The development of a flash flood severity index

Abstract

Flash flooding is a high impact weather event that requires clear communication regarding severity and potential hazards among forecasters, researchers, emergency managers, and the general public. Current standards used to communicate these characteristics include return periods and the United States (U.S.) National Weather Service (NWS) 4-tiered river flooding severity scale. Return periods are largely misunderstood, and the NWS scale is limited to flooding on gauged streams and rivers, often leaving out heavily populated urban corridors. To address these shortcomings, a student-led group of interdisciplinary researchers came together in a collaborative effort to develop an impact-based Flash Flood Severity Index (FFSI). The index was proposed as a damage-based, post-event assessment tool, and preliminary work toward the creation of this index has been completed and presented here. Numerous case studies were analyzed to develop the preliminary outline for the FFSI, and three examples of such cases are included in this paper. The scale includes five impact-based categories ranging from Category 1 very minor flooding to Category 5 catastrophic flooding. Along with the numerous case studies used to develop the initial outline of the scale, empirical data in the form of semi-structured interviews were conducted with multiple NWS forecasters across the country and their responses were analyzed to gain more perspective on the complicated nature of flash flood definitions and which tools were found to be most useful. The feedback from these interviews suggests the potential for acceptance of such an index if it can account for specific challenges.

The challenge of forecasting impacts of flash floods: test of a simplified hydraulic approach and validation based on insurance claim data

“…Although data of this sort are generally confidential, they may be accessible through partnerships with insurance companies. Other sources of information on flash flood impacts could also be used, such as the logs of emergency services, emergency calls, information shared on social networks (USDHS, 2012;Jongman et al, 2015;Tkachenko et al, 2017), or information gathered in the field after or during the event (Ortega et al, 2009;Ruin et al, 2014). This information is also affected by uncertainties and severe biases, especially in flash flood situations: absence of information due to local breakdowns of communication networks, reduction of social network activity, and partial filling of emergency logs in strongly affected areas during the turmoil of the event.…”

“…A large number of simultaneous alarms may be generated in the case of a significant rainfall event by such highly distributed flash flood forecasting systems. And it is now recognised that end users, such as emergency managers, who have little time for situation analysis and decision making during flash floods, crucially need rapid assessment of the possible field consequences and damage severity (Schroeder et al, 2016;Cole et al, 2016). Moreover, a direct forecast of possible field consequences opens the possibility for assessing the performance of flash flood forecasting systems in ungauged areas, based on reported consequences, as a surrogate for measured flood discharges (Versini et al, 2010a;Naulin et al, 2013;Javelle et al, 2014;Moncoulon et al, 2014;Saint-Martin et al, 2016;Le Bihan et al, 2016).…”

Spatial Analysis of High-Resolution Radar Rainfall and Citizen-Reported Flash Flood Data in Ultra-Urban New York City

“…Researchers have turned to flash flood reports, in which trained observers detail flash flood extent and damage, to identify flooding and have used these reports, along with more common stream gaging records, to create flash flood databases (in the U.S. [29] and in Europe [30]). Other researchers have called for the development of a Flash Flood Severity Index, based on damage, similar to the Enhanced Fujita scale for tornadoes [31]. Meanwhile, some researchers are turning to citizen science to record the presence of flooding or to verify hydrodynamic model output [32,33] and have created smartphone apps to report flooding [34].…”

Gridded Flash Flood Risk Index Coupling Statistical Approaches and TOPLATS Land Surface Model for Mountainous Areas

“…The method was shown to have poor skill in forecasting flash flooding when applied operationally for flash flood forecasting in western United States [60]. The FFSI is a damage-based post-event assessment tool with five categories ranging from 1 to 5, from least to most destructive, and still additional refinement is needed [61].…”