Journal of Water Resources Planning and Management

A set of nested models was applied to provide useful strategy to evaluate the storage, water yield and the operational performance of the multipurpose Mula reservoir in India. Insufficient yield from the reservoir for the purpose of water supply and irrigation has led to the need for reevaluation. These nested models were applied in tandem using linear programming (LP), dynamic programming (DP), artificial neural networks (ANN), hedging rules (HRs), and simulation. An LP-based yield model (YM) has been used to reevaluate the annual yields available from the reservoir for water supply and irrigation. The yields obtained from the YM have been refined by two DP models, viz; the controlled output DP (CODP) and the controlled inventory DP (CIDP). The prespecified annual release reliabilities and the yield deficits were similar to that used in the YM. In this approach, the ANN models use a hybrid model in the stochastic generation of monthly inflows to the reservoir for studying its operational performance. With the ANN, both the sigmoidal (DPN) and the radial basis function network (RBN) evaluated reservoir performance using results of the DP model. Continuous HR (CHR) and discrete HR (DHR) were also used for modeling the reservoir performance. The results show that the YM estimates the annual multiyields accurately. The data generated by the hybrid models preserve the characteristics of the historical data. The RBN is better than the DPN models. In the CHR, the sets of hedging trigger obtained from the YM gave an acceptable performance. The DHR reduces the number of reservoir empty conditions and hazards of droughts. The study reveals that the project may fail to fulfill the irrigation demands.

This paper presents a procedure for optimal medium-term scheduling (e.g., over a time window of 5 years) of rehabilitation and leakage detection interventions in a water distribution system given predetermined budget constraints. The decisional variables are the interventions to be scheduled, that is, which pipes to replace and when (in which year), and where and when (in which zone and year) to carry out leakage detection surveys, while the objectives are to minimize the volumes of water lost and break repair costs. The optimizer used is the NSGA II multiobjective genetic algorithm. It is assumed that the budget allocated for leakage detection and pipe rehabilitation (proactive interventions) represents a separate expenditure item from that for the repair of breaks (reactive interventions). In particular, whereas repair costs are subject to minimization, the budget for proactive interventions is allocated on a yearly basis during the scheduling period and a constraint is determined by the fact that the amounts budgeted must be completely spent, year after year: this reflects the customary practice of water utilities, which strive to spend the entire budget available to them, since any residual amount may not be reallocated to the same expenditure item in the year or years to come. The multiobjective optimization procedure does not produce one optimal solution, but rather the Pareto front of nondominated solutions. Some considerations for identifying a range of solutions within this front, from which to choose the one to apply, are thus discussed. The procedure is applied to a real water distribution system using advanced models to represent the various processes that characterize the problem, namely, leakages as a function of pipe age and pressure, actual nodal discharges released as a function of head, time series of pipe breaks, etc. and the results obtained show that the proposed procedure may be a useful decision support tool for scheduling leakage detection campaigns and rehabilitation interventions.

Anticipated future temperature changes in the mountainous U.S. Pacific Northwest will cause reduced spring snow pack, earlier melt, earlier spring peak flow and lower summer flow in transient rain-snow and snowmelt dominant river basins. In the context of managed flood control, these systematic changes are likely to disrupt the balance between flood control and reservoir refill in existing reservoir systems. To adapt to these hydrologic changes, refill timing and evacuation requirements for flood control need to be modified. This work poses a significant systems engineering problem, especially for large, multiobjective water systems. An existing optimization/simulation procedure is refined for rebalancing flood control and refill objectives for the Columbia River Basin for anticipated global warming. To calibrate the optimization model for the 20th century flow, the objective function is tuned to reproduce the current reliability of reservoir refill, while providing comparable levels of flood control to those produced by current flood control practices. After the optimization model is calibrated using the 20th century flow the same objective function is used to develop flood control curves for a global warming scenario which assumes an approximately $2°\text{C}$ increase in air temperature. Robust decreases in system storage deficits are simulated for the climate change scenario when optimized flood rule curves replace the current flood control curves, without increasing monthly flood risks.

