Remote Sensing of Environment 86 (2003) 385 – 400
www.elsevier.com/locate/rse
Satellite and ground-based microclimate and hydrologic analyses
coupled with a regional urban growth model
S. Traci Arthur-Hartranft a,*, Toby N. Carlson b, Keith C. Clarke c
a
Penn State University, P.O. Box 8048, Philadelphia, PA 19101, USA
Department of Meteorology, Pennsylvania State University, University Park, PA 16802, USA
c
Department of Geography, University of California at Santa Barbara, Santa Barbara, CA 93106, USA
b
Received 25 March 2002; received in revised form 25 September 2002; accepted 28 December 2002
Abstract
Urban development is shown to induce predictable changes in satellite-based measures of radiant surface temperature and
evapotranspiration fraction—as long as certain features of the development are known. Specifically, the vegetation changes that accompany
the development and the initial climatic state of the land parcel must be noted. Techniques are also developed for quantifying the effects of
urbanization on the surface hydrology at a watershed scale. Streamflow and precipitation data are related graphically in order to determine a
watershed’s general ratio of stormwater runoff to rainfall, along with any changes in the ratio over time. Four distinct runoff responses,
separated by season and antecedent moisture conditions, are distinguishable for a particular basin, with the response during the non-summer
months under typical antecedent moisture conditions the most representative of and responsive to land-use patterns. This particular runoff
response can be estimated from satellite-derived land cover patterns and certain physical attributes of a basin. These satellite-based
microclimate and hydrologic analyses are coupled with an existing urban growth model (SLEUTH). The SLEUTH urban growth model
simulates future development scenarios for a region of interest. The resulting changes in urban land use lead to the evolution of site-specific
climate and hydrology based on the scheme that is presented in this paper. This study, as well as related tools and bodies of knowledge, can
be used to broaden the scientific basis behind land-use management decisions.
D 2003 Elsevier Science Inc. All rights reserved.
Keywords: Urban environment; LANDSAT; Development; Hydrology; Modeling
1. Introduction
At the beginning of the 21st century, the modern metropolis has evolved into a vast urban field with multiple centers
(Spirn, 1984). The environmental impact of the land cover
change associated with this urbanization pattern is the basis
of the work presented in this paper. Local land cover, i.e.,
grass, concrete, soil, water, etc., largely dictates the energy
exchanges that occur between the earth and atmosphere and
thus, is one of the primary determinants of a site’s microclimate. The idea that an architect can design a building and
its immediate surroundings in order to achieve a desirable
microclimate is now a traditional one. Much less commonly,
* Corresponding author. Current affiliation: Lockheed Martin Remote
Sensing Systems Integration, P.O. Box 8048, Philadelphia, PA 19101,
USA. Tel.: +1-610-531-5592.
E-mail address: [email protected] (S.T. Arthur-Hartranft).
however, is the idea extended to an entire region. Individual
land-use decisions become integrated into meso-climatic
zones that mirror the form of urban development, but rarely
is the theory that a regional planner can ‘‘control the mesoclimate of broad zones of the city’’ put into practice
(Chandler, 1976). As late as 1996, despite many attempts
to model the effects of urban development on the local
climate, complete models for urban planning were unavailable and there was ‘‘still a striking gap between climate and
design’’ (Eliasson, 1996).
The first two authors, Carlson and Arthur (2000), took a
step towards closing this gap by using the technique of
multiple linear regression to formulate equations for predicting the effect of various urban development plans on the
micro-scale surface temperature and moisture. Data for the
analysis was obtained from a combination of satellite remote
sensing and surface climate modeling; the validity of the
parameters used (fractional vegetation cover, radiant surface
temperature, evapotranspiration fraction, surface moisture
0034-4257/03/$ - see front matter D 2003 Elsevier Science Inc. All rights reserved.
doi:10.1016/S0034-4257(03)00080-4
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S.T. Arthur-Hartranft et al. / Remote Sensing of Environment 86 (2003) 385–400
availability and percentage developed land) has been previously established (Arthur, Carlson, & Ripley, 2000; Carlson, Arthur, & Ripley, 1997; Gillies, Carlson, Cui, Kustas,
& Humes, 1997; Owen, Carlson, & Gillies, 1998). This
paper continues the work presented in Carlson and Arthur
(2000), as well as introduces a new parameter, stormwater
runoff, which specifically addresses surface hydrology.
Additionally, it presents a demonstration of the potential
to operationalize the research by coupling the microclimate
and surface hydrology results with a cellular automaton
urban growth model (SLEUTH) developed by the third
author (Clarke, Hoppen, & Gaydos, 1996).
