An Analysis of the Geographic Distribution of Flash Flood

Events across the Southeastern United States

# Jeffrey C. Dobur

NOAA/National Weather Service

Southeast River Forecast Center

Peachtree City, Georgia

National Weather Association Digest

Submitted: March 2006

> **Abstract**
>
> Flooding contributes to more than 100 deaths each year across the
> United States with most deaths due to flash flooding. Though most
> flash floods are caused by intense rainfall, the magnitude of a flash
> flood is dramatically affected by pre-existing physical factors such
> as ground cover, topography and soil moisture. This study looks at two
> of these pre-existing physical factors by examining the relationship
> between the number of flash flood events within a county to both the
> county population density and mean county slope.
>
> Results showed that with increasing county population density there is
> an increase in the average annual number of flash flood events. With
> increasing mean county slope there is also an increase in the average
> annual number of flash flood events. A composite factor was developed
> to account for both population density and mean slope together. This
> factor when compared to the average annual number of flash flood
> events indicates that densely populated areas within hilly to high
> terrain are the most susceptible to flash flooding. It is the hope
> that these results will highlight hydrologically vulnerable areas that
> are prone to flash flooding in the southeastern United States. With
> this improved understanding, National Weather Service (NWS) forecaster
> situational awareness will increase, leading to more accurate and
> timely flash flood warnings.
>
> **1. Introduction**
>
> The National Weather Service (NWS) defines a flash flood event as a
> flood which occurs within six hours or less of the causative event.
> (NWS, 2005) This fast occurring type of flood is often a threat to
> life and property. In fact, flash flooding is the convective
> storm-related event across the United States that is associated with
> the most fatalities (Doswell et al., 1996). For this reason, the NWS
> has greatly improved flash flood prediction through better forecasting
> tools and techniques during the past decade. These advances have come
> mainly in the improvement of heavy precipitation forecasting, a key
> component of flash flood prediction. However, the challenge of flash
> flood forecasting is made more difficult by the interaction of the
> meteorology with hydrology. A given precipitation event's chance of
> producing a flash flood are dramatically affected by such factors as
> soil moisture, the size of the drainage basin, the topography of the
> basin, the amount of urban use within the basin, etc. Thus, a flash
> flood event is many times a result of the combined effects of a
> meteorological event with a particular hydrological situation (Doswell
> et al, 1996).
>
> The primary objective for the study is to examine the number of flash
> flood events in counties across the southeastern United States
> relative to county population density (PD) and/or a mean county slope.
> It is theorized that counties with high population density and/or high
> mean county slope should have a greater potential for flash flood
> events than counties that are rural and/or flat. The intent of the
> author is to show a relationship between the number of flash flood
> events to urbanization and topography. It is the hope that this paper
> will enhance forecaster awareness to an inherent flash flood potential
> caused by these two factors.
>
> **2. Methodology**
>
> Flash flood event data was taken from the National Weather Service
> Verification Database for the period 1994 through 2004 inclusive. A
> total of 6566 flash flood events across 713 counties were utilized in
> the study covering an eight state area (Alabama, Florida, Georgia,
> Mississippi, North Carolina, South Carolina, Tennessee, and Virginia)
> within the southeastern United States. This total area encompassed
> 374,905 square miles. The number of flash flood events per county was
> normalized by the county area per 1000 square miles. County population
> density data was calculated from Environmental Systems Research
> Institute (ESRI) Geographic Information Systems (ESRI, 2000) data. To
> account for variations in county area that may influence the number of
> events, counties less than 100 square miles were excluded from the
> analysis which consisted of 36 municipality counties in Virginia.
> Slope data was calculated using ESRI Arc/Info Geographic Information
> Systems utilizing USGS 1-degree Digital Elevation Map (DEM) data.
>
> Counties were grouped into three groups for analysis (Fig. 1). Average
> population density for the dataset was 138 people per square mile with
> a standard deviation of 255. Thus the author established that the
> "Urban" county group included all counties whose population density
> exceeded 393 people per square mile. The "Suburban" county group
> included all counties whose population density was equal to or between
> 138 to 393 people per square mile, while the "Rural" county group
> included all counties with population density less than 138 people per
> square mile.
>
> ![](media/image1.jpeg){width="2.8090277777777777in"
> height="2.5493055555555557in"}
>
> **Fig. 1.** Population Density Groups utilizing 1999 Census data
> provided through ESRI Arcview.
>
> Mean county slope was calculated using the USGS 1-degree DEM data in
> ArcGIS with Spatial Analysis Extension. Average county slope was
> calculated at 0.754 with a standard deviation of 0.990. The data was
> classified into three different groups (Fig. 2). The "High Terrain"
> group included counties whose mean slope was greater than 1.744. Based
> on the statistics and knowledge of the topography, the author
> established that counties with a mean slope equal to or between 0.400
> and 1.744 were classified as "Hilly Terrain", while counties with mean
> slope less than 0.400 were considered "Flat Terrain".
>
> ![](media/image2.jpeg){width="3.0541666666666667in"
> height="2.5597222222222222in"}
>
> **Fig. 2.** Mean County Slope Factor derived from USGS elevation data
> using ArcGIS.
>
> Utilizing both the mean county slope along with the county population
> density, a merged field was created called the "Composite Group" (Fig.
> 3). By cross-referencing the population density groups and the mean
> county slope groups, a total of 9 groups would be defined (three
> population density groups by three mean slope groups). Only 8 of these
> groups were possible in this study as no counties meet the criteria
> for both "Urban Group" and "High Terrain", and thus no "Urban-High"
> group was created (Table 1).

