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  <front>
    <journal-meta>
<journal-id journal-id-type="publisher">NHESS</journal-id>
<journal-title-group>
<journal-title>Natural Hazards and Earth System Sciences</journal-title>
<abbrev-journal-title abbrev-type="publisher">NHESS</abbrev-journal-title>
<abbrev-journal-title abbrev-type="nlm-ta">Nat. Hazards Earth Syst. Sci.</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">1684-9981</issn>
<publisher><publisher-name>Copernicus Publications</publisher-name>
<publisher-loc>Göttingen, Germany</publisher-loc>
</publisher>
</journal-meta>

    <article-meta>
      <article-id pub-id-type="doi">10.5194/nhess-17-439-2017</article-id><title-group><article-title>The influence of an extended Atlantic hurricane season on inland flooding
potential in the southeastern United States</article-title>
      </title-group><?xmltex \runningtitle{The influence of an extended Atlantic hurricane season}?><?xmltex \runningauthor{M.~H.~Stone and S.~Cohen}?>
      <contrib-group>
        <contrib contrib-type="author" corresp="yes" rid="aff1">
          <name><surname>Stone</surname><given-names>Monica H.</given-names></name>
          <email>mhstone@crimson.ua.edu</email>
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Cohen</surname><given-names>Sagy</given-names></name>
          
        </contrib>
        <aff id="aff1"><institution>Department of Geography, The University of Alabama, Tuscaloosa,
35487-0322, USA</institution>
        </aff>
      </contrib-group>
      <author-notes><corresp id="corr1">Monica H. Stone (mhstone@crimson.ua.edu)</corresp></author-notes><pub-date><day>21</day><month>March</month><year>2017</year></pub-date>
      
      <volume>17</volume>
      <issue>3</issue>
      <fpage>439</fpage><lpage>447</lpage>
      <history>
        <date date-type="received"><day>29</day><month>September</month><year>2016</year></date>
           <date date-type="rev-request"><day>4</day><month>November</month><year>2016</year></date>
           <date date-type="rev-recd"><day>14</day><month>February</month><year>2017</year></date>
           <date date-type="accepted"><day>24</day><month>February</month><year>2017</year></date>
      </history>
      <permissions>
<license license-type="open-access">
<license-p>This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/3.0/">http://creativecommons.org/licenses/by/3.0/</ext-link></license-p>
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</permissions><self-uri xlink:href="https://nhess.copernicus.org/articles/17/439/2017/nhess-17-439-2017.html">This article is available from https://nhess.copernicus.org/articles/17/439/2017/nhess-17-439-2017.html</self-uri>
<self-uri xlink:href="https://nhess.copernicus.org/articles/17/439/2017/nhess-17-439-2017.pdf">The full text article is available as a PDF file from https://nhess.copernicus.org/articles/17/439/2017/nhess-17-439-2017.pdf</self-uri>


      <abstract>
    <p>Recent tropical cyclones, like Hurricane Katrina, have
been some of the worst the United States has experienced. Tropical cyclones
are expected to intensify, bringing about 20 % more precipitation, in the
near future in response to global climate warming. Further, global climate
warming may extend the hurricane season. This study focuses on four major
river basins (Neches, Pearl, Mobile, and Roanoke) in the southeastern United
States that are frequently impacted by tropical cyclones. An analysis of the
timing of tropical cyclones that impact these river basins found that most
occur during the low-discharge season and thus rarely produce riverine
flooding conditions. However, an extension of the current hurricane season
of June–November could encroach upon the high-discharge seasons in these
basins, increasing the susceptibility for riverine hurricane-induced
flooding. Our results indicate that 28–180 % more days would be at risk
of flooding from an average tropical cyclone with an extension of the
hurricane season to May–December (just 2 months longer). Future research
should aim to extend this analysis to all river basins in the United States
that are impacted by tropical cyclones in order to provide a bigger picture
of which areas are likely to experience the worst increases in flooding risk
due to a probable extension of the hurricane season with expected global
climate change in the near future.</p>
  </abstract>
    </article-meta>
  </front>
<body>
      

