Data challenges in developing and managing hydraulic models for complex water supply systems

Offline and online modelling (digital twins) of a water supply network (operational twin) is a process that integrates the various sources of information defining the network and its operation, adjusted to the measurements collected during its exploitation, and supported by hydraulic equations that ensure consistency. Hydraulic models, seen as a centralised repository of coherent information, face their greatest challenge in data management. These data characterise topology (static), determine operation (dynamic), record consumption or estimate demand (calculations and forecasts), and fit the model to real system measurements. Furthermore, they must address the challenge of automating integration, verification, and validation processes for the entire dataset, systematically and periodically. This study sketches a process for evaluating information from an initial starting point.

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Publication Details

Journal
Digital Water
Published
2026-10-28
DOI
https://doi.org/10.1080/28375807.2026.2747294
Primary Topic
Water Systems and Optimization
Type
article
Field-Weighted Citation Impact
0.00
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article

Data challenges in developing and managing hydraulic models for complex water supply systems

Juan Fernández González, Juan Carlos Ortega Mier, Antonio Lastra de la Rubia
Digital Water
Water Systems and Optimization
article

Data challenges in developing and managing hydraulic models for complex water supply systems

Juan Fernández González, Juan Carlos Ortega Mier, Antonio Lastra de la Rubia
article en

Abstract

Offline and online modelling (digital twins) of a water supply network (operational twin) is a process that integrates the various sources of information defining the network and its operation, adjusted to the measurements collected during its exploitation, and supported by hydraulic equations that ensure consistency. Hydraulic models, seen as a centralised repository of coherent information, face their greatest challenge in data management. These data characterise topology (static), determine operation (dynamic), record consumption or estimate demand (calculations and forecasts), and fit the model to real system measurements. Furthermore, they must address the challenge of automating integration, verification, and validation processes for the entire dataset, systematically and periodically. This study sketches a process for evaluating information from an initial starting point.

Digital WaterVol. 4(1)
Clean water and sanitation
Openalex Percentile: Top 19%
Water Systems and Optimization
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