examining the impact of land use/land cover characteristics on flood losses
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Examining the impact of land use/landcover characteristics on flood lossesSamuel Brodya, Russell Blessingb, Antonia Sebastianc & PhilipBedientc
a Departments of Marine Sciences/Urban Planning, Texas A&MUniversity, 200 Seawolf Pkwy, Galveston, TX 77553, USAb Center for Texas Beaches and Shores, Texas A&M University atGalveston, Galveston, TX 77553, USAc Department of Civil and Environmental Engineering, RiceUniversity, Houston, TX USAPublished online: 06 Jun 2013.
To cite this article: Samuel Brody, Russell Blessing, Antonia Sebastian & Philip Bedient (2014)Examining the impact of land use/land cover characteristics on flood losses, Journal ofEnvironmental Planning and Management, 57:8, 1252-1265, DOI: 10.1080/09640568.2013.802228
To link to this article: http://dx.doi.org/10.1080/09640568.2013.802228
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Examining the impact of land use/land cover characteristics
on flood losses
Samuel Brodya*, Russell Blessingb, Antonia Sebastianc and Philip Bedientc
aDepartments of Marine Sciences/Urban Planning, Texas A&M University, 200 Seawolf Pkwy,Galveston, TX 77553, USA; bCenter for Texas Beaches and Shores, Texas A&M University
at Galveston, Galveston, TX 77553, USA; cDepartment of Civil and Environmental Engineering,Rice University, Houston, TX, USA
(Received 8 November 2012; final version received 1 May 2013)
Characteristics of the built environment and overall local-level land use patterns areincreasingly being attributed to greater surface runoff, flooding and resultingeconomic losses from flood events. Specific configurations of impervious surfaces andland cover may be as important to determining a community’s flood risk as baselineenvironmental conditions. This study addresses this issue by statistically examiningthe impacts of adjacent land use and land cover (LULC) on flood damage recorded onparcels within a coastal watershed in southeast Texas. We analyse empirical models toidentify the influence of different LULCs surrounding over 7900 properties claiminginsured flood losses from 1999–2009. Results indicate that specific types ofsurrounding LULCs impact observed flood losses and provide guidance on howneighbourhoods can be developed more resiliently over the long term.
Keywords: flood losses; land use; land cover; Texas; resilience
1. Introduction
Flooding and associated flood impacts have long been linked to urbanisation and the
proliferation of impervious surfaces (Anderson 1970; Hall 1984). Numerous researchers
have examined and quantified the impact of urban development, particularly as it relates
to surface runoff, using small experimental plots, hydrological simulations and watershed
level land use changes (Tourbier and Westmacott 1981; Tong 1990; Im, Brannan, and
Mostaghimi 2003). The advent of computer software, such as SWMM, GIS L-THIA,
HEC-HMS, HEC-RAS, etc. has made the prediction of runoff from land use change even
more widespread. In contrast, little observational and empirical research has been
conducted on the role of surrounding land use/land cover (LULC), particularly using the
parcel unit of analysis. Even less work has been done to quantify the amount of property
damage incurred as a result of LULC patterns. Increasingly, changes in the built
environment and local-level land use configurations are attributed to flooding and
resulting economic losses from flood events (Brody et al. 2011). Specific patterns of
impervious surfaces and land cover may contribute to a community’s flood risk as much
as baseline environmental conditions (Brody, Kim, and Gunn 2013). We pose the
following research question: do LULCs affect flood losses on adjacent properties and if
so, which types have the most impact?
Our study addresses this question directly by statistically examining the impacts of
*Corresponding author. Email: [email protected]
� 2013 University of Newcastle upon Tyne
Journal of Environmental Planning and Management, 2014
Vol. 57, No. 8, 1252–1265, http://dx.doi.org/10.1080/09640568.2013.802228
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adjacent LULC on flood damage recorded on parcels within the Clear Creek watershed in
southeast Texas. Specifically, we analyse spatially-weighted linear regression models to
identify the influence of multiple LULC categories within a 0.5 mile buffer surrounding
over 7900 properties claiming insured flood losses from 1999–2009. Control variables
measuring various environmental, socio-economic and built environmental
characteristics within the watershed statistically isolate the effect of each LULC category
on property damage from floods. Results provide insights for local planners, developers
and homeowners on the degree to which different land use patterns affect flood impacts
and how neighbourhoods can be developed more resiliently over the long term. Our study
is intended to complement and extend previous work on development and flooding by
taking a less conventional approach to predicting the impacts of LULC.