The resulting regional urban planning model will enable
users, for the first time, to clearly incorporate surface
microclimatic and hydrologic changes into decision-making. The SLEUTH model is used to drive future development scenarios, while the start-up microclimate and surface
hydrology for the region of interest are determined from a
satellite-derived land cover map. As the SLEUTH model
predicts new growth and generates changes in land cover,
site-specific climate and hydrology evolve based on the
work presented here. The SLEUTH growth prediction is
thus advanced beyond a qualitative look at development
patterns and towards an environmental assessment of a
region’s future. Additionally, the quantitative information
derivable from multi-spectral satellite imagery is finally
presented in a form that is of use to policy makers.
2. Methodology
2.1. Microclimate
2.1.1. Background
The urban heat island has been described as ‘‘one of the
most clearly established examples of inadvertent modification of climate’’ (Roth, Oke, & Emery, 1989). When
detection of heat islands is based on thermal infrared
satellite and airborne data, the heat island intensity is
greatest during the day and least at night—the opposite of
results from studies based on air temperatures (Roth et al.,
1989). The use of a daytime radiant surface temperature to
monitor the impact of land-use change emphasizes urbanization’s influence on the surface energy balance, a feature
which also allows for the extraction of surface moisture
properties. Soil wetness is the atmospheric boundary condition second only to sea surface temperature in its impact
on climate; over warm continental areas during the spring
and summer, it is considered the most important factor
(Dirmeyer, Dolman, & Sato, 1999). Providing moisture
for evaporation, it increases the surface latent heat release
and thus limits the daily maximum temperature (Dai,
Trenberth, & Karl, 1999). Vegetation, with its ability to
transpire, also plays a large role in the distribution of energy
between the latent heat flux and the sensible heat flux.
When soil moisture is not limiting, higher levels of vegeta-
tion result in increased transpiration and redistribute the
energy fluxes towards substantially reduced Bowen ratios
and thus cooler and moister near-surface climates (Bounoua
et al., 2000).
These three physical parameters (radiant surface temperature, evapotranspiration/soil moisture, and vegetation) are
combined in the ‘triangle method’ as presented in the works
of Carlson, Capehart, and Gillies (1995), Carlson, Gillies,
and Perry (1994), Gillies and Carlson (1995), Gillies et al.
(1997) and Owen et al. (1998). The method conceptually
relates variations in satellite-derived radiant surface temperature and fractional vegetation cover, while coupling the
interpretation of their association to an inverse modeling
scheme.
2.1.2. Calculations from imagery
Radiant surface temperature (T) was derived from the
thermal band of the Landsat Thematic Mapper (TM) sensor.
At-sensor radiances were converted to apparent temperatures using an empirical form of Planck’s function. To
correct to surface values, MODTRAN (an atmospheric
radiative transfer model) was applied using a standard
mid-latitude summer sounding, the mean terrain altitude
and an estimate of atmospheric visibility (Kneizys et al.,
1996). An NDVI (normalized difference vegetation index)
was defined using the TM’s near infrared and visible red
wavelength bands. For these wavelengths, conversion of the
at-sensor reflectances to surface values was not necessary
since a scaled NDVI, as used here, is insensitive to atmospheric correction (Carlson & Ripley, 1997).
Scaling anchor points for both the radiant surface temperature (T) and the NDVI were determined using each image’s
T/NDVI scatterplot. Fig. 1, adapted from Carlson and
Arthur (2000), displays the recurring shape that is seen for
such plots—as long as the image used covers a large,
heterogeneous area and as long as it is taken during normal
sunlit drying conditions. If the image is acquired shortly
after rain or during a drought, the shape will be modified.
Once the cloud and water pixels are removed from Fig. 1,
the scatterplot resembles a truncated triangle. The radiant
surface temperature of any given pixel is hypothesized as
determined by the relative amounts of bare surface and
vegetation viewed by the sensor, as well as the surface
moisture conditions.
