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  • View in gallery

    Maximum sea level along the U.S. coast facing the Atlantic basin. This conceptual diagram illustrates sea level as the sum of SLR that has occurred since the end of 2010 and the maximum storm surge height that is “felt” when a TC makes landfall anywhere along the U.S. coast facing the Atlantic basin. The letters (a),(b), and (c) represent coastal flooding, extra coastal flooding, and extreme coastal flooding, respectively. Note that the lower and upper parts of the y axis use two different linear scales.

  • View in gallery

    TC fatalities against the sum of maximum surge heights for each year between 1990 and 2011. Squares indicate years that were used for the linear regression; diamonds indicate years that were not used for the linear regression. [See text for regression Eq. (1). The regression equation including the data point for 2005 is y = 7.5581x − 46.127; R2 = 0.1545]. Note that the linear scale used on the upper part of the y axis is different from the linear scale used on the lower part (<35 yr−1) of the y axis.

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Human Lives at Risk because of Eustatic Sea Level Rise and Extreme Coastal Flooding in the Twenty-First Century

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  • 1 International Human Dimensions Programme on Global Environmental Change, United Nations University, Bonn, Germany
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Abstract

Sea level rise (SLR) is a topic of increasing importance, as global warming continues to drive it at the global level and other factors such as land subsidence also affect it at the local level. Economic and human-based approaches have been taken to assess its impact on society. However, quantifications of the effect of SLR on mortality have not been extensive. Therefore, the objective of this study is to quantify the relative impact of SLR on mortality due to extreme coastal flooding for 2011–2100. First, an empirical relationship between annual storm surges caused by tropical cyclones (TCs) and associated fatalities is established. Next, a conceptual framework is introduced to measure rises in sea level due to gradual SLR and temporary storm surges on a common scale called cumulatively raised sea level. An analysis applying SLR projections to this framework shows that, in addition to the deaths that occur because of coastal flooding due to TCs, at least 84–139 deaths due to extra coastal flooding caused by SLR may occur in the United States by 2100, in the absence of coastal population changes, adaptation, and protection failure. Higher-than-expected rates of SLR due to increased discharge from polar glaciers will raise this estimate to 277. Protection failure will also result in more fatalities. Conversely, adaptation, even when combined with coastal population increases, may lead to fewer fatalities.

Current affiliation: Independent scholar and translator, Kashiwa-shi, Chiba-ken, Japan.

Corresponding author address: Yosuke Adachi, Kashiwa-shi, Chiba-ken, Japan. E-mail: adachi.ysk@gmail.com

Abstract

Sea level rise (SLR) is a topic of increasing importance, as global warming continues to drive it at the global level and other factors such as land subsidence also affect it at the local level. Economic and human-based approaches have been taken to assess its impact on society. However, quantifications of the effect of SLR on mortality have not been extensive. Therefore, the objective of this study is to quantify the relative impact of SLR on mortality due to extreme coastal flooding for 2011–2100. First, an empirical relationship between annual storm surges caused by tropical cyclones (TCs) and associated fatalities is established. Next, a conceptual framework is introduced to measure rises in sea level due to gradual SLR and temporary storm surges on a common scale called cumulatively raised sea level. An analysis applying SLR projections to this framework shows that, in addition to the deaths that occur because of coastal flooding due to TCs, at least 84–139 deaths due to extra coastal flooding caused by SLR may occur in the United States by 2100, in the absence of coastal population changes, adaptation, and protection failure. Higher-than-expected rates of SLR due to increased discharge from polar glaciers will raise this estimate to 277. Protection failure will also result in more fatalities. Conversely, adaptation, even when combined with coastal population increases, may lead to fewer fatalities.

Current affiliation: Independent scholar and translator, Kashiwa-shi, Chiba-ken, Japan.