1.1. Land use/land cover and flood impacts
A major factor when considering the relationship between LULC and flooding is the
amount of impervious surfaces incorporated into development patterns. Conversion of
natural landscapes to urban or suburban developments can reduce the functionality of
hydrological systems (Wheater and Evans 2009). Large areas of impervious surface can
reduce rainfall infiltration into the soil and increase surface runoff into nearby streams
and rivers (Paul and Meyer 2001; Gill et al. 2007). For example, Arnold and Gibbons
(1996) asserted that stormwater runoff within a drainage basin will nearly double with
only a 10–20% increase in impervious surfaces. More recently, White and Greer (2006)
found that as impervious surface in the Penasquitos Creek watershed in southern
California expanded from 9 to 37%, total runoff was amplified approximately 200% over
their study period (1973 to 2000). Overall, empirical and simulated studies suggest that
increased surface runoff from impervious surface coverage is an important aspect of
flood risk because it can translate into heightened frequency and severity of flooding in
vulnerable areas.
Impervious surfaces are also implicated in increased peak discharge (Brezonik and
Stadelmann 2002). Under these conditions, the lag time between the centre of
precipitation volume and runoff volume is shortened so that floods peak more rapidly
(Hirsch et al. 1990). When rainfall is unable to infiltrate into the soil, rapid onset of
floods can occur because runoff enters water bodies more quickly (Hsu, Chen, and Chang
2000; Hey 2002). For example, Rose and Peters (2001) found that peak discharge
increases approximately 80% in urban catchments containing greater than 50%
impervious area. Similarly, Burns et al. (2005) noticed a 300% increase for a basin with
an impervious area of only 11% when examining mean peak discharges for 27 storms in
the Croton River Basin in New York.
Finally, the resulting increase in surface runoff and peak discharge from development-
based land uses exacerbate human losses. For example, Brody et al. (2007a) found that an
increase in impervious surfaces coincided with a significant increase in stream flow
exceedances over a 12-year period across 85 coastal watersheds in Texas and Florida. A
subsequent study of 37 coastal counties in Texas found that each square metre of
impervious surface added to the landscape translated into approximately $3602 of added
property damage caused by floods per year from 1997–2001 (Brody et al. 2008).
Adverse impacts from floods do not stem solely from the percentage of impervious
surfaces present within the landscape, but the intensity with which they are clustered.
That is, the pattern and form of development becomes important for predicting flood
losses over time. For example, a low density, sprawling suburban setting with 25%
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impervious cover may result in a different degree of impact compared to a high-intensity,
clustered urban development pattern with 85% paved surfaces. Indeed, our previous
research showed that, even when controlling for multiple environmental and socio-
economic variables, the type of development pattern within counties/parishes along the
Gulf of Mexico coast significantly affects the degree to which communities report
property damage from floods (Brody et al. 2011). Perhaps unexpectedly, jurisdictions
with large amounts of spatially-focused, high-intensity development patterns claimed, on
average, reduced amounts of property damage. This finding supports the efficacy of well-
defined urban cores associated with smart growth approaches to development. It appears
that older, denser urban development is generally situated away from areas exposed to
flooding (such as the floodplain), so this development pattern can lead to more resilient
local communities over the long term. In addition, high density urban areas may be more
likely to have in place a co-ordinated system of flood mitigation infrastructure that can
appropriately handle large amounts of runoff. In general, urbanised communities have
greater resources and commitment to provide municipal-level drainage and flood
protection infrastructure. Finally, dense urban cores are more likely comprised of multi-
storey buildings where only the ground floors may flood.
In contrast, increasing percentages of low-intensity development greatly exacerbate
property damage from flooding. This result supports the argument that outwardly
expanding, low-density development patterns can compromise hydrological systems and
amplify surface runoff by spreading-out impervious surfaces across a watershed (Brody,
Kim, and Gunn 2013). This form of development is also more likely to infringe upon
flood-prone areas that were previously left untouched.
Another type of LULC that has become an increasingly important factor in predicting
flood impacts is based on the presence (or loss of) naturally-occurring wetlands. A large
amount of simulated and observational work has been conducted on mostly rainfall-based
flooding, where wetlands help to reduce flooding and associated losses because of their
ability to hold, store and slowly release accumulated runoff (Bullock and Acreman,
2003).
