Journal of Water Resources Planning and Management

Stormwater harvesting (SWH) is an important water-sensitive urban design (WSUD) approach that provides an alternate water supply source and improves runoff quality through integrated systems of stormwater best management practice (BMP) technologies. In SWH system design, practitioners must account for trade-offs between cost, supply volume, and water quality improvement performance, which depend on design decisions for the type, size, and spatial distribution of BMPs. As such, the design of SWH systems with distributed BMPs is a complex, multiobjective optimization problem with a large decision space. This paper presents a multiobjective optimization framework to assess trade-offs in spatially distributed SWH system designs. The framework was applied to a case study for a housing development in Adelaide, South Australia. The results illustrated the potential benefits of distributing BMPs in an integrated SWH system where space at the catchment outlet is limited. Trade-offs between volumetric reliability and total suspended solids (TSS) reduction indicate that large gains in TSS reduction can be achieved with limited reduction in volumetric reliability. Concept designs in low-cost/moderately reliable and low-cost/high TSS reduction trade-off regions contained biofilters in locations receiving large inflows.

Accurate prediction of water demand is essential for optimum management of water resources and sustainable growth and development. Recently, models based on artificial neural networks (ANNs), in combination with data preprocessing techniques, have been used for water demand prediction due to their ability to handle large amounts of complex nonlinear data. Discrete wavelet transform (DWT) is one of the most widely employed data preprocessing techniques, and is used in combination with ANNs to improve prediction accuracy and extend prediction lead time. However, DWT is known to have serious drawbacks, and the accuracy and prediction lead times of the models have not been satisfactory. In this study, multiplicative season algorithm (MSA) is applied for the first time as an alternative data preprocessing technique in the area of hydrology and its performance is compared with DWT. The outputs of MSA and DWT are used as inputs to a multilayer perceptron (MLP) in order to develop combined models called discrete wavelet transform–multilayer perceptron (DWT-MLP) and multiplicative season algorithm–multilayer perceptron (MSA-MLP), which are compared with the stand-alone MLP model. The results demonstrate that MSA is a better preprocessing technique than DWT and, thus, that MSA captures periodicity and converts nonstationary time series into stationary time series better than DWT.

Modeling water resources networks is often input-intensive because of network size and complexity. This paper introduces a ranking automation for networks (RANK) tool that weights node connections based on flow capacity and direction and that automates the process to rank nodes that are stable, topologically significant, and redundant. Application to the 55-node, 73-link lower Bear River water system that stretches from southern Idaho to the Great Salt Lake, Utah shows that stable nodes do not depend on other nodes and are typically middle junctions; unstable nodes are located downstream. The most topologically significant nodes make other nodes unstable when added or removed, and they occur throughout the network. The most redundant node pairs have few but identical connections. Results can help water system modelers and planners identify and prioritize locations to (1) transfer water; (2) build, expand, remove, or abandon plans for dams; (3) adopt conservation measures; (4) develop alternative supplies; (5) monitor flows; and (6) protect environmental features. Network spatial resolution, link direction, and data used to weight links influence RANK results.

In the context of household water demand generation with high time resolution (down to 1 s), two different categories of models have been proposed, aimed at representing instantaneous demand as the superimposition of pulses with constant intensity. The two categories differ in the spatial scale considered for demand generation. In detail, the models of the first category generate the demand as a whole at the household spatial scale. Those of the second category instead generate the demand at the scale of the microcomponents—that is, the various fixtures producing water demand pulses—and sum the microcontributions to obtain the total instantaneous demand of the household. The models of the first category are parameterized based on demand measurements. The models of the second category (actually, only one model exists, named SIMDEUM, standing for SIMulation of water Demand, an End-Use Model) are instead parameterized as a function of information derived from surveys, such as household occupants’ age and behavior, number and kind of household fixtures. This paper presents a review of the models for water demand pulse generation. In this review, the main differences in the modeling structure are described and the various parameterization methods are presented. Though the two categories of model operate at different spatial scales, three models—that is, two models of the first category, Poisson rectangular pulse (PRP) and PRP with correlated pulse durations and intensities (cor-PRP), and model SIMDEUM for the second category—are chosen as representative and are applied to a case study in Milford, Ohio, for which demand measurements and information about the household fixtures and occupants are all available. The merits and limits of the models are highlighted in the applications.

The estimate of current real losses in water distribution networks (WDN) is crucial to plan investments for rehabilitation, assess the rise of leakage over time, and possibly drive procedures for failure identification and repair. However, many WDNs worldwide do not have flow/pressure monitoring within the system yet, and the inlet water volume or flowrate is the only recorded data. Developing reliable procedures to estimate real losses in such circumstances is essential to assess the leakage phenomenon and eventually drive the upgrade of existing monitoring systems. This work proposes a simple bottom-up methodology to estimate leakage using WDN inflow data series while only exploiting the seasonal fluctuation of water consumptions. It resorts to a data-assimilation strategy whose formulation is consistent with the physical behavior of WDNs and requires the estimate of only a few numerical parameters. Additionally, the methodology allows the estimation of the user’s daily and night water consumptions, thus being useful to verify or integrate other leakage estimate methods. The methodology is discussed and demonstrated on both synthetic and real WDNs.

