Assessing the impact of climate change on water demand is a challenging task. This paper proposes a novel methodology that quantifies this impact by establishing a link between water demand and weather based on climate change scenarios, via Coupled General Circulation Models. These models simulate the response of the global climate system to increasing greenhouse gas concentrations by reproducing atmospheric and ocean processes. In order to establish the link between water demand and weather, Random Forest models based on weather variables were used. This methodology was applied to a district metered area in Naples (Italy). Results demonstrate that the total district water demand may increase by 9–10% during the weeks with the highest temperatures. Furthermore, results show that the increase in water demand changes depending on the social characteristics of the users. The water demand of employed users with high education may increase by 13–15% when the highest temperatures occur. These increases can seriously affect the capacity and operation of existing water systems.

Nowadays, in the definition of effective approaches for the sustainable management of water pressurized systems, the assessment of water leakages in water supply and distribution systems represents a key aspect. Indeed, the large water volumes dispersed yearly provoke relevant environmental, technical and socio-economic costs. Worldwide, many water systems show alarming levels of water losses, due to both the poor sealing of joints and the presence of cracks, enhanced by a high pressure level greater than that strictly required for assuring a proper service level to users. With the aim of analysing the correlation between pressure and leakages, in this work the results of an experimental and a numerical Computational Fluid Dynamics (CFD) investigation are provided and discussed. With reference to a drilled PVC-A (Polyvinyl Chloride-Alloy) pipe, a new-generation plastic material for water systems use, an experimental investigation was first carried out at the Laboratory of Hydraulics of the University of Naples Federico II, aimed at assessing the leakage-pressure relation for transversal rectangular orifices. A CFD model was then implemented and calibrated with experimental results, to different geometric configurations of the orifice, with the aim of assessing the dependence of the orifice geometry and orientation on the calibration of leakage law parameters.

This paper aims to show how the total demand signal (i.e., the sum of all user demands) of a district metered area (DMA) can be reconstructed using simple linear models starting from the readings taken at influential user locations with a preassigned temporal resolution (e.g., 1 h). By comparing the total reconstructed demand pattern with the inflow into the DMA, which is usually monitored at DMA boundaries using flowmeters, water utilities have the potential to identify occurrences of anomalous events, such as pipe bursts, unauthorized consumption, and leakage increases. To address this issue, two procedures are proposed that are applicable when smart meters at the end of life were previously installed at all locations and when no smart meter was present, respectively. The first procedure is based on the application of the stepwise regression that measures the demand time series aimed at both the selection of user locations and the model calibration, whereas Fisher’s test or economic considerations is used as stopping criterion. The second methodology consists of the application of criteria to identify the subset of representative locations on the basis of available data and practical considerations (user typology and consumption on the annual bill). In addition, a new method to calibrate the model using billed annual consumptions is provided. Both methodologies enable the construction of linear models that express the total pattern as a function of single-user consumption patterns, allowing the reconstruction of the original signal. Both procedures were applied to a case study in Naples (Italy) with a total of 1,406 users, that is, 1,067 residential users and 339 non-residential users. The results proved that, as expected, the accuracy of the total demand pattern reconstruction of both procedures increases as the sample size of the representative locations grows. However, the results indicating the trade-off between the sample size and the goodness of the fit reveal that the accuracy is good even for low model order (25–50 users selected). Indeed, the coefficient of determination, $R^2$ , of the fit increases from 0.66 to 0.83 for 25 and 50 users selected, respectively, and the accuracy becomes almost perfect for a size of 100 (i.e., approximately 7% of the total number of users considered). Overall, this paper demonstrates that an effective strategy for the accurate characterization of the total demand in a DMA consists of distributing the preassigned number of smart meters between the different categories of users present in that DMA (i.e., residential and non-residential) and privileging users with the highest annual consumption.

This paper presents a novel methodology for the generation of demand time series at water distribution network (WDN) users. After subdividing the day into an integer number of time steps with order of magnitude of 1 h, the methodology is based on two phases. First, it generates, for each user and for each time step of the day, demand time series of the first attempt, which are consistent with the measured time series in terms of mean, standard deviation, and skewness. This is done with a beta probability distribution with tunable bounds or with a gamma distribution with shift parameter. In the refinement phase, rank cross-correlations between users and at all temporal lags are imposed on the generated demand time series through a single Copula-based re-sort. The effectiveness of the methodology is proven in two real case studies with different numbers of users—namely, the literature case study of Milford, Ohio, and a novel Italian site. The demand time series obtained from the spatial aggregation of the generated user demand time series preserves very well mean and standard deviation of the measured aggregated demand time series. The preservation of skewness and temporal cross-correlations at all lags is very satisfactory. A procedure is also presented to reconcile the generated demand time series with demand pulses generated at fine time step, thus enabling reconstruction of demand at any time step.

This paper presents a comparison between two procedures for the generation of water demand time series at both single user and nodal scales, a top-down and a bottom-up procedure respectively. Both procedures are made up of two phases. The top-down procedure adopted includes a non-parametric disaggregation based on the K-nearest neighbours approach. Therefore, once the temporal aggregated water demand patterns have been defined (first phase), the disaggregation is used to generate water demand time series at lower levels of spatial aggregation (second phase). In the bottom-up procedure adopted, demand time series for each user and for each time step are generated applying a beta probability distribution with tunable bounds or a gamma distribution with shift parameter (first phase). Then, a Copula based re-sort is applied to the demand time series generated to impose existing rank cross-correlations between users and at all temporal lags (second phase). For the sake of comparison, two case studies were considered, both of which are related to a smart water network in Naples (Italy). The results obtained show that the bottom-up procedure performs significantly better than the top-down procedure in terms of rank-cross correlations at fine scale. However, the top-down procedure showed a better performance in terms of skewness and rank cross-correlation when the aggregated demands were considered. Finally, the level of aggregation in nodes was found to affect the performance of both the procedures considered.