Corresponding author address: Yosuke Adachi, Kashiwa-shi, Chiba-ken, Japan. E-mail: adachi.ysk@gmail.com

1. Introduction

The implications of global warming for human society are huge, and efforts are being made to address the problem globally by forming a binding agreement (Kuipers 2011; UNFCCC 2014). While the physical aspects of the problem began to be researched in the nineteenth century (Weart 2008), recent decades have seen a sharp increase in the amount of research in the social sciences (Goodall 2008, Fig. 1), and collaborative research that spans multiple disciplines continues to be called for in order to effectively tackle the problem in the future (Shaman et al. 2013). Such efforts sometimes take the form of examining the interface of physical and social parameters, which plays an important role in studying the social impacts of global warming.

An established way to measure the wide-ranging effects of global warming on society involves measuring its economic impact (e.g., Titus 1992; Fankhauser 1995; Nordhaus and Boyer 2000). This involves synthesizing economic analyses of specific aspects of society, such as agriculture (Rosenzweig and Parry 1994), biodiversity (Chapin et al. 2000), and sea level rise (SLR, which hereafter refers to eustatic SLR unless otherwise stated; Neumann et al. 2011; Darwin and Tol 2001; Hinkel et al. 2014; Anthoff et al. 2010).

Another approach involves counting the number of people, the basis of the human capital in a society (UNU-IHDP and UNEP 2012), who are affected by global warming. One way in which global warming affects people is through SLR. The effect of SLR on populations has been explored from a multitude of perspectives. Studies accounting for future changes in coastal populations have shown that more people will be flooded by storm surges that accompany severe storms with the progression of SLR (Nicholls 2002; Nicholls and Tol 2006). Potential increases in hurricane intensities, combined with SLR, may also contribute to coastal communities being more prone to floods (Karim and Mimura 2008; McInnes et al. 2003; Woodruff et al. 2013). When population increase in coastal zones, SLR, and increased storminess are considered together, population increase has the greatest relative importance in increased exposure to coastal flooding (Nicholls et al. 2008). This highlights the importance of considering population changes along the coast, in the context of the observed migration to coastal areas in many parts of the world and the expected migration from the coast in response to SLR and flood damage. Adaptation is also important in determining future impact (Hinkel et al. 2014; Neumann et al. 2011). A common approach to project future potential impact on people involves computing vulnerability, which is a function of population exposed to a flooding event of a certain magnitude occurring at a certain recurrence interval. This vulnerability is evaluated in the context of scenarios of evolving important factors, such as adaptation, population change, increased storm intensity, and SLR. Another way to project future impact is to assess relationships between environmental conditions and actual impacts that have been experienced in the past (environment–impact relationships) and apply these to future climate change scenarios. This approach has been taken for other aspects of global warming, such as thermal stress (McGeehin and Mirabelli 2001) and vector-borne diseases (McMichael et al. 2003), to estimate future mortality or morbidity. Although SLR is a gradual process that is not generally considered to cause loss of life, this study takes the “environment versus impact” approach to quantify future fatalities that may be caused by the extreme coastal flooding due to SLR.

2. Theory and analyses

A tropical cyclone (TC, which hereafter refers to hurricanes, tropical storms, and subtropical storms) is characterized by strong winds that pile water up and push it onto shore as it approaches a coast. A TC is accompanied by low air pressure, which also raises the sea level. The combination of mainly these two effects causes a storm surge, with the former effect typically contributing 85% relative to the latter contributing 5%–10% (Masters 2013). These storm surges cause coastal flooding, which is responsible for much of the loss of life due to TCs (McInnes et al. 2003; Masters 2013). Therefore, a systematic relationship between storm surges and TC fatalities was explored.

In this study, the term “extreme coastal flooding” is used to refer to the combined effect of coastal flooding due to storm surges and the additional extent to which water levels during a flooding event are raised compared to a baseline value due to SLR. Extra coastal flooding is the effect of this additional extent. Coastal flooding, extra coastal flooding, and extreme coastal flooding are caused by changes in the sea level, as expressed by (a), (b), and (c) in Fig. 1, respectively, during a TC landfall.

Fig. 1.
Fig. 1.

Maximum sea level along the U.S. coast facing the Atlantic basin. This conceptual diagram illustrates sea level as the sum of SLR that has occurred since the end of 2010 and the maximum storm surge height that is “felt” when a TC makes landfall anywhere along the U.S. coast facing the Atlantic basin. The letters (a),(b), and (c) represent coastal flooding, extra coastal flooding, and extreme coastal flooding, respectively. Note that the lower and upper parts of the y axis use two different linear scales.

