Influence of a season on hourly and daily variations in water demand patterns in a rural water supply line – case study

This article presents the results of a study on hourly and daily variations in water demand patterns, depending on a season. The study was conducted in the years 2014-2015 on a selected rural water supply line. The analysis was based on values of hourly water demand as measured by a water meter coupled with a recording device. The research showed that both the volume and fluctuations in daily water demand were higher in the spring and the summer, versus the autumn and the winter. This was most probably caused by water consumption for additional purposes, specific for rural areas. Individual water demand was the highest in the summer, and the lowest in the winter. Two peaks for hourly water demand were determined for the analyzed seasons. The morning peak always occurred at 7 a.m. on working days, while on days off work it fell at 9 a.m. or 10 a.m., depending on the season. The evening peak always fell at 8 p.m., regardless of a season or a day of a week. On working days, the evening peak was always higher than the morning one, while on days off work the morning peak was higher than the evening one in the autumn and in the winter, and both peaks were the same in the spring and in the summer.

A bottom-up approach of stochastic demand allocation in a hydraulic network model: a sensitivity study of model parameters

An “all pipes” hydraulic model of a drinking water distribution system was constructed with a bottom-up approach of demand allocation. This means that each individual home is represented by one demand node with its own stochastic water demand pattern. These water demand patterns were constructed with the end-use model SIMDEUM. A sensitivity test with respect to the resulting residence times was performed for several model parameters: time step, spatial aggregation, spatial correlation, demand pattern and number of simulation runs. The bottom-up approach of demand allocation was also compared to the conventional top-down approach, i.e. a single demand multiplier pattern is allocated to all demand nodes with the base demand to account for the average water demand on that node. The models were compared to measured flows and residence times in a small network. The study showed that the bottom-up approach leads to realistic water demand patterns and residence times, without the need for any flow measurements. The stochastic approach of hydraulic modelling, with a 15 minute time step, some spatial aggregation and 10 simulation runs, gives insight into the variability of residence times as an added feature beyond the conventional way of modelling.