| Summary: | CHAPTER 1 | INTRODUCTION 1.1 | TROPICAL CYCLONE RAINFALL HAZARD The intense rainfall associated with tropical cyclones (TC) is a significant hazard to human life and property. From 1963 to 2012, extreme rainfall was the most frequent cause of tropical cyclone-related fatalities in the United States (Rappaport 2014). Climate models have indicated, with high confidence, that TC rainfall rates in the North Atlantic will increase due the effects of global warming (Knutson et al. 2010; Wuebbles et al. 2017), with projected increases of 5%-20% (Walsh et al. 2016). Furthermore, the mean translational speed of North Atlantic TCs over land has decreased by 20% since the mid-20th century (Kossin 2018). These changes have important implications since more intense, slower-moving storms are more likely to linger for long durations and to generate extreme rainfall totals. The widely-used Saffir-Simpson scale assigns TCs a hazard category from one to five based on maximum sustained wind speed, with “Category 5” being the most catastrophic. However, maximum wind speed is a not a reliable indicator of overall hazard. Six of the ten deadliest TCs in the U.S. in the fifty years from 1963 to 2012 were tropical storms or Category 1 hurricanes at landfall (Rappaport 2014), and the Saffir-Simpson scale does not provide an accurate estimate of potential damage for a TC post-landfall (Senkbeil and Sheridan 2006). While a TC’s wind speed over land is typically much weaker than over the ocean (Sparks 2003), the rainfall threat may still be high post-landfall. Additionally, the area most impacted by extreme rainfall during a particular TC may be both spatially and temporally distinct from the region with the highest winds. These factors point to the need for an alternative means of characterizing and communicating TC rainfall threats. Hurricane Harvey (2017) and Hurricane Florence (2018) highlighted the hazard posed by TC rainfall and the challenge of effectively communicating this information to the public. Hurricane Florence, for example, approached the East Coast of the United States as a Category 4 hurricane, before weakening and making landfall as a Category 1 storm. The storm generated extreme rainfall and severe flooding throughout the Carolinas. Over 900 mm of rainfall was recorded in Elizabethtown, North Carolina, breaking the state’s record for TC rainfall. There is anecdotal evidence that the downgrade in Florence’s category as it approached the coast was perceived by members of the public as an indication of reduced hazard, resulting in some residents choosing not to evacuate (Achenbach and Wax-Thibodeaux 2018). The previous year, in 2017, Hurricane Harvey stalled over southeastern Texas for several days, making landfall as a Category 4 hurricane and weakening to a tropical storm shortly thereafter. Over 1500 mm of rain fell in Nederland, Texas—the highest TC-related rainfall total ever recorded in the United States. Experts and the media attempted to use recurrence intervals to contextualize the associated rainfall and flooding, with some media outlets reporting that Harvey was a “500-year” or “1000-year” event (Ingraham 2017; Lind 2017; Samenow 2017), while some outlets noted the shortcomings of these claims (Koerth- Baker 2017; D’Angelo 2017; Bledsoe 2017). 1.2 | RECURRENCE INTERVALS Recurrence interval (i.e. return period) estimates are critical to the fields of engineering design and probabilistic hazard and risk assessment. They are typically estimated by fitting a statistical distribution to a time series of observations (Coles 2001). A rainfall or flood event with a “500-year” recurrence interval has a 1 in 500 (0.2%) probability of being exceeded in any year (i.e. annual exceedance probability); such an event will occur, on average, once every 500 years in a stationary (i.e. unchanging) climate. The usage of these statistical measures is complicated by the fact that storm duration is a critical component in determining the impacts of an extreme rainfall event. A one-day, 500-year rainfall event could generate localized flash flooding, for example, while a three-day, 500-year event could cause widespread flooding in large watersheds. Previous attempts to develop TC classification systems have highlighted how differences in storm duration result in different magnitudes of TC impacts (Senkbeil and Sheridan 2006). Rainfall records are rarely longer than 75 years in the United States and are considerably shorter in many other parts of the world. Thus, rainfall recurrence interval estimates tend to be subject to substantial sampling uncertainty because the period of record is often substantially shorter than the desired quantile. Rainfall distributions are typically derived from point-scale observations (such as rain gauges), describing the distribution of extreme rainfall at a specific location, but with a limited ability to describe distributions at larger spatial scales. Additionally, when new events occur that lie outside the range of previously observed rainfalls, recurrence interval estimates should, in principle, be updated to reflect these new records. These changes can be quite large, as seen in the revised “Atlas 14” precipitation frequency estimates from the National Oceanic and Atmospheric Administration (NOAA) for the state of Texas, released in September 2018. An earlier estimate of the 100-year rainfall in Houston, based on rainfall records up to the 1960s, was approximately 330 mm (13 inches) in 24 hours (Hershfield 1961); this was updated to 457 mm (18 inches) in Atlas 14 using the most up-to-date data for the region, including observations of rainfall from Harvey (Perica et al. 2018). In addition to these statistical challenges, recurrence intervals can be confusing and misleading to the public (Keller et al. 2006). Probabilities and frequencies are abstract concepts, creating room for misinterpretation (Schneider 2016). For example, there is a common misperception that multiple “100-year” events cannot occur within a short timeframe. Statistically, however, there is a 26.4% chance of two or more “100-year” events of the same duration occurring at a particular location within any century-long period; this issue is complicated even further when events of varying durations are considered. Additionally, the understanding of probabilistic metrics is highly individual—the same metric can have different meanings for different users based on their own perception of risk (Schneider 2016). A growing number of studies highlight the importance of “experiential processing” in everyday decision-making – the idea that decisions are often made by relating current situations to events that individuals can recall from prior personal experience or recent media reports and images (Marx et al. 2007). Investigations into how individuals process information related to weather hazards have shown some shortcomings of current approaches, including recurrence intervals, and some have proposed alternatives that could convey this information more effectively (Schroeder et al. 2016; Lave and Lave 1991; Wachinger et al. 2013). A survey of residents in a flood-prone community in Texas in the United States, for example, highlighted how residents were more concerned about a potential flooding hazard when concrete information about the nature of flooding was provided, as opposed to abstract probabilities (Bell and Tobin 2007). Preparedness ahead of high-impact floods in 2015 and 2016 in the United Kingdom may have been reduced because residents had trouble adequately conceptualizing the magnitude of the flooding, which exceeded any that had occurred in recent memory (Cologna et al. 2017). 1.3 | OUTLINE Given the well-documented shortcomings of recurrence intervals in the context of communicating the hazard associated with extreme rainfall events (including tropical cyclones), this paper seeks to devise an alternative metric to more effectively quantify and communicate rainfall hazard. This metric – the Extreme Rainfall Multiplier (ERM) – expresses the magnitude of extreme rainfall as a multiple of the climatologically derived 2-yr rainfall value and is derived in Chapter 2. The ERM metric is applied retrospectively to historical TCs from 1948 to 2017 in Chapter 3, highlighting both the most extreme TC rainfall events (including Hurricane Harvey, which had the highest ERM value during this period) and the wide geographical distribution of these events in the eastern and southern United States. This analysis also allows for the development of regional-scale (rather than local-scale) recurrence interval estimates for extreme TC rainfall. Chapter 4 explores the potential utility of ERM to characterize extreme rainfall hazard in a forecast context, as well as other ways to improve the public’s general understanding using informal science communication methods. Future applications of the ERM metric and general conclusions are presented in Chapter 5.
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