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.

Lago Loíza reservoir in east-central Puerto Rico is one of the primary sources of public water supply for the San Juan metropolitan area. To evaluate and predict the Lago Loíza water budget, an artificial neural network (ANN) technique is trained to predict river inflows. A method is developed to combine ANN-predicted daily flows with ANN-predicted 30-day cumulative flows to improve flow estimates. The ANN application trains well for representing 2007–2012 and the drier 1994–1997 periods. Rainfall data downscaled from global circulation model (GCM) simulations are used to predict 2050–2055 conditions. Evapotranspiration is estimated with the Hargreaves equation using minimum and maximum air temperatures from the downscaled GCM data. These simulated 2050–2055 river flows are input to a water budget formulation for the Lago Loíza reservoir for comparison with 2007–2012. The ANN scenarios require far less computational effort than a numerical model application, yet produce results with sufficient accuracy to evaluate and compare hydrologic scenarios. This hydrologic tool will be useful for future evaluations of the Lago Loíza reservoir and water supply to the San Juan metropolitan area.

Urban drainage networks are under constant pressure caused by a permanently changing environment. For proactive planning, management, and adaptation of urban water infrastructure, a robust and comprehensive knowledge base is needed as decision support. This paper shows an integrated approach for decision support consisting of urban development modeling, climate projections, hydrodynamic sewer modeling, and Web-GIS visualization of interactive risk maps for pluvial flooding. The application on an Alpine case study demonstrates the benefits of such a combined consideration of different drivers. Climate projections of precipitation are based on four climate projections produced specifically for the Alps on a 0.1° grid. In order to obtain precipitation data with an adequate resolution for urban drainage modeling, the projections are downscaled using a statistical downscaling method. The results indicate a trend toward more intense heavy precipitation during summer until 2050. The effect of this trend can either be compensated or amplified by city development. It is shown that population growth on its own does not necessarily lead to a decrease of system performance, but that clever spatial planning can reduce negative effects. The visualization helps to involve stakeholders, politicians, and the public by illustrating model results for strategic planning and in workshops.

To estimate pipe roughness and nodal demand parameters in water distribution networks, a method based on head loss adjustment is proposed. By using weighted least squares (WLS), model-simulated head losses are adjusted to minimize the sum of the squares of the corrections (differences between simulated and adjusted values) of simulated head losses under the constraints of head and flow measurements. Pipe roughness coefficients (Hazen-Williams $C$-factors) are computed by using the ratio of the simulated to adjusted head losses and refined by using boundary-fence and gross-error detection techniques. With the adjusted $C$-factors and head losses, pipe flows are computed to rectify nodal demands, which are limited in their boundary fences. After the demand multipliers are computed and filtered by using gross-error detection techniques, a large number of nodal demands make an advance. The loop of model simulation, head loss adjustment, and roughness and demand calibrations runs iteratively from different starting points of $C$-factor under multiple loading conditions, then realistic roughness and demands are achieved. The method was verified on two benchmark networks—a hypothetical network and a real network—by using noisy head and flow measurements. The results show that the method is effective for dual estimation of roughness and demand parameters.

This paper proposes a system-characteristics and graph theory–based water distribution system (WDS) model classification scheme that is based on system function and topology. Various parameters are examined to determine the most adequate parameter(s) for describing a WDS. The classification scheme is applied to a single hypothetical and 25 real systems. The primary indicator to classify a WDS function (transmission or distribution networks) is the length-weighted average pipe diameter. The average nodal demand and histogram of total length of each pipe diameter are applied as secondary measures. A new parameter, defined as the branch index ($BI$), is used to further classify a branched network by estimating the degree of branching within a WDS. The degree of looping and a second level of classification are based on the meshedness coefficient ($MC$), but only after the system is reduced to eliminate nonessential nodes. $BI$ values are compared with other system-structure metrics in the literature including link density ($LD$), average node degree ($AND$), $MC$, and clustering coefficient ($CC$). To that end, Pearson correlation coefficients are computed across the set of other metrics for 26 systems. The correlation analysis reveals that several graph-theory system-structure metrics are highly correlated.

Contaminant intrusion into water distribution networks can have disastrous consequences. This event poses a threat to public health and could result in political instability and social problems. After the contaminant and its source are detected, the main issues are consequences management and contaminant withdrawal from the water distribution network. To do this, several actions are required, including containing contaminant spread by closing valves and opening hydrants to flush out the polluted water. These actions may lead the network to a critical state in which the pressure of some nodes falls below the standard measure. In these network conditions, a pressure driven analysis of the water distribution network would be necessary. In this study, a combination of hydraulic simulation based on pressure driven analysis and particle swarm optimization is used to minimize the negative effects of contaminated water on public health as an immediate response after the contaminant entrance into water is detected. Based on the type of contaminant, two different emergency response strategies are introduced. Multiobjective optimization is performed when a nonharmful contaminant has entered the network. In this case, the main objectives are to minimize the impacts of contaminated water on consumers and the cost of operations. In another response strategy, reducing the time the network requires to return to a normal state is the objective of single-objective optimization when the contaminant is harmful. The proposed model was applied on the water distribution network of Sabzevar in northeastern Iran. The results proved the model’s credibility and its applicability in real water distribution networks, even in cases of contaminant intrusion.

Real-time hydropower operations planning requires many optimization models in order to efficiently manage the hydropower system. The short-term hydropower model is used on a daily basis to dispatch the volume of water available to the turbines. This paper considers a stochastic short-term model. Uncertainty of inflows is represented with scenario trees, which are a discrete representation of the continuous distribution of the inflows. In addition, the complexity needed in the scenario trees to maximize the energy production in a rolling-horizon framework is investigated. Methods to generate scenario trees require input parameters that may be laborious to determine, and defining the structure of the tree in a real-time context is time consuming. Three comparisons are conducted to assess the complexity required in the scenario trees to obtain a good operational solution for the short-term hydropower optimization model. The first involves generating a set of scenario trees built from inflow forecast data over a rolling horizon. The second replaces the entire set of scenario trees by the median scenario. The third replaces the set of trees by scenario fans. The method used to build scenario trees requires three parameters: number of stages, number of child nodes at each stage, and aggregation of the period covered by each stage. The question of finding the best values of these parameters is formulated as a black-box optimization problem that maximizes energy production over the rolling horizon. Numerical experiments with real data on three hydropower plants in series suggest that using a set of scenario trees is preferable to using the median scenario, but using a fan of scenarios yields a comparable solution with less computational effort.

This paper presents an economic analysis of pressure control solutions for leakage and pipe burst reduction. In detail, it explores the operating conditions under which the installation of conventional mechanical pressure reducing valves (PRVs) or remotely real-time controlled (RTC) valves are cost effective compared to a scenario with no control. For a range of system sizes, hydraulic extended period simulations and empirical formulas were used to estimate leakage rates and pipe bursts, respectively, in numerous operational scenarios, including different precontrol leakage levels and demand patterns, the absence of pressure control, and the installation of a PRV or RTC valve. The total cost of the controlled system, including the installation cost of the control device, the flow-dependent operation and maintenance (O&M) cost, and the pipe burst repair cost over the planning horizon, was compared with the water-related O&M and pipe burst repair costs of the uncontrolled system. The results pointed out that no pressure controls are needed if leakage and the variable O&M cost of water are low. When these variables are high, remote RTC is attractive, especially when the demand pattern is peaked and the system is large. For more moderate cost and leakage, a conventional PRV may be better than RTC, especially in small systems and for relatively smooth demand patterns.