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  • Author or Editor: Rebecca D. Marjerison x
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Rebecca D. Marjerison, M. Todd Walter, Patrick J. Sullivan, and Stephen J. Colucci

Abstract

Flash floods cause more fatalities than any other weather-related natural hazard and cause significant damage to property and infrastructure. It is important to understand the underlying processes that lead to these infrequent but high-consequence events. Accurately determining the locations of flash flood events can be difficult, which impedes comprehensive research of the phenomena. While some flash floods can be detected by automated means (e.g., streamflow gauges), flash floods (and other severe weather events) are generally based on human observations and may not reflect the actual distribution of event locations. The Storm Data–Storm Events Database, which is produced from National Weather Service reports, was used to locate reported flash floods within the forecast area of the Binghamton, New York, Weather Forecast Office between 2007 and 2013. The distribution of those reports was analyzed as a function of environmental variables associated with flood generation including slope, impervious area, soil saturated hydraulic conductivity k sat, representative rainfall intensity, and representative rainfall depth, as well as human population. A spatial conditional autoregressive model was used to test the hypothesis that flash flood reports are made more frequently in areas with higher populations, even when other flood-generating processes are considered. Slope, soil saturated hydraulic conductivity, and impervious area are significant predictors of flash flood reports. When population is added as a predictor, the model is similarly robust, but impervious area and k sat are no longer significant predictors. These results may challenge the assumption that flash flood reports are strongly biased by population.

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