International Journal of Geosciences, 2013, 4, 891-897
http://dx.doi.org/10.4236/ijg.2013.45083 Published Online July 2013 (http://www.scirp.org/journal/ijg)
Modeling Urban Hydrology: A Comparison of New
Urbanist and Traditional Neighborhood Design Surface
Runoff
Christopher Andrew Day1, Keith Allen Bremer2
1Department of Geography and Geosciences, University of Louisville, Louisvil l e, USA
2Department of Geography, Texas State University-San Marcos, San Marcos, USA
Received April 30, 2013; revised May 31, 2013; accepted June 28, 2013
Copyright © 2013 Christopher Andrew Day, Keith Allen Bremer. This is an open access article distributed under the Creative Com-
mons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work
is properly cited.
ABSTRACT
Urban development generally leads to an increase in impervious cover resulting in a greater volume of surface runoff
following storm activity. However, the type of urban development in place strongly controls the degree of impervious
cover generated. Traditional neighborhood designs focus on a medium-to-low urban density spread over larger areas,
while new urbanist neighborhood designs incorporate more diversity by increasing urban density across smaller areas.
The purpose of th is study is to model and compare the poten tial surface runoff for two urban neighborh oods in Austin,
Texas-Circle C Ranch, a traditional neighborhood design, and Mueller, a new urbanist development for a 10-year
24-hour storm scenario. Potential surface runoff was calculated by layering various geospatial datasets representing the
physical characteristics of both study sites with in the Watershed Modeling System (WMS) to configure the HEC-HMS
runoff model. Results initially imply that the higher density new urban ist neighborhood significantly increases total an d
peak storm runoff compared to the traditional neighborhood. However, a greater number of residential units are avail-
able at Mueller over the same area as Circle C Ranch. When taking this into account the increased potential surface
runoff is negated at the new urbanist site. Although new urbanist neighborhoods will usually contain more residential
units than traditional developments when compared at the same scale, the higher urban density associated with these
neighborhoods demand the development of more effective stormwater retention systems to cope with a potential in-
crease in surface runoff.
Keywords: Urban Hydrology; New Urbanism; Runoff Modeling; Land Use
1. Introduction
Urban development affects the amount of potential sur-
face runoff generated during storms by changing the
amount of impervious cover across the landscape [1-3].
In addition to increasing surface runoff, urban develop-
ment also modifies the volume of groundwater recharge,
lowers water tables, increases peak discharge, and de-
creases base flow during drier periods [4,5]. These modi-
fications depend on the type of urban development in
place.
New urbanism is a type of sustainable development
that is designed to reduce automobile use, increase
walking and cycling, and increase the diversity of land
uses while incorporating traditional and new practices of
planning at all scales [6]. Moreover, new urbanism is a
type of low impact development (LID) that contains
elements such as cluster development and bio-retention.
LID can mitigate problems associated with storm water
runoff by increasing resilience and utilizing best man-
agement practices [7,8]. Traditional neighborhood de-
velopment (TND) is limited to the neighborhood scale
and incorporates traditional planning practices such as
large lot and single family zoning [9]. TND are not con-
sidered as LID unless further steps have been taken to
implement specific LID features. New urbanism is touted
as a more environmentally sustainable development than
TND, which will typically contain greater amounts of
impervious cover [10].
While research implies that LID does often reduce to-
tal stormwater runoff and increase the runoff lag time
when compared to TND [11-13], more research needs to
be carried out which compare neighborhoods of similar
C
opyright © 2013 SciRes. IJG