Scaled values, represented by N* and T*, are defined as:
N* ¼
NDVI NDVIo
NDVIs NDVIo
T* ¼
T Tmin
Tmax Tmin
ð1Þ
NDVIs is set at the value where the scatterplot folds over
and becomes flatter near the top of the distribution of points
and where it is assumed that NDVI has reached saturation at
100% vegetation cover. NDVIo defines the base of the
triangle where it is assumed bare soil is represented. Tmax
is the result of extrapolating the ‘‘warm edge’’ down to a
possible hottest surface, and Tmin is representative of the full
S.T. Arthur-Hartranft et al. / Remote Sensing of Environment 86 (2003) 385–400
387
Fig. 1. Scatterplot of radiant surface temperature and NDVI adapted from Carlson and Arthur (2000). An area of approximately 90 100 km for an AVHRR
scene near Philadelphia, PA is represented.
canopy temperature, generally similar to that of the air. N*
and T* thus each vary between 0 and 1. An N* of 0
represents bare soil and 1, full vegetation cover. A T* of 0
is typical of a surface at air temperature and 1, of the hottest
surface in an image. The scaled NDVI was then converted
to a physical index, fractional vegetation cover (Fr), defined
as the proportion of a pixel covered by vegetation and
related though Fr c N2* (Choudhury, Ahmed, Idso, Reginato, & Daughtry, 1994; Gillies & Carlson, 1995).
The final transformed scatterplots of fractional vegetation
cover (Fr) and scaled radiant surface temperature (T*) were
then compatible with the output from a soil – vegetation –
atmosphere transfer (SVAT) model (Carlson, 1986; Carlson,
Dodd, Benjamin, & Copper, 1981). The underlying constraint in this model is the balance of the energy fluxes at the
earth’s surface. Initialization was based on a ‘‘typical’’
morning sounding along with land surface parameters
representative of the study area. An equation for predicting
the fraction of net radiation (Rn) used in evaporative
processes (ET/Rn—ranging from 0 to 1) as a function of
Fr and T* was developed from the model output using
multiple regression analysis. The physical interpretation of
the T*/Fr scatterplot was used to set the domain for the
output of the SVAT model simulation. Contours of constant
ET/Rn were overlaid on the space defined by the T*/Fr
scatterplot by interpreting the defined ‘‘warm edge’’ (see
Fig. 1) as the upper limit to possible surface temperatures
due to dryness.
T*, Fr and ET/Rn were generated via satellite imagery in
order to monitor microclimate changes over time and their
possible correspondence with alterations in land use. Thus,
the Landsat TM images were georeferenced to a UTM map
base, each with a total root mean square error less than 1,
i.e., the average positional error was less than approximately
30 m. Each scene was then re-sampled to a 25-m grid using
the nearest neighbor criteria. To generate land cover maps
from the TM images, a maximum likelihood supervised
classification scheme was used. Six classes were clearly
separable: dense development (commercial), less dense
development (residential), bare soil, short vegetation, forest
and water. For use with the SLEUTH urban growth model,
however, classes were combined into four Anderson Level I
land cover categories: urban or built-up land, agricultural
land, forested land and water (Anderson, Hardy, Roach, &
Witmer, 1976). An error analysis of each satellite-derived
land cover map was completed by generating an error
matrix (Congalton, 1991). The overall accuracy and the
KHAT statistic from the error matrix were set at a threshold
of 80% for acceptance. Off-diagonal errors were used to
determine inter-class confusion and guide reclassification
efforts.
When microclimate and land use are combined, Table 1
shows that the average T* and ET/Rn values for a given land
class vary little from image to image. Note, however, that
for a given year, T* increases and ET/Rn decreases as the
land class changes from forest to agriculture to residential to
high density development—reflecting warming and drying.
Images were selected for clear days in the absence of clouds,
Table 1
Average ET/Rn and T* values for all of the pixels classified in each land
cover class for the given year of image acquisition
Land cover class
1987
1997
1987
1997
(mean ET/Rn) (mean ET/Rn) (mean T*) (mean T*)
Forest
Agriculture
(bare/vegetated)
Residential
High density
0.61
0.58
0.62
0.57
0.15
0.29
0.13
0.30
0.54
0.46
0.55
0.45
0.35
0.51
0.33
0.53
The sampled area was a 61-mile2 basin in southeastern Pennsylvania.
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rain and abnormal climatological conditions such as
drought, helping to assure that the only substantial, nonreversible surface climate changes observed over time were
due to urbanization.
2.2. Surface hydrology
2.2.1. Background
If urbanization is observed to affect the local energy
balance through its influence on the turbulent fluxes of
latent and sensible heat, the hydrologic balance must likewise be impacted. The water balance for an urban volume
can be expressed as:
p þ I ¼ r þ E þ DS
ð2Þ
where p and I serve as the system inputs—precipitation and
piped-in water, respectively—r is net runoff, E is evapotranspiration, and DS is the net water storage change. Since
the latent heat term in the energy balance equation is based
on the mass flux of water, E, the two balances are inextricably linked. The typical view is that urbanization leads to
a decrease in both evapotranspiration and storage and an
increase in runoff. Traditional stormwater management
tends to focus on the latter. Detailed computer models are
available that attempt to simulate the urban system of pipes,
gutters, sewers and storage ponds/reservoirs in order to
predict storm runoff with a focus on pre- and post-development conditions.