Both anecdotal and empirical research suggest that wetlands may reduce or slow
precipitation-based flooding (Mitch and Gosselink 2000; Lewis 2001). Some research
also suggests wetlands reduce the impact of storm surge caused by hurricanes (Costanza
et al. 2008). Empirical research in Texas and Florida indicates the value of naturally
occurring wetlands in reducing the adverse impacts of floods. When controlling for
multiple socio-economic and geophysical contextual characteristics, Brody et al. (2008)
found that the loss of wetlands across 37 coastal counties in Texas from 1997 to 2001
significantly increased the observed amount of property damage from floods. On average,
wetland alteration as measured by the number of approved permits added over $38,000 in
property damage to a jurisdiction per flood. A companion analysis for every county in
Florida showed similar results (Brody et al. 2008). In this case, the alteration of wetlands
increased the average property damage per flood by over $400,000. Given the evidence,
we expect that wetland loss will exacerbate property damage from floods even over
larger study areas, such as the Gulf of Mexico coast.
Other types of LULC may also influence the extent of flood loss reported at the parcel
level. For example, modern agriculture operations are thought to affect surrounding
properties in terms of flood impacts. Soil compaction from ploughs and heavy machinery,
loss of vegetative cover and rapid drainage off properties are all implicated in increasing
surface runoff and lead to potential surrounding flood losses (O’Connell et al. 2007;
Wheater and Evans 2009). In particular, reduction in soil infiltration rates and water
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storage capacity can significantly increase overland flow into adjacent areas (Carroll et al.
2004). In their long-term study of south-east England, Boardman, Evans, and Ford (2003)
found a significant relationship between the presence of autumn-sown cereal fields and
local ‘muddy floods’. It should be noted that muddy floods stemming from agricultural
land uses for the most part adversely impact off-farm properties.
Forest lands surrounding a property may also play a role in determining the extent of
damage experienced during a flood event. On the one hand, forested areas lack
impervious surfaces that reduce infiltration and expedite runoff into local streams. Trees
and woody plants also help to slow and trap runoff, reducing flood peaks more so than
other types of land cover (McCulloch and Robinson 1993). On the other hand, forest
lands along the coast (particularly in the Gulf of Mexico and similar environments) can
be located in low-lying areas comprised of floodplains. Houses situated adjacent to or
within forested bottom-lands are extremely vulnerable to flooding regardless of LULC
composition.
Finally, open space, such as parks and local preserves may reduce adverse impacts to
houses located on adjacent parcels. Open space land use usually contains much less
impervious surface compared to other development types, such as commercial or high-
density residential. With the presence of ball-fields and playgrounds, drainage is usually
well-maintained and runoff appropriately captured. Finally, developed open space is
most often integrated into the spatial fabric of local neighbourhoods such that their flood-
reducing effects will be more evident. Gill et al. (2007), for example, found that under
the right conditions, green space can significantly reduce surface run-off in Manchester,
England.
One of the major limitations of the research cited above is the spatial scale at which it
has been conducted. Conclusions drawn from both observational and simulated data were
made at a watershed or jurisdictional (city or county/parish) level. Lumped hydrologic
models, such as HEC-HMS (USACE 2000), which are used to explicitly simulate
watershed hydrology, lack the ability to capture small variations in LULC on flooding at
the parcel level, while distributed models, such as SWMM (Huber and Dickinson 1988),
are limited in their application to large watersheds by data availability and computational
power. Recent studies have used hydrologic modelling to examine the impact of Low
Impact Development technologies on peak flow and runoff volumes in small watersheds
or neighbourhoods (Willams and Wise 2006; Deitz and Clausen 2008), but few have
directly examined the impact of adjacent LULC on flooding at the parcel level using
statistical methods. We hope our research outlined below offers an important initial step
in examining flood impacts at the site level.
2. Research methods
We selected as the study area the Clear Creek watershed, which is located 20 miles south
of Houston, Texas. This 197 square mile area is situated adjacent to Galveston Bay on the
Gulf of Mexico coast and encompasses the four counties of Galveston, Brazoria, Harris
and Fort Bend (Figure 1). The watershed is drained primarily by Clear Creek and
associated tributaries. Clear Creek itself is a tidally influenced bayou that terminates as it
enters Clear Lake, which then opens into west Galveston Bay. The Clear Creek
Watershed typifies the Gulf Coast physical environment, with very little topographic
relief, large percentages of floodplain area, wide floodplain boundaries and exposure to
frequent storm events. Over the study period, from 1999 to 2009, both multiple small-
scale and major flooding impacted the Clear Creek Watershed. For example, Tropical
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Storm Allison in 2001 dropped over 18 inches of rain across the study area, becoming the
costliest tropical storm in US history. Overall, significant increases in residential
development in vulnerable areas over the last decade have resulted in large amounts of
property damage caused by repetitive flooding events.