Urban water supply headworks systems are usually designed to provide high security against drought. The best way to evaluate this security is to use Monte Carlo simulation which is computationally expensive. The advent of parallel computing technology in conjunction with genetic algorithms has made it practicable to optimize operation for drought security. Nonetheless, computation turnaround times remain long. This paper presents a simple heuristic called replicate compression to improve Monte Carlo efficiency. It exploits the well known concept of a critical period. In a high reliability system there should be few critical periods. Therefore, restricting simulation to such periods should bring about substantial savings in computational effort. It was found for problems where the objective function evaluation is only affected by what happens during critical periods, replicate compression provides an effective means for substantially reducing simulation effort. The case study involving a nine-reservoir urban headworks system showed the actual reduction in effort depended on the stress experienced by the system, which in turn affected the frequency of critical periods. Even when the objective function is affected by decisions outside the critical period, replicate compression may provide a useful result by helping to guide the specification of a reduced search space for the genetic algorithm. This strategy can bring about substantial savings in turnaround time.

The drought frequency index (DFI) is employed in this study as a drought indicator and as a trigger mechanism for multireservoir system operations during drought. The DFI characterizes droughts according to their duration and intensity, using a probabilistic criterion that takes into account the persistence of extreme low precipitation values. Performances with and without the DFI are evaluated, using reliability and resilience indices, for the Conchos river basin—a tributary of the Rio Grande/Bravo basin between United States and Mexico—through a multilevel nonlinear optimization procedure oriented to reduce water deficits to the United States and maximize net benefits for farmers in Mexican irrigation districts. Results show that the inclusion of the DFI improves the reliability of both reservoirs and water deliveries to users during periods of drought, which reflects an overall improvement of net benefits associated with crop production in Mexican irrigation districts.

To respond to growing concerns related to potential contamination ingress via backflow and/or terrorist threats to drinking water, a data mining approach is developed. Use of this data mining approach, in conjunction with a maximum likelihood procedure provides the means to identify the location and time of an intrusion event, based on limited sensor data. Uncertainties in water demand, sensor measurement, and modeling, are demonstrated to be highly relevant and necessary to be considered in the contamination identification problem. The effectiveness of the data mining method is demonstrated using a case study network where it takes only 3 min to identify a multiple injection event using five sensors in a 285 node water distribution network, including consideration of the aforementioned sources of uncertainty. The effectiveness of the method ensures the ability for a rapid-response to an abnormal event, and consequently, minimizes exposure risks of water consumers.

A developing understanding of instream flow needs now supports maintenance of ecological flow regimes rather than a fixed, time-invariant regulatory minimum flow. This shift is reflected in management of streamflow change created by existing reservoirs and flow diversion structures. With an emphasis on regime-based approaches a new connectivity is demanded between reservoir operations and the resulting downstream flow conditions. Complicating this situation is reservoir management that may be legally limited to flood control and water supply requirements reducing options for supplying downstream needs. This paper proposes an approach to reservoir operation that is based on ecological flow regime concepts. Reservoir operation is guided by six hydrologic indicators selected both to meet the specific flow needs of the local indigenous fish community and to satisfy authorized reservoir operational rules. The approach is based on incorporation of ecology and life history requirements of the fish community in the decision making process to better define and meet flow needs. Optimization using nondominated sorting genetic algorithms provides a basis for reservoir operational schemes that are expected to provide benefit to fish communities downstream while also meeting authorized reservoir storage needs.

Deterioration of water distribution systems and the optimal allocation of limited funds for their rehabilitation represent crucial challenges for water utility managers. Decision makers should be provided with a set of “informed” solutions to select the best rehabilitation plan with regard to available resources and management strategies. In a risk-based scenario, such an approach should result in an element-wise prioritization scheme based on individual pipe rehabilitation/replacement effectiveness. This manuscript describes a framework for devising a short-term decision support tool for pipe replacement. The approach allows for the introduction of economic, technical, and management rationales as separate objectives to produce a pipe-wise prioritization scheme which is achieved by ranking pipes selected during a multiobjective (MO) evolutionary optimization of replacement scenarios. Such a procedure helps overcome the doubts in choosing among the solutions obtained by MO evolutionary optimization due to the diverse sets of pipes selected for replacement even when they are economically comparable. The effectiveness of the entire framework is demonstrated on a real U.K. water distribution system.

