Urban Water Consumption at Multiple Spatial and Temporal Scales. A Review of Existing Datasets

Over the last three decades, the increasing development of smart water meter trials and the rise of demand management has fostered the collection of water demand data at increasingly higher spatial and temporal resolutions. Counting these new datasets and more traditional aggregate water demand data...

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Veröffentlicht in:Water (Basel) Jg. 13; H. 1; S. 36
Hauptverfasser: Di Mauro, Anna, Cominola, Andrea, Castelletti, Andrea, Di Nardo, Armando
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.01.2021
Schlagworte:
Algorithms
Consumption
data collection
Datasets
Freshwater resources
Keywords
Mathematical models
temporal variation
water
Water conservation
Water shortages
Water supply
ISSN:2073-4441, 2073-4441
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Zusammenfassung:Over the last three decades, the increasing development of smart water meter trials and the rise of demand management has fostered the collection of water demand data at increasingly higher spatial and temporal resolutions. Counting these new datasets and more traditional aggregate water demand data, the literature is rich with heterogeneous urban water demand datasets. They are characterized by heterogeneous spatial scales—from urban districts, to households or individual water fixtures—and temporal sampling frequencies—from seasonal/monthly up to sub-daily (minutes or seconds). Motivated by the need of tracking the existing datasets in this rapidly evolving field of investigation, this manuscript is the first comprehensive review effort of the state-of-the-art urban water demand datasets. This paper contributes a review of 92 water demand datasets and 120 related peer-review publications compiled in the last 45 years. The reviewed datasets are classified and analyzed according to the following criteria: spatial scale, temporal scale, and dataset accessibility. This research effort builds an updated catalog of the existing water demand datasets to facilitate future research efforts end encourage the publication of open-access datasets in water demand modelling and management research.
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ISSN:2073-4441
2073-4441
DOI:10.3390/w13010036