The spread of the COVID-19 pandemic induced many countries, including Italy, to implement social distancing measures and to 6 suspend the majority of educational and working activities, which significantly impacted peoples' lifestyles. To support water utilities in 7 understanding the impacts of the COVID-19 pandemic on water consumption and improving water distribution system resilience, the effects 8 of the lockdown were investigated with reference to a residential district metered area (DMA) in the city of Rovigo (northern Italy), in which 9 smart monitoring of water consumption at the level of individual users started in 2019. The water consumption recorded during the lockdown 10 period was analyzed at different levels of temporal and spatial aggregation and compared with the consumption recorded in the same period of 11 the previous year. The results show that, during the lockdown period, the overall water consumption in this mainly residential area increased 12 by 18%. Moreover, water consumption was observed to be more spread out over the day, with a decrease (and a delay) in peak morning 13 consumption, which was particularly evident on weekdays.

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Leakages in water distribution systems have great economic and environmental impacts and are a major issue for water utilities. In this work, the water balance and the Minimum Night Flow (MNF) method for evaluating the amount of water loss, as well as the power and Fixed and Variable Area Discharge (FAVAD) equations for analyzing the relationship between leakage and pressure, were applied to a fully monitored District Metered Area (DMA) located in Gorino Ferrarese (FE, Italy). Time series of (a) the water consumption of each user, (b) the DMA inflow, and (c) the pressure at the DMA inlet point were monitored with a 5 min time step. The results of an analysis carried out by exploiting the collected time series highlighted that: (a) The application of the MNF method based on literature values can lead to significant inaccuracies in the presence of users with irregular consumption, and (b) the estimation of the parameters of the power and FAVAD equations is highly affected by the amounts and types of observed data used.

Leakage in water distribution systems is an important issue and of major interest for water utilities. In this study, the Minimum Night Flow (MNF) method to quantify the amount of water lost and the equations representing the relationship between pressure and leakage in power and FAVAD (Fixed and Variable Area Discharge) forms were applied to a District Metered Area (DMA) located in Gorino Ferrarese (FE, Italy) equipped with smart meters. The analysis carried out by exploiting the collected time series of user water consumption, DMA inflow, and pressure highlighted that: (a) the MNF method can lead to significant inaccuracy in leakage estimation in the presence of users with irregular consumptions, when based on literature values, and (b) the estimation of the parameters of the power and FAVAD equation is highly affected by the number and types of observed data used.

Model-based methods for leakage localization in water distribution systems have recently been gaining more attention. These methods identify the leakage position by comparing the measured network data with the corresponding values simulated by a hydraulic model. In this study two model-based methods already proposed in literature, one based on the Sensitivity Matrix method and the other one on the Linear Approximation method, are analysed and compared to each other. The methods are applied to the same case study network, exploiting only data provided by pressure sensors. Various analyses are undertaken in order to investigate the main critical issues tied to the two methods, i.e. a) the use of different amounts of data averaged over different time windows, b) the impact of the model’s accuracy in terms of water demands and pipe roughness, and c) the effect of the number of pressure measuring points. The results show that higher efficiency is obtained by considering the hourly averaged data all together. Moreover, the Linear Approximation method is on average 3 times more accurate than the Sensitivity Matrix when a perfect hydraulic model is used, even with a reduced number of pressure sensors. However, when a hydraulic model and/or measured data affected by errors are considered, the Sensitivity Matrix is more accurate, with an average error almost 10% lower than the Linear Approximation.