+-------------------------+----------------------+--------------------+
| > **Category**          | > **PD (100 sq       | > **Slope**        |
|                         | > miles)**           |                    |
+-------------------------+----------------------+--------------------+
| > ***Urban-Hilly***     | > \> 394             | > 0.400 -1.744     |
+-------------------------+----------------------+--------------------+
| > ***Urban-Flat***      | > \> 394             | > \< 0.400         |
+-------------------------+----------------------+--------------------+
| > ***Suburban-High***   | > 138-393            | > \> 1.744         |
+-------------------------+----------------------+--------------------+
| > ***Suburban-Hilly***  | > 138-393            | > 0.400 -1.744     |
+-------------------------+----------------------+--------------------+
| > ***Suburban-Flat***   | > 138-393            | > \< 0.400         |
+-------------------------+----------------------+--------------------+
| > ***Rural-High***      | > \< 138             | > \> 1.744         |
+-------------------------+----------------------+--------------------+
| > ***Rural-Hilly***     | > \< 138             | > 0.400 -1.744     |
+-------------------------+----------------------+--------------------+
| > ***Rural-Flat***      | > \< 138             | > \< 0.400         |
+-------------------------+----------------------+--------------------+

> **Table 1.** Composite Groups and criteria
>
> ![](media/image3.jpeg){width="3.3069444444444445in"
> height="2.8368055555555554in"}
>
> **Fig. 3.** Composite Groups
>
> The average number of county flash flood events per 1000 square miles
> was calculated for each of the population density groups, mean county
> slope groups, and the composite groups. Correlation coefficients
> comparing the relationship between the average annual number of flash
> flood events per 1000 square miles and the three groups were also
> calculated. The intervals depicted within the correlation analysis
> were computed by using every tenth percentile within each of the three
> groups to provide for an even breakdown in the groups for comparison
> to flash flood events. To further support the analysis of the
> relationship between flash flood events to both slope and population
> density, the number of county flash flood events per 1000 square miles
> will be shown geographically providing a comparison to known urban and
> high slope areas in the southeastern United States.
>
> **3. Results**
>
> *a. Population Density Groups*
>
> When comparing flash flood events against county population density
> groups, data showed that the urban county group had the highest
> density of flash flood events with an average of 3.0 flash flood
> events per 1000 square miles per year through the 11-year period. The
> suburban county group observed 2.3 events per 1000 square miles per
> year with the rural county group at 1.6 events per 1000 square miles
> per year (Fig. 4).
>
> ![](media/image4.wmf){width="3.0in" height="2.3993055555555554in"}
>
> **Fig. 4.** The annual number of flash flood events per 1000 sq miles
> by population density group.
>
> Correlation coefficients between county population density intervals
> and the annual number of county flash flood events per 1000 sq miles
> for those intervals were computed to be 0.94 (Fig. 5).
>
> ![](media/image5.wmf){width="3.002083333333333in"
> height="2.401388888888889in"}
>
> **Fig. 5.** The average annual number of flash flood events per 1000
> sq miles compared to population density intervals with linear
> trendline.
>
> Results show that there is a strong positive relationship between the
> average annual number of flash flood events and county population
> density. The urban county group represents nearly twice as many flash
> flood events as the rural county group. This connection between
> increasing population density and the number of flash floods may be
> attributed to several reasons. We must keep in mind that an event must
> be observed and reported, and thus counties with denser populations
> may have an increased chance of both observing and reporting flash
> floods. Another reason may be linked to the fact that with increasing
> population density, an increase in impervious surface is often the
> result. The effects of added impervious surface and urbanization on
> the flood hydrograph include increased total runoff volumes and peak
> flow rates. The changes in hydraulic efficiency associated with
> artificial channels, curbing, gutters, and storm drainage collection