Citation: Weather, Climate, and Society 7, 2; 10.1175/WCAS-D-13-00063.1

a. Analysis of tropical cyclone fatalities

The number of fatalities caused by a TC depends on its intensity, the protection system, and the storm surge warning system in place in a coastal community. As most storm surge warning systems are administered at the national level, the analysis focuses on that level. Based on data availability for past TC tracks, storm surges, and TC fatalities, the United States was chosen as the site for analysis. For each TC landfall (see appendix A for a definition) on the coast of one of the 19 states (from Maine to Texas) with a coast facing the Atlantic Ocean or the Gulf of Mexico (hereafter, the Atlantic basin) for the period between 1990 and 2011, the Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model developed by the National Weather Service (Jelesnianski et al. 1992) was used to reproduce the maximum height of storm surges that occurred within a 150-km radius of the landfall location. The accuracy of SLOSH is ±20% (Glahn et al. 2009). The sum of these maximum surge heights for each year is plotted against the corresponding annual fatalities due to TCs in Fig. 2 (see Tables A1 and A2 in appendix A for values).

Fig. 2.
Fig. 2.

TC fatalities against the sum of maximum surge heights for each year between 1990 and 2011. Squares indicate years that were used for the linear regression; diamonds indicate years that were not used for the linear regression. [See text for regression Eq. (1). The regression equation including the data point for 2005 is y = 7.5581x − 46.127; R2 = 0.1545]. Note that the linear scale used on the upper part of the y axis is different from the linear scale used on the lower part (<35 yr−1) of the y axis.

Citation: Weather, Climate, and Society 7, 2; 10.1175/WCAS-D-13-00063.1

The positive correlation involving most of the data points in Fig. 2 shows that within the arrangement of a functioning society and protection system, the number of fatalities that occur almost every year due to TCs is proportional to the extent the sea level is raised because of storm surges during TC landfalls. The mortality rate derived from this correlation is used to elucidate the effect of SLR in the next section. The data points for 2001 and 2005, which are not included in the regression equation [Eq. (1); see Fig. 2], deserve further attention.
e1

The fatalities attributed to TCs by the National Weather Service (2013a, hereafter NWS13) and the National Climatic Data Center (2011, hereafter NCDC11) are not all due to storm surges. Other direct causes of death include wind and freshwater flooding (including flash flooding) associated with TCs. Meanwhile, SLOSH does not simulate rainfall or wind damage, so the effects due to these causes are not modeled. In a case like the year 2001, when 22 of the 23 deaths were caused by flash flooding due to a tropical storm (National Climatic Data Center 2001), the correlation in Fig. 2 does not capture the data point. When the 22 deaths due to flash flooding are removed from the 2001 fatalities, the data point is congruent with the correlation.

In 2005, Hurricane Katrina caused catastrophic damage to the Gulf coast. Katrina is often considered a hurricane that caused the failure of an existing protection system (Nicholls et al. 2008; Nicholls and Cazenave 2010). Therefore, the data point for 2005 including Katrina (~1000 of the 1016 deaths for 2005 are attributed to Katrina) should belong to a different cluster of data points for conceptual years when one of the TC landfalls caused many more fatalities than the other landfalls because a protection system in place in a community failed. It is part of a theoretical cluster of data points that are different from the rest of the data points within the analyzed period that form the actual, observed cluster of data points and that characterize normal years in the modern United States. Here, a “normal year” is defined as a year when a functioning protection system remains largely effective for all the TC landfalls experienced during that year. Subtracting the deaths attributed to Katrina and the corresponding storm surge height allows the data point for 2005 to be congruent with the other data points. Equation (1) shows the mortality rate in a normal year.

