Journal of Water Resources Planning and Management

Small urbanized areas (UAs) as defined by the U.S. Census and the Environmental Protection Agency (EPA) have a population between 50,000 and 100,000, and a population density of at least 1,000 people per square mile. Under Phase II of the National Pollutant Discharge Elimination System, storm-water managers in these UAs must preserve predevelopment hydrology through compliance with qualitative regulations—an often impracticable task given the limited resources of these municipal authorities and the diffuse hydrologic impacts of urbanization. The objective of this paper is to assist storm-water managers by providing a geographic information system (GIS) workflow supported by Python scripting that produces a geographic database of storm-water infrastructure that can subsequently be used to create a fully parameterized hydrologic/hydraulic model. Guidance on calibrating this model is then provided with suggestions for measuring discharge in storm sewer systems. The application of these objectives is demonstrated in a watershed in Blacksburg, Virginia, where a unique municipal-academic partnership fostered the development of this work. Finally, improvements and further research ideas are suggested for storm-water management in small UAs.

Water distribution system model parameter calibration is an important step to obtain a representative system model, such that it may be applied to understand system operational performance, often in real time. However, few approaches have attempted to quantify uncertainty in calibrated parameters, model predictions, and consider the sensitivity of model predictions to uncertain parameters. A probabilistic Bayesian approach is applied to calibrate and quantify uncertainty in the pipe roughness groups of an Epanet2 hydraulic model of a real-life water distribution network. Within the applied Bayesian framework, the relative performance of formal and informal Bayesian likelihoods in implicitly quantifying parameter and predictive uncertainty is considered. Both approaches quantify posterior parameter uncertainty with similar posterior distributions for parameter values (mean and standard deviation). However, the uncertainty intervals identified with the informal likelihood are too narrow, regardless of the behavioral threshold applied to derive these bounds. In contrast, the formal Bayesian approach produces more realistic 95% prediction intervals based on their statistical coverage of the observations. This results as the error model standard deviation is jointly inferred during calibration, which also helps to avoid potential overconditioning of the posterior parameter distribution. However, posterior diagnostic checks reveal that the prediction intervals are not valid at percentiles other than the 95% interval as the assumptions of normality, residual homoscedasticity, and noncorrelation, often assumed in hydraulic model calibration, do no hold. More robust calibration requires the development of error models better suited to the nature of residual errors found in water distribution system models.

This work presents a new paradigm for the design of stormwater detention basins. Through simulation and optimization, this work seeks to find the design of a detention basin outlet control structure that reduces the ecological impairment to downstream water bodies. Ecological impairment is measured using the ecodifference, a metric that represents the hydrologic alteration in the stream. The ecodifference is defined as the weighted sum of the eco-flow statistics, nine hydrological flow statistics that have been shown to be particularly relevant to ecological quality. In this simulation-optimization methodology, a genetic algorithm (GA) is combined with a hydrologic simulator of a detention pond. The GA is used to design the openings in the outlet control structure that minimizes the ecodifference. Constraints are used to meet current municipality design regulations. The simulation-optimization methodology is been applied to a case study site, and resulting designs demonstrate that improvements in ecological flows can be achieved via alteration of two to four outlet control openings, while meeting design regulations. The ecodetention ponds designed here primary improve the low flow series. Applying this ecological design approach to alternative volume-reducing best management practices could further improve the ecological flow series.

This paper proposes the use of online retrained metamodels for the optimization of water distribution system (WDS) design. In these metamodels, artificial neural networks (ANNs) are used to replace the full hydraulic and water quality simulation models and differential evolution (DE) is utilized to carry out the optimization. The ANNs in the proposed online DE-ANN model are retrained periodically during the optimization in order to improve their approximation to the appropriate portion of the search space. In addition, a local search strategy is used to further polish the final solution obtained by the online DE-ANN model. Three case studies are used to verify the effectiveness of the proposed online retrained DE-ANN model for which both hydraulic and water quality constraints are considered. In order to enable a performance comparison, a model in which a DE is combined with a full hydraulic and water quality simulation model (DE-EPANET2.0) and an offline DE-ANN model (ANNs are trained only once at the beginning of optimization) are established and applied to each case study. The results obtained show that the proposed online retrained DE-ANN model consistently outperforms the offline DE-ANN model for each case study in terms of efficiency and solution quality. Compared with the DE-EPANET2.0 model, the proposed online DE-ANN model exhibits a substantial improvement in computational efficiency, while still producing reasonably good quality solutions.

Robustness is generally defined as an ability of a system to maintain its function under a defined set of disturbances. To introduce robustness to the water distribution systems (WDSs) design, chance constrained, or so-called reliability-based models have been formulated. Under variations in system parameters, such as nodal demands and pipe roughness, system reliability is generally measured as the probability that the stochastic nodal pressures will be higher than an allowable minimum pressure limit. However, chance constraints may not be the best formulation to improve system robustness because it focuses on the likelihood of failure under a specified set of conditions rather than developing a solution that consistently provides adequate service. In addition, the reliability-based design requires defining the demand condition, its probability distribution and its statistics, which are not straight forward in practice. To address these difficulties, a robustness index that limits the range of the system function variability is posed here and incorporated in a two objective optimization problem. Resulting designs are compared with those from the reliability constraint formulation. The authors demonstrate that the robustness-based design improves resilience relative to the reliability-based design.

Nutrient-intensive agriculture involving nitrogen application rates exceeding crop requirements results in excessive loads in surface waters, contributing substantially to hypoxia in coastal ecosystems and violations of EPA drinking water nitrate standards. There is growing interest in the cultivation of perennial grasses for bioethanol production, which may address these problems and reduce greenhouse gas emissions. Some of these grasses can be grown in constructed wetlands, which offer additional nitrate removal by denitrification in the wetland benthos. The present work simulates and optimizes a hypothetical wetland system near Camargo, Illinois. The wetland system is assumed to draw water from the Embarras River and is designed to treat non-point-source nutrient loadings and produce harvestable biomass for ethanol production. An integrated model of biomass production, nutrient removal, and cost is developed to assess and optimize the relationship between wetland cost and nutrient removal. The wetland shows a profit only when some costs are excluded from the analysis; however, consideration of the environmental benefit could change the cost-benefit outcome.

This paper presents a technique to quantify water savings due to implementation of water restrictions by adopting water restriction indexes as a continuous numerical predictor variable in regression analysis. The adopted modeling technique compares four methods: yearly base difference method, weighted average method, before and after method, and expected use method. These methods are applied to single and multiple dwelling residential sectors in the Blue Mountains region, Australia. In the study, three forms of multiple regression techniques are adopted: raw data, semi-log, and log-log. The model performances are evaluated by a number of statistics such as relative error, Nash-Sutcliffe coefficient, and percentage bias. Moreover, the potential of using the water restriction savings and water conservation savings as continuous predictor variables in the water demand forecasting model is investigated. The performances of different modeling techniques are evaluated using split-sample and leave-one-out cross-validation methods. The yearly base difference method is found to quantify the water savings more accurately in that the savings due to Level 1, Level 2, and Level 3 water restrictions are found to be approximately 9, 18, and 20%, respectively, for the single dwelling residential sector and approximately 4, 8, and 9%, respectively, for the multiple dwelling residential sector. The semi-log model coupled with yearly base difference method is found to perform the best in predicting water demand for both the single and multiple dwelling residential sectors with a relative error of about 3%.