2.1. Concept measurement
Flood damage, the dependent variable for the study, was measured as the dollar amount
(contents plus building damage) of claims paid per household under the National Flood
Insurance Program (NFIP). The NFIP is administered through FEMA (Federal Emergency
Management Agency) to provide flood insurance to residents and businesses. In
participating communities, residential structures within the 100-year floodplain with a
mortgage are required to purchase flood insurance. While insured loss does not cover all
damage incurred from a flood event, the market penetration of NFIP policies is so high
within our study area that these loss figures more accurately reflect overall flood damage
than in regions where the NFIP is less prevalent. The dollar value of individual insured
claims for both building contents and structures was aggregated at the parcel level and
log-transformed to better approximate a normal distribution (see Table 1). Out of
approximately 31,000 policies purchased, 7942 made claims during the study period. Each
claim was geocoded using the centroid of the parcel and analysed in a Geographic
Information System (GIS).
LULC variables were derived from the NOAA C-CAP dataset using Landsat
Thematic Mapper data at 30 metre resolution from both 2001 and 2006. Because
comparable remote sensing datasets for the study area were available for only these two
years, we tied LULC variables to the nearest claim, temporally. For example, a flood-
damaged parcel in 2002 would be tied to the 2001 dataset, while a loss in 2007 would be
Figure 1. Clear Creek watershed study area.
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Table1.
Conceptmeasurement.
Measurement
Source
Range
Mean
Standarddeviation
Dependentvariable
Floodloss
Logged
NFIP
claimsforfloodlosses
from
1999–2009
NFIP
0–13.56
7.70
4.24
AbsoluteNFIP
claimsforfloodlosses
from
1999–2009in
USdollars
NFIP
$0–780,798.7
$33,999.61
$53,642.67
Landuse/landcovervariables
Highintensity
development
80%þ
impervioussurfacewithin
0.5milebuffer
NOAA,CoastalChange
&AnalysisProgram
0–47.61
5.32
5.49
Medium
intensity
development
50–79%
impervioussurfacewithin
0.5milebuffer
NOAA,CoastalChange
&AnalysisProgram
0–84.60
26.96
19.82
Lowintensity
development
21–49%
impervioussurface
within
0.5milebuffer
NOAA,CoastalChange
&AnalysisProgram
0–57.01
21.09
10.36
Developed
open
space
Green
space;<20%
impervioussurface
within
0.5milebuffer
NOAA,CoastalChange
&AnalysisProgram
0–68.57
11.53
9.29
Agriculture
Croplandswithin
0.5milebuffer
NOAA,CoastalChange
&AnalysisProgram
0–87.70
3.70
7.89
Forest
Deciduous,evergreen,andmixed
forestcover
within
0.5milebuffer
NOAA,CoastalChange
&AnalysisProgram
0–38.41
7.09
7.56
Grass
>80%
herbaceousvegetation
within
0.5milebuffer
NOAA,CoastalChange
&AnalysisProgram
0–30.94
2.03
2.18
Scrub
<20%
shrubsless
than
16feettall
within
0.5milebuffer
NOAA,CoastalChange
&AnalysisProgram
0–13.55
1.03
1.71
Barren
Earth
material;<20%
vegetation
cover
within
0.5milebuffer
NOAA,CoastalChange
&AnalysisProgram
0–7.73
0.10
0.29
Palustrinewetland
Non-tidalforested,scrub/shrub,
andem
ergentdominated
vegetation
NOAA,CoastalChange
&AnalysisProgram
0–43.39
4.24
6.67
Estuarinewetland
Tidalforested,scrub/shrub,
andem
ergentdominated
vegetation
NOAA,CoastalChange
&AnalysisProgram
0–22.25
0.85
2.67
Environmentalcontrolvariables
Elevation
Feet
NationalElevationDataset
0–19
6.69
4.59
Precipitation
Inches
per
month
PRISM
0.02–21.47
13.62
5.14
Structuralcontrolvariables
Property
value
Assessedtaxvalue
County
taxoffice
0–5.56eþ
07
219,395
1,122,545
Ageofstructure
Yearbuilt
FEMAPolicy
Data
1900–2009
1972.61
88.19
Floodplain
Position
In-outof100-yearfloodplain
FEMAQ3data
0/1
0.54
0.49
Spatiallag
Nearestneighbourspatialautocorrelation
FEMANFIP
data
�0.63-4.05
0.008
0.611
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attributed to the 2006 data. We extracted and analysed the following previously classified
11 variables: high (contains 80–100% constructed materials), medium (50–79%
constructed materials), and low intensity (21–49% constructed materials) development;
developed open space (less than 20% constructed materials), agriculture, grassland,
palustrine wetland, estuarine wetland, forest, scrub and barren land (for more
information, see www.noaa.gov/landcover).