The reality is that not all years are normal years. Indeed, the return period of a Katrina-scale hurricane is estimated at ~20 yr (Elsner et al. 2006), and there is no guarantee that widespread destruction of a similar degree will be prevented when the next Katrina strikes (Kates et al. 2006). To examine sensitivity to fatality estimates if we account for the unquantified possibility that a Katrina-scale hurricane in the future causes the same kind of destruction that was experienced in 2005, a weighted mortality rate is needed. First, a nonnormal year is defined as a year when one of the TC landfalls during that year causes the protection system to fail, resulting in catastrophic destruction. The sum of maximum surge heights and fatalities for 2005 are then used to estimate the mortality rate for a nonnormal year at 32.1 fatalities per meter of raised sea level (1016 fatalities per 31.7 m = 32.1 fatalities per meter). Using this value and the mortality rate in a normal year, a weighted mortality rate is calculated for a 20-yr period during which there are 19 normal years and one nonnormal year. This weighted mortality rate is 2.23 fatalities per meter of raised sea level.

b. Analysis of the effect of SLR on human lives

Equation (1) shows that for a normal year between 1990 and 2011, each meter of sea level raised cumulatively due to the highest storm surge generated by each TC landfall in the Atlantic basin of the U.S. mainland during that year caused 0.67 fatalities. Next, this finding is used to quantify the effect of SLR. First, the baseline year is fixed at 2010. The eustatic sea level will keep rising for the rest of the twenty-first century. Therefore, the base level from which sea level will be raised by storm surges will rise continuously. Therefore, whatever the amount of cumulatively raised sea level (CRSL) in a given year due to TCs (this will fluctuate because of interannual variations in TC landfalls), there will always be the second component to CRSL due to SLR that has occurred since 2010, proportional to the number of landfalls that occur during that year. This concept is illustrated in Fig. 1. The algebraic expression is
e2
where ϕi is CRSL in year i, σi is the sum of maximum surge heights in year i, and λi is SLR that occurred between 2010 and year i multiplied by the number of landfalls in year i. All terms are in units of meters per year. With this concept, it is possible to replace the x-axis label of Fig. 2 with “CRSL for year i (ϕi) (m yr−1)” and use Eq. (1) to account for sea level raised because of both storm surges and SLR since 2010. The major requirement of this conceptual framework is that coastal communities remain static at 2010 levels. In other words, it assumes that no adaptation will take place during the twenty-first century. In addition, population changes are not considered, and the SLR that occurred between 1990 and 2011 is regarded as negligible.

Below are the calculations to account for what λi means in terms of loss of life during the twenty-first century for an idealized scenario in which all years are normal years. A recent assessment that synthesizes the broad literature available for SLR projections in the twenty-first century provides lower- and higher-end projections (RCP 2.6 and 8.5) of global mean SLR of 44m and 73 cm, respectively, by 2100 compared to 1986–2005 (IPCC 2013). Applying a linear rise in eustatic sea level and a constant value of 7.3 TC landfalls per year (average for the 22-yr period from 1990 to 2011), each year from 2011 onward will have 3.1 cm (5.1 cm; hereinafter, the higher-end value is shown in parentheses after the lower-end value) added to CRSL compared to the previous year as the contribution from SLR. This amount of CRSL due to SLR is applied to Eq. (1) to find that an additional 0.020 (0.034) lives will be at risk each year from 2011 on. A summation of these at-risk lives due to SLR for all years between 2011 and 2100 suggests that 84 (139) of the lives lost to TCs by the end of 2100 will be due to extra coastal flooding caused by SLR if coastal communities do not adapt. These are deaths that may occur in addition to the deaths that occur because of coastal flooding by TCs without the effect of SLR. Enhanced dynamic ice discharge from polar ice sheets may lead to larger rises in sea level [e.g., 1.25 m (Cuffey and Paterson 2010), 2 m (Pfeffer et al. 2008)], but these projections exceed the probabilistically quantified ranges given by the Intergovernmental Panel on Climate Change (IPCC; Church et al. 2014). In this study, a 125-cm rise in global mean sea level by 2100 is defined as a “very high end” scenario in which 277 lives are at risk (see Table 1 for more values).

Table 1.

Human lives at risk for 2011–2100 under three SLR scenarios and using two mortality rates. The lower rate is used when all years are normal (idealized scenario). The higher rate is used when there is one nonnormal year during a 20-yr period (less idealized scenario).