> systems typical of an urban setting increase the velocity of flow and
> magnitude of flood peaks. (Chow, 1988) These hydrologic
> characteristics of an urban environment affect the hydrologic response
> and can lead to flash flooding. A third basis for the connection
> between population density and the increase in flash flood events may
> be credited to an enhancement in rainfall intensity within urban
> areas. Shepherd and Burian (2003), as well as Huff and Changnon early
> on (1973), found elevated rainfall rates over and downwind of urban
> areas in their studies.
>
> *b. Mean Slope Factor Groups*
>
> The comparison of the county mean slope and the annual number of
> county flash flood events per 1000 square miles indicated that
> counties with mountainous and hilly terrain tend to have more flash
> flood events than those counties with flat terrain. The high terrain
> group though the 11-year period (1994-2004) had on average 3.3 events
> per 1000 square miles per year. The hilly terrain group observed 2.1
> events per 1000 square miles per year, while the flat terrain group
> reported only an annual average of 1.2 events per 1000 square miles
> (Fig. 6).
>
> ![](media/image6.wmf){width="2.997916666666667in"
> height="2.3944444444444444in"}
>
> **Fig. 6.** The average number of flash flood events per year by mean
> county slope group.
>
> Correlation coefficients between the mean county slope intervals and
> the average annual number of flash flood events per 1000 square miles
> for those intervals were computed to be 0.88 (Fig. 7). The likely
> reason for the link between mean county slope and the number of flash
> flood events is due to the fact that higher sloped areas have a
> greater potential for faster flow velocities due to gravity. These
> faster flow velocities can contribute to a faster responding channel
> or stream which can lead to a flash flood event. In addition, soils in
> steeper terrain tend to be of shallow depth due to erosion and other
> physical mechanisms. The shallow, rocky soils can promote more
> efficient runoff as the soil will be less capable for both
> infiltration and vegetation growth. Moreover, although most flash
> floods are caused by deep convection induced by synoptic forcing or
> convective heating, a strong updraft can also be caused or enhanced by
> orographic forcing. Chen et al found landscape variability at both
> large and small scales significantly affects the location and
> intensity of moist convection (2001). In the Big Thompson Canyon flash
> flood in 1975 and the Rapid City Flash Flood in 1972, both studied by
> Maddox et al (1978), orographic lifting in the eastern slopes of the
> Rockies played a notable role in producing rainfall sufficient for
> flash flooding.
>
> ![](media/image7.wmf){width="2.997916666666667in"
> height="2.4006944444444445in"}
>
> **Fig. 7.** The average annual number of flash flood events compared
> to mean county slope intervals with linear trendline.
>
> *c. Composite Groups*
>
> Comparison of the "Composite Groups" to flash flood events showed that
> the "Suburban-High" group observed the most flash flood events with an
> average annual 4.7 flash flood events per 1000 square miles. The
> "Rural-Flat" group observed the least number of events with an average
> of 0.9 flash flood events per 1000 square miles annually (Fig. 8).
>
> ![](media/image8.wmf){width="2.997916666666667in" height="2.39375in"}
>
> **Fig. 8.** The average number of flash flood events per year by the
> population density-mean county slope composite groups.
>
> In examining the correlations between the "Composite Groups" and flash
> flood events, population density and mean county slope were normalized
> to the corresponding average value. These two normalized values were
> then multiplied together to calculate a "Composite Group" factor. The
> "Composite Group" factor was then used to determine correlations
> between the composite group and the number of flash flood events. In
> comparing the every tenth percentile intervals of the "Composite
> Group" factor and the average annual number of flash flood events per
> 1000 square miles, correlations coefficients were 0.95, indicating a
> strong positive relationship (Fig. 9). More importantly, the
> correlation between the merged factors and the number of flash flood
> events is slightly greater than the correlation when comparing county
> population density or mean county slope alone.
>
> ![](media/image9.wmf){width="2.997916666666667in"
> height="2.3958333333333335in"}
>
> **Fig. 9.** The average annual number of flash flood events compared