C. A. DAY, K. A. BREMER
892
size and scale in order to make further accurate assess-
ments of LID and their impact on stormwater runoff.
Several obstacles pertinent to stormwater runoff have
been noted concerning LID planning. Many current zon-
ing and regulatory statutes can hinder the implementation
of LID concepts and philosophies [14]. These features
include minimum street width for public services, con-
crete curbs and gutters, the absence of runoff collection
ponds due to public health concerns, and other elements
that may not fit into th e v isually p leasin g aesth etic d esign
[14]. As a result, a comparison of three urban neighbor-
hoods ranging from high-to-low density actually found
that the medium density neighborhood displayed the
longest peak run off lag times d ue to more effective us age
of stormwater retention systems [15].
An increase in geospatial and modeling capability has
increased the opportunity of analyzing urban develop-
ment impacts on stormwater runoff in recent years. Re-
mote sensing data coupled with geographic information
science (GIS) systems and runoff modeling software
have been used more frequently to study the interaction
between rainfall events and urban surfaces leading to
runoff [16-18]. The purpose of this research is to utilize
these techniques to model and compare the poten tial sur-
face runoff for two similar-sized new urbanist and tradi-
tional neighborhoods in Austin, Texas.
2. Study Area
The study area includes two neighborhoods, one new
urbanist, and one traditionally developed neighborhood
in Austin, Texas (Figure 1). Austin-Mueller (Mueller) is
a new urbanist neighborhood located in north-central
Austin approximately three miles from downtown Austin
on the site of the city’s old Robert Mueller airport.
Mueller is the most recent master-planned community in
Austin that focuses on new urbanism as a vehicle for
sustainability including a mixture of home types, sizes,
and price ranges. Circle C Ranch is a traditional neigh-
borhood development that originated in the late 1980s.
The neighborhood contains mostly single-family homes
that are situated on medium to large lots with traditional
planning practices in place [19].
Regarding physical characteristics that may impact
stormwater runoff, Austin receives, on average, 870 mm
precipitation annually [20]. The majority of this total
occurs in the months of April and May when violent
storms develop from Pacific cold fronts moving rapidly
across the south-central Texas region, resulting in severe
flooding [21]. Another important factor concerning run-
off is the soil which heavily controls the amount of infil-
tration-to-surface-runoff ratio during storm events. Soils
may be classified into one of four hydrologic groups (A,
B, C, D) that reflect their drainage capability. Group A
soils are characterized by high infiltration rates to give
low runoff potential following precipitation, while group
D soils have low infiltration rates to increase runoff po-
tential [22]. Soil coverage across both sites is typical of
the south-central Texas region.
Mueller is dominated by the Lewisville and Altoga se-
ries soils which range from well-to-moderately drained
silty-clay soils underlain by fractur ed chalk or limestone,
classified in the B-C soil hydrologic groups. Smaller in-
stances of the Houston Black and Patrick soil series are
also present at these sites classified into the moder-
ately-to-poorly drained B-D soil hydrologic grouping. At
Circle C Ranch, the Tarrant soil series dominates as a
stony-clay type soil (hydrologic group C) with the mod-
erately well-drained (C group) Speck series present to the
south and west of the site [22].
3. Methods
The methodology workflow incorporates a series of
geospatial data sources and techniques in order to calcu-
late potential surface runoff at both study areas (Figure
2). Land use/cover data were obtain ed for bo th sites from
1m resolution Digital Orthophoto Quarter Quad (DOQQ)
images from 2010. In order to directly compare the run-
off generated between the two sites, the larger Circle C
Figure 1. Study are as within Austin, Texas.

C. A. DAY, K. A. BREMER 893
Figure 2. Methodology workflow.
Ranch site was trimmed down to match the area of
Mueller, using road boundaries within th e sub -div ision as
the new boundaries for Circle C Ranch. This gave two
images covering an equal area of 0.7 km2 with the Muel-
ler site containing 751 residential units and Circle C
Ranch 511.
The imagery was initially loaded in ArcMap before
performing a supervised classification technique using
the maximum likelihood algorithm. Following a visual
inspection of the images, four land cover classes were
identified as urban/impervious, forest, grass, and surface
water (Figure 3). The classification accuracy was veri-
fied by rechecking the classified images with the original
imagery. The classified images were then loaded into the
Watershed Modeling System (WMS) software and com-
bined with a digital elevation model (DEM) to calculate
slope and hydraulic length (the longest flow path across
each site, L) for both sites. Finally, soil coverages, con-
taining the soil hydrologic groups for the soils at both
sites, from the State Soil Geographic Database (STA-
TSGO) were loaded into the model in order to calculate
infiltration losses during storm activity, similar to previ-
ous research techniques [23] (Figure 4).
Surface runoff was calculated using the HEC-HMS
model for a 10-year 24 hour storm scenario based on the
surface and soil hydrologic group cover for each site.
The HEC-HMS model was originally developed by the
US Army Corps of Engineers (US ACE) as a lumped-
parameter model, capable of routing surface flow into a
series of drainage basins towards an outlet [23,24]. Va-
rious methods are available within HEC-HMS to de-
termine runoff versus infiltration. The Soil Conservation
Service (SCS) method was chosen for this study based on
its success at modeling surface runoff in other urban
runoff studies [18,25,26], and the availability of the nec-
essary physical data at both study sites in Austin. It is
also ideally suited for modeling drainage areas of less
than 2000 acres (~8 km2) [27].
Figure 3. Landcover classification from DOQQ imagery for
Mueller (left) and Circle C Ranch (right).
Figure 4. DEM and soil coverage for Mueller (left) and Cir-
cle C Ranch (right).
The SCS method calculates initial precipitation losses
(the initial abstraction) and ultimately the volume of wa-
ter available for surface runoff based on soil perme-
ability and land cover by prescribing a predetermined

C. A. DAY, K. A. BREMER
894
“curve number” to each surface and soil hydrologic group
cover (Eq uat ions (1) and (2)).

2
PIa
PIaS

 Q (1)
Q = runoff depth;
P = 24-hour storm precipitation depth;
Ia = initial abstraction (0.2S);
S = infiltration/retention losses (Equation (2)).
1000 10
CN