High intensity development consists primarily of concrete, asphalt and other
constructed materials accounting for 80 to 100% of the total cover for each 30�30 meter
pixel. This variable signifies heavily built-up urban centres and large constructed surfaces
in suburban and rural areas. Medium intensity development consists of constructed
materials covering 50–79% of total area. This variable captures multi and single-family
housing areas, especially in suburban neighbourhoods (neither high nor medium intensity
variables distinguish between single or multi-storey buildings). Low intensity
development includes areas with a mixture of constructed materials and large amounts of
vegetation or other cover. Constructed materials for this variable account for 21–49% of
the total area, and commonly includes single-family housing areas, especially in rural
neighbourhoods. The developed open space variable contains some constructed
materials, but consists mostly of managed grasses or low-lying vegetation planted in
developed areas for recreation, erosion control or aesthetic purposes. This variable
captures public parks, ball fields and other recreational use areas. Constructed surfaces
account for less than 20% of total land cover.
Agriculture was measured by combining cultivated crops intensely managed for
production and pasture areas consisting of grasses planted for livestock grazing or hay
crops. Grassland measures areas dominated (over 80%) by herbaceous vegetation. These
areas are not subject to intensive management such as tilling, but can be utilised for
grazing livestock. We combined the following three land classifications to derive the
forest variable: deciduous, evergreen and mixed forest. Trees in each category are over
16 feet tall and greater than 20% of the total vegetation cover. The scrub land cover
variable contains areas dominated by shrubs less than 5 metres tall, with shrub canopy
typically accounting for greater than 20% of total vegetation. This variable includes tree
shrubs, young trees in an early successional stage or trees stunted by environmental
conditions. Barren land cover was measured as areas of bedrock, mines, gravel pits and
other accumulations of earth material. In general, vegetation for this class accounts for
less than 10% of total cover. Finally, we measured two types of wetlands land cover
types within the study area. First, palustrine wetlands were measured by combining three
remote sensing classes: tidal and non-tidal forested, scrub/shrub and emergent dominated
vegetation. Second, we included in our analysis an estuarine wetland variable comprised
of the same three vegetation types, but only for tidal areas. This variable better captures
wetlands associated with the direct coastline influenced by saltwater. It should be noted
that the study area entirely comprises clay-based soils.
The percentage area for each LULC was calculated within a 0.5 mile buffer around
each parcel incurring flood loss during the study period. We selected a 0.5 mile buffer as
the most reasonable distance surrounding LULC will impact a specific parcel within the
Clear Creek watershed. We based this measurement decision by measuring the average
diameter of developed patches across the study area. Using this procedure, we
determined that a 0.5 mile buffer would best represent the run-off effects of surrounding
LULC. In addition, we took into account the spatial resolution of our data, local
topography and drainage network. We also conducted a sensitivity analysis using buffer
distances of 0.25 and 1 mile, which yielded the same overall results.
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Several contextual control variables were also measured and analysed in the statistical
models. Under structural characteristics, the mean assessed value (based on local tax
records for 2011, the date closest to the study period when all properties were available)
for each damaged parcel was calculated as a key economic control variable. Multiple
parcels in the county tax record were missing assessment values. In these instances, we
dropped the observation for the dataset. Assessed value was also used in an alternate
specification of the dependent variable (insured loss/assessed value), but did not yield
significantly difference results than including this variable as a separate independent
statistical control. The year in which each structure was built was also incorporated into
the statistical models. The year of construction ranged from 1900 to 2009. As a measure
of risk exposure, we also calculated whether a parcel was in or out of the 100-year
floodplain using FEMA Q3 data. Fifty-five per cent of all claims were located outside of
the floodplain.
We also included several environmental baseline controls. Traditionally, the biggest
predictor of flooding and flood damage is precipitation. We measured this variable based
on the annual rainfall amounts recorded in the PRISM (Parameter-elevation Regressions
on Independent Slopes Model) Climate Group dataset. Monthly precipitation data were
mapped at a scale of 2.5-arcmin (approximately 4 � 4 km grid cells) normal and joined
to the flood damaged parcel with the data time period corresponding to the month of the
claim. Precipitation estimated for each cell is an average over the entire area of that cell.
It is important to note here that the vast majority of flood claims happened during long
duration storm events that accounted for nearly all of the rainfall that occurred during an
entire month. This rainfall pattern is common along the Gulf Coast and will, for the most
part, mask any potential inaccuracies due to the temporally low resolution dataset. That
said, PRISM datasets are considered to be the highest-quality spatial climate data
currently available. Elevation of each property was measured using 1/9 arc-second
(approximately 3 � 3 metre grid cells) LIDAR data from the National Elevation Dataset.