<sec id="Ch1.S1" sec-type="intro">
  <title>Introduction</title>
      <p>In the southeastern United States tropical cyclones are some of the most
severe rain events (Schumacher and Johnson, 2006). While tropical cyclones
occur less frequently than other rain-producing events, they cause the most
damage because they cover a large geographic area and often cause widespread
flooding (Greenough et al., 2001; Mousavi et al., 2011; Schumacher and
Johnson, 2006). On average, tropical cyclones occurring in the southeast
bring 240.4 mm of rain in a 24 h period (Schumacher and Johnson, 2006).
The severity of flooding following tropical cyclone events is a function of
tropical storm frequency; landfall location; precipitation intensity; and,
for coastal areas, mean sea level (Irish and Resio, 2013). In addition to
flooding, these storms cause further damage from their strong winds
(Greenough et al., 2001; Mousavi et al., 2011), and they frequently can
cause tornadoes and landslides (Greenough et al., 2001; National Science
Board (NSB), 2007).</p>
      <p>Coastal communities in the United States, especially along the east coast and
the Gulf Coast, are most at risk of the flooding, strong winds, and heavy
precipitation associated with tropical cyclones (Irish et al., 2014).
Approximately half of the United States population lives within only
<inline-formula><mml:math id="M1" display="inline"><mml:mo>∼</mml:mo></mml:math></inline-formula> 80 km of the coast (NSB, 2007), and, on average, areas that are
prone to tropical cyclones are 5 times more heavily populated than the rest
of the nation (Frey et al., 2010). About 70 million people live in
hurricane-prone areas (Greenough et al., 2001). Recent increases in coastal
populations and development in coastal areas are posing an increasing risk to
coastal infrastructure and human life (Greenough et al., 2001; Irish et al.,
2014). Based off of 2010 estimates, 39 % of US homes are located in
coastal counties, an 8 % increase since 2000 (NOAA, 2013). The monetary
losses from hurricanes are increasing; in 2006 dollars, average annual losses
were USD 1.3 billion from 1949 to 1989, USD 10.1 billion from 1990 to
1995, and USD 35.8 billion from 2002 to 2007 (NSB, 2007). Flooding from
high storm surges during hurricanes has caused approximately 14 600 deaths
over the last century; about 50–100 deaths occur per hurricane event
(Greenough et al., 2001). In addition to deaths caused by flooding,
hurricanes can cause a variety of health impacts, including illnesses that
result from ecological changes (changes in the abundance and distribution of
disease-carrying insects, rodents, mold, and fungi), damage to healthcare
infrastructure and reduced access to healthcare services, damage to water and
sewage systems, overcrowded conditions in shelters, and psychological effects
from the trauma faced by victims (Greenough et al., 2001).</p>
      <p>Several studies have looked at the influence of tropical cyclones on river
flooding in small catchments. Kostaschuk et al. (2001) investigated
tropical-cyclone-induced flooding in the Rewa River system in Viti Levu, Fiji. They
observed that rainstorms caused a higher number of floods but that floods
caused by tropical cyclones were much larger (Kostaschuk et al., 2001).
Waylen (1991) conducted a partial duration series flood analysis for the
Santa Fe River in Florida and found similar results. Tropical-cyclone-induced
floods were found to occur less often than floods from other
rain-producing events. However, they tended to have larger magnitudes and
longer durations (Waylen, 1991). Specifically, they found that tropical
cyclone floods were <inline-formula><mml:math id="M2" display="inline"><mml:mo>∼</mml:mo></mml:math></inline-formula> 3 times larger and <inline-formula><mml:math id="M3" display="inline"><mml:mo>∼</mml:mo></mml:math></inline-formula> 2
times longer than other floods (Waylen, 1991).</p>
      <p>Greenhouse gases in the atmosphere not only increase atmospheric
temperature but also can lead to increased sea surface temperatures (Irish
et al., 2014). The warmer the sea surface temperature, the greater the
intensity of tropical cyclones. Thus, global warming may intensify tropical
cyclones, such that storms may tend to have higher storm surge levels (Frey
et al., 2010; Irish et al., 2014; Mousavi et al., 2011). The
Intergovernmental Panel on Climate Change (IPCC) predicts that global
sea surface temperatures will increase 1.1–6.4 <inline-formula><mml:math id="M4" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C over the next
century (Irish and Resio, 2013; Mousavi et al., 2011). Sea surface
temperatures need to be at or above <inline-formula><mml:math id="M5" display="inline"><mml:mo>∼</mml:mo></mml:math></inline-formula> 26.7 <inline-formula><mml:math id="M6" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C for
tropical cyclones to form (Steenhof and Gough, 2008). The current hurricane
season extends from June to November; however, longer seasons (i.e., storms
occurring before June and/or after November) have been occurring in recent
years (Dwyer et al., 2015). While research on this topic is not conclusive,
there is some indication that global climate change may lead to a change in
the Atlantic hurricane season (Dwyer et al., 2015). There is an 8 %
increase in a tropical cyclone's central pressure for each 1 <inline-formula><mml:math id="M7" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C
increase in tropical sea surface temperature (Irish and Resio, 2013; Irish
et al., 2014; Mousavi et al., 2011). Further, there is a 3.7 % increase
in a tropical cyclone's wind speed for each 1 <inline-formula><mml:math id="M8" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C increase in
tropical sea surface temperature (Irish et al., 2014). Climate models also
suggest that precipitation rates from tropical cyclones may increase by 20 %
by 2100 (Geophysical Fluid Dynamics Laboratory (GFDL), 2016; Knutson et
al., 2010).</p>
      <p>Numerous studies have indicated that global climate warming may intensify
tropical cyclones and is very likely to result in sea level rise (Bronstert
et al., 2002; Frey et al., 2010; Greenough et al., 2001; Irish and Resio,
2013; Irish et al., 2014; Kostaschuk et al., 2001; Mousavi et al., 2011;
Ouellet et al., 2012). Major hurricanes, those that are category 3 or higher
on the Saffir–Simpson scale, are the most likely to intensify (Frey et al.,
2010; Mousavi et al., 2011). However, there is some debate about changes in
tropical cyclone frequency. Some research predicts that tropical cyclone
frequency will increase (e.g., Greenough et al., 2001; Ouellet et al., 2012),
while other research suggests that tropical cyclones are likely to intensify
with global climate warming but occur less frequently (e.g., Irish and
Resio, 2013; Kostaschuk et al., 2001).</p>
      <p>Several studies about the effects of climate change on tropical cyclone
intensity have been conducted for the Corpus Christi, TX area (Frey et al.,
2010; Mousavi et al., 2011). Frey et al. (2010) determined how severe
historical hurricanes would be if they were to occur in the current climate,
and those predicted for the 2030s and 2080s. They found that, in all three
climate scenarios, storm surge flood depth, area of flood inundation,
population affected, and economic damages would all increase compared to the
historical levels (Frey et al., 2010). In a follow-up study by Mousavi et al. (2011),
sea level rise and tropical cyclone intensification, due to
global warming, are likely to equally contribute to increased flood depths.</p>
      <p>While there has been much focus on the impact of tropical cyclones on
coastal flooding, there has been little research on how these high-intensity
precipitation events affect the hydrology of streams just inland of coastal
areas. Further, few studies have focused on how inland flooding is likely to
be altered with an extended hurricane season in the near future due to
likely global climate change. This study investigates the potential increase
in flooding risk with an extension of the hurricane season on four rivers in
the southeastern United States. The goal is to help determine how flooding
potential may change in the near future in order to elucidate the impact
such changes may have on communities in the southeastern United States.</p>
</sec>
<sec id="Ch1.S2">
  <title>Study areas</title>
      <p>This research is focused on the southeastern United States, where tropical
cyclone events occur quite frequently and where severe flooding following
these events can have profound impacts on the prosperity of communities.
Specifically, four river basins (Neches, Pearl, Mobile, and Roanoke) were
selected for analysis (Fig. 1; Table 1). These four basins were chosen to be
in areas that experience tropical cyclones and a high number of severe
hurricanes. Currently, tropical cyclones impacting these four basins rarely
cause flooding. As is shown later in this paper, this is primarily due to
the overlap of the current hurricane season with the low-discharge seasons
on these four rivers. However, an extension of the hurricane season, such
that it encroaches upon the high-discharge seasons on these rivers, could
likely lead to increases in flooding following tropical cyclones that impact
these basins.</p>

<?xmltex \floatpos{t}?><table-wrap id="Ch1.T1"><caption><p>Gage location in and size of the four study basins.</p></caption><oasis:table frame="topbot"><?xmltex \begin{scaleboxenv}{.82}[.82]?><oasis:tgroup cols="5">
     <oasis:colspec colnum="1" colname="col1" align="left"/>
     <oasis:colspec colnum="2" colname="col2" align="left"/>
     <oasis:colspec colnum="3" colname="col3" align="right"/>
     <oasis:colspec colnum="4" colname="col4" align="right"/>
     <oasis:colspec colnum="5" colname="col5" align="left"/>
     <oasis:thead>
       <oasis:row rowsep="1">  
         <oasis:entry colname="col1">River basin</oasis:entry>  
         <oasis:entry colname="col2">Near</oasis:entry>  
         <oasis:entry colname="col3">Latitude</oasis:entry>  
         <oasis:entry colname="col4">Longitude</oasis:entry>  
         <oasis:entry colname="col5">Basin size</oasis:entry>
       </oasis:row>
     </oasis:thead>
     <oasis:tbody>
       <oasis:row>  
         <oasis:entry colname="col1">Neches</oasis:entry>  
         <oasis:entry colname="col2">Silsbee/Evadale, TX</oasis:entry>  
         <oasis:entry colname="col3">30.374</oasis:entry>  
         <oasis:entry colname="col4"><inline-formula><mml:math id="M9" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>94.094</oasis:entry>  
         <oasis:entry colname="col5">25 117 km<inline-formula><mml:math id="M10" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:math></inline-formula></oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">Pearl</oasis:entry>  
         <oasis:entry colname="col2">Slidell, LA</oasis:entry>  
         <oasis:entry colname="col3">30.374</oasis:entry>  
         <oasis:entry colname="col4"><inline-formula><mml:math id="M11" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>89.774</oasis:entry>  
         <oasis:entry colname="col5">22 894 km<inline-formula><mml:math id="M12" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:math></inline-formula></oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">Mobile</oasis:entry>  
         <oasis:entry colname="col2">Mt. Vernon, AL</oasis:entry>  
         <oasis:entry colname="col3">31.094</oasis:entry>  
         <oasis:entry colname="col4"><inline-formula><mml:math id="M13" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>87.974</oasis:entry>  
         <oasis:entry colname="col5">110 955 km<inline-formula><mml:math id="M14" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:math></inline-formula></oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">Roanoke</oasis:entry>  
         <oasis:entry colname="col2">Williamston, NC</oasis:entry>  
         <oasis:entry colname="col3">35.864</oasis:entry>  
         <oasis:entry colname="col4"><inline-formula><mml:math id="M15" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>76.904</oasis:entry>  
         <oasis:entry colname="col5">25 963 km<inline-formula><mml:math id="M16" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:math></inline-formula></oasis:entry>
       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup><?xmltex \end{scaleboxenv}?></oasis:table></table-wrap>