The work presented in this paper differs from these
runoff prediction models in that its goal is to develop an
index that can monitor change in the hydrologic environment in a manner compatible with the output from the
SLEUTH urban growth model. The method is essentially
what is known as ‘‘black box’’—the output, storm runoff, is
produced in response to the input, total storm precipitation,
without detailed consideration of the physical processes
involved (Dingman, 1994). As with Woodruff and Hewlett
(1970), the premise of this work was not ‘‘to probe delicate
relationships among obscure parameters that are difficult to
determine, but rather to identify physical attributes of watersheds that can be easily measured on available maps, yet
might readily indicate how the watershed handles its storm
precipitation.’’
Surface hydrology is defined as the spatial and temporal
storage and redistribution of rainfall as it falls on or enters into
the soil (Engman, 1997). The various physical paths the
rainfall may take collectively generate what is known as the
storm hydrograph, a plot of discharge vs. time during the
period when a stream or river is affected by precipitation. This
plot characterizes the hydrologic response of the basin. The
work presented in this paper closely follows Boughton (1986,
1987), which present a graphical technique for determining
the proportion of a watershed that contributes overland flow
for different storms, as well as Boyd, Bufill, and Knee (1993,
1994). Graphical analysis was used to determine if urban-
ization resulted in a greater proportion of the basin contributing to runoff for a given storm depth. Changes in the
contributing area were related to the general land-use types
and patterns that are predictable by the SLEUTH urban
growth model. The focus for this study was on the storm
runoff signal rather than total yield or low flows.
2.2.2. Study area and stream data methodology
Eleven watersheds and seven rain gauges were selected
in southeastern Pennsylvania. Site selection was limited by
the availability of daily mean discharge data from the United
States Geological Survey (USGS) and 24-h precipitation
data from the National Climatic Data Center (NCDC). The
selected basins also had to lie within cloud-free areas of the
Landsat TM scenes that were used for land cover mapping.
The sites were preferably small (less than about 60 mile2),
relatively urbanized, and exhibiting urban growth over the
study period—although some sites with no growth were
chosen as controls, and one basin with a drainage area of
324 mile2 was also included. All data was downloaded from
the internet at http://waterdata.usgs.gov/nwis/sw/ and http://
www.ncdc.noaa.gov. Discharge data was recorded in cubic
feet per second (cfs) and precipitation data in hundredths of
inches. All data was left in the units in which it was
acquired. Due to the empirical nature of the equations in
this study, it is necessary that these same units be used
throughout this paper, despite the convention for SI units.
Each watershed was checked for changes in baseflow during
the study period by examining October 1 daily discharge
values over time; no significant trends were found. This date
(the start of the water year) was chosen since it represents
the approximate point in time when the soil moisture should
be drawn down to its lowest annual value and flow is
generally supported by groundwater (Black, 1991).
Total storm runoff was related to total storm precipitation
for approximately 20 years worth of data in each basin. The
total storm runoff depth for each rain event was approximated by removing the previous day’s flow as baseflow and
then applying the storm’s daily mean discharge value to a
24-h period and dividing by the basin’s area. Negative
values, representing periods of drawdown in streamflow,
were set to zero runoff. This method assumed that the
stream’s response lasts only for the given day, so that the
flow returns to pre-storm levels within 24 h. It thus had the
potential to miss a substantial amount of runoff during any
recession flow that may occur after the primary day of the
rain event. This error should have been minimal, however,
since the majority of the basins studied were relatively
small. The 324-mile2 basin did display a general lag
between a day’s rain event and the discharge response.
The precipitation values for this site were thus moved
forward in time one day in an attempt to auto-correct the
data. An alternative technique to determine stormflow from
USGS daily mean discharge data was presented by Woodruff and Hewlett (1970) and is recommended for any similar
studies in the future.