Table 1.

Potentially, larger numbers of deaths can occur by including the possibility that a Katrina-scale hurricane will cause protection failure. Calculations based on the weighted mortality rate result in estimated fatalities by 2100 that are 3.4 times higher than in the scenario of only normal years. For example, 466 deaths may occur for the higher-end scenario (see Table 1 for further values). However, there is large uncertainty in the numbers calculated for this less idealized scenario for a number of reasons. First, the mortality rate for a nonnormal year was calculated based on one pair of values for the year 2005. Second, the number of deaths that occurred because of Katrina is uncertain. Third, improved protection systems and/or changes in coastal populations can greatly affect whether a Katrina-scale hurricane will cause protection failure, and if it does, how many lives it might take. This sensitivity analysis suggests that accounting for the unquantified possibility that a Katrina-scale hurricane in the future causes the same kind of destruction that was experienced in 2005 will cause significantly more fatalities than those calculated for the idealized scenario of only normal years. A rigorous quantification beyond this qualitative conclusion is not conducted given the limitations of this study and the challenges involved.

3. Discussion

The current consensus is that there will “more likely than not” (>50%) be an increase in TC intensity in the North Atlantic basin in the late twenty-first century (IPCC 2013). Although this is an important concern, it is beyond the scope of this study, which focuses on the role of SLR in extreme coastal flooding that puts human lives at risk. TC frequency is relevant because the contribution of SLR to CRSL is directly proportional to the number of annual TC landfalls. There is much spread in the projected frequency of TCs over the twenty-first century (Christensen et al. 2014), so the average number of annual landfalls for the period 1990–2011 has been applied to the twenty-first century beyond 2010. If there is a change in TC frequency for this period, the resulting number of fatalities will change in direct proportion to the change in frequency. In other words, a ±25% change in frequency would lead to a ±25% change in fatalities, resulting in 63–104 (104–173) lives at risk. Extending the TC statistics back to 1950 yields an annual average landfall rate of 6.6 (see Table B1). This is about 9% less than the 1990–2011 average, and calculations based on this value yield 76 (126) lives at risk.

Coastal population increase and adaptation will tend to increase and reduce fatalities, respectively. These two counteracting effects were explored together in the context of future SLR scenarios by Hinkel et al. (2013). They predict that adaptation will outweigh the population increase effect, thereby decreasing the number of people flooded annually by the end of the twenty-first century compared to the present. They consider improvement of protection via dikes as the specific mode of adaptation. For example, in their “business as usual” scenario, similar to the higher-end scenario in this study, the number of people flooded globally in 2100 is about 9% of the number in 2000 (4 million per year), highlighting the effect of adaptation. Although comparing numbers involving people flooded and people killed may not be straightforward, their study shows that population increase and adaptation considered together will probably reduce the estimates given here. There are other forms of adaptation, such as retreat from the coast and improved storm surge warning systems (Klein et al. 2001), that would reduce our estimates.

Other factors that could affect the estimates are advancements in SLR projection and noneustatic effects on local SLR. Lower rates of projected SLR would reduce the estimates while higher rates would increase them. Meanwhile, SLR at the local level is affected by steric effects, glacial isostatic adjustments, dynamic effects due to changes in ocean circulation, and land subsidence. The latter two are the most relevant to the U.S. Atlantic coast in addition to eustatic effects. A rapid rise in sea level compared to the global mean has been observed in the northeastern United States north of Cape Hatteras, North Carolina, due to a weakening Atlantic meridional overturning circulation (AMOC; Sallenger et al. 2012). This trend is expected to continue in the decades ahead, when New York City, for instance, could experience an extra 15–21-cm rise in sea level superimposed on the global mean SLR by 2100 due to these dynamic effects (Yin et al. 2009). In a similar region, the southern Chesapeake Bay, land subsidence due to groundwater withdrawal has boosted the observed rate of local SLR to 3.5–4.4 mm yr−1, which is about twice as high as the global mean, for the past several decades (Eggleston and Pope 2013). These two effects may increase our estimates of fatalities, but a quantitative estimate has not been made because our model, which treats the whole U.S. mainland facing the Atlantic basin as one unit, cannot resolve the spatial variations in local SLR predicted along the coastline between Maine and Texas.

