The potential of proxy water level measurements for calibrating urban pluvial flood models.

Urban pluvial flood models need to be calibrated with data from actual flood events in order to validate and improve model performance. Due to the lack of conventional sensor solutions, alternative sources of data such as citizen science, social media, and surveillance cameras have been proposed in literature. Some of the methods proposed boast high scalability but without an on-site survey, they can only provide proxy measurements for physical flooding variables (such as water level). In this study, the potential value of such proxy measurements was evaluated by calibrating an urban pluvial flood model with data from experimental flood events conducted in a 25 × 25 m facility, monitored with surveillance cameras and conventional sensors in parallel. Both ideal proxy data and actual image-based proxy measurements with noise were tested, and the effects of measurement location and measurement noise were investigated separately. The results with error-free proxy data confirm the theoretic potential of such measurements, as in half of the calibration configurations tested, ideal proxy data increases model performance by at least 70% compared to sensor data. However, image-based proxy data can contain complex correlated errors, which have a complex and predominantly negative effect on performance.

The Potential of Knowing More: A Review of Data-Driven Urban Water Management.

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.