S.T. Arthur-Hartranft et al. / Remote Sensing of Environment 86 (2003) 385–400
Additional manual processing was used in order to
reduce potential sources of error. First, events that were
contaminated by snowfall, as indicated by the NCDC 24-h
precipitation records, were automatically removed from the
analysis. A scan was then run for implausible events—that
is, days when the runoff generated was greater than the
rainfall. For the basins studied here, these events generally
displayed one of two scenarios: a heavy snowfall had
recently occurred or the rain event lasted for 2 or more
consecutive days, such that the tail-end precipitation
became associated with the main discharge response in
the data. If the event was the first rainfall after a previous
snow, it could have induced melting of the snowpack and
increased the runoff above the day’s actual precipitation
depth. Such events were removed since they involved
other physical processes than the straightforward rainfall – runoff relationship that was being sought. If the
rainfall was observed to span consecutive days, the daily
rainfall amounts and relevant discharge values were cumulated into one storm, with all runoff values calculated
relative to pre-storm conditions. For the remaining ‘inconsistent’ cases where neither of the above scenarios was
applicable, the data could often be corrected by replacing
presumed misrepresentative rainfall measurements with
data from a nearby site or, in the presence of particularly
strong spatial rainfall gradients, the event was removed
from the dataset.
Since the amount of runoff from any given storm will
depend highly on the soil moisture deficiency at the beginning of rainfall, the final rainfall –runoff pairs were stratified
by antecedent moisture conditions, using the antecedent
precipitation index (API), given by Kohler and Linsley
(1951) as:
APIi ¼ Pi þ 0:92APIiI
ð3Þ
The index for any given day, i, is equal to 0.92 of the
previous day’s value. The choice of this recession factor is
not critical, although values generally range from 0.85 to
0.90 over most of the eastern and central portions of the
United States (Kohler & Linsley, 1951). If rain occurs on
any given day, then the amount of rain observed is added to
that day’s index. Since the concern is the API at the start of
the rain event, each pair of rainfall – runoff values was
related to the previous day’s API.
Essentially, the API acts as a surrogate for a basin’s soil
moisture status, which integrates much of the land surface
hydrology by controlling the infiltration and surface runoff
processes (Engman, 1997). Histograms of all the rain
events’ API values suggested some natural cutoff points
for separating dry antecedent conditions from typical and
saturated conditions. API values less than 0.5 in. were
assumed to represent extremely dry conditions and those
greater than 3.0 in., extremely moist or saturated conditions.
All events in between were considered to be a collection of
typical scenarios.
389
The annual time series of discharge and precipitation
displayed a seasonal pattern, with summer events generating less runoff than non-summer events with the same API.
The effect that antecedent precipitation has on subsequent
storm runoff strongly depends on the extent to which the
precipitation is dissipated between storms through such
processes as evapotranspiration. To account for this, the
API recession factor could be set to vary throughout the
year, or, alternatively, a second variable—season—can be
introduced into the analyses. For this study, the rainfall –
runoff points were further stratified by season. Events
occurring between June 21 and October 1 were defined as
part of the summer regime, while those that occurred
outside this time period were designated as non-summer
events. These dates were selected based on an annual water
budget for southeastern New York (Black, 1991). Potential
evapotranspiration was noted to exceed the actual value
around mid-June, and a decrease in storage occurred until
near the end of September. The particular rainfall – runoff
relationships obtained in this study are specific to the
geographical region. For example, model simulations of
soil moisture regimes in Pennsylvania identified all of the
sites as lying within the region of the greatest summer
deficit (Waltman et al., 1997).
The rainfall – runoff pairs, stratified by both season and
API, were used to generate characteristic plots of runoff vs.
rainfall for each basin (Fig. 2). The plots were used to
determine the fraction of the basin that contributed to
streamflow during a storm event, as well as the initial
rainfall losses that must have occurred before runoff began.
If a basin is fully saturated from prior rain events, 100%
runoff will theoretically occur from both pervious and
impervious surfaces, and the slope of the runoff vs. rainfall
plot will be 1 and the intercept 0. Alternatively, a basin
entirely covered by impervious surfaces will produce a
similar graph. With this interpretation, the slope in Fig. 2
represents the fraction of the drainage basin that contributes
to runoff, and the intercept represents the depth of rainfall
that must occur before runoff commences (Boyd et al.,
1993). The separation of rainfall – runoff events by season,
and then further by API within each season, succeeded in
discriminating four rainfall – runoff lines, such that the
source area for runoff was less in the summer and in general,
for dry antecedent conditions. The least squares linear
regression best-fit line was used within each group to
estimate the contributing area.
2.3. SLEUTH urban growth model
On their own, the climatological and hydrological analyses are limited in function as far as their practical role in
urban planning. The ability to estimate changes in these
environmental parameters is most useful if a scenario for
the growth that drives these changes can be made. Since the
animation of urban growth alone is not capable of providing an environmental evaluation, coupling such work
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S.T. Arthur-Hartranft et al. / Remote Sensing of Environment 86 (2003) 385–400
Fig. 2. Visualization of the runoff vs. rainfall graphical method used to characterize a basin’s runoff response.
results in an enhancement of both efforts. This paper
presents one of the first attempts at using the SLEUTH
urban growth model as a basis for assessing the ecological
and climatic impacts of urban change and estimating the
sustainable level of urbanization in a region (Clarke,
Hoppen, & Gaydos, 1997).
