Journal of Water Resources Planning and Management

The town of Yucca Valley located in the southwest part of the Mojave Desert in southern California relies on groundwater pumping from the Warren groundwater basin as its sole source of water supply. This significant dependency has resulted in a large imbalance between groundwater pumpage and natural recharge, causing groundwater levels in the basin to decline more than 90 m from the late 1940s to 1994. Consequently, an artificial recharge program proposed by the Hi-Desert Water District, which provides water service to the town of Yucca Valley, was implemented for the purpose of recovering the groundwater levels; however, the rise in groundwater levels has caused nitrate $\left({\text{NO}}_3\right)$ concentration to increase simultaneously. The purpose of this study is to develop an optimal pump and recharge strategy for a planned conjunctive-use project to remove the high-nitrate concentration while maintaining groundwater levels at desired elevations at specified locations as well as meeting water demand. An optimization/management model is formulated with a linear objective function and nonlinear constraints. The response matrix approach is used to link the optimization model with the simulation model. Because of nonlinearity, the response matrix is updated and iteration is required for convergence. A systematic scheme is also developed for finding a feasible initial policy. Three different scenarios are considered in the management model. The results obtained from each scenario are analyzed and discussed.

Water lost through leakage from water distribution networks is often appreciable. As pressure increases on water resources, there is a growing emphasis for water service providers to minimize this loss. The objective of the work presented in this paper was to assess the online application and resulting benefits of an artificial intelligence system for detection of leaks/bursts at district meter area (DMA) level. An artificial neural network model, a mixture density network, was trained using a continually updated historic database that constructed a probability density model of the future flow profile. A fuzzy inference system was used for classification; it compared latest observed flow values with predicted flows over time windows such that in the event of abnormal flow conditions alerts are generated. From the probability density functions of predicted flows, the fuzzy inference system provides confidence intervals associated with each detection, these confidence values provide useful information for filtering and ranking alerts. Additionally an accurate estimate of abnormal flow magnitude is produced to further aid in ranking of alerts. A water supply system in the U.K. was used for a case study with near real-time flow data provided by general packet radio service. The online burst alert system was constructed to operate alongside an existing flat-line alarm system, and continuously analyze a set of 144 DMAs every hour. The new system identified a number of events and alerts were raised prior to their detection in the control room; either through flat-line alarms or customer contacts. Examples are given of alert correlation with burst reports and subsequent mains repairs for a 2-month trial period. Forty four percent of alerts were found to correspond to bursts confirmed by repair data or customer contacts, 32% of alerts were confirmed as unusual short-term demand from manual analysis, 9% were related to known industrial events, and only 15% were ghosts. The results indicate that the system is an effective and viable tool for online burst detection in water distribution systems with the potential to save water and improve customer service.

For flood damage reduction analysis, the standard for measuring risk is expected annual damage. This index represents a long-term average annual flood damage for a given structure or area. We compute this index by integrating a probability-damage function. In practice, we develop the required function by developing a water level (stage)-probability function and an elevation-damage relationship. The analysis becomes complex when the stage-probability function is dependent upon multiple flooding sources. For example, this occurs when an area is protected by multiple leveed reaches or can be flooded from more than one stream. Here we propose alternative analysis frameworks for computing expected annual damage considering this complicating factor. An example illustrates that appropriate methods depend on the alternatives evaluated, the correlation of discharges, and the number of levee reaches.

Floods in both riverine and coastal zones can cause significant damage to infrastructure, including possible structural failure of buildings. Methodologies commonly used to estimate flood damage to buildings are typically based on aftermath surveys and statistical analyses of insurance claims data. These methodologies rarely account for flooding hydrodynamics, and thus do not differentiate between the damage caused by floodwater contact and those caused by floodwater velocity. A new stochastic methodology has been developed to estimate the direct impact of flood actions on buildings and to determine the expected damage. Building vulnerability is modeled based on analytical representations of the failure mechanisms of individual building components. The flood actions generated during different flooding events are assessed and compared to the resistance of each building component. The assessed flood actions include: hydrostatic and hydrodynamic forces, waves, turbulent bores, debris impacts, and time-dependent local soil scour. Monte Carlo simulation was used to synthetically expand the available building data, to perform load-resistance analysis, and to account for the uncertainty of input parameters. The primary result from this study is the expected flood damage to individual buildings, and it is expressed as a three-dimensional functions dependent on both floodwater depth and floodwater velocity. The results show how floodwater velocity can increase the magnitude of the flood damage outcome compared to those that solely consider water depth. This demonstrates the real need for considering floodwater hydrodynamics in the vulnerability assessment of buildings located in flood prone areas. Although the present study focuses on the vulnerability of reinforced concrete frame buildings with infill concrete-block walls, the methodology can also be applied to other types of structures. This methodology could serve as a decision-making tool to assist engineers and emergency management agencies to identify zones of high risk, and to implement the necessary preventive measures and mitigation strategies to minimize the adverse impact of potential flooding events.