S (2)
CN = curve number for areal soil and land cover.
Higher curve numbers result from land cover and soil
hydrologic groups that allow decreased infiltration, re-
sulting in a greater volume of water made available for
surface runoff. By overlaying the classified land cover
data with the soil hydrologic group coverage data, a
composite curve number could be generated for each site
(Equation (3)) [24] .
comp
ii
i
A
CN
CN
A



(3)
CNcomp = composite curve number;
Ai = drainage area of each area with uniform land and
soil coverage;
CNi = curve number of each Ai.
The curve numbers used in Equations (2) and (3) for
soil hydrologic groups and various land cover surfaces
are given in Table 1.
Runoff volumes were then generated to produce hy-
drographs which determined the peak runoff in cubic
meters per second (cms) and lag time between peak pre-
cipitation and runoff at each site. The 24-hour storm pre-
cipitation depth in equation 1 was taken from a 10-year
24 hour storm scenario for the Austin area based on the
availability of local historical hydrological data for mo-
del calibration later (Table 2). Within the WMS model-
ing software the SCS method initially estimates basin lag
time using the physical basin parameters in Equation (4),
(Table 3):
0.7
0.8 1
1900
S
TL Y

lag  (4)
Tlag = basin lag time;
L = hydraulic length;
S = infiltration/retention losses (Equation (2));
Y = mean slope.
Calibration of the HEC-HMS model is normally
achieved by comparing the modeled runoff with ob-
served runoff obtained from a US Geological Survey
streamgauge at the outlet of the modeled catchment site
[23,29]. This was not directly possible as neither site
Table 1. Example runoff curve numbers for various land
covers by soil hydrologic group [27].
Land Cover Soil Hydrologic Grou p
A B C D
Impervious Surfaces 98 98 98 98
Woods/Forest 30 55 70 77
Grass 39 61 74 80
Surface Water 0 0 0 0
Table 2. Approximate precipitation depths for a 10-y ear 24-
hour storm in the Austin area [28].
Time period Precipitation depth (mm)
15 min 35.6
1 hour 68.6
2 hours 86.4
3 hours 94.0
6 hours 109.2
12 hours 121.9
24 hours 152.4
Table 3. SCS model parameters gene r ated by WMS.
Site Hydraulic
length, L (m)ªInfiltration losses, S Slope, Y (%)
Basin lag
time, Tlag
(hr)
Mueller 994 1.6 1.8 0.5
Circle C
Ranch 1020 3.2 2.3 0.62
aAlthough m eters are given, the equation re qu ires L input in feet.
contained an active stream gauge for model comparison
located at the site outlets. To account for this, calibration
of the runoff model took place by comparing the peak
flow generated from a 10-year 24-hour storm with the
observed peak flow from the nearest active stream gauge
located approximately 2.4 km downstream from the Muel-
ler site (Boggy Creek USGS# 08 158035). In this case th e
model ran using the initial cond itions calculated b y HEC-
HMS from the physical site data, before adjusting the
key parameter, initial abstraction, to match the propor-
tional observed peak runoff generated at Boggy Creek.
This took into account the larger catchment area of the
Boggy Creek gauge location. Initial peak runoff was
overestimated, and subsequent lag times underestimated,
as a result of low initial abstraction parameter values
generated by the model. This was corrected by in-
creasing the initial abstraction value until the peak
runoff value at Mueller proportionally matched the
value at the Boggy Creek site, similar to the approach
adopted by previous urban runoff modeling research

