Monitoring nonrevenue water performance in intermittent supply

Journal Article (2019)
Author(s)

T.M.Y. AL-Washali (IHE Delft Institute for Water Education, TU Delft - Sanitary Engineering, Sana'a University)

Saroj Kumar Sharma (IHE Delft Institute for Water Education)

Fadhl AL-Nozaily (Sana'a University)

Mansour Haidera (Sana'a University)

Maria D. Kennedy (TU Delft - Sanitary Engineering, IHE Delft Institute for Water Education)

Research Group
Sanitary Engineering
Copyright
© 2019 T.M.Y. Al-Washali, Saroj Sharma, Fadhl Al-Nozaily, Mansour Haidera, M.D. Kennedy
DOI related publication
https://doi.org/10.3390/w11061220
More Info
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Publication Year
2019
Language
English
Copyright
© 2019 T.M.Y. Al-Washali, Saroj Sharma, Fadhl Al-Nozaily, Mansour Haidera, M.D. Kennedy
Research Group
Sanitary Engineering
Issue number
6
Volume number
11
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Abstract

Water utilities should monitor their nonrevenue water (NRW) levels properly to manage water losses and sustain water services. However, monitoring NRW is problematic in an intermittent water supply regime. This is because more supplied water to users imposes higher volumes of NRW, and supplying significantly less water results in an unmet water demand but interestingly less NRW. This study investigates the influence of the amount of water supplied to a distribution system on the reported level of NRW. The volume and indicators of NRW all vary with variations in the system input volume (SIV). This is even more critical for monitoring NRWfor systems shifting from intermittent to continuous supply. To enable meaningful monitoring, the NRW volume should be normalised. Addressing that, this research proposes two normalisation approaches: regression analysis and average supply time adjustment. Analysis of the NRW performance indicators showed that regression analysis enables the monitoring of NRW and tracking its progression in an individual system only, but not for a comparison with other systems. For comparing (or benchmarking) a water system to other systems with different supply patterns, the average supply time adjustment should be used. However, this approach presents significant uncertainties when the average supply time is less than eight hours per day.