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Using volunteered geographic information (VGI) to supplement disaster risk management systems, including forecasting, risk assessment, and disaster recovery, is increasingly popular. This attention is driven by difficulties in detection and characterization of hazards, as well as the rise of VGI appropriate for characterizing specific forms of risk. Flash-flood historical records, especially those that are impact-based, are not comprehensive, leading to additional barriers for flash-flood research and applications. In this paper we develop a method for associating VGI flood reporting clusters against authoritative data. Using Hurricane Harvey as a case study, VGI reports are assimilated into a spatial analytic framework that derives spatial and temporal clustering parameters supported by associations between Waze's community-driven emergency operations center and authoritative reports. These parameters are then applied to find previously unreported likely flash flood-events. This study improves the understanding of the distribution of flash flooding during Hurricane Harvey and shows potential application to events in other areas where Waze data and reporting from official sources, such as the National Weather Service, are available. [ABSTRACT FROM AUTHOR]

International Research Institute for Climate and Society, Climate School, Columbia University, New York, USA. Correction to: I Scientific Reports i https://doi.org/10.1038/s41598-022-08751-7, published online 28 March 2022 The original version of this Article omitted affiliations for Chris Lowrie, Andrew Krusckiewicz and Miriam Nielsen. [Extracted from the article]