> to the population density-mean county slope composite factor intervals
> with linear trendline
>
> *d. Geographic Distribution of Flash Flood Events*
>
> To further support the results of the study, an assessment of the
> geographic distribution on the number of county flash flood events per
> 1000 square miles was completed. The resulting map depicts elevated
> numbers of flash flood events in many of the major urban areas of the
> southeast with emphasis on urban areas in the depicted high terrain
> urban areas. Urban areas including Raleigh-Durham,
> Greenville-Spartanburg, Atlanta, Nashville, Birmingham, Mobile,
> Jackson, Memphis, Jacksonville, and Tampa show elevated numbers of
> flash flood events relative to surrounding areas (Fig. 10).
>
> ![](media/image10.jpeg){width="3.423611111111111in"
> height="3.370138888888889in"}
>
> **Fig. 10.** Spatial distribution of the average annual number of
> flash flood events per 1000 square miles by county across the
> southeastern United States from 1994-2004. Red color shades indicate
> areas of elevated numbers of flash flood events.
>
> **4. Conclusion**
>
> It is apparent that the number of flash flood events by county in the
> southeast United States can be influenced by both population density
> and mean county slope. This association between flash flooding to both
> mean county slope and population density provides an important
> awareness to inherent flash flood hydrologic vulnerabilities. For
> forecasters at the National Weather Service Forecast Offices who are
> responsible for issuing flash flood warnings, having an understanding
> of the areas which are hydrologically vulnerable can greatly enhance
> situational awareness to a potential flash flood event. This enhanced
> awareness can often lead to increased warning time and thus mitigation
> and protection of lives and property. In addition, at the NWS River
> Forecast Centers, daily flash flood rainfall guidance, used by NWS
> warning forecasters as criteria to issue flash flood warnings, should
> also be representative of the relative hydrologically vulnerable
> areas. Further studies into identifying higher resolution hydrologic
> vulnerable areas would provide even greater awareness, and could be
> incorporated into NWS warning programs such as the Flash Flood
> Monitoring Program, or FFMP.
>
> **Acknowledgements**
>
> The author would like to thank Reggina Cabrera from the National
> Weather Service Southeast River Forecast Center (SERFC) for her
> extensive review of this paper, Mark Love from SERFC for his guidance
> on obtaining mean slope data, and Wylie Quillian, John Feldt and Brad
> Gimmestad from SERFC and James Noel of the National Weather Service
> Ohio River Forecast Center (OHRFC) for their suggestions on
> improvements.
>
> **Author**
>
> Jeff Dobur currently serves as a Senior Hydrologist at the National
> Weather Service Southeast River Forecast Center (SERFC) in Peachtree
> City, Georgia. Prior to this, he has served as a Hydrologic Analysis
> and Support Meteorologist at SERFC, a Public and Aviation Forecaster
> at the NWSFO Peachtree City, GA, a Hydrologist with the Ohio River
> Forecast Center (OHRFC) and a Meteorologist at the NWSFO in
> Wilmington, Ohio, in addition to working as an Assistant to the State
> Climatologist in Ohio. His educational background includes a BS in
> Atmospheric Sciences from the Ohio State University (1997) and
> additional graduate course work in Hydrology/Civil Engineering from
> the University of Cincinnati.
>
> ***References***
>
> Chen, Fei, T. Warner, and K. Manning, 2001. Sensitivity of orographic
> moist convection to landscape variability: A Study of the Buffalo
> Creek, Colorado, flash-flood case of 1996. . *J. Atmos. Sci.,* **58**,
> 3204-3223.
>
> Chow. V.T., Maidment. D.R., and Mays, L.W., 1988. Applied Hydrology.
> 152 p.
>
> Doswell, C. A., III, H. E. Brooks, and R. A. Maddox, 1996: Flash flood
> forecasting: An ingredients based methodology. *Weather Forecasting,*
> **11,** 560--581.
>
> ESRI,, 2000: ARC/INFO Version 8.0. Environmental Systems Research
> Institute, Inc.
>
> Huff, F. A. and S. A. Changnon, Jr., Precipitation modification by
> major urban areas, Bull. Amer. Soc., 54, (12), 1220-1232, 1973.
>
> Maddox, Robert A., L. R. Hoxit, C. F. Chappell, and F. Caracena, 1978:
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>
> NWS, 2005: NWS Directive 10-950, http://www.nws.noaa.gov/directives/.
>
> Shepherd, J.M., and S.J. Burian, 2003: Detection of urban-induced
> rainfall anomalies in a major coastal city. *Earth Interactions,*
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