The SLEUTH urban growth model has been calibrated
for and applied to datasets of the Baltimore– Washington
corridor and the San Francisco Bay area (Clarke & Gaydos,
1998; Crawford-Tilley, Acevedo, Foresman, & Prince,
1996; Kirtland et al., 1994). Based on cellular automata,
the model begins with a set of initial conditions and then
uses local rules to describe how each cell changes in
response to its neighbors. From these responses, complex
behavior emerges across the whole grid of cells (Theobald
& Gross, 1994). Calibration of the model uses historical
maps of land use and urban extent to determine the
coefficients that will guide the growth rules during a
predictive run. These decision rules and their resulting
behavior are based primarily on the physical factors that
control development patterns. A satellite-derived land cover
map of the study region provides the starting point for the
predictive run, and additional GIS-based layers such as
slope, reserve areas, water bodies, and road networks drive
the future development scenarios. Despite the use of guiding
growth coefficients, the model is not stagnant. It has the
ability to self-modify, meaning that different time periods
will be dominated by different growth behavior (Clarke &
Gaydos, 1998).
In this paper, the purpose of the use of the SLEUTH
model is not to generate a thorough and validated growth
run for a particular study site. The intent rather is to
present a demonstration of a proof of concept for making
the hydrology and climate analyses operational. The
result is not necessarily the production of definitive
predictions but rather the development of a useful tool
for scenario planning—a tool for imagining, testing and
choosing between possible futures (Clarke & Gaydos,
1998).
3. Results and discussion
3.1. Microclimate
In the work of Carlson and Arthur (2000), the equations
for predicting changes in scaled radiant surface temperature,
DT*, and evapotranspiration fraction, D(ET/Rn), due to
growth-induced land-use transitions were developed from
NOAA AVHRR and Landsat TM satellite analyses of
Chester County, Pennsylvania. The Landsat TM satellite
data was used strictly for land cover mapping and served as
the basis for determining the change in urban development
that occurred within each 1-km2 AVHRR pixel. The climate
parameters—temperature, vegetation and surface moisture—were all derived from the AVHRR data. For the work
presented in this paper, the AVHRR imagery was no longer
used, and instead, 1-km2 averages of TM-derived climate
variables formed the basis of the analyses.
A comparison of results from this new TM-based work
with the work of Carlson and Arthur (2000), DT* and DET/
Rn multiple regression equations demonstrate improved R2
values, as well as increased significance for individual
coefficients. However, the TM-based analyses do not identify the land class ‘‘near a large water body and natural
reserve area’’ as distinct. This was the class that, in the work
of Carlson and Arthur (2000), stood out as especially
sensitive to urbanization. In general, the sensor-based data
and equations in this paper and those by Carlson and Arthur
(2000) cannot be interchanged. For each sensor, the data
that was used to develop the equations had slightly different
ranges and inter-variable relationships, so one sensor’s
equation will not yield physical results when used with
the other sensor’s data. Overall though, the comparison
S.T. Arthur-Hartranft et al. / Remote Sensing of Environment 86 (2003) 385–400
391
Table 3
Initial conditions for use with the climate module in the SLEUTH urban
growth model
served to validate the original work. With the exception of
the one distinct initial land class, the similarity in the local
climate change predicted for various scenarios from these
completely independent analyses was encouraging.
Although based on the same techniques, one set of equations used 1-km2 averages of TM climate variables for
watersheds throughout southeast Pennsylvania, while the
other set of equations used AVHRR climate variables, a
different study site and different TM scenes for land cover
mapping.
When used with the SLEUTH urban growth model, the
DET/Rn and DT* predictions are no longer one-time calculations. The climate fields are updated each year that a landuse map is predicted based on any new growth that has
occurred, as well as the surface climate conditions from the
previous time step. Since the parameter that was predicted in
the last time step should not be used to generate a new
prediction in the present step, the form of the original
equations given in Carlson and Arthur (2000) was altered
slightly. The initial ET/Rn value (ET/Rn0) was removed from
the DET/Rn equation, and likewise, the initial T* value (T*0)
was removed from the DT* equation. The resulting final
equations for use with the SLEUTH urban growth model are
given in Table 2. The original equations used the surface
moisture availability (Mo) as an indicator of surface wetness
in the DT* equation. Also an output of the SVAT model,
isopleths of Mo can be overlaid on the T*/Fr scatterplot and
defined for each pixel in an image. When used with the
SLEUTH model, however, Mo is only available for the first
time step and so it was replaced with the predicted variable,
ET/Rn.