      <p>Gaging stations along these rivers were chosen to be inland of coastal areas
so that tidal fluctuation and storm surge would not be factors when
analyzing discharge, and far enough downstream to include as much of the
basins as possible. These four basins were selected to represent a range of
sizes and geographic locations that exist throughout the southeastern
United States. United States Geological Survey (USGS) gages were used where
data were available for the period extending from 1998 to 2014. In many cases
(Pearl, Mobile, and Roanoke) USGS stream gages for these basins either did
not have daily discharge data or did not have a long enough history of daily
discharge data, or if sufficient daily discharge data were available, the
location of the gaging station was either too close to the coast where there
were tidal fluctuations or too far upstream in the catchment such that only
a small fraction of the catchment was flowing to the gaging station. In
these situations, Dartmouth Flood Observatory (DFO) satellite river gages
were used (Brakenridge et al., 2016).</p>
</sec>
<sec id="Ch1.S3">
  <title>Methods</title>
<sec id="Ch1.S3.SS1">
  <title>Frequency and timing of tropical cyclones</title>
      <p>NOAA's Atlantic hurricane database (HURDAT2) (Landsea et al., 2015) was used to determine when
tropical cyclones passed over the four basins. For each tropical cyclone
event on record, this dataset provides information on the year, month, day,
time, latitude, longitude, maximum sustained wind speed (in knots), minimum
pressure (in millibars), and several wind speed radii extents for points
along a tropical cyclone's track (where points are spaced at 6 h intervals).
The data provided in the HURDAT2 dataset are downloadable in a text file
format. A Python script was developed to extract this information in order to
create point shapefiles of tropical cyclone paths that could be analyzed in
GIS. The paths of tropical cyclones between 1998 and 2014 were buffered to a
width of 300 mi (<inline-formula><mml:math id="M17" display="inline"><mml:mo>∼</mml:mo></mml:math></inline-formula> 500 km), the average precipitation extent of a
tropical cyclone (Darby et al., 2013). Then, a selection-by-location
procedure was used to determine which buffered tropical cyclones passed over
each of the basins. The coordinates of the buffered points along tropical
cyclone paths passing over the basins were then used to look up the
corresponding dates each storm passed over each basin in the HURDAT2 dataset.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F1"><caption><p>Location of the study basins analyzed in this study (blue); colored
dots represent points along the tracks of all tropical cyclones since 1998
that have impacted the study basins, where the color/size of the dot
indicates the severity of the storm at that location (see legend). (Hurricane
track data were retrieved from NOAA's Atlantic hurricane database, HURDAT2
(Landsea et al., 2015); basin boundary data were retrieved from USGS's
National Hydrography Dataset, NHD, and Watershed Boundary Dataset, WBD (USGS,
2016); basemap is from ESRI.)</p></caption>
          <?xmltex \igopts{width=241.848425pt}?><graphic xlink:href="https://nhess.copernicus.org/articles/17/439/2017/nhess-17-439-2017-f01.png"/>

        </fig>

      <?xmltex \floatpos{t}?><fig id="Ch1.F2" specific-use="star"><caption><p>Comparison of average monthly discharge (blue bars) with the
number of tropical cyclones occurring each month (yellow line) from
1998 to 2014 for the Neches <bold>(a)</bold>, Pearl <bold>(b)</bold>, Mobile <bold>(c)</bold>, and Roanoke <bold>(d)</bold> basins.</p></caption>
          <?xmltex \igopts{width=497.923228pt}?><graphic xlink:href="https://nhess.copernicus.org/articles/17/439/2017/nhess-17-439-2017-f02.png"/>

        </fig>

      <?xmltex \floatpos{t}?><fig id="Ch1.F3" specific-use="star"><caption><p>Comparison of monthly discharge maximum–minimum range (red bars)
with the number of tropical cyclones occurring each month (yellow line) from
1998 to 2014 in the Neches <bold>(a)</bold>, Pearl <bold>(b)</bold>, Mobile <bold>(c)</bold>,
and Roanoke <bold>(d)</bold> basins.</p></caption>
          <?xmltex \igopts{width=497.923228pt}?><graphic xlink:href="https://nhess.copernicus.org/articles/17/439/2017/nhess-17-439-2017-f03.png"/>

        </fig>

      <?xmltex \floatpos{t}?><fig id="Ch1.F4" specific-use="star"><caption><p>Bankfull discharge (black lines), flow duration curves (blue
curves), and flow duration curves with discharge increased due to the
average tropical cyclone (red curves) for the current hurricane season on
the Neches <bold>(a)</bold>, Pearl <bold>(b)</bold>, Mobile <bold>(c)</bold>, and Roanoke <bold>(d)</bold> rivers.</p></caption>
          <?xmltex \igopts{width=497.923228pt}?><graphic xlink:href="https://nhess.copernicus.org/articles/17/439/2017/nhess-17-439-2017-f04.png"/>

        </fig>

      <?xmltex \floatpos{p}?><fig id="Ch1.F5" specific-use="star"><caption><p>Bankfull discharge (black lines), flow duration curves (blue
curves), and flow duration curves with discharge increased due to the
average tropical cyclone (red curves) for an extended May–December hurricane
season on the Neches <bold>(a)</bold>, Pearl <bold>(b)</bold>, Mobile <bold>(c)</bold>, and Roanoke <bold>(d)</bold> rivers.</p></caption>
          <?xmltex \igopts{width=497.923228pt}?><graphic xlink:href="https://nhess.copernicus.org/articles/17/439/2017/nhess-17-439-2017-f05.png"/>

