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Gauging Through the Crowd: A Crowd‐Sourcing Approach to Urban Rainfall Measurement and Storm Water Modeling Implications

Accurate rainfall measurement at high spatial and temporal resolutions is critical for the modeling and management of urban storm water. In this study, we conduct computer simulation experiments to test the potential of a crowd‐sourcing approach, where smartphones, surveillance cameras, and other devices act as precipitation sensors, as an alternative to the traditional approach of using rain gauges to monitor urban rainfall. The crowd‐sourcing approach is promising as it has the potential to provide high‐density measurements, albeit with relatively large individual errors. We explore the potential of this approach for urban rainfall monitoring and the subsequent implications for storm water modeling through a series of simulation experiments involving synthetically generated crowd‐sourced rainfall data and a storm water model. The results show that even under conservative assumptions, crowd‐sourced rainfall data lead to more accurate modeling of storm water flows as compared to rain gauge data. We observe the relative superiority of the crowd‐sourcing approach to vary depending on crowd participation rate, measurement accuracy, drainage area, choice of performance statistic, and crowd‐sourced observation type. A possible reason for our findings is the differences between the error structures of crowd‐sourced and rain gauge rainfall fields resulting from the differences between the errors and densities of the raw measurement data underlying the two field types.