During equation development, satellite images were used
to derive each pixel’s initial T* and ET/Rn values. Users of
the SLEUTH urban growth model cannot be expected to
obtain satellite images of their areas and work with both an
image processing program and a SVAT model to determine
these values. Thus, Table 3 can be used to initialize the
climate variables from the land-use map that serves as initial
input to the growth model. T* and ET/Rn are estimated by
aggregating the individual pixels of the user’s land cover
map into 1-km2 areas and determining the percentage of
developed land within the parcel and the nature of the
remaining land (i.e., wooded or agricultural).
The result of the initial land class on the predicted
climate change and the general behavior of the regression
Undeveloped agricultural
Undeveloped wooded
Lightly developed, otherwise agricultural
Lightly developed, otherwise wooded
Moderately developed, otherwise agricultural
Moderately developed, otherwise wooded
Greater than 50% developed
T *0
Fr0
ET/Rn0
0.26
0.19
0.29
0.26
0.35
0.33
0.43
48.2
68.7
46.0
60.0
43.8
54.2
39.4
0.59
0.60
0.57
0.58
0.54
0.55
0.50
Undeveloped regions were defined by less than 10% developed land,
lightly developed by 10% to 25%, and moderately developed by 25% to
50%. The remaining land was predominantly agricultural if the 1-km2
region was less than 5% forested. If the undeveloped land was classified as
at least 10% forests, the remaining land was set as predominantly wooded.
equations themselves can be understood through a series of
land-use change scenarios. These scenarios were applied to
each initial land type based on the sequence of inputs for
Ddev and DFr given in Table 4. The idea was to start with
the greatest impact—equal and opposite changes in development and vegetation—and then progress to the most
‘‘friendly’’ form of development, i.e., that which actually
increases the amount of vegetation. This scenario could
occur if, for example, landscaped residential development
occurs on bare agricultural fields. Stages in between these
extremes represent development with no impact on vegetation (for example, commercial development on barren land)
or development with a low level of impact on vegetation
(for example, residential development that takes care to
remove only enough vegetation for the actual house area).
The DT* and DET/Rn equations in Table 2 were used for
each of these scenarios within a descriptive initial land class;
then the sequence of scenarios was repeated for the next
initial class. The results are displayed in Fig. 3. The
descriptive land classes for the initial conditions are given
in the graphs from left to right, just as they are presented in
Table 3 from top to bottom. The majority of the points lie
above the zero line for the DT* graph, indicating that radiant
surface temperature tends to increase with development. In
contrast, the majority of the points for the DET/Rn graph lie
below the zero line, indicating that with development,
surface moisture tends to decrease.
In addition, three patterns can be observed in the graphs.
First, there is the general trend of decreasing climate impact
Table 2
TM-based equations in the same format as that presented in the work of Carlson and Arthur (2000)
Y = a + b(DFr) + c(Ddev) + d(T *0 ) + e(Fr0) + f (ET/Rn0)
DT*
DET/Rn
a
b
c
d
e
f
R2
F-significance
0.4926
0.1656
0.0035
0.0016
0.0019
0.0010
N/A
0.2761
0.0020
0.0016
1.0742
N/A
0.79
0.75
< 0.0001
< 0.0001
Fr is fractional vegetation coverage; dev is developed land; T* is the scaled radiant surface temperature, and ET/Rn is the fraction of net radiation used in
evapotranspiration. D indicates that the change in the independent variable over time is what is important to the analysis, while a subscript 0 indicates an
emphasis on the variable’s initial conditions.
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Table 4
Inputs for growth scenarios
Note how the sequence starts with development that has the greatest impact
on vegetation then gradually decreases this impact to the point where
vegetation is actually increased. Within each of these scenarios, there is
another sequence occurring from the largest magnitude changes to the
lowest (for example, 40 to 5).
the more developed the 1-km2 parcel is before the growth
modeling process begins. A second pattern is found within
each descriptive land class; it shows the greatest increases in
temperature and decreases in moisture for the first development scenarios, i.e., those that are most destructive of the
vegetation. The third pattern lies within each of these
general scenarios; the greatest impact is typically observed
for the largest Ddev values. An exception to this third
pattern is noted for the rather improbable final scenario—
when the increases in vegetation are nearly equal to the
increases in development. In this situation, the equations are
dominated by the magnitude of the vegetation term. This
means that when the vegetation increase is great enough
(even though development is equally large), the parcel
actually becomes cooler or wetter.
