TY - JOUR
T1 - The Potential of Knowing More
T2 - A Review of Data-Driven Urban Water Management
AU - Eggimann, Sven
AU - Mutzner, Lena
AU - Wani, Omar
AU - Schneider, Mariane Yvonne
AU - Spuhler, Dorothee
AU - Moy De Vitry, Matthew
AU - Beutler, Philipp
AU - Maurer, Max
N1 - Publisher Copyright:
© 2017 American Chemical Society.
PY - 2017/3/7
Y1 - 2017/3/7
N2 - 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 network-based 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.
AB - 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 network-based 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.
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U2 - 10.1021/acs.est.6b04267
DO - 10.1021/acs.est.6b04267
M3 - Review article
C2 - 28125222
AN - SCOPUS:85019674860
SN - 0013-936X
VL - 51
SP - 2538
EP - 2553
JO - Environmental Science and Technology
JF - Environmental Science and Technology
IS - 5
ER -