assessment and implementation framework. The proposed approach was applied to selected sites with high feasibility of dam rehabilitation in Korea’s major river basins considering different climate change scenarios.

Potential for energy recovery exists at any point within a water distribution system where the mechanical energy of excess water pressure can be converted into electrical energy. Energy conversion decreases the average operating pressure within a system, which in turn reduces water losses from leakages in the system due to the proportionality of leakage and pressure. This paper explores the incorporation of a genetic algorithm (GA) in a procedure to optimize the location and size of energy-recovery turbines (ERT) within a water distribution system based on maximizing recovered energy and reduced water losses evaluated on an economic basis and assigned a differentiated weighted importance. The developed procedure was tested on a well-known pressure management benchmark network as well as a water network from previous studies. Where previous studies on the benchmark network were only focused on pressure management, the current procedure produced results on pressure management with the added benefit of an analysis on both energy recovery and leakage reduction. The procedure provides municipal and water utility managers with a better-informed basis for pressure management and energy-recovery decision making.

The standard method for estimating instantaneous peak water use in premise plumbing systems (PPSs) is Hunter’s (1940) design curve, showing water demand versus fixture units. In recent years, federal regulations have mandated efficient water fixtures and, coincidentally, per capita water use has declined dramatically. As a consequence, Hunter’s iconic design curve no longer applies to many contemporary PPSs. In this study, high-resolution indoor water-use data from more than 1,000 single-family homes across the United States were analyzed to estimate the peak hour probability of use ($p$-values) for efficient water fixtures. Concurrent water-use profiles in different single-family households were combined to determine fixture $p$-values for multiapartment buildings. Results show that during the peak hour in single-family homes, $p$-values ranged from 0.5% to 5.5% at typical household fixtures. In a multiunit complex, the $p$-values tend to diminish as the number of units in the building increases, converging to baseline slightly lower than the single-family case. The $p$-values reported here can be used to help predict peak indoor water demand in residential buildings. These $p$-values represent current residential water-use habits and provide a first step in efforts to update Hunter’s classic method for estimating peak indoor water demand in buildings.

Condition assessment or defect detection of a pipeline is a difficult inverse problem. This paper proposes a general linear model framework that can approximately describe a wide range of pipeline condition assessment and defect detection problems. More specifically, the system response is governed by a linear function of a pipe property at discrete locations along a pipe, such that the pipe property can be reconstructed via a least-squares fit to the measured response. Real pipe systems in general involve a large number of uncertain pipe characteristics, limited data, and a very high level of noise, such that the inverse problem is ill-posed. The well-known Tikhonov regularization scheme is employed on the linear model to provide a general solution for the ill-posed inverse problem. The optimal regularization parameter, which is crucial and problem-dependent such that no universal approach always generates satisfactory results, are decided via the generalized cross validation (GCV) and L-curve approaches. The proposed general linear model and inverse problem methodologies are illustrated via two application examples: time-domain impulse response function extraction using least-squares deconvolution and leakage detection based on a frequency-domain linearized model. In both examples, numerical and experimental results demonstrate the significance of the regularization parameter and the merits of the GCV and L-curve methods in the pipeline condition assessment problems.

This paper estimates feasible water savings for a sample of nine urban water districts in California during the height of the 2012–2016 drought, just prior to the implementation of mandatory water-use reductions, using household production theory and stochastic frontier analysis. Estimates of feasible savings are compared to mandated reductions and actual reductions in each district. Although the mandated reductions were generally feasible, the results reported here show that they had asymmetric impacts across districts and tended to impose larger burdens on some disadvantaged groups.

In water distribution systems, the substitution of regulating valves by pumps as turbines (PATs) is sustainable if the energy recovery balances the installation and maintenance costs, with a comparable and efficient reduction of the volume lost by leakage. Hence, the installation feasibility and regulation of a PAT require analysis of the introduced effects on the components of the energy, mass, and cost balances. In this paper these effects are explored by means of a case study, i.e., the water distribution system of Trondheim, Norway. Different hydraulic regulations of a PAT are analyzed to maximize the recovered energy, considering the daily variation of the system functioning conditions and the substitution of valves of different kinds, meaning a constant opening degree, a fixed value of pressure at a node of the system, and a control in time of the setting. A feasible solution for the Trondheim network is shown, consisting of the implementation of two twin PATs in parallel with a discontinuous functioning of one of them.

In this technical note, new considerations and modifications are introduced to a recent design optimization model, developed by the first author, that is able to find the optimal active capacity for a water supply reservoir admitting a predefined minimum number of failure periods, a maximum failure magnitude, or both. It is shown that this model actually finds the best capacity to implement; however, to keep failure indexes within accept limits, the reservoir should also be operated optimally. A simulation-optimization approach is then formulated to find the best capacity even if the reservoir is to be operated by the standard operation policy (SOP), as in typical design procedures that take failure probabilities into account. Applications of both current and modified strategies are carried out and their results compared and commented on.

The effects uncertainty on water quality predictions by the free water distribution modeling software created by the US Environment Protection Agency (EPANET2) are assessed using both design of experiments (DOE) and Monte Carlo simulation (MCS). For the test system evaluated, the DOE approach describes the effects of uncertainty with as few as eight model executions and yet shows good agreement with the more computationally demanding MCS approach. Surprisingly, first order disinfectant decay models are less sensitive to uncertainty than water age, even though an additional rate coefficient is required. This is likely because disinfectant residuals are bounded between the input concentration and zero, while water age is not. At nodes affected by water storage, up to 40% of the variability in disinfectant residual is attributed to uncertainty in tank mixing fraction (TMF). Due to seasonal and diurnal changes in water temperature, density, and mixing behavior, the TMF is inherently uncertain. Strategies to address this include MCS or sensitivity analysis using DOE. Inclusion of a wall decay coefficient appears to increase model sensitivity to uncertainty.