In this paper, pressure management through pressure-reducing valves (PRVs) is discussed with particular reference to metaheuristic optimization techniques, which have proven to be very effective in producing good results with reduced use of computational resources. In particular, the application of the harmony search (HS) method to the strategic location and setting of a prefixed number of PRVs is proposed and discussed. A single-objective optimization problem that aims at the leakage reduction through the minimization of the water pressures is defined. A double harmonic component is adopted for taking into account both the location and setting of each PRV. The hydraulic constraints handled by simulation software are considered as well. Two examples from literature are analyzed to allow comparisons. Good results compared to reduced computational time are obtained.

A comprehensive water-energy-carbon (WEC) nexus model for an urban water system (UWS) using system dynamics is proposed to assist municipalities, urban developers, and policy makers for neighborhood water planning and management. The proposed model and decision support tool was developed for the operational phase of UWSs. The model was validated using historical water and energy consumption data (2005–2014) of Penticton (British Columbia, Canada). Spearman’s correlation coefficients between water and energy, water and carbon, and energy and carbon were 0.94, 0.89, and 0.83, respectively, revealing highly significant interconnections. The energy for water was $11.1\mathrm{MWh}/\mathrm{ML}$, water for energy was $\mathrm{6,512}\mathrm{L}/\mathrm{MWh}$, and carbon emissions were $124.4\mathrm{kg}{\mathrm{CO}}_2\mathrm{e}/\mathrm{MWh}$ from energy use and $120.8\mathrm{kg}{\mathrm{CO}}_2\mathrm{e}/\mathrm{ML}$ from wastewater processes. A Monte Carlo–based sensitivity analysis showed residential outdoor irrigation and water heating energy for showers and dishwashers have higher contribution to model variability. The intervention analysis reveals significant differences in savings in water, energy, and carbon for various water and energy-based interventions in UWSs and the developed tool is well capable for analyzing these dynamic savings.

The division of water distribution networks (WDNs) into district metering areas (DMAs) is a challenging issue and can be effective for analysis, planning, and management purposes. This contribution proposes a novel two-step strategy for DMA planning. The first step is optimal segmentation design by maximizing the WDN-oriented modularity index versus minimizing the number of conceptual cuts (i.e., not accounting for devices). The second step is the actual optimal DMA design, returning the positions of flow meters and closed valves at the conceptual cuts. Since closed valves change the system’s hydraulic paths, the implementation of DMAs could allow reductions in pressure and leakages through the WDN. Optimal DMA design is therefore achieved by solving a three-objective optimization minimizing the number of flow meters and total unsupplied customer demand while maximizing the reduction of background leakages; thus, pressure-driven modeling is mandatory. The effectiveness and flexibility of the procedure is demonstrated using the Apulian and Exnet networks. Results shows that DMA implementation can allow leakage reduction in systems with excess hydraulic capacity.

Monitoring water quality in drinking water distribution systems is the basis for proactive approaches to prevent or manage emerging water quality issues, and such a monitoring requires a strategic selection of relevant and representative monitoring sites. GISMOWA is a new GIS and risk-based analysis tool to identify and prioritize pipe segments for water quality monitoring and to comply with existing monitoring and sampling guidelines. The tool was designed to integrate multiple parameters categorized as (1) hydraulic and structural weaknesses in the system, e.g., residence time; (2) external threats, e.g., contaminated sites; and (3) sensitive consumers, e.g., hospitals, in a GIS environment. The tool used a multicriteria decision analysis to evaluate multiple monitoring site parameters and map zones particularly suitable for water quality monitoring. GISMOWA was applied to Danish water distribution systems as a transparent and simple-to-use tool facilitating a complete overview of the distribution system, including sensitive consumers and consumers in general, thus fulfilling a precondition for a HACCP-based monitoring strategy of drinking water.

Drinking water distribution networks (DWDNs) are vital urban infrastructures. These systems are susceptible to widespread contamination. It is necessary to provide a minimum level of performance for DWDNs by decreasing their vulnerability to contamination. In this paper, a framework is proposed for the evaluation of DWDN response to biological and chemical contaminants. A simulation model is developed for analysis of a DWDN in the eastern part of Tehran, Iran. Various scenarios are defined based on type of contaminants, reactions with chlorine, contaminants’ dose and injection duration, and chlorine initial concentration to see how the network would respond to the spread of contamination. Based on the network’s ability to provide demand with acceptable quality, an index is suggested for vulnerability assessment. To mitigate the network’s vulnerability, chlorine injection and pipe closures are suggested. Results show that the proposed index can be used for identifying the DWDN critical nodes and for comparison of network performance in different scenarios. The results also indicate the key role of residual chlorine in contaminant decay and in assessing DWDN response to widespread contamination.