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The potential of knowing more: A review of data-driven urban water management

Abstract:
The promise of collecting and utilizing large amounts of data has never been greater in the history of urban water management (UWM). This paper reviews several data-driven approaches which play a key role in bringing forward a sea change. It critically investigates whether data-driven UWM offers a promising foundation for addressing current challenges and supporting fundamental changes in UWM. We discuss the examples of better rain-data management, urban pluvial flood-risk management and forecasting, drinking water and sewer network operation and management, integrated design and management, increasing water productivity, wastewater-based epidemiology and on-site water and wastewater treatment. The accumulated evidence from literature points toward a future UWM that offers significant potential benefits thanks to increased collection and utilization of data. The findings show that data-driven UWM allows us to develop and apply novel methods, to optimize the efficiency of the current networkbased approach, and to extend functionality of today’s systems. However, generic challenges related to data-driven approaches (e.g., data processing, data availability, data quality, data costs) and the specific challenges of data-driven UWM need to be addressed, namely data access and ownership, current engineering practices and the difficulty of assessing the cost benefits of data-driven UWM.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1021/acs.est.6b04267

Authors


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Institution:
University of Oxford
Division:
SSD
Department:
SOGE
Sub department:
Environmental Change Institute
Role:
Author
ORCID:
0000-0003-3655-2328
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Role:
Author
ORCID:
0000-0002-6954-8360
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Role:
Author
ORCID:
0000-0002-3124-3705
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Role:
Author
ORCID:
0000-0003-3397-2773


Publisher:
American Chemical Society
Journal:
Environmental Science & Technology More from this journal
Volume:
51
Issue:
5
Pages:
2538-2553
Publication date:
2017-01-26
Acceptance date:
2017-01-26
DOI:
EISSN:
1520-5851
ISSN:
0013-936X


Pubs id:
pubs:827101
UUID:
uuid:25a1ccc7-ed14-4ea4-a324-ceba7259263c
Local pid:
pubs:827101
Source identifiers:
827101
Deposit date:
2018-06-13

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