Peak water demand is one of the most stringent operative conditions for a Water Distribution System (WDS), not only for the intensity of the event itself, but also for its recurring nature. The estimation of the maximum water demand is a crucial aspect in both the design and management processes. Studies in the past have tackled this issue with deterministic approaches, even if peak phenomena are distinctly random. In this work, probabilistic models have been developed to study and forecast the daily maximum residential water demand. Some probability distributions have been tested by means of statistical inferences on different data samples related to three monitored WDS. The parameter estimations of the proposed equations have been related to the number of supplied users. Furthermore, this work investigates time scaling effects on the effectiveness of the proposed distributions and relations. Corrective factors that take into account the effect of time averaging step on the above-mentioned parameters have been proposed.

In water networks characterized by a significant variation in ground elevations the necessity of pumping water in some areas is complicated by a conflicting requirement to reduce excess pressures in other areas. This and the increasing cost of electricity has led to the use of Pumps-operating-As-Turbines (PATs) devices that can reduce pressure (and leakage) whilst harvesting energy. This paper presents a methodology for optimal water distribution system (WDS) management, driving the optimization by minimizing the surplus pressure at network nodes and the operational pumping costs and maximizing the income generated through energy recovery. The method is based on a highly parallelized Evolutionary Algorithm, employing an hydraulic solver to evaluate hydraulic constraints. Water demands at network nodes are considered as uncertain variables modelled by using a probabilistic approach in order to take into account unknown future demands. The approach is demonstrated in different case studies. Results obtained highlight that the economic benefits of installing PATs for energy recovery in conjunction with a combined pump-scheduling and pressure management regime is especially related to the input network characteristics. Further analysis of the importance of the probabilistic approach and of the influence of the interval time step adopted for the optimization has been evaluated.

Side weirs are hydraulic structures widely used in practice and extensively studied in the technical literature. The flow along a side weir is a classic example of spatially varied flow, the study of which is generally performed by means of a momentum or energy approach. The momentum approach is usually more accurate but requires the experimental estimation of correction coefficients leading to a formulation that is difficult to apply to a practical design. The energy approach, instead, is not as accurate when the flow depths on the side weir are significantly higher than the same weir crest. A novel, simplified theoretical model for the study of single side weir in a circular pipe, operating in supercritical flow, is proposed. The model originates from an experimental analysis of the flow power variation along the side weir. The proposed model has shown good performance in free surface representation and is capable of estimating the lateral outflow with good accuracy. The suggested formulation is...

The problem of water distribution system rehabilitation is formulated here as a multiobjective optimisation problem under uncertainty. The two objectives are to minimise the structural rehabilitation cost and to maximise the hydraulic reliability of the system. In this context, reliability is defined as a probability of simultaneously satisfying minimum pressure head constraints at all nodes in the network. An economic analysis has been performed, taking into account not only the structural costs but also lost revenue owing to the volume of water required by users but not supplied to them because of the structural inadequacy of the network. Because of the uncertainty in water demand, a probabilistic approach is used within the optimisation model. The probabilistic distribution and its parameters were estimated through an experimental study conducted on a real water distribution network. The recently developed robust Non-dominated Sorting Genetic Algorithm II optimisation algorithm is used to solve the optimisation problem. The methodology presented allows the identification of the specific optimal solution of the Pareto front that corresponds to minimal structural cost and minimal lost revenue yet corresponding to a high level of reliability – the economic level of reliability. This value could be assumed as the optimal solution or as a lower threshold, which restricts the gamut of technically feasible solutions that could be adopted in a rehabilitation programme.

Climate change is increasingly influencing the water cycle, hindering the effective management of water resources in various sectors. Lazio, central Italy, exhibits a wide range of climatic conditions, stretching from the Tyrrhenian coast to the Apennines. This study assessed a crucial aspect of climate change, focusing specifically on reference evapotranspiration (ETo) and its associated hydrological variables. The seasonal Mann–Kendall (MK) test was used to assess trends in gridded data. The K-means algorithm was then applied to divide Lazio into four homogeneous regions (clusters), each characterized by distinct trends in hydrological variables. The analysis revealed statistically significant increasing trends (p ≤ 0.01) in temperature, solar radiation, and ETo, with more marked effects observed in the coastal and hilly clusters. In contrast, statistically significant decreasing trends (p ≤ 0.01) were observed for relative humidity, while no statistically significant trends (p > 0.01) were observed for precipitation. This study’s methodology, combining trend analysis and clustering, provides a comprehensive view of ETo dynamics in Lazio, aiding in pattern recognition and identifying regions with similar trends.

The behavior of residential users in the request of water influences the demand patterns which characterize the water consumption at each node of the network during the time. This stochastic variable influences greatly the performance of hydraulic networks and a better understanding of residential water consumption variability might allow for a more effective design or management of Water Distribution Systems. In order to contribute to this analysis, stochastic models are herein defined and proposed for representing demand variation, especially at the peak condition. This study is based on an experimental approach, comparing empirical data collected in several real water distribution networks which refer to different habits and number of users. Statistical inferences on large data samples have allowed defining probabilistic models and relative parameters which characterize the maximum water consumption at network nodes. The study has been developed considering both the deterministic cyclical component of the demand variability and its uncertain component that accommodates the random nature of the water requirement. Results obtained have shown that Gumbel and Log-Normal distributions best represent the peak water demand for all the monitoring systems considered.