Real-time state estimates (SEs) of nodal demands in a water distribution system (WDS) can be developed using data from a supervisory control and data acquisition (SCADA) system. These estimates provide information for improved operations and customer service in terms of energy consumption and water quality. The SE results in a WDS are significantly affected by measurement characteristics, i.e., meter types, numbers, and topological distributions. The number and type of meters are generally selected prior to a SCADA layout. Thus, selecting measurement locations is critical. The aim of this study is to develop a methodology that optimally locates field measurement sites and leads to more reliable SEs. An optimal meter placement (OMP) problem is posed as a multiobjective optimization form. Three distinctive objectives are formulated: (1) minimization of nodal demand estimation uncertainty; (2) minimization of nodal pressure prediction uncertainty; and (3) minimization of absolute error between demand estimates and their expected values. Objectives (1) and (2) represent the model precisions while Objective (3) describes the model accuracy. The OMP is solved using a multiobjective genetic algorithm (MOGA) based on Pareto-optimal solutions. The trade-off between model precision and accuracy is clearly observed in two case studies and it is recommended to use both criteria as objectives. It is also concluded that the proposed objectives are more appropriate for OMP purposes compared to calibration sampling design studies in which minimization of metering costs (i.e., number of meters) is used as one of the multiple objectives. The MOGA saves computational effort while providing optimal Pareto solutions compared to full enumeration for a small hypothetical network. For real networks, GA solutions, although not guaranteed to be globally optimal, are improvements over those obtained using less robust methods or designers’ experienced judgment.

To fulfill the objective of providing acceptable level of service to customers, the water managers have to plan how to operate, maintain, and rehabilitate the system under budget constraints. The model presented in this paper uses risk cost as an appropriate framework to define the optimal replacement time prediction based on the balance between investment for replacing and expenditures for maintaining the asset. An economic analysis compares the costs associated with maintaining an existing pipe in service, being completely depreciated or not, to the cost of replacing or rehabilitating the pipe. On this basis, the right time in the future to rehabilitate the pipeline can be determined. The costs associated with an existing pipe include direct operational and maintenance costs and indirect costs, such as those associated with risk of failure. The optimal replacement time is identified as the year in which the cost to maintain the existing stock of pipes exceeds the investment to replace it. A dynamic programming tool was developed to search the vast combinatorial solution space of the problem. The model was applied, with the aim of supporting management decisions, to the wastewater network of Oslo in Norway, managed by Oslo Vann og Avløpsetaten, using available real-world information to estimate expected costs of maintenance and rehabilitation. The results show that a constant value for lifetime should not be applied to all the pipelines in the stock, as currently done by the utility for long-term investment; rather it is wiser to define different values for different cohorts of pipelines to reduce the uncertainties associated with generalizations for simplification. The model has been applied to wastewater pipes but is in principle valid for any aging infrastructure.

The effective implementation of total maximum daily loads (TMDLs) usually requires that relationships between terrestrial contaminant loadings and instream concentrations be estimated using deterministic fate and transport (DFT) models. The limitations of using conventional DFT models are that model predictions do not converge to observations as source loadings approach their calibrated values, and model-prediction errors are not explicitly included in the model output. A normalization approach is proposed that yields an accurate convergence to observations and can explicitly account for prediction errors. The proposed approach is demonstrated using field data collected at the Little River Experimental Watershed in Georgia, where source-load reductions are related to the confidence of compliance with a water-quality standard.

