Potential Performance of Hydraulic State Estimation in Water Distribution Networks

Emilio Ruiz (), Sarai Díaz () and Javier González ()
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Emilio Ruiz: University of Castilla-La Mancha
Sarai Díaz: University of Castilla-La Mancha
Javier González: University of Castilla-La Mancha

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2022, vol. 36, issue 2, No 19, 745-762

Abstract: Abstract Hydraulic State Estimation (HSE) is a suitable tool to gain real-time understanding of water supply systems. This technique enables to estimate the most likely hydraulic behavior in the network, as well as its associated uncertainty, from available measurements. HSE has been successfully applied to trunk mains, but additional work is required to implement it on Water Distribution Networks (WDNs). The reason for this is that WDNs generally have less real-time information than trunk mains. The new telemetry devices that can be installed at water service connections provide an opportunity to gain distributed information that could be used to monitor WDNs using HSE. However, these technologies often provide records with different time intervals (i.e. sampling rates), which should be leveled to the same time resolution for HSE application. This poses a challenge: combining information associated with different temporal scales, especially when there are larger time intervals than the monitoring time resolution, which requires considering additional uncertainty due to temporal disaggregation. The aim of this paper is twofold. First, to propose a methodology that enables to systematically level records with different time intervals to a same time resolution. This makes the application of HSE to WDNs affordable and enables to consistently evaluate the associated uncertainty. Second, to analyze the potential of HSE in WDNs using a case study. This paper thus presents a systematic framework to assess HSE at different resolution levels and highlights the importance of increasing the information available within the distribution level to reduce uncertainty.

Keywords: Uncertainty; Temporal scale; Spatial scale; Water demand; Demand variance; Microcomponent model (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11269-021-03056-2

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