        </fig>

</sec>
<sec id="Ch1.S3.SS2">
  <title>Determining bankfull discharge</title>
      <p>Daily discharge data for the outlet of each of the basins over the period
from 1998 to 2014 were obtained from either the USGS or the DFO's satellite
river discharge measurements. The DFO sites provide daily measures of
discharge beginning 1 January 1998 (Brakenridge et al., 2012). Discharge is
estimated from NASA and the Japanese Space Agency TRMM microwave data
(Brakenridge et al., 2012). This dataset is particularly useful because it
allows the user to place gaging stations at any location along world rivers.
Brakenridge et al. (2012) tested the accuracy of DFO satellite river
discharge measurements and reported regression <inline-formula><mml:math id="M18" display="inline"><mml:mrow><mml:msup><mml:mi>r</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:mrow></mml:math></inline-formula> values &gt; 0.6.
They also provide a site-specific “quality assessment” which, for
sites in the United States, is based on calculating the Nash–Sutcliffe (NS)
statistics for the DFO site and near gaging station hydrographs (Brakenridge
et al., 2015). For the Mobile River site, for example, the DFO quality
assessment ranking is 2 (fair), which means that the NS statistics were
&gt; 0.44. However, since both bankfull and time series discharge
are estimated from the same source in this study, while the absolute value
may somewhat differ from the actual discharge, temporal trends and
fluctuation magnitude were found to be well captured. This is clearly
evident in the Mobile River DFO site
(<uri>http://floodobservatory.colorado.edu/SiteDisplays/467.htm</uri>).</p>
      <p>Using the daily
discharge data obtained, the log Pearson type III statistic (Interagency
Advisory Committee on Water Data (IACWD), 1982) was calculated for each
basin. The log Pearson type III statistic can be used to provide an
“industry standard” of bankfull discharge for a river at a particular
gaging station; times when discharge is greater than the bankfull discharge
indicate the occurrence of a flood (IACWD, 1982). In the study of tropical
cyclone floods in Fiji by Kostaschuk et al. (2001), the log Pearson type III
statistic was found to represent their partial duration flood series more
accurately than the Pareto distribution, even though it tended to
underestimate the largest flows slightly.</p>
      <p>The log Pearson type III statistic was calculated using maximum yearly
discharge values from 1998 to 2014:
            <disp-formula id="Ch1.E1" content-type="numbered"><mml:math id="M19" display="block"><mml:mrow><mml:mi>log⁡</mml:mi><mml:mi>Q</mml:mi><mml:mo>=</mml:mo><mml:mi>log⁡</mml:mi><mml:mover accent="true"><mml:mi>Q</mml:mi><mml:mo mathvariant="normal">‾</mml:mo></mml:mover><mml:mo>+</mml:mo><mml:mi>K</mml:mi><mml:mi mathvariant="italic">σ</mml:mi><mml:mo>,</mml:mo></mml:mrow></mml:math></disp-formula>
          where <inline-formula><mml:math id="M20" display="inline"><mml:mi>Q</mml:mi></mml:math></inline-formula> is the discharge of some return period, <inline-formula><mml:math id="M21" display="inline"><mml:mrow><mml:mi>log⁡</mml:mi><mml:mover accent="true"><mml:mi>Q</mml:mi><mml:mo mathvariant="normal">‾</mml:mo></mml:mover></mml:mrow></mml:math></inline-formula> is the
average of the log <inline-formula><mml:math id="M22" display="inline"><mml:mi>Q</mml:mi></mml:math></inline-formula> maximum discharge values, <inline-formula><mml:math id="M23" display="inline"><mml:mi>K</mml:mi></mml:math></inline-formula> is the frequency factor
(found using the <inline-formula><mml:math id="M24" display="inline"><mml:mi>K</mml:mi></mml:math></inline-formula> frequency factor table, which is based upon return period
and the skew coefficient), and <inline-formula><mml:math id="M25" display="inline"><mml:mi mathvariant="italic">σ</mml:mi></mml:math></inline-formula> is the standard deviation of the
log <inline-formula><mml:math id="M26" display="inline"><mml:mi>Q</mml:mi></mml:math></inline-formula> discharge values (Oregon State University (OSU), 2005). The variance
can be found using Eq. (2):
            <disp-formula id="Ch1.E2" content-type="numbered"><mml:math id="M27" display="block"><mml:mrow><mml:msup><mml:mi mathvariant="italic">σ</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msup><mml:mo>=</mml:mo><mml:mstyle displaystyle="true"><mml:mfrac style="display"><mml:mrow><mml:msubsup><mml:mo>∑</mml:mo><mml:mn mathvariant="normal">1</mml:mn><mml:mi>n</mml:mi></mml:msubsup><mml:mo>(</mml:mo><mml:mi>log⁡</mml:mi><mml:mi>Q</mml:mi><mml:mo>-</mml:mo><mml:mi>log⁡</mml:mi><mml:mover accent="true"><mml:mi>Q</mml:mi><mml:mo mathvariant="normal">‾</mml:mo></mml:mover><mml:msup><mml:mo>)</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:mrow><mml:mrow><mml:mi>n</mml:mi><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:mfrac></mml:mstyle><mml:mo>,</mml:mo></mml:mrow></mml:math></disp-formula>
          where <inline-formula><mml:math id="M28" display="inline"><mml:mi>n</mml:mi></mml:math></inline-formula> is the number of maximum discharge values (i.e., the number of years)
(OSU, 2005). The skew coefficient can be found using (OSU, 2005)
            <disp-formula id="Ch1.E3" content-type="numbered"><mml:math id="M29" display="block"><mml:mrow><mml:msub><mml:mi>C</mml:mi><mml:mi>s</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mspace linebreak="nobreak" width="0.125em"/><mml:mstyle displaystyle="true"><mml:mfrac style="display"><mml:mrow><mml:mi>n</mml:mi><mml:mo>∑</mml:mo><mml:mo>(</mml:mo><mml:mi>log⁡</mml:mi><mml:mi>Q</mml:mi><mml:mo>-</mml:mo><mml:mi>log⁡</mml:mi><mml:mover accent="true"><mml:mi>Q</mml:mi><mml:mo mathvariant="normal">‾</mml:mo></mml:mover><mml:msup><mml:mo>)</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:msup></mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mi>n</mml:mi><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn><mml:mo>)</mml:mo><mml:mo>(</mml:mo><mml:mi>n</mml:mi><mml:mo>-</mml:mo><mml:mn mathvariant="normal">2</mml:mn><mml:mo>)</mml:mo><mml:mo>(</mml:mo><mml:msup><mml:mi mathvariant="italic">σ</mml:mi><mml:mn mathvariant="normal">3</mml:mn></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:mfrac></mml:mstyle><mml:mo>.</mml:mo></mml:mrow></mml:math></disp-formula>
          The bankfull discharge was calculated using a return period of 2.33,
following Waylen (1991).</p>
</sec>
<sec id="Ch1.S3.SS3">
  <title>Analyzing the effects of an extended hurricane season on flooding
susceptibility</title>
      <p>An analysis was performed to determine how many days from 1998 to 2014 during
the hurricane season would have been at risk of flooding were an average
tropical cyclone to have occurred on any given day. For each tropical
cyclone in each basin from 1998 to 2014 the discharge the day before the event
was compared to the peak discharge during the storm in order to determine the increases in
discharge due to the tropical cyclones. For each basin, these increases
in discharge were averaged to determine the average increase in discharge
due to a tropical cyclone.</p>
      <p>For each June–November day from 1998 to 2014, the daily discharge in the Neches
River was increased by the average increase in discharge due to a tropical
cyclone experienced by the Neches Basin. This increased discharge due to an
average tropical cyclone was compared with the bankfull
discharge value on each individual day for the Neches River. A day with a
discharge greater than bankfull discharge indicates that the
Neches River likely would have flooded on this day if an average tropical
cyclone were to have impacted this basin. Similar analyses were conducted
for the Pearl, Mobile, and Roanoke basins.</p>
      <p>The above methodology was then repeated with an extended Atlantic hurricane
season of May–December. A 1-month extension of the present June–November
Atlantic hurricane season (Dwyer et al., 2015) was considered because
several May (1 month outside the current hurricane season) tropical cyclones
have impacted the Roanoke Basin in 2007, 2009, and 2012. The HURDAT2
dataset also indicates the occurrence of some May, as well as some December,
Atlantic tropical cyclones. These data were then compared to the percentage
of days susceptible to tropical-cyclone-induced flooding in the current
hurricane season.</p>
</sec>
</sec>
<sec id="Ch1.S4">
  <title>Results</title>
<sec id="Ch1.S4.SS1">
  <title>Tropical cyclone frequency and timing</title>
      <p>From 1998 to 2014 (17 years), 15 tropical cyclones impacted the Neches Basin,
28 impacted the Pearl Basin, 30 impacted the Mobile Basin, and 36 impacted
the Roanoke Basin. The number of tropical cyclones impacting each basin each
year has not been constant over the period of study. The years 2004 and 2005
had high numbers of storms in every basin, and in recent years there have
been very few storms. For example, in 2004 and 2005 most basins experienced
two or more tropical cyclones, while in 2013 and 2014 only the Roanoke Basin
was impacted by tropical cyclones (and only one in each year). Most notably,
almost all tropical cyclones impacting these four basins occur during
low-discharge seasons, when flood risk is minimized (Figs. 2 and 3).</p>
</sec>
<sec id="Ch1.S4.SS2">
  <title>Effects of an extended hurricane season on flooding
susceptibility</title>
      <p>On average, tropical cyclones increased discharge (calculated from the
difference between peak discharge and discharge the day before the storm) by
97.85 m<inline-formula><mml:math id="M30" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">3</mml:mn></mml:msup></mml:math></inline-formula> s<inline-formula><mml:math id="M31" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> on the Neches River, 226.71 m<inline-formula><mml:math id="M32" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">3</mml:mn></mml:msup></mml:math></inline-formula> s<inline-formula><mml:math id="M33" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> on the
Pearl River, 787.25 m<inline-formula><mml:math id="M34" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">3</mml:mn></mml:msup></mml:math></inline-formula> s<inline-formula><mml:math id="M35" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> on the Mobile River, and 101.26 m<inline-formula><mml:math id="M36" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">3</mml:mn></mml:msup></mml:math></inline-formula> s<inline-formula><mml:math id="M37" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>
on the Roanoke River (Table 2). The average percent increase
in discharge following a tropical cyclone impact in all four rivers was 92 % (Table 2).</p>