4. Conclusions

This study quantifies the human lives at risk because of SLR and the extreme coastal flooding it accounts for on the U.S. mainland facing the Atlantic basin. An empirical analysis relates TC fatalities to the amount of sea level raised during a year due to storm surges. A conceptual framework using the CRSL scale since 2010 is used to estimate that 84–139 of the fatalities caused by TCs between 2011 and 2100 may be due to extra coastal flooding caused by SLR; very rapid discharge of ice from polar glaciers may raise the upper end of this estimate to 277. These deaths may occur in addition to the deaths that occur because of coastal flooding by TCs, and they would not occur in the absence of SLR.

The major limitation to this estimate is that adaptation to SLR and coastal population increase have been omitted. These will tend to reduce and increase loss of life, respectively. However, a previous study (Hinkel et al. 2013) shows that despite these counteracting tendencies, the effect of adaptation via dike protection may be greater, suggesting that accounting for these effects may reduce our fatality estimate. The estimate is based on an idealized scenario in which the protection system does not fail in the way it did when Hurricane Katrina struck the Gulf coast. Accounting for the possibility of protection failure significantly increases this estimate by a factor of about 3. However, there is much uncertainty in accounting for the possibility of protection failure. This is because of a lack of data and because it is difficult to assess how prepared the protection system will be for the next Katrina-scale hurricane. In addition, accounting for dynamic effects on SLR, which is not done in this study, may increase the estimated fatalities by 2100, as a weakening AMOC is expected to raise the sea level in some parts of the northeastern United States more than the global mean. The ongoing land subsidence in the southern Chesapeake Bay region is another factor that could increase the estimates. This analysis was carried out for the U.S. mainland facing the Atlantic basin. Similar analyses carried out for other regions should yield different parameters for the storm surge–fatality relationship and thus different estimates of lives at risk due to the proportion of extreme coastal flooding caused by SLR.

Acknowledgments

I thank Anantha Duraiappah, Pablo Munoz, and Elorm Darkey for discussions. I thank UNU-IHDP for support. I thank three reviewers for comments that greatly improved the manuscript. I declare no competing financial interests.

APPENDIX A

Methods: Procedure for Creating Fig. 2

a. Sum of maximum surge heights

1) Tropical cyclone tracks and landfalls

TC track data were obtained from the hurricane database HURDAT (Hurricane Research Division 2012, hereafter HRD12; see “Track Maps and Data by Year”). The tracks were laid on top of a map of the U.S. coastline (NASA 2000), using ArcGIS 10 World Geodetic System 84 (WGS 84). When HURDAT2 (see “HURDAT2” in HRD12) provided an additional measurement point (with time and location) specifically for landfall, this measurement point was added to the HURDAT dataset.

The definition of a TC landfall is when a segment (defined as a line connecting two measurement points) of a track completely crosses a coastline onto land. When the track of the same TC crosses a coastline onto land again after once exiting into sea, it is considered another landfall. This methodology tends to record multiple landfalls when a TC moves parallel to a coast.

In cases where there was a measurement point at landfall, the time and date of that measurement point as the landfall time was noted. Otherwise, landfall time was determined by linear interpolation between the two measurement points on either side of the landfall location.

2) Maximum surge heights

The Display Program of SLOSH (hereafter, SDP; National Weather Service 2013b) was used to determine maximum surge height. The values of Maximum Envelope of Water (MEOW), with land elevation subtracted, were viewed for the relevant SLOSH basin(s) for each landfall. The maximum surge height for the landfall is the value of the grid cell with the largest value within a 150-km radius of the landfall location, rounded up to the nearest foot (~30 cm).