The average elevation is approximately 22 feet, which illustrates the low-lying
topography of the Texas coastal plain.
2.2. Data analysis
We analysed the data using linear regression models to explain the variation in insured
flood losses across the study area. A Global Moran’s I statistic revealed the presence of
significant (p <0.000) spatial-autocorrelation in the dependent variable. To correct for this
bias, we estimated the spatial lag using a nearest-neighbour function. The lag variable was
calculated based on eight nearest neighbours of flood damage parcels. Sensitivity analysis
and model fit suggested eight nearest-neighbours was the most appropriate statistical
threshold. Once created, we included the spatial lag variable in the regression model as a
statistical control. Diagnostics also revealed the presence of heteroskedacticity in the data,
leading us to estimate the model with robust standard errors. No other statistical biases
were found. Both non-standardised and standardised (‘beta’) regression coefficients were
reported for regression models. Beta coefficients are useful to compare the relative strength
of various predictors within a single regression model. Because the beta coefficients are
measured in standard deviations, instead of the original units of the variables, they can be
effectively compared to one another. Essentially, the betas are the coefficients one would
obtain if the outcome and predictor variables were all transformed standard scores before
running the regression. Thus, the coefficient for the jth regressor indicates how many
standard deviations y would change given a 1-standard deviation change in xj.
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3. Results
Over the 11-year study period, we mapped and analysed across the Clear Creek watershed
9792 NFIP-based flood damage claims totalling over $356 million (in 2009 dollars), most
of which stemmed from structural losses. Over 50% of these losses occurred outside of
the 100-year floodplain. The average loss per claim across the watershed was $36,585
($26,902 for building and $9683 for contents) (for more information on descriptive
statistics, see Brody et al. 2013).
As shown in Table 2, baseline environmental contextual controls behave as expected.
Parcels located at higher elevations (note the average for the study area is only 6.7 feet
above sea level) incurred significantly less property damage (p < 0.001). Elevation
proved to be an especially important variable for mitigating flood loss during surge based
events, such as Hurricane Ike in 2008. Precipitation is by far the most powerful predictor
variable in the model (aside from the spatial lag), where increasing amounts of rainfall
result in increased property damage (p < 0.001). Together, environmental controls
explain approximately 10% of the variation in the dependent variable.
The four structural control variables by comparison almost double the explanatory
power of the statistical model. On average higher assessed property values (more
expensive homes) correspond with less insured damage. This result may seem counter-
intuitive, but more expensive homes are usually located in developments with high
quality drainage systems and their owners are more likely to invest in mitigation
alternatives, such as elevating lower floors, sum pumps, drains, etc. Over the long term,
these modifications can result in a significant reduction in the adverse impacts associated
with floods. The age of the structure does not have a statistically significant impact on
insured flood loss. Newer homes may have better building codes or drainage systems, but
they tend to be located in more flood-prone areas that were originally considered
undesirable. As expected, the position of a structure in relation to the 100-year floodplain
has a major effect on the likelihood of flood damage. Those properties located outside the
floodplain are less exposed to flood risk and therefore report significantly less (p < 0.001)
property damage from flooding events. While over half of the total number of flood
claims is located outside of the floodplain, a significant dollar amount of loss remains
within these boundaries. Finally, the spatial lag control based on the strong presence of
spatial autocorrelation within the dependent variable is significant, where increasing
spatial clustering of claims results in greater amounts of flood loss.
Specific types of land use and land cover surrounding flood-impacted parcels also
affect the amount of insured damage incurred by property owners. Built environment
land uses have a particularly strong effect, indicating the intensity of neighbourhood
development is important when considering resiliency to floods. For example, increasing
percentages of high intensity development lead to greater dollar amounts of flood damage
(p < 0.001). Within the Clear Creek Watershed, these areas consist of large shopping
centres located off major highways. Even with drainage infrastructure, the large amount
of impervious surfaces exacerbates runoff and associated flooding. However, surrounding
low intensity development characterised by sprawling, low-density residential
development patterns has a far greater positive effect on property damage caused by
floods. Based on the standardised betas (standardised coefficients in the regression
model) shown in Table 2, the average impact on flooding is over three times greater
under low intensity development patterns than high intensity. In contrast, neighbourhoods
dominated by medium intensity land use development (50–79% imperviousness) are
significantly less likely to experience large amounts of flood loss (p < 0.001). These
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Table2.
