Journal of Water Resources Planning and Management

This research investigated relationships between the most notable characteristics of end-use events, namely, event duration, volume, and intensity, in order to categorize water use as being indoor or outdoor. Three classification models were developed, calibrated, and compared using more than 200,000 household end-use events that were recorded independently in Australia and South Africa. The three methods were also compared to a practice-based limit classification scheme. The classification model presented in this paper correctly apportions $\sim 81\%$ of the indoor end-use event volumes and $\sim 98\%$ of the outdoor end-use event volumes, thus reinforcing the value of basic smart water meter data sets as a source of useful information for water demand management.

This study examines the impact of advanced metering infrastructure (AMI) end-user demand metering failure on water distribution system (WDS) operation and management. To address this issue, our first step is to develop a burst detection algorithm that compares total end-user demands with system inflow rates. Western Electric Company (WEC) rules are applied to test for anomalies in the time series of normalized differences between supply and withdrawal. Then, hydraulic model prediction and burst detection performance are evaluated for fully reporting and missing AMI demand conditions using synthetically generated end-user demands for a network in Tucson, Arizona. Three imputation methods [zero, historical mean (HM), and distribution sampling (DS) method] are applied to replace missing AMI data and are compared for a range of missing data percentages. Based on the numerical experimental results, HM imputation method is the most useful tool to replace missing WDS AMI data. This scheme resulted in the lowest hydraulic model prediction errors and low false-alarm rates while maintaining high burst detection probability. However, more false alarms are raised as the percentage of missing data increases. To solve the problem, the guidelines for optimal WEC rule application are identified for a range of missing demand levels.

Drinking water systems commonly use manual or grab sampling to monitor water quality, identify or confirm issues, and verify that corrective or emergency response actions have been effective. In this paper, the effectiveness of regulatory sampling locations for emergency response is explored. An optimization formulation based on the literature was used to identify manual sampling locations to maximize overall nodal coverage of the system. Results showed that sampling locations could be effective in confirming incidents for which they were not designed. When evaluating sampling locations optimized for emergency response against regulatory scenarios, the average performance was reduced by 3%–4%, while using optimized regulatory sampling locations for emergency response reduced performance by 7%–10%. Secondary constraints were also included in the formulation to ensure geographical and water age diversity with minimal impact on the performance. This work highlighted that regulatory sampling locations provide value in responding to an emergency for these networks.

Rapid and informed response is needed to ensure effective water management during droughts, including reliable and immediate data synthesis, near-real-time forecasting, and model-based decision support for water operations. A service-driven approach has been developed previously to couple river modeling and genetic algorithm (GA) optimization services for determining optimal water allocation strategies under daily drought scenarios. However, the computational effort in handling the constraints, which involves executing computationally intensive models repetitively, is a major obstacle to enabling an effective real-time Web application for decision support. The objective of this work is to develop a computationally efficient metamodel approach to reduce the computational burden of the simulation-optimization model. Two types of metamodels are developed: a pretrained metamodel that is built offline before the optimization and an adaptive metamodel that is built and updated during the optimization. The metamodel is a classifier algorithm that evaluates whether a constraint is satisfied, which simplifies the prediction and leverages the metamodel’s role in water management. The metamodel framework was tested for a drought event in the Upper Guadalupe River Basin, Texas, in April 2015 and the performance of the different approaches is compared. A conservative model, which narrows the feasible region by increasing the constraint probability threshold, is needed with the pretrained metamodel to ensure convergence to a feasible near-optimal solution, but not for the adaptive metamodel. The results also show that the adaptive metamodel GA performs best in model accuracy and reduces computation time by 58%, compared with the pretrained metamodel GA, which reduces computation time by 78% but does not reliably obtain the optimal solution. The approach also does not require users to select classifiers, tune parameters, or execute simulation models offline. Therefore, a prototype Web interface is implemented that uses the best-performing adaptive metamodel approach to more efficiently assist decision makers with real-time drought management.

This paper analyses and compares different layouts for the installation of pumps used as turbines (PATs) to advantageously couple pressure regulation with hydropower generation in water distribution networks (WDNs). The layouts differ in terms of the type of regulation adopted for adjusting PAT operation to time-varying conditions of available flow and head during the day. In fact, while offering a number of feasible layouts for PAT installation (hydraulic regulation, electric regulation, coupled hydraulic and electric regulation), the literature provides no clear indication as to the layout deemed most suitable. The paper contributes to resolving the open question of determining the most effective and appropriate installation layout for different flow and head patterns by means of a comparative cost-benefit analysis—specifically, technical, and economic analysis—of the layouts considered. Overall, the results from four case studies that vary in terms of available flow and head show that the hydraulic regulation-based installation layout represents the best trade-off between energy produced and installation costs. The solely electrical regulation option exhibits poor flexibility for accommodating yearly demand variations.

A climatically driven, water system modeling framework that captures a full range of interacting components at necessary spatiotemporal scales was developed for the Southwestern United States, including a detailed representation of California’s water supply and demand network, within the Water Evaluation and Planning (WEAP) system. Credibility of the model as a tool for water-energy analysis was garnered through evaluation of the historic regional water balances, including metrics of water supply, water use, groundwater storage, environmental requirements, and the energy use associated with water. The ability of the model to address policy-relevant questions was demonstrated by evaluating the impacts of a future 30-year drought scenario that included a realistic 2°C warming trend through 2050. With drought, the total energy used to supply water decreases by about 3% compared to the historic use, and warming alone reduces overall energy use by a modest 1% due to overall reductions in water delivery. The strongest signal of persistent warming expresses itself in groundwater storage, which decreases by an additional 9% beyond the impact of drought alone. A water conservation scenario was used to demonstrate the response of the water sector to the types of conservation strategies being pursued at local and state levels, with conservation reducing water use by about 10% and energy use by 3% relative to historic levels. Such measures overcome the warming trend, but it is demonstrated that persistent drought challenges groundwater conservation strategies and reductions in energy use by the water sector, leading to further overdraft of aquifers and increased energy use.