C. A. DAY, K. A. BREMER 895
[23,29]. Adjustment of the initial abstraction value for
Circle C Ranch followed based on the lower CN value
for that site (Table 4).
4. Results
The Mueller site contained a much greater proportion of
urban/impervious cover, totaling 50% compared to the
Circle C Ranch coverage of 36% (Figure 3, Table 5).
The impervious area of the Mueller ne ighborhood is also
clustered around a central area, surrounded by non-im-
pervious surfaces, which typifies new urbanist develop-
ments.
The Circle C Ranch site displays a more uniform
spread of all surfaces, with impervious surfaces distrib-
uted across the entire site. While Mueller does display
17% more grass coverage, the majority of the Circle C
Ranch site is covered in forest, to taling 51% compared to
Mueller’s 16%. Mueller also includes 4% surface water
coverage in the form of two ponds located to the south
and northwest of the site.
Regarding runoff, initially the two hydrographs pro-
duced by the model appear similar, but closer inspection
reveals three key differences between Mueller and Circle
C Ranch in response to the 10-year storm scenario (Fig-
ure 5). Firstly, the peak runoff increased by 64% from
0.99cms at Circle C Ranch to 1.55 cm at the Mueller site.
Secondly, the storm lag time displayed a lower value by
31 minutes at Mueller, which equated to a 59% decrease
in time from Circle C Ranch storm response. Lastly, the
runoff coefficient (proportion of rainfall to runoff), in-
creased by 5.9% at Mueller, again highlighting that a
greater proportion of rainfall during the storm becomes
surface runoff at this location. The results suggest that
the new urbanist site at Mueller actually generates the
greater volume of stormwater runoff (42,000 m3 vs.
35,700 m3 at Circle C Ranch). Furthermore, with both
Table 4. Curve numbers and initial abstraction values used
in model.
Site Default Initial
Abstraction (Ia) Calibrated Initial Abstraction (Ia) Curve Number
Mueller 0.2 0.26 86
Circle C Ranch 0.2 0.32 78
Table 5. Proportion of surface cover at Circle C Ranch and
Mueller sites.
Surface cover Circle C Ranch Mueller
Impervious 36% 50%
Forest/Woods 51% 16%
Grass 13% 30%
Water 0% 4%
sites displaying similar physical properties in terms of
area, relief, hydraulic length and soil hydrologic group
characteristics, the greater extent of impervious surface
coverage compared to the traditional site at Circle C
Ranch is chiefly responsible for this.
However, it must be addressed that new urbanist de-
velopments focus on clustered development practices
that have a higher density of residential development
than a traditional urban development practice over a
similar area. In this case Mueller contains 751 residential
units compared to Circle C Ranch’s 511, a total differ-
ence of 240 units over the 0.7 km2 area. Taking this into
account Circle C Ranch would theoretically generate a
greater volume of runoff at 69,863 m3 per 1000 units vs.
55,925 m3 per 1000 units at Mueller, a difference of just
under 14,000 m3. As a result Circle C Ranch and other
similar traditional urban developments, taken as a whole,
will likely generate a greater volume of surface runoff
than their new urbanist counterparts in terms of their total
footprint on the landscape.
Of further note are the landscaped reten tion systems in
place at the Mueller site which are designed to limit the
effects of stormwater runoff, practices that are often not
included across traditional developments. Bio-retention
ponds are key features of new urbanist developments
which aim to capture and store excess runoff following
storm events. Mueller has two such ponds in place, to the
north and south which have been aesthetically land-
scaped into the development blueprint. The DEM data-
sets used in this study do not capture any of these large-
scale landscaping changes implemented at the Mueller
site, assuming that the majority of stormwat er runoff w ill
follow the original topography and drainage patterns.
However the purpose of this paper was to investigate the
potential surface runoff generated from this kind of de-
velopment in comparison to a traditional neighborhood.
The fact that new urbanist sites will often cluster their
development in a bid to reduce the overall footprint of
the site means that without these kinds of retention sys-
tems in place a greater volume of runoff could potentially
be generated and lag times reduced following storm
events as seen in this study.
5. Conclusions
A modeling framework has been developed to analyze
the impacts of urban neighborhood design on storm run-
off for the city of Austin, Texas. By layering a series of
datasets that represent the physical landscape ( land cover,
soil, and relief) within the Watershed Modeling System
(WMS) the HEC-HMS runoff model has generated peak
runoff and storm lag times for a new urbanist and tradi-
tional neighborh ood. Th e results imply that wh en directly
comparing these types of urban design on a similar scale,
the new urbanist neighborhood has the propensity to

C. A. DAY, K. A. BREMER
896
Figure 5. Modele d r unoff hydrographs for Circle C Ranch (top) and Mueller (bottom).
generate larger peak flows and shorter lag times as a
function of the high density urban footprint associated
with this type of neighborhood. Consequently it is im-
perative that flood retention or reduction measures are
included in these neighborhood designs in order to miti-
gate the impacts of potential flooding both within and
surrounding these new urbanist neighborhoods. Further-
more, while new urbanist neighborhoods have LID ele-
ments designed within them to reduce runoff and pollut-
ants at a larger scale these results suggest more research
is needed to determine how well, at the smaller scale,
these elements work with other neighborhood designs
and to what level they reduce or increase pollutant run-
off.
The methodology employed in this research demon-
strates the potential of combining and manipulating a
series of datasets within GIS and modeling software to
ascertain the potential surface runoff generated within
urban areas at the sub-drainage basin scale. However
further research should also be conducted that compares
potential runoff output from infiltration abstraction me-
thods other than the SCS method as employed in this
research. Also with the increase in development of new
urbanist neighborhood s within US cities, similar research
may be conducted that compares the potential runoff
between these neighborhoods. Their non-traditional de-
velopment and design often makes them unique from one
another and thus could generate significantly different
runoff outputs from similar storm scenarios.
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