This paper discusses various water management issues facing federal, state, and local agencies charged with managing the water resources of the Rio Grande River Basin and its major tributaries. The Rio Grande—3,058 km $\left(=\mathrm{1,900}\text{mi}\right)$ long—is the fourth longest river in the United States. The river's basin is $\mathrm{870,236}{\text{km}}^2$ $\left(=\mathrm{336,000}{\text{mi}}^2\right)$ and for roughly two-thirds of its length it forms the United States–Mexican border. It is a major recreational resource providing world class trout fishing near its headwaters in Colorado's San Juan Mountains and shoreline, angling, and boating opportunities near the Colorado–New Mexico border. The Rio Grande is the principal tourist attraction of Big Bend National Park and flows through downtown Albuquerque and El Paso. Many reaches are wide and broad, but almost all are relatively shallow and not navigable by commercial ships. Nevertheless, it is one of the most important renewable water resources of the southwestern United States and North America. The issue of the “manageability” of the river in the face of social forces and disparate administrative jurisdictions that adversely impact Rio Grande flows is a thread linking various sections of the paper together. The length of the river; the fact that major reaches lie in Colorado, New Mexico, and Texas; and its unique role as an international boundary pose complex management problems. The allocation status quo formed by the complex nexus of existing river laws make it difficult to reshape Rio Grande management.

Booster disinfection has been shown to be an effective means of maintaining more stable chlorine residuals in a water distribution system. It has been suggested that booster disinfection could also be a viable means of protecting a population against contamination. We simulated random contamination events in a model water distribution system with an optimized sensor network. A disinfection boost was simulated to begin the instant the contamination reached a sensor, and a range of decay coefficients were applied to the contaminant to simulate reaction with the disinfectant. Cumulative distribution curves of the volume of consumed contaminated water for various response levels were prepared to analyze how each response affected the vulnerability of the system. This analysis illustrated that a boost-response system could be effective in significantly reducing the volume of consumed contaminated water, but only in very specific circumstances. Most importantly, the booster must be located at a node with high reachability. Further, if the disinfectant cannot rapidly inactivate the contaminant, the effectiveness of a boost-response system is much reduced.

Urban development of rural land can be a concern for water resources and wildlife. The quantity and quality of surface water runoff and groundwater recharge can be significantly affected by urbanization. Base flow in streams and cold-water habitat, e.g., for trout, depend on groundwater. If water recharge to aquifers is reduced, and surface runoff is increased, streamflow and cold-water habitat can be adversely affected. An investigation of the changes to groundwater recharge resulting from the urbanization of a rural/natural area in the Vermillion River watershed in Minnesota was conducted. Well established principles and relationships for the estimation of infiltration (Soil Conservation Service (SCS) and Green-Ampt (GA) methods] and evapotranspiration [Food and Agriculture Organization (FAO)-56 Penman-Monteith) were applied, and percolation or groundwater recharge was determined using a water budget approach. The relationships were assembled in three models called the FAO-SCS, the FAO-GA, and the GA models. Four scenarios of land development representing progressive stages of urbanization (past, present, plus $50\text{years}$ , and plus $100\text{years}$ ) were developed and groundwater recharge was simulated for each scenario from predevelopment to fully developed. In 2005, about 60% of the $6.78{\mathrm{km}}^2$ $\left(\mathrm{1,675}\text{acres}\right)$ watershed of a tributary to the Vermillion River was undeveloped agricultural and natural land, and about 40% was developed urban residential and commercial land. Model estimates of groundwater recharge under the 2005 land-use conditions ranged from 12 to 24% of $829\mathrm{mm}$ of annual precipitation. The full urban development of the study site would result in a decrease of groundwater recharge to a range of 9 to 14% of precipitation. To make this projection, the impervious areas were raised from an average of 18% for the present scenario to an average of 36%. The case study shows that the increase of 18% in impervious area may decrease the recharge by about 20 to 40% of its present value. A change of this magnitude will substantially affect the water supply to the aquifer which discharges into the Vermillion River which is classified as a cold-water (trout) stream.