The tide level at the time and location of landfall is either low [lower than mean sea level (MSL) minus one-third of the tide amplitude], mean (higher than MSL minus one-third of the tide amplitude and lower than MSL plus one-third of the tide amplitude), or high (higher than MSL plus one-third of the tide amplitude). Tide predictions, provided by the Tide Display Program as part of SDP, of tide gauge stations in physical proximity to the landfall location were used. The tide amplitude was determined based on the tide level at high tide that is used by SLOSH simulations in the relevant basin. The simulation results for mean tide were used for landfalls that occur at low tide as well as at mean tide.

The forward (moving) speed is recorded for each measurement point in HURDAT. If there was no measurement point at the landfall location, the forward speed at landfall was specified as the range of forward speeds at the measurement points before and after landfall. The direction at landfall is the direction of the segment of the track immediately before landfall if there is a measurement point at landfall. Otherwise, it is the direction of the track segment that crosses the coastline.

The TC category at landfall is based on the information provided with each measurement point and the North Atlantic Hurricane Tracking Chart (HRD12, see “Track Maps and Data by Year”).

For basins where tropical storm simulation results were not available, the following extrapolation method to determine maximum surge height generated by tropical/subtropical storm landfalls was developed. For each of the seven basins that have tropical storm simulations, two directions and tabulated the maximum surge height within the entire basin for five TC categories (tropical storm and category 1–4 hurricanes) for three forward speeds were chosen. Based on the differences between the maximum surge heights for the category1–4 hurricanes, a separate “expected” value of maximum surge height for the tropical storm was calculated, so that this calculated value could be compared to the simulated value. The formula for the calculated tropical storm maximum surge height value is
eq1
where TS is the calculated tropical storm maximum surge height, Cn is the simulated maximum surge height for a category n hurricane, and α is a calibrated integer. The units are all in feet. Finally, the differences between calculated and simulated values of tropical storm maximum surge height for the 42 cases (7 basins × 6 cases) described above were added and α adjusted so that the sum of these differences becomes the smallest. In this way, α was determined to be 3.

See Table A1 for all the data required to determine the sum of maximum surge heights.

Table A1.

Maximum storm surge heights for TC landfalls between 1990 and 2011. Landfalls are named by the TC name when there is only one landfall associated with the TC. When there are multiple landfalls, the first landfall is indicated as “-1” after the TC name, the second as “-2,” etc. Asterisks indicate that a maximum surge height value is estimated by extrapolation [see appendix A, section a(2)] for a tropical/subtropical storm landfall. Tide station names are those available in the Tide Display Program of SDP. Basin name is as defined in SDP. Direction is specified in degrees when available; otherwise it is indicated in 16 directions. Abbreviations are as follows: Tide, tide level; Fwd, forward moving speed of TC; Dir, direction; Cat, TC category; S.h., maximum storm surge height; TS, tropical storm; SS, subtropical storm; C1–C5, category 1–5 hurricane.

Table A1.

b. Fatalities

TC fatality numbers for 1995–2011 were obtained from the Annual Summaries of NCDC11, which are summarized in one table in NWS13 (www.nws.noaa.gov/om/hazstats/resources/weather_fatalities.pdf). TC fatality numbers for 1990–94 were determined by subtracting the fatalities that occurred outside the 19 states (NCDC11, see monthly publications) from the nationwide total (NWS13). See Table A2 for values.

Table A2.

TC fatalities in the United States from 1990 to 2011. The sum of the fatalities inside and outside of the 19 states facing the Atlantic basin for each year equals the number of U.S. fatalities attributed to TCs by the National Weather Service (the 73-yr summary for 1940–2012 is available at NWS13 and annual summaries for 1996–2011 are available at NCDC11). Abbreviations are as follows: AR, Arkansas; CA, California; GU, Guam; HI, Hawaii; PR, Puerto Rico; VI, Virgin Islands.

Table A2.

APPENDIX B

TC Landfall Frequency for 1950–2011

The numbers needed to determine landfall frequency for 1950–2011 are summarized in Table B1.

Table B1.

Yearly averages of TC landfalls on the U.S. mainland facing the Atlantic basin from 1950 to 2011. The number of TC landfalls as defined in this study is counted for each of six periods between 1950 and 2011 (total landfalls). Based on these totals, yearly averages are computed both for each period and as cumulative averages from the beginning of each period until 2011.

Table B1.

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