Regressionmodelsexplaininginsuredfloodlosses,1999–2009.
Model1
Model2
Model3
Variable
bt
beta
seb
tbeta
seb
tbeta
se
Elevation
�0.217
�19.49��
��0
.233
0.0111
�0.0601
�5.20��
��0
.0647
0.0116
�0.0642
�4.5
���
�0.0691
0.0143
Precipitation
0.2523
26.23��
�0.3038
0.0096
0.249
27.42��
�0.2998
0.0091
0.2906
28.73��
�0.3499
0.0101
Assessedvalue
0�2
.13�
�0.0243
00
�1.15
�0.0215
0Yearbuilt
�0.0011
�1.31
�0.0216
0.0008
�0.0009
�1.15
�0.0182
0.0008
Floodplain
�0.8302
�9.54��
��0
.0964
0.087
�0.9407
�10.45��
��0
.1092
0.09
Spatiallag
2.6368
31.71��
�0.3707
0.0831
2.3668
27.65��
�0.3327
0.0856
Highintensity
dev.
0.0346
3.59��
�0.044
0.0096
Medium
intensity
dev.
�0.0118
�4.84��
��0
.056
0.0024
Lowintensity
dev.
0.0569
12.47��
�0.143
0.0046
Open
space
�0.0145
�2.59��
�0.0325
0.0056
Agriculture
�0.002
�0.22
�0.0037
0.0094
Grassland
�0.0736
�2.62��
�0.0386
0.0281
Forest
�0.0294
�0.33
�0.005
0.09
Barren
0.1039
0.63
0.0073
0.1662
Scrub
0.1032
2.28�
0.0423
0.0453
Wetlandpalustrine
�0.0475
�4.73��
��0
.0706
0.0101
Wetlandestuarine
�0.0131
�0.78
�0.0085
0.0169
Constant
5.6332
41.82��
�0.135
7.2997
4.43��
�1.648
5.7974
3.59��
�1.6134
N7942
7942
7942
R-squared
0.0936
0.2341
0.2551
Adjusted
R-squared
0.0911
0.2334
0.2534
Notes:
� p<0.05;��p<0.01;��� p
<0.001.
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areas usually consist of fairly dense, more recently-built suburban developments, such as
master-planned communities. Finally, the presence of surrounding developed open space,
which has the least amount of impervious cover compared to the other built environment
land uses, significantly reduces the amount of recorded flood damage (p ¼ 0.01). Parks
and recreation areas which comprise mostly green space and vegetation seem to provide
a buffer against negative flood impacts.
In terms of what we consider undeveloped LULC, agricultural operations surrounding
a flooded property have a negative, but statistically insignificant effect on the dependent
variable. Large percentages of adjacent grassland, however, do decrease observed
property damage from floods, where p > 0.01. Herbaceous plants with non-woody stems
may be enough to capture surface run-off without causing ponding. Forest, barren and
scrub land covers, which make up a very small amount of total area, have a negligible
impact on flooding. Finally, naturally-occurring wetlands within the study area seem to
play an important role in influencing the degree of property damage from floods.
Estuarine wetlands located primarily along the coastline do not have a significant effect.
However, freshwater palustrine wetlands surrounding a property significantly attenuate
flood losses (p < 0.001). In fact, this wetland type has the strongest flood damage
reduction effect among all LULCs in the model. This result corroborates previous work
examining the relationship between wetlands and flooding (Brody et al. 2007b).
4. Discussion
Our statistical results indicate that the local configuration of land use plays an important
role in predicting the amount of property damage caused by floods at the parcel level.
Although the overall explanatory power of LULCs is low compared to baseline
environmental and structural characteristics, several categories nevertheless have a
significant impact on the amount of adjacent flood losses.
Specifically, the intensity of development based on the percentage of impervious
surface within a 30-metre grid has the most profound statistical implications when
considering flood risk reduction. A surrounding built environment pattern dominated by
medium intensity development typical of planned suburban communities appears to have
the greatest effect or magnitude on reducing the amount of insured losses incurred across
the watershed. These developments typically have better on-site drainage mechanisms as
well as neighbourhood level stormwater infrastructure, such as retention/detention ponds
with higher capacity to offset the increases in surface runoff. Because this pattern of
development also includes interspersed areas of vegetation (including parks and golf
courses), there is also more opportunity to incorporate green space for flood control. This
argument is supported by our finding that developed open space land use decreases
property damage from floods. Local parks, playing fields and other recreational facilities
consist primarily of green space that can act as a storm buffer to surrounding properties.