A risk-management approach is proposed for estimating total maximum daily load (TMDL) load reductions in pathogen-impaired streams. The approach is based on reducing the observed $\alpha$ -percentile concentration to the $\alpha$ -percentile water-quality standard with a $\beta$ -percentile level of confidence. The required reduction is determined using an explicit policy decision of acceptable risk. A review of the methodologies currently being used to determine TMDL load reductions in eight southeastern states shows a lack of uniformity in TMDL methodology and the use of either unknown implicit or arbitrary explicit margins of safety. Application of the proposed approach to these cases demonstrates that the confidence of compliance of the adopted TMDL load reductions are mostly less than 50%. This indicates that the adopted TMDL load reductions would likely result in not meeting applicable water-quality standards.

This paper reviews existing approaches to system resilience, and proposes a scheme to quantify the resilience of water resource systems. In general, three aspects of resilience are considered in water resource systems: (1) that against crossing a performance threshold; (2) that for response and recovery after disturbances; and (3) that of adaptive capacity. Conventionally these aspects are treated separately without considering possible functional relationships or interdependencies. We argue that the adaptive capacity of the system is better treated as an input variable of the other two aspects of resilience, and that response/recovery may be considered only when the performance threshold is not crossed. Because of the dynamic and evolving nature of water resource systems, proper consideration of uncertainty and associated information, whether obtained from well-defined numerical data or vague linguistic articulation, is essential for better understanding and proper management of their resilience. Constantly monitoring, adjusting, and engaging in long-term planning for resilience enhancement is important to maintaining a resilient system.

This paper explores how estimated construction costs and projected revenue from sales could influence the decision to maintain open spaces in predevelopment condition and to implement a decentralized swale-based storm-water management system characteristic of low impact development. Four development alternatives were explored for a hypothetical residential subdivision. Hedonic price analysis was used to develop a price equation for the Gainesville, Fla. residential market. The hedonic analysis indicated that reduction in lot size to preserve open-space and conversion from a curb and gutter storm-water drainage system to a swale-based system would both result in a lower sale price per lot. Estimated construction costs indicated that this same combination of site planning and storm-water system design would result in the lowest per lot construction cost. The ratio of revenue (sale price) to construction cost showed that a development incorporating lot size reductions for open-space preservation and a swale-based storm-water management system was the preferred option over the first half of the study period, but was not in the second half.

A set of risk factors and a fault tree methodology in conjunction with fuzzy logic is described to assess potential contributing factors to failures in water supply systems. The fault tree methodology establishes the structure of the risks, and the fuzzy logic analysis translates qualitative risk data into probabilities. The methodology affords guidance to the decision-making process indicating when the risk is severe and what constitutes a contributory factor that warrants specific attention. The model is applied to a case study in North Battleford, Saskatchewan and sensitivity tests are used to demonstrate the model’s utility to assess the implications of individual factors.

After several decades in service, the deterioration of storm-water pipe assets is inevitable. The deterioration of storm-water pipes is characterized by structural deterioration and hydraulic deterioration. Condition assessment using closed circuit television (CCTV) inspection is often carried out to assess the structural condition of pipes. However, the knowledge on the condition of storm-water pipe assets is still limited for strategic planning of maintenance and rehabilitation, because generally only a small sample is CCTV-inspected and in almost all cases, these pipes are inspected once only due to high costs. The challenge for researchers is to use the sample of CCTV-inspected pipes for developing mathematical models that can predict the structural condition of remaining pipes as well as the future condition of pipes. In this present study, the deterioration pattern of storm-water pipes is constructed on the basis that each pipe has its own deterioration rate due to its pipe factors. Based on this, two mathematical models using multiple logistic regression (MLR) and probabilistic neural networks (PNN) are developed for predicting the structural condition of individual pipes. The MLR model was calibrated using the maximum likelihood method and the PNN model was trained using a genetic algorithm (GA). The predictive performances of both models were compared using CCTV data collected for a local government authority in Melbourne, Australia. The results showed that the PNN model was more suited for modeling the structural deterioration of individual storm-water pipes than the MLR model. Furthermore, the use of GA improved the training results of the PNN model compared to the trial and error method.