The modification of land and water resources for human use alters the natural hydrologic flow regime of a downstream receiving body of water. The natural flow regime is essential for sustaining biotic structure and equilibrium within the ecosystem. Best management practices mitigate the increased storm water runoff due to increased imperviousness and are typically designed and located within a watershed to match peak and minimum flows for a small set of targeted design storms. Ecosystems are, however, affected by all the characteristics of a long-term flow regime, including the magnitude, duration, frequency, and timing of flows. A more environmentally sustainable approach for watershed development is presented based on the minimization of differences in the characteristics of the flow regime between predevelopment and postdevelopment conditions. The indicator of hydrologic alteration (IHA) is a set of 33 hydrologic indices that characterize a flow regime and, coupled with the range of variability approach (RVA), can be used to evaluate a development strategy for its alteration of the long-term hydrologic flow regime. This paper presents a methodology to identify watershed management strategies that will have a minimal impact on the flow regime and downstream ecosystems. This methodology utilizes a metric that evaluates development strategies based on an IHA/RVA analysis implemented within a simulation-optimization framework. Continuous simulation of urban runoff for different land use strategies is enabled through the use of the storm water management model, and the resulting long-term hydrograph is analyzed using IHA/RVA. Development is allocated within subcatchments to maintain a predefined minimum level of total development while minimizing the hydrologic alteration. A hybrid optimization approach based on genetic algorithm and Nelder-Meade approaches is used to identify optimal land use allocation. Further analysis is conducted to identify alternative development patterns that allocate impervious development maximally differently among subcatchments while achieving similarly low alteration in the hydrologic flow regime.

The impacts of climate change on medium-term reservoir operations for the Peribonka water resource system (Quebec, Canada) were evaluated with annual and seasonal hydropower production indicators and flood control criteria. According to simulations under the current operating rules in a climate change context, the tendency is for a reduction in mean annual hydropower production and an increase in spills, despite an increase in the annual average inflow to the reservoirs. The main results indicate that annual mean hydropower would change by $-12\text{\hspace{0.17em}to\hspace{0.17em}}+2\mathrm{\%}$ , and spills by $-49\text{\hspace{0.17em}to\hspace{0.17em}}+152\mathrm{\%}$ . A broad range of climate projections—a combination of five general circulation models with two greenhouse gas scenarios each—were used in order to evaluate the uncertainty of these future potential climates on floods and hydroelectric production. Climate projections were downscaled with the change factor method (also called the Delta method) at a horizon centered in 2050. To represent natural variability, a stochastic weather generator was used to produce 30 synthetic climate series of 30 years each, representative of each climate change projection as well as of the climate of the control period. The hydrological impacts of climate change were evaluated with a lumped hydrological model and the hydrological regimes were analyzed according to spring flood characteristics and the average inflows. In general, the projections indicate an increase in annual inflow, earlier peaks and greater volumes during the spring flood. The analyses show that a power plant managed with a reservoir is sensitive to the operating rules and that these rules should be re-examined in order to take account of new seasonal hydrological contexts.

This paper applies two-stage stochastic quadratic programming to optimize conjunctive use operations of groundwater pumping and artificial recharge with farmer’s expected revenue and cropping decisions. The two-stage programming approach allows modeling of water and permanent crop production decisions, with recourse for uncertain conditions of hydrology, annual crops, and irrigation technology decisions. Results indicate potential gains in expected net benefits and reduction in income variability from conjunctive use, with increase in high value permanent crops along with more efficient irrigation technology.

Econometricians have long studied the effect of price on residential water demand and the impact on water use of the rate (tariff) structure in which price signals are embedded. This paper applies an existing deductive model of residential water use for the intermittent supply system in Amman, Jordan and simulates demand responses across a cross section of households over many uniform, increasing block, and linear price (quadratic charge) rate structures at historically low and significantly higher prices. Results show inelastic piped water demand responses for all rate structures at historically low prices similar to findings from a prior econometric study for Amman. However, piped water demand turns more elastic when prices rise above $\mathrm{\$}0.50/{\text{m}}^3$ with uniform rates showing the most elastic response. But results also highlight several complications to determine and interpret price elasticity of demand under different rate structures. They also illustrate trade-offs among rate structures and rate structure components for key rate-setting objectives such as to encourage water conservation, recover costs, promote efficiency, and more equitably allocate costs among users.

A computational protocol for particle tracking in wellfields with lakes is proposed. The protocol is demonstrated in the Northwest Wellfield, which is the largest wellfield in Florida and contains many lakes. Comparison of the results with existing regulatory travel-time contours shows that the proposed method produces more realistic results in terms of reflecting the presence of lakes.