<?xmltex \floatpos{p}?><table-wrap id="Ch1.T2" specific-use="star"><caption><p>Flooding risk from 1998 to 2014 for the four study basins with the
current hurricane season and with an extended May–December hurricane season.</p></caption><oasis:table frame="topbot"><oasis:tgroup cols="5">
     <oasis:colspec colnum="1" colname="col1" align="left"/>
     <oasis:colspec colnum="2" colname="col2" align="left"/>
     <oasis:colspec colnum="3" colname="col3" align="right"/>
     <oasis:colspec colnum="4" colname="col4" align="right"/>
     <oasis:colspec colnum="5" colname="col5" align="left"/>
     <oasis:thead>
       <oasis:row>  
         <oasis:entry colname="col1">Basin</oasis:entry>  
         <oasis:entry colname="col2">Increase in discharge</oasis:entry>  
         <oasis:entry colname="col3">Days at risk</oasis:entry>  
         <oasis:entry colname="col4">Days at risk with</oasis:entry>  
         <oasis:entry colname="col5">Increase in risk</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1"/>  
         <oasis:entry colname="col2">due to average</oasis:entry>  
         <oasis:entry colname="col3">with June–Nov season</oasis:entry>  
         <oasis:entry colname="col4">May–Dec season</oasis:entry>  
         <oasis:entry colname="col5">with extended</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">  
         <oasis:entry colname="col1"/>  
         <oasis:entry colname="col2">tropical cyclone</oasis:entry>  
         <oasis:entry colname="col3">(% of time period)</oasis:entry>  
         <oasis:entry colname="col4">(% of time period)</oasis:entry>  
         <oasis:entry colname="col5">season</oasis:entry>
       </oasis:row>
     </oasis:thead>
     <oasis:tbody>
       <oasis:row>  
         <oasis:entry colname="col1">Neches</oasis:entry>  
         <oasis:entry colname="col2">97.85 m<inline-formula><mml:math id="M38" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">3</mml:mn></mml:msup></mml:math></inline-formula> s<inline-formula><mml:math id="M39" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula></oasis:entry>  
         <oasis:entry colname="col3">30</oasis:entry>  
         <oasis:entry colname="col4">44</oasis:entry>  
         <oasis:entry colname="col5"><inline-formula><mml:math id="M40" display="inline"><mml:mrow><mml:mo>+</mml:mo><mml:mn mathvariant="normal">14</mml:mn></mml:mrow></mml:math></inline-formula> days</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1"/>  
         <oasis:entry colname="col2"/>  
         <oasis:entry colname="col3">(0.96 %)</oasis:entry>  
         <oasis:entry colname="col4">(1.06 %)</oasis:entry>  
         <oasis:entry colname="col5">(47 % increase)</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">Pearl</oasis:entry>  
         <oasis:entry colname="col2">226.71 m<inline-formula><mml:math id="M41" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">3</mml:mn></mml:msup></mml:math></inline-formula> s<inline-formula><mml:math id="M42" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula></oasis:entry>  
         <oasis:entry colname="col3">39</oasis:entry>  
         <oasis:entry colname="col4">50</oasis:entry>  
         <oasis:entry colname="col5"><inline-formula><mml:math id="M43" display="inline"><mml:mrow><mml:mo>+</mml:mo><mml:mn mathvariant="normal">11</mml:mn></mml:mrow></mml:math></inline-formula> days</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1"/>  
         <oasis:entry colname="col2"/>  
         <oasis:entry colname="col3">(1.25 %)</oasis:entry>  
         <oasis:entry colname="col4">(1.20 %)</oasis:entry>  
         <oasis:entry colname="col5">(28 % increase)</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">Mobile</oasis:entry>  
         <oasis:entry colname="col2">787.25 m<inline-formula><mml:math id="M44" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">3</mml:mn></mml:msup></mml:math></inline-formula> s<inline-formula><mml:math id="M45" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula></oasis:entry>  
         <oasis:entry colname="col3">10</oasis:entry>  
         <oasis:entry colname="col4">28</oasis:entry>  
         <oasis:entry colname="col5"><inline-formula><mml:math id="M46" display="inline"><mml:mrow><mml:mo>+</mml:mo><mml:mn mathvariant="normal">18</mml:mn></mml:mrow></mml:math></inline-formula> days</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1"/>  
         <oasis:entry colname="col2"/>  
         <oasis:entry colname="col3">(0.32 %)</oasis:entry>  
         <oasis:entry colname="col4">(0.67 %)</oasis:entry>  
         <oasis:entry colname="col5">(180 % increase)</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">Roanoke</oasis:entry>  
         <oasis:entry colname="col2">101.26 m<inline-formula><mml:math id="M47" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">3</mml:mn></mml:msup></mml:math></inline-formula> s<inline-formula><mml:math id="M48" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula></oasis:entry>  
         <oasis:entry colname="col3">50</oasis:entry>  
         <oasis:entry colname="col4">84</oasis:entry>  
         <oasis:entry colname="col5"><inline-formula><mml:math id="M49" display="inline"><mml:mrow><mml:mo>+</mml:mo><mml:mn mathvariant="normal">34</mml:mn></mml:mrow></mml:math></inline-formula> days</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1"/>  
         <oasis:entry colname="col2"/>  
         <oasis:entry colname="col3">(1.61 %)</oasis:entry>  
         <oasis:entry colname="col4">(2.02 %)</oasis:entry>  
         <oasis:entry colname="col5">(68 % increase)</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">Average</oasis:entry>  
         <oasis:entry colname="col2">92 % increase</oasis:entry>  
         <oasis:entry colname="col3">32</oasis:entry>  
         <oasis:entry colname="col4">52</oasis:entry>  
         <oasis:entry colname="col5">63 % increase in #</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1"/>  
         <oasis:entry colname="col2"/>  
         <oasis:entry colname="col3">(1.04 %)</oasis:entry>  
         <oasis:entry colname="col4">(1.24%)</oasis:entry>  
         <oasis:entry colname="col5">of days at risk</oasis:entry>
       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup></oasis:table></table-wrap>