Although there is extensive work related to design and implementation of model-driven optimal groundwater management, evaluation of the performance of those models is less reported. This hinders transferability of lessons learned and wide adoption of specific policies. This paper reports a study designed to evaluate the effectiveness of a real-time optimal groundwater management tool, optimized regional operations plan (OROP), used in the Tampa Bay region (located on the west coast of Florida in the United States) since 1998. The overarching question in this study is how different groundwater levels would have been at monitoring sites if such an operational decision-supporting tool was not implemented. The proposed framework is comprised of multiple steps, including generating a non-OROP groundwater pumping scenario that would have happened without the decision support tool in place, running a modified optimization model in retrospective mode, and comparing water level between OROP-based and non-OROP–based pumpage scenarios. The framework is applied to historical data in the past decade (2010–2019). In generating an alternative pumpage scenario, realistic model constraints (e.g., pump capacity, pipeline hydraulics, and network) were used to generate wellfield pumpage to meet demands. Wellfield pumpage is then disaggregated at individual wells. Differences in resulting water levels at monitoring wells, at multiple timescales, between the OROP and non-OROP pumpage scenarios were calculated. Results reveal that although at a weekly timescale the effectiveness of OROP seems minimal over the region, the cumulative effectiveness at the monthly and annual timescales is remarkable. It is also found that benefit of using OROP is even higher during dry seasons when groundwater levels are more critical in maintaining wetland health. The proposed framework presented in this study can be applied to other areas in evaluating groundwater management strategies.

Spill operations of hydropower facilities can generate supersaturated total dissolved gases (TDGs) that can negatively impact fish residing in downstream habitats by causing gas bubble disease and mortality. Assessment of such impact and management of TDG can be challenging, particularly in river systems with multiple dams, because this requires investigation of complex physical processes related to gas transfer and dissolved gas generation in spillways as well as its transport, mixing, and dissipation in the riverine environment. In this study, an integrated analytical platform was developed to model system-wide total dissolved gas levels during spill events. To test its functionality, TDG monitoring data from the lower Columbia River hydropower system was evaluated for different operational conditions. The system includes contributions from the dams on two other regulated tributaries. In addition to facility-specific estimates of TDG production, the system model provided TDG distribution in the river system, which provides rapid and accurate estimations for impact assessment or mitigation. A ranking process was developed to address cumulative TDG risk for multifacility spill operations. This involved estimation of risk scores considering severity of supersaturation level, river depth compensation, and exposure duration for a given spill event, and grouping the scores into four risk categories defined as none, low, moderate, and high. This resulted in a risk assessment framework that identifies the potential risk zones and the degree and extent of fish habitat impacted for the combined facility operations in a complex river system. This framework can be used as a tool to help inform water management decisions for regulatory compliance and environmental performance, and provide a strategic assessment of the relative need for mitigation action.

The estimation of nodal water demands for water distribution systems has been extensively researched over the last decades. However, demand estimation performance is dependent on selected nodal demand aggregation and available sensor locations. Despite a variety of water demand clustering approaches already proposed, a comprehensive methodology capable of generating cluster solutions with practical interpretation and maximum accuracy is still lacking. To achieve that goal, the current research presents an innovative clustering methodology based on network flow measurements. The procedure follows two primary steps: (1) determination of optimal flow sensor locations, and (2) an integrated approach for cluster identification, which includes cluster scenario generation, demand estimation, and identification metrics. The effectiveness of the proposed method was tested on a synthetic case study with realistic generated spatial patterns. Results demonstrate that finding a high-quality cluster solution is possible by utilizing (1) additional flow sensors installed according to the proposed V-optimal early split methodology, and (2) cluster selection based upon a likelihood metric. In general, the metrics used for both sensor location and cluster identification were found to be critical to identifying the best set of clusters.

While pressure-reducing valves (PRVs) are commonly used to regulate pressure and reduce leakage in water distribution networks (WDNs), research in recent years has increasingly focused on coupling hydropower generation with pressure control, wherein the excess head is used for generating power instead of being dissipated through the PRVs. In this case, a programmable logic controller (PLC)-based control system is required to control valve operation (in case of hydraulic regulation) and impeller speed (in case of electric regulation). Because the optimal operation of the system requires solving an optimization problem with mathematical operations too complex for the PLC to handle, an external optimization module is required and coupled to an OPC server so as to allow communication between the optimization routine and the PLC. However, issues associated with implementing any external device in such a solution diminish system reliability. This paper therefore presents a robust alternative approach for advantageously solving the optimization problem directly within the PLC operating cycle, involving only simple yet effective mathematical and logical operations. The proposed methodology also allows identifying the range of flow discharges for which hydraulic and electric regulation can be used alone, thus not requiring coupled regulation. Besides using dimensionless variables representing the machine characteristic curves, and a flowchart showing the different operations at varying flow discharge and available head values, the capability of the proposed methodology was confirmed through numerical experiments, which in all cases returned the optimal solution to the problem. Moreover comparative analysis including an appropriate case study from the literature also decisively showed the results derived from the methodology to coincide with those obtained using an optimization tool.