Parks also often double as runoff detention areas to hold and slowly release water on to
adjacent parcels. Open space land use can be strategically sited within neighbourhoods
not just to provide recreational opportunities, but also to reduce the adverse impacts of
localised flooding.
In contrast, low intensity, more sprawling development patterns significantly increase
flood losses. In fact, this development category has the strongest overall statistical effect of
all LULCs in our model, where a percentage increase in surrounding low-intensity
development translates into, on average, approximately $1734 in additional property
damage caused by floods. Low-density development patterns can compromise hydrological
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systems and amplify surface runoff by spreading out impervious surfaces across a
watershed, placing more structures and residents at risk from flooding over a larger area.
Furthermore, unlike well-established urban centres, low-density suburban and ex-urban
communities are less likely to have adequate storm drainage systems and other
infrastructure to accommodate increase increased surface runoff. Drainage in these areas is
often the responsibility of an individual subdivision (land divided into pieces for the
purpose of development), which could simply convey runoff offsite through hardened
channels and exacerbate downstream flood impacts. Finally, sprawling residential
development on the periphery of older urban areas and outside extraterritorial jurisdictions
(ETJs) (government exercised authority outside it normal boundaries) may be more likely
to encroach on floodplains that were originally left as open space or low-impact uses.
Despite the expectation that agricultural land uses will exacerbate flood losses, our
results show a negative, albeit statistically insignificant relationship. Closer investigation
of agricultural operations within the Clear Creek watershed reveals that only a fraction
are cultivated crops involving active tilling and soil compaction. Almost all of the
agricultural acreage is classified as pasture or hay fields which produces far less surface
runoff that may negatively impact adjacent parcels. In general, we find that more passive
agricultural uses can be successfully integrated into a residential setting without causing
flood damage to surrounding properties during a storm event.
Our results also support the notion that more naturally occurring land cover, such as
grass and forestland, help reduce flood-related losses. Once we controlled for elevation,
both these surrounding land covers significantly reduce reported property damage
throughout the study period. Grass and forest cover are effective for capturing and
slowing surface runoff before it reaches residential parcels. Forest cover has a stronger
effect, most likely because it contains more woody materials that help retard and retain
overland flow.
Finally, our results corroborate previous work at larger spatial scales supporting the
flood attenuation properties of naturally occurring wetlands (Highfield and Brody 2006;
Brody et al 2008). In particular, palustrine wetlands consisting of freshwater
environments significantly reduce observed flood losses over a variety of storm types. It
is the second-most powerful LULC variable in our analysis and should be considered an
important flood mitigation tool when considering the location of future development. In
contrast, we did not find any statistical evidence that estuarine wetlands provide flood
damage reduction functions, even for surge-based events. The estuarine wetlands in our
study area comprise salt marsh, which may not suppress surge compared to other
vegetation types, such as mangroves.
5. Conclusion and future research
Overall, our study finds that the patterns with which we develop and use the landscape
have important implications when establishing flood resilient communities. Planners and
decision makers should factor in the local pattern of LULC when designing flood
reduction programmes and be more strategic when deciding where future development
should take place. Local land uses should also be coupled with regional structural
mitigation strategies, such as dikes and levees. Our study focuses on a coastal watershed
in upper Texas, but the findings should translate to other similar coastal settings
nationally and internationally. For example, planners in countries with traditional land
use patterns, such as England, Australia and the Netherlands, can gain insight from our
results on the factors contributing to flood loss.
Journal of Environmental Planning and Management 1263
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While this study is one of the first to examine flood losses over a large number of
individual parcels, it should be considered only a first step in a more thorough
investigation of the topic. First, our analysis only considers one watershed and
approximately 9700 parcels. Future research should expand the study area to include
multiple watersheds in different coastal settings. This approach would enable researchers
to compare across different watershed settings, not just specific parcels. Second, we only
examine the percentage of specific LULCs within a 0.5 mile buffer around each damaged
property. Additional work should be done to determine the specific mixture of land uses
and their synergistic effects on impacting flood losses. Adjacent development patterns,
including connectivity, may also be important variables to consider. This line of inquiry
will provide more information on how to plan for and develop more flood resilient
communities. Third, future analyses should include additional proximity and site-specific
variables. For example, measuring a property’s distance from the floodplain, coastline
and other physical features will help better explain the relationship between residential
location and flood vulnerability. Fourth, future study should include additional control
variables to further isolate the effects of LULC, if the data are available, including
drainage system characteristics, structural and non-structural mitigation measure, soil
properties, etc. Finally, future research should investigate the type of flooding event (e.g.
precipitation or tidal-based) and its impact on the characteristics of loss over time.
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