      <p>The Roanoke River was the most susceptible to potential flooding from an
average tropical cyclone in the current hurricane season scenario. On about
50 (out of 3111)
days in the 1998–2014 June–November hurricane seasons the
Roanoke River would be above bankfull discharge and at risk of flooding from
an average tropical cyclone (Fig. 4d; Table 2). That is, about 1.61 % of
days would be susceptible to potential flooding were an average tropical cyclone
to occur (Table 2). The Mobile River showed the least susceptibility with
only about 10 days (or 0.32 % of the time). The average susceptibility for
potential tropical-cyclone-induced flooding for all four rivers was about 32
days (or 1.04 % of the time) (Fig. 4; Table 2). The
extended hurricane season showed greater flooding risk for all four of the
rivers. Again, the flood risk was greatest on the Roanoke River (84 days,
or 2.02 % of the time) and least on the Mobile River (28 days, or
0.67 % of the time) (Fig. 5; Table 2). On average, the
extended hurricane scenario led to about 20 more days per basin that likely
would be at risk of a flood were the average tropical cyclone to occur
(Table 2). Over the 17 seasons, this is a 63 % increase in the number of
days at risk of flooding, or an increase from 1.9 to 3.1 days yr<inline-formula><mml:math id="M50" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>.</p>
</sec>
</sec>
<sec id="Ch1.S5" sec-type="conclusions">
  <title>Discussions and conclusions</title>
      <p>Most tropical cyclones impacting these four basins occur during September,
or the middle of the low-discharge season (Figs. 2 and 3). The current
hurricane season coincides primarily with the low-discharge seasons of the
four basins. Thus, tropical cyclones rarely cause flood events on these
rivers, even though they bring high amounts of precipitation, because they
occur primarily during the low-discharge season. This is in contrast to
tropical cyclones in Southeast Asia, for example, which are frequent during
the monsoon season, causing widespread inland flooding (Darby et al., 2013).
Some May tropical cyclones have already occurred in the Roanoke Basin during
2007, 2009, and 2012, and NOAA's HURDAT2 dataset contains other May and
December tropical cyclones occurring in the Atlantic Ocean. This suggests
that, while tropical cyclones rarely led to inland flooding from 1998 to 2014 in
the four basins, a future extension of the hurricane season, such that it
encroaches upon the high-discharge season in these rivers, has the potential
to considerably enhance flooding risks.</p>
      <p>Adding the months of May and December increases the number of days during
the year that fall within the hurricane season by 34 %. For just a
34 % increase in the length of the hurricane season, there was, on average,
a 63 % increase in the number of days at risk of a
tropical-cyclone-induced flood along these southeast rivers (Table 2). When
averaged over the 17-year period analyzed in this study, the number of days
at risk of tropical-cyclone-induced flooding increases from 1.9 to
3.1 days yr<inline-formula><mml:math id="M51" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>. While 3 day yr<inline-formula><mml:math id="M52" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> may not seem substantial, it not only represents
a 63 % increase, but it is also a conservative number, as it excludes
predicted enhancements in the intensity and/or frequency of future tropical
cyclones (Bronstert et al., 2002; Frey et al., 2010; Greenough et al., 2001;
Irish and Resio, 2013; Irish et al., 2014; Kostaschuk et al., 2001; Mousavi
et al., 2011; Ouellet et al., 2012). Further, this research does not
consider synergistic effects due to the potential interplay between May
and/or December tropical cyclones and midlatitude cyclones, which could
increase precipitation and flooding risk even further.</p>
      <p>The timing of the hurricane season in relation to the high- and low-discharge
seasons is crucial to understanding flooding risk following tropical cyclones
on these rivers. The Mobile and Roanoke rivers showed the greatest increase
in flooding risk (68 and 180 % respectively) in an extended May–December
hurricane season as compared to the Neches and Pearl rivers (Table 2). The
Pearl River showed the least increase in flooding risk following the average
tropical cyclone (28 %) in an extended May–December hurricane season.
While the Neches, Mobile, and Roanoke rivers tend to have slightly higher
discharges in May than in June, discharges in the Pearl River are slightly
lower in May than June. Thus, this study reveals not only that flooding risk
following tropical cyclones is expected to increase if the hurricane season
is extended due to global climate warming but also that this increase will
not be uniform across the southeastern United States. Rivers with
high-discharge seasons in May and December, such as the Mobile and Roanoke
rivers, are likely to be most affected by a lengthened hurricane season.</p>
      <p>The main limitation of this study is its use of average statistics. Future
work could extend this study to look at increase in flood risk not only due
to the average tropical cyclone but also due the full range of tropical cyclones that a
basin is likely to experience (the tropical cyclone with the maximum
increase in discharge, the tropical cyclone with the minimum increase in
discharge, etc.). For instance, given that tropical cyclones are likely to
intensify (Bronstert et al., 2002; Frey et al., 2010; Greenough et al.,
2001; Irish and Resio, 2013; Irish et al., 2014; Kostaschuk et al., 2001;
Mousavi et al., 2011; Ouellet et al., 2012), flooding risk in an extended
hurricane season likely could exceed the results presented in this paper,
although May and December tropical cyclones likely could be weaker than
mid-season storms. Further, more explicit modeling of future tropical
cyclone dynamics using a stochastic approach, rather than average
statistics, could potentially produce a more robust understanding of the
effects of future climate dynamics on flood susceptibility. Because the
high-discharge season varies from basin to basin, extending this study to other
basins along the east and Gulf coasts would allow for a fuller understanding
of which areas in the southeastern United States are likely to be more at
risk of flooding following tropical cyclones due to an extension of the
hurricane season in response to global climate warming.</p>
</sec>

      
      </body>
    <back><notes notes-type="dataavailability">

      <p>The HURDAT2 dataset is available at
<uri>http://www.nhc.noaa.gov/data/</uri>. The DFO data is available at
<uri>http://floodobservatory.colorado.edu/DischargeAccess.html</uri>.
The results of this study are not publicly available because they are
being used in a follow-up study.</p>
  </notes><notes notes-type="competinginterests">

      <p>The authors declare that they have no conflict of interest.</p>
  </notes><ack><title>Acknowledgements</title><p>We wish to thank   Jason Senkbeil and   Peter Waylen for their guidance
in this research. We are thankful for the help of   G. Robert Brakenridge
for his help in installing river gages for some of the study basins with the
Dartmouth Flood Observatory. And lastly, thank you to The University of
Alabama for funding portions of this research.<?xmltex \hack{\newline}?><?xmltex \hack{\newline}?>
Edited by: T. Wagener <?xmltex \hack{\newline}?>
Reviewed by: two anonymous referees</p></ack><ref-list>
    <title>References</title>