3.2. Surface hydrology
Table 5 provides an initial exploration of the runoff vs.
rainfall plots in terms of runoff source area. The relationships given are a result of applying the graphical method of
Fig. 2 to summer events during the 1980s for one of the
study basins in southeastern Pennsylvania. As stated in
Section 2.2, API values less than 0.5 in. were assumed to
represent extremely dry conditions and those greater than
3.0 in., extremely moist or saturated conditions. All events
in between were considered a collection of typical scenarios.
In Table 5, as the antecedent conditions became moister, the
slope of the best-fit trend line increased, indicating that
more of the basin was contributing to runoff. On the other
hand, during extremely dry events (API < 0.5), it was likely
that the impervious surfaces directly connected to the drain-
age system dominated the runoff response. This fact can be
seen in the data by the low slope, as well as the intercept at
zero—i.e., those few surfaces that generated runoff ( < 1%
of the basin in this case) did so at the first sign of rainfall.
Based on such an interpretation, for the data in Table 5,
about 10% of the basin contributed to runoff after approximately 0.02 in. of rain for typical API values, while under
very wet antecedent conditions, it took approximately 0.04
in. of rain to then tap into about 22% of the basin.
A unique response for each of the three API scenarios
was distinguishable for this particular basin. In general,
however, the events with API values above 3.0 in. did not
present a clear relationship between rainfall and runoff. For
the rest of this work, only two categories are presented:
API < 0.5 in. for extremely dry antecedent conditions and
API z 0.5 in. for typical conditions. When these API categories were also separated by season, four distinct runoff–
rainfall relationships emerged for most of the study basins.
Fig. 4 provides an example of these relationships using a
different southeastern Pennsylvania basin during the 1980s.
The linear regression best-fit trend line is included on each
plot. Note the difference in scale between typical and dry
conditions. The relationship shown in Fig. 4 between the
runoff vs. rainfall slopes for this particular basin is typical of
results in this study. The greatest runoff for a given rain
event was generated in the non-summer months under
typical antecedent moisture conditions (API z 0.5 in.).
When antecedent moisture was very low, the runoff was
reduced. Overall, there was less runoff in the summer, with
the lowest amounts of the year generated during this season
under dry conditions (API < 0.5 in.).
These patterns are readily understandable in terms of
physical processes (Pielou, 1998). During the non-summer
months in southeastern Pennsylvania, the cool temperatures
and scarcity of growing vegetation allow evapotranspiration
to consume only so much water, often much less than that
supplied by precipitation. Particularly under typical antecedent moisture conditions, more of a basin’s pervious areas
have experienced a recharge in soil moisture, and the
proportion of the basin contributing to storm runoff
increases. During the summer months, when plants are
actively growing and temperatures are warm, transpiration
and evaporation are vigorous and rapid, such that actual
evapotranspiration is generally held below its potential by a
lack of water. High potential evapotranspiration values, soil
moisture withdrawals and possible water deficits help to
explain why the summer slopes in Fig. 4 are lower than the
corresponding ones during the non-summer months.
Using a different technique, Betson (1964) also found a
seasonal runoff pattern, with high runoff occurring during
the winter and low runoff during the summer. During the
winter, when the amount of moisture stored in the soil
profile was high, runoff occurred from a somewhat larger
proportion of the watershed than during the summer, when
the contributing area was smaller. Likewise, using a water
balance model, Grimmond and Oke (1986) showed that in
S.T. Arthur-Hartranft et al. / Remote Sensing of Environment 86 (2003) 385–400
393
Fig. 3. Results of the growth scenarios for DT* and DET/Rn. The equations and the initial conditions for each descriptive land type were used from Tables 2 to
3, while the DFr and Ddev values were determined by cycling through the growth scenarios in Table 4.
the winter ‘‘when evapotranspiration is limited and the soil
moisture store is well stocked,’’ 90% of the losses are as
runoff, while in the summer ‘‘the primary output is to the
atmosphere.’’ Human activities, however, do have the
potential to alter this typical runoff pattern. For example,
summer irrigation of suburban lawns was found to artificially stock the soil moisture in Vancouver, British Columbia, and significantly impact the city’s natural seasonal
water balance (Grimmond & Oke, 1986).
In general, for the runoff vs. rainfall plots over all of the
southeastern Pennsