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.

This paper presents a new method for identifying leakage zones of water distribution systems. A large water network is first divided into a number of zones. The zone number is used as the category label of the multiclass support vector machine (M-SVM), which is trained with the data set generated by simulation of the possible leakages using a hydraulic model. The trained M-SVM is used as the leakage zone identification model and applied to determine the likely leakage zones with the observed field data. Two case studies are presented in this paper to demonstrate the effectiveness of the method. The results indicate that this method has many unique advantages in solving the nonlinear and high-dimensional pattern recognition problem with a small sample data set. Together with the method of pressure-dependent leakage detection (PDLD), the proposed approach enables engineers to improve the effectiveness and efficiency of leakage detection for large water distribution systems.

To limit the high cost of surfactant-enhanced aquifer remediation (SEAR) for clearing dense nonaqueous phase liquids (DNAPLs), the simulation-optimization technique is generally adopted for determining the optimal remediation strategy in advance. The simulation model requires an uncertainty analysis, and incorporating the results into the SEAR strategy optimization process is critical. However, previous studies have rarely involved corresponding problems. In the present study, an uncertainty analysis is performed by combining a Monte Carlo random simulation with the Sobol’ global sensitivity analysis to assess the contribution of different parameters to the remediation efficiency and distribution characteristics of the simulation model outputs. The surrogate model technique based on Kriging was used to reduce the high computational load of the sensitivity and uncertainty analyses. The results of the sensitivity analysis showed that the porosity is the most important parameter with the largest influence on the remediation efficiency at a weight of 70%, followed by the oleic phase dispersity at a weight of 27%; the influence from variations in other parameters can be neglected. The rebuilt surrogate model with fewer input variables performed significantly better than the one built before the sensitivity analysis for all performance evaluation indices. Uncertainty in the aquifer parameters resulted in clear variations in the simulation model outputs. Output fluctuations from the average were nearly 2.5%. The results of this study showed that the failure risk of a given remediation strategy can be obtained based on the distribution of model outputs.

Hydraulic models have been widely used for design, analysis, and operation of water distribution systems. As with all hydraulic models, water demands are one of the main parameters that cause the most uncertainty to the model outputs. However, the calibration of the water demands is usually not feasible attributable to the limited quantity of available measurements in most real water networks. This paper presents an approach to calibration of the demand multiplier factors under an ill-posed condition where the number of measurements is less than the number of parameter variables. The problem is solved using a genetic algorithm (GA). The results show that not only is the GA able to match the calibrated values at measured locations, but by using multiple runs of the GA model, the flow rates and nodal heads at nonmeasured locations can be estimated. Three case studies are presented as an illustration of the problem. The first case study is a small network that demonstrates the calibration model. The second case study shows a comparison between the genetic algorithm model and a singular value decomposition model. The last case study is a large network that allows for practical considerations in applying the proposed methodology to a realistic context.

A conjunctive-use model is developed for management of groundwater and surface water resources via mixed integer linear fractional programming (MILFP). The objective of the conjunctive-use model is to maximize the ratio of groundwater usage to surface water usage through a water supply network. A conditional head constraint is imposed to the conjunctive-use model to maintain aquifer sustainability. A transformation approach is introduced to transform the conditional head constraint into a set of mixed integer linear constraints in terms of groundwater head. Groundwater head is further linearized with respect to pumping rates that are decision variables. Eventually, the conjunctive-use model is to solve a successive MILFP problem by updating the response matrix in each iteration. To make an MILFP problem tractable, the study develops a transformation technique along with the Charnes–Cooper transformation approach to transform an MILFP problem into an equivalent problem of mixed integer linear programming (MILP) to be solved by CPLEX. The proposed conjunctive-use model is applied to northern Louisiana. A water supply network is proposed to utilize four existing reservoirs as alternative resources in order to raise groundwater level in the Sparta aquifer to acceptable target level in Ouachita, Lincoln, and Union Parishes while maximizing groundwater pumping. The results show that the conjunctive-use management framework increases groundwater levels by an average of 6.96 m (22.82 ft) from 2001 to 2010 by reducing total groundwater withdrawal 28.93%, which is counterbalanced by reservoir water.

This study determines the optimal operational strategies for a complex water resource system. These strategies are derived from four conflicting objective functions: maximization of net irrigation benefits, maximization of employment generation, minimization of cultivation cost, and maximization of revenue generation from industrial and municipal supplies. Multiobjective fuzzy linear programming (MOFLP) models, i.e., the maximum-minimum (max-min) operator, two-phase MOFLP (TPMOFLP), and fuzzy compromise approach (average operator), are derived from the individual linear programming solutions of these objective functions. The performances of the derived MOFLP models are compared for inflows that have different probabilities of exceedance with reference to the overall degree of satisfaction, irrigation intensity, and optimized values of the relevant objective functions. The cropping pattern obtained from the recommended MOFLP model, i.e., average operator Case-I (overall degree of satisfaction $\lambda =0.75$), is compared with the actual cropping pattern in the command area in recent years to highlight the need for the developed model. The irrigation intensity for the whole command area from the recommended MOFLP model (104.6%) has been found to be significantly higher than the actual cropping patterns adopted in recent years. The net irrigation benefits, employment generation, cost of cultivation, and municipal and industrial revenue obtained from the recommended MOFLP model are Rs 11,058.27 million, 33,414.62 thousand work days, Rs 5,622.20 million, and Rs 2,686.25 million, respectively. Additionally, the performance of the water resource system using the recommended model is simulated based on 36 years of historical data and 100 years of synthetically generated data and is measured in terms of the performance indices reported by previous studies. The analyses show that the irrigation deficit will increase from $11.22\times {10}^6{\mathrm{m}}^3$ for the past 36 years to $26.67\times {10}^6{\mathrm{m}}^3$ over the next 100 years. The monthly (MFID) and annual frequency irrigation deficits (AFID) will rise from 7.17% (past) to 13.92% (in next 100 years) and from 52.77% (past) to 82% (in next 100 years), respectively.

Every water distribution system (WDS) fails partially or completely at some point or points during its lifetime. Measurement of reliability for a WDS under various failure conditions is necessary. Numerous techniques have been developed for estimating WDS reliability. This study provides an in-depth review of the relevant research literature (subsequently referred to simply as “literature”) in the context of mathematical methods for measuring WDS reliability. It was found that the literature is vague as types of failure (mechanical, hydraulic, and water quality failure) and techniques of reliability analysis are mixed together inappropriately. This research organized and classified the available techniques into three major categories and discussed which technique should be used depending upon the type of a failure. A particular state of failure could have several simultaneous unfavorable impacts on a WDS operation. Deep insight into the relevant literature revealed that simultaneous multiaspect/state analysis has received relatively no attention. The research demonstrated the need for simultaneous multiaspect/state performance analysis of a WDS.

Security issues have become increasingly important within distribution systems, which have led to the development of event detection algorithms (EDAs) to provide timely detection of intrusion events. The current study develops a localized model-based event detection algorithm that utilizes nonspecific water quality sensors to identify water quality anomalies. The proposed EDA focuses on evaluating a series of multivariate error signals between the observed signals and the model estimated signals based on a moving time-window of error statistics. The likelihood of the multivariate error signals is estimated using the product of univariate kernel density estimation (KDE), which is a type of nonparametric representation of the error distribution. A comprehensive analysis was performed using synthetic events to explore the combination of the moving window-pairs and bandwidth with respect to three injection strengths and two injection durations. In addition to the synthetic events, the EDA was also evaluated using a more realistic approach that simulates the water quality parameters in response to two real contaminants (KCN and nicotine) based on previously developed water quality dynamic models. Overall, the model-based EDA was capable of detecting anomalous water quality events through the statistical evaluation of multivariate error signals with performance related to the magnitude of the event. The results indicate that smaller events resulting from hydraulic/transport dynamics can have a significant impact on the EDA performance, which are not typically considered in most EDA evaluations. The results of the proposed EDA also illustrate that sensor performance should be considered within other water security activities such as the optimal design of sensor-based contaminant warning systems.

Water year types (WYTs), whereby years are classified by river runoff quantity compared to historical runoff, are one tool to help make major water management decisions. Increasingly, these decisions include instream flow requirements (IFRs) below dams for river ecosystem management. However, WYTs are typically based on assumptions of stationarity, and are thus rendered less meaningful with climate change. Hydrologic alteration resulting from climate change means that a WYT-based IFR scheme using stationary historical observations might inadvertently result in long-term river management outcomes inconsistent with original water management goals. This study assesses the management implications of assuming hydrologic nonstationarity in a WYT-based IFR scheme in California’s upper Yuba River and demonstrates a rolling period of record as a climate adaptation strategy. The existing, nonadaptive water management scheme leads to vastly different possible water allocation outcomes than originally planned for. Results indicate that water year types, if regularly updated, can help maintain historical instream flow distributions. However, gains toward maintaining desired IFRs are obfuscated by future increases in unmanaged reservoir spill. These findings indicate that hydroclimatic uncertainty can partially be accounted for with simple modifications to existing operating rules for reservoirs, though other, risk-based management approaches are also likely needed.

Exploratory simulation allows analysts to discover scenarios in which existing or planned water supplies may fail to meet stakeholder objectives. These robustness assessments rely heavily on the choice of plausible future scenarios, which, in the case of drought management, requires sampling or generating a streamflow ensemble that extends beyond the historical record. This study develops a method to modify synthetic streamflow generators by increasing the frequency and severity of droughts for the purpose of exploratory modeling. To support management decisions, these synthetic droughts can be related to recent observed droughts of consequence for regional stakeholders. The method approximately preserves the spatial and temporal correlation of historical streamflow in drought-adjusted scenarios. The approach is demonstrated in a bottom-up planning context using an urban water portfolio design problem in North Carolina, a region whose water supply faces both climate and population pressures. Synthetic scenarios are used to simulate the implications for reliability and cost if events with similar severity to the recent 2007–2008 drought become more frequent under climate change, and in general, the system-level consequences of increasingly frequent and/or severe droughts. Finally, synthetically generated drought extremes are compared with runoff projections derived from downscaled climate model output, serving to support bottom-up robustness methods in water systems planning.

In this study, an optimal reservoir refill hedging rule (RHR) is developed under hydrologic uncertainty using a two-stage model. Based on the probability distribution of the maximum refill water availability at the end of refill season, three possible cases exist: unfilled without flood damage, complete filling without flood damage, and complete filling with flood damage. These cases are characterized based on relationships among storage capacity, expected storage buffer, and maximum safe excess discharge. Karush–Kuhn–Tucker (KKT) conditions for the two-stage model show that the optimal refill operation equates the expected marginal loss of conservation benefit from not complete filling (i.e., ending storage of refill period less than storage capacity) and the expected marginal flood damage from levee overtopping downstream, unless constrained by capacity constraints. A RHR curve, which is analogous to water supply hedging and flood hedging rules, is drawn and shows the trade-off between the two objectives. The optimal refill hedging release decision shows a linear relationship with expected current water availability for a wide range of water conservation benefit functions (linear, concave, or convex). Several operational results are derived. A large downstream flood conveyance capacity and remaining storage capacity allow for a smaller current release and greater storage of water. Relative economic drivers are important; a greater economic potential for flood damage drives a greater release of water in the current stage, and vice versa. Below a critical forecast uncertainty value, improving forecasts reduces the volume of water released, although the opposite effect occurs above this critical value. Finally, the Danjiangkou Reservoir case shows that the RHR, combined with a rolling horizon decision approach, performs better than current rule curves and leads to a gradual dynamic refilling based on forecast information, indicating its potential for practical use.