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Syvitski, J. P. M., and Fekete, B. M.: Calibration of satellite measurements
of river discharge using a global hydrology model, J. Hydrol., 475, 123–136,
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Cohen, S., and Nghiem, S. V.: Experimental satellite-based river discharge
measurements: Technical summary, available at:
<uri>http://floodobservatory.colorado.edu/SatelliteGaugingSites/technical.html</uri>
(last access: 1 September 2016), 2015.</mixed-citation></ref>
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<uri>http://floodobservatory.colorado.edu/DischargeAccess.html</uri>, last access:
1 September 2016.</mixed-citation></ref>
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Bronstert, A., Niehoff, D., and Büger, G.: Effects of climate and
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capabilities, Hydrol. Process., 16, 509–529, 2002.</mixed-citation></ref>
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Darby, S. E., Leyland, J., Kummu, M., Räsänen, T. A., and Lauri, H.:
Decoding the drivers of bank erosion on the Mekong river: The roles of the
Asian monsoon, tropical storms, and snowmelt, Water Resour. Res., 49,
2146–2163, 2013.</mixed-citation></ref>
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Dwyer, J. G., Camargo, S. J., Sobel, A. H., Biasutti, M., Emanuel, K. A.,
Vecchi, G. A., Zhao, M., and Tippett, M. K.: Projected twenty-first century
changes in the length of the tropical cyclone season, J. Climate, 28,
6181–6192, 2015.</mixed-citation></ref>
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Frey, A. E., Olivera, F., Irish, J. L., Dunkin, L. M., Kaihatu, J. M., Ferreira,
C. M., and Edge, B. L.: Potential impact of climate change on hurricane
flooding inundation, population affected, and property damages in Corpus
Christi, J. Am. Water Resour. As., 46, 1049–1059, 2010.</mixed-citation></ref>
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</mixed-citation></ref><?xmltex \hack{\newpage}?>
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Greenough, G., McGeehin, M., Bernard, S. M., Trtanj, J., Riad, J., and
Engelber, D.: The potential impacts of climate variability and change on
health impacts of extreme weather events in the United States, Environ.
Health Persp., 109, 191–198, 2001.</mixed-citation></ref>
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Irish, J. L. and Resio, D. T.: Method for estimating future hurricane flood
probabilities and associated uncertainty, J. Waterw. Port. C. Div., 139,
126–134, 2013.</mixed-citation></ref>
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Irish, J. L., Sleath, A., Cialone, M. A., Knutson, T. R., and Jensen, R. E.:
Simulations of Hurricane Katrina (2005) under sea level and climate
conditions for 1900, Climatic Change, 122, 635–649, 2014.</mixed-citation></ref>
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determining flood flow frequency (Bulletin #17B), Hydrology Subcommittee,
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Kostaschuk, R., Terry, J., and Raj, R.: Tropical cyclones and floods in
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Knutson, T. R., McBride, J. L., Chan, J., Emanuel, K., Holland, G., Landsea,
C., Held, I., Kossin, J. P., Srivastava, A. K., and Sugi, M.: Tropical
cyclones and climate change, Nat. Geosci., 3, 157–163, 2010.</mixed-citation></ref>
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available at: <uri>http://www.nhc.noaa.gov/data/</uri> (last access: 1 September
2016), 2015.</mixed-citation></ref>
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Mousavi, M. E., Irish, J. L., Frey, A. E., Olivera, F., and Edge, B. L.: Global
warming and hurricanes: the potential impact of hurricane intensification
and sea level rise on coastal flooding, Climatic Change, 104, 575–597, 2011.</mixed-citation></ref>
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National Oceanic and Atmospheric Administration (NOAA): National coastal
population report: Population trends from 1970–2020, Department of
Commerce, 2013.</mixed-citation></ref>
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National Science Board (NSB): Hurricane warning: The critical need for a
national hurricane research initiative, National Science Foundation,
Arlington, VA, 2007.</mixed-citation></ref>
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<uri>http://streamflow.engr.oregonstate.edu/analysis/floodfreq/</uri>, last
access: 23 August 2016, 2005.</mixed-citation></ref>
      <ref id="bib1.bib20"><label>20</label><mixed-citation>
Ouellet, C., Saint-Laurent, D., and Normand, F.: Flood events and flood risk
assessment in relation to climate and land-use changes: Saint-François
River, Southern Québec, Canada, Hydrolog. Sci. J., 57, 313–325, 2012.</mixed-citation></ref>
      <ref id="bib1.bib21"><label>21</label><mixed-citation>
Schumacher, R. S. and Johnson, R. H.: Characteristics of U.S. extreme rain
events during 1999–2003, Weather Forecast., 21, 69–85, 2006.</mixed-citation></ref>
      <ref id="bib1.bib22"><label>22</label><mixed-citation>
Steenhof, P. A. and Gough, W. A.: The impact of tropical sea surface
temperatures on various measures of Atlantic tropical cyclone activity,
Theor. Appl. Climatol., 92, 249–255, 2008.</mixed-citation></ref>
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<uri>https://nhd.usgs.gov/data.html</uri>, last access: 1 September 2016, 2016.</mixed-citation></ref>
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Waylen, P. R.: Modeling the effects of tropical cyclones on flooding in the
Santa Fe river basin, Florida, GeoJ., 23, 361–373, 1991.</mixed-citation></ref>

  </ref-list><app-group content-type="float"><app><title/>

    </app></app-group></back>
    <!--<article-title-html>The influence of an extended Atlantic hurricane season on inland flooding potential in the southeastern United States</article-title-html>
<abstract-html><p class="p">Recent tropical cyclones, like Hurricane Katrina, have
been some of the worst the United States has experienced. Tropical cyclones
are expected to intensify, bringing about 20 % more precipitation, in the
near future in response to global climate warming. Further, global climate
warming may extend the hurricane season. This study focuses on four major
river basins (Neches, Pearl, Mobile, and Roanoke) in the southeastern United
States that are frequently impacted by tropical cyclones. An analysis of the
timing of tropical cyclones that impact these river basins found that most
occur during the low-discharge season and thus rarely produce riverine
flooding conditions. However, an extension of the current hurricane season
of June–November could encroach upon the high-discharge seasons in these
basins, increasing the susceptibility for riverine hurricane-induced
flooding. Our results indicate that 28–180 % more days would be at risk
of flooding from an average tropical cyclone with an extension of the
hurricane season to May–December (just 2 months longer). Future research
should aim to extend this analysis to all river basins in the United States
that are impacted by tropical cyclones in order to provide a bigger picture
of which areas are likely to experience the worst increases in flooding risk
due to a probable extension of the hurricane season with expected global
climate change in the near future.</p></abstract-html>
<ref-html id="bib1.bib1"><label>1</label><mixed-citation>
Brakenridge, G. R., Cohen, S., Kettner, A. J., De Groeve, T., Nghiem, S. V.,
Syvitski, J. P. M., and Fekete, B. M.: Calibration of satellite measurements
of river discharge using a global hydrology model, J. Hydrol., 475, 123–136,
2012.
</mixed-citation></ref-html>
<ref-html id="bib1.bib2"><label>2</label><mixed-citation>
Brakenridge, G. R., Kettner, A., Syvitski, J., Overeem, I., De Groeve, T.,
Cohen, S., and Nghiem, S. V.: Experimental satellite-based river discharge
measurements: Technical summary, available at:
<a href="http://floodobservatory.colorado.edu/SatelliteGaugingSites/technical.html" target="_blank">http://floodobservatory.colorado.edu/SatelliteGaugingSites/technical.html</a>
(last access: 1 September 2016), 2015.
</mixed-citation></ref-html>
<ref-html id="bib1.bib3"><label>3</label><mixed-citation>
Brakenridge, G. R., De Groeve, T., Kettner, A., Cohen, S., and Nghiem, S. V.:
River Watch, Version 3, University of Colorado, Boulder, available at:
<a href="http://floodobservatory.colorado.edu/DischargeAccess.html" target="_blank">http://floodobservatory.colorado.edu/DischargeAccess.html</a>, last access:
1 September 2016.
</mixed-citation></ref-html>
<ref-html id="bib1.bib4"><label>4</label><mixed-citation>
Bronstert, A., Niehoff, D., and Büger, G.: Effects of climate and
land-use change on storm runoff generation: Present knowledge and modeling
capabilities, Hydrol. Process., 16, 509–529, 2002.
</mixed-citation></ref-html>
<ref-html id="bib1.bib5"><label>5</label><mixed-citation>
Darby, S. E., Leyland, J., Kummu, M., Räsänen, T. A., and Lauri, H.:
Decoding the drivers of bank erosion on the Mekong river: The roles of the
Asian monsoon, tropical storms, and snowmelt, Water Resour. Res., 49,
2146–2163, 2013.
</mixed-citation></ref-html>
<ref-html id="bib1.bib6"><label>6</label><mixed-citation>
Dwyer, J. G., Camargo, S. J., Sobel, A. H., Biasutti, M., Emanuel, K. A.,
Vecchi, G. A., Zhao, M., and Tippett, M. K.: Projected twenty-first century
changes in the length of the tropical cyclone season, J. Climate, 28,
6181–6192, 2015.
</mixed-citation></ref-html>
<ref-html id="bib1.bib7"><label>7</label><mixed-citation>
Frey, A. E., Olivera, F., Irish, J. L., Dunkin, L. M., Kaihatu, J. M., Ferreira,
C. M., and Edge, B. L.: Potential impact of climate change on hurricane
flooding inundation, population affected, and property damages in Corpus
Christi, J. Am. Water Resour. As., 46, 1049–1059, 2010.
</mixed-citation></ref-html>
<ref-html id="bib1.bib8"><label>8</label><mixed-citation>
Geophysical Fluid Dynamics Laboratory (GFDL):
<a href="http://www.gfdl.noaa.gov/global-warming-and-hurricanes" target="_blank">http://www.gfdl.noaa.gov/global-warming-and-hurricanes</a>, last access: 23
August 2016.

</mixed-citation></ref-html>
<ref-html id="bib1.bib9"><label>9</label><mixed-citation>
Greenough, G., McGeehin, M., Bernard, S. M., Trtanj, J., Riad, J., and
Engelber, D.: The potential impacts of climate variability and change on
health impacts of extreme weather events in the United States, Environ.
Health Persp., 109, 191–198, 2001.
</mixed-citation></ref-html>
<ref-html id="bib1.bib10"><label>10</label><mixed-citation>
Irish, J. L. and Resio, D. T.: Method for estimating future hurricane flood
probabilities and associated uncertainty, J. Waterw. Port. C. Div., 139,
126–134, 2013.
</mixed-citation></ref-html>
<ref-html id="bib1.bib11"><label>11</label><mixed-citation>
Irish, J. L., Sleath, A., Cialone, M. A., Knutson, T. R., and Jensen, R. E.:
Simulations of Hurricane Katrina (2005) under sea level and climate
conditions for 1900, Climatic Change, 122, 635–649, 2014.
</mixed-citation></ref-html>
<ref-html id="bib1.bib12"><label>12</label><mixed-citation>
Interagency Advisory Committee on Water Data (IACWD): Guidelines for
determining flood flow frequency (Bulletin #17B), Hydrology Subcommittee,
US Department of the Interior, Reston, VA, 1982.
</mixed-citation></ref-html>
<ref-html id="bib1.bib13"><label>13</label><mixed-citation>
Kostaschuk, R., Terry, J., and Raj, R.: Tropical cyclones and floods in
Fiji, Hydrolog. Sci. J., 46, 435–450, 2001.
</mixed-citation></ref-html>
<ref-html id="bib1.bib14"><label>14</label><mixed-citation>
Knutson, T. R., McBride, J. L., Chan, J., Emanuel, K., Holland, G., Landsea,
C., Held, I., Kossin, J. P., Srivastava, A. K., and Sugi, M.: Tropical
cyclones and climate change, Nat. Geosci., 3, 157–163, 2010.
</mixed-citation></ref-html>
<ref-html id="bib1.bib15"><label>15</label><mixed-citation>
Landsea, C., Franklin, J., and Beven, J.: Atlantic hurricane database,
available at: <a href="http://www.nhc.noaa.gov/data/" target="_blank">http://www.nhc.noaa.gov/data/</a> (last access: 1 September
2016), 2015.
</mixed-citation></ref-html>
<ref-html id="bib1.bib16"><label>16</label><mixed-citation>
Mousavi, M. E., Irish, J. L., Frey, A. E., Olivera, F., and Edge, B. L.: Global
warming and hurricanes: the potential impact of hurricane intensification
and sea level rise on coastal flooding, Climatic Change, 104, 575–597, 2011.
</mixed-citation></ref-html>
<ref-html id="bib1.bib17"><label>17</label><mixed-citation>
National Oceanic and Atmospheric Administration (NOAA): National coastal
population report: Population trends from 1970–2020, Department of
Commerce, 2013.
</mixed-citation></ref-html>
<ref-html id="bib1.bib18"><label>18</label><mixed-citation>
National Science Board (NSB): Hurricane warning: The critical need for a
national hurricane research initiative, National Science Foundation,
Arlington, VA, 2007.
</mixed-citation></ref-html>
<ref-html id="bib1.bib19"><label>19</label><mixed-citation>
Oregon State University (OSU):
<a href="http://streamflow.engr.oregonstate.edu/analysis/floodfreq/" target="_blank">http://streamflow.engr.oregonstate.edu/analysis/floodfreq/</a>, last
access: 23 August 2016, 2005.
</mixed-citation></ref-html>
<ref-html id="bib1.bib20"><label>20</label><mixed-citation>
Ouellet, C., Saint-Laurent, D., and Normand, F.: Flood events and flood risk
assessment in relation to climate and land-use changes: Saint-François
River, Southern Québec, Canada, Hydrolog. Sci. J., 57, 313–325, 2012.
</mixed-citation></ref-html>
<ref-html id="bib1.bib21"><label>21</label><mixed-citation>
Schumacher, R. S. and Johnson, R. H.: Characteristics of U.S. extreme rain
events during 1999–2003, Weather Forecast., 21, 69–85, 2006.
</mixed-citation></ref-html>
<ref-html id="bib1.bib22"><label>22</label><mixed-citation>
Steenhof, P. A. and Gough, W. A.: The impact of tropical sea surface
temperatures on various measures of Atlantic tropical cyclone activity,
Theor. Appl. Climatol., 92, 249–255, 2008.
</mixed-citation></ref-html>
<ref-html id="bib1.bib23"><label>23</label><mixed-citation>
United States Geological Survey (USGS): Get NHD data, available at:
<a href="https://nhd.usgs.gov/data.html" target="_blank">https://nhd.usgs.gov/data.html</a>, last access: 1 September 2016, 2016.
</mixed-citation></ref-html>
<ref-html id="bib1.bib24"><label>24</label><mixed-citation>
Waylen, P. R.: Modeling the effects of tropical cyclones on flooding in the
Santa Fe river basin, Florida, GeoJ., 23, 361–373, 1991.
</mixed-citation></ref-html>--></article>