Small-scale hydropower is emerging as a decentralized source to satisfy local demand for electricity. In water supply systems, microhydropower can be used for energy recovery associated with excessive pressure control. However, there is a lack of specific solutions for applications within networks where discharges are highly variable and there are limitations of pressure. An arrangement of microturbines specially conceived for water supply networks is proposed, based on a recently tested microturbine for inline installation in pipes. The installation of up to four turbine units is possible within a buried chamber created around an existing pipe. The location of the chambers is analyzed using an optimization algorithm that considers two objective functions: energy production and economic value. The feasibility of the proposed arrangement for a microhydropower plant was assessed through a case study of a subgrid of the water supply network of Lausanne, Switzerland. A detailed analysis of the cost breakdown revealed that further to the electromechanical equipment, the number of isolation valves required in each layout may have an important role on the investments.

Multiple cascaded hydropower reservoir systems serving the same area need to cooperate to increase overall benefits and reliability. To meet system demand while balancing among cascaded systems, both centralized and distributed five segment energy available based operating rules, making power decisions for subsystems and the whole system, are proposed for cooperation across multiple cascaded hydropower reservoirs. Unlike existing methods, the cooperative operating rules are optimized to the global objective of maximizing the minimum system power production, with constraints on local cascade minimum energy generation to confine the possible local profit earnings or losses because of cooperation, making the operating rules more acceptable for cooperative operations among local cascade system agents. The operating rule optimization models are solved using a genetic algorithm. A case study for three cascaded systems in southwest China shows that distributed and centralized cooperative operating rules can increase minimum total power by more than 3,600 MW and 4,700 MW, respectively, in the dry season with little energy changes in each cascaded system, taking advantages of reservoir storages and different timing of inflows. The centralized rule is superior to the distributed rule for increasing minimum power, although the distributed rule is more effective to control the energy for each cascaded system. The distributed and centralized rules adapt to higher subsystem energy constraints and higher interbasin compensation demands.

Best management practices (BMPs) such as bioretentions, dry and wet ponds, porous pavement, and many other methods are widely used to reduce the runoff volume and the concentration of sediments and nutrients in the runoff. Numerical models are used to develop and assess water quality management plans that, among others, include BMPs. Those models need to take into account the BMP effectiveness to remove pollutants, being a function of the volume of daily runoff, in order to estimate their performance under a range of different climate scenarios. Circumventing the lack of monitoring data, this study used the Environmental Protection Agency’s system for urban stormwater treatment and analysis integration model (SUSTAIN) to run 22 years (1984–2005) of runoff data from the Patuxent River (Maryland) through seven types of BMPs. It was found that BMP effectiveness decreases sooner, steeper, and deeper with increasing sizes of storm events than assumed in the Chesapeake Bay Phase 5 watershed model. At a minimum, the resulting performance curves should differentiate among BMPs.

This paper describes the development and application of a spreadsheet model to evaluate effects of water management on diversion modified flow regimes, enabling the exploration of novel ways to meet proposed environmental flow standards. Mill Creek, a northern California river with an altered flow regime that impacts aquatic species, was used as a case study. Test cases examined how water management alternatives, such as groundwater pumping, water rights transfers, and water exchange agreements, can improve environmental flow allocations given irrigation water demands. Four test cases include passage flows for Chinook salmon and steelhead trout, a minimum instream flow, 80% of natural flow, and a spring recession flow with functional flow components. The model identified late October as consistently water-scarce, even in wet years. These analyses suggest that fall shortages for fish migration could be eliminated through a water exchange agreement combined with use of wells. All cases except the minimum fish passage flow case required acquisition of the largest water rights to decrease environmental shortages by over 80%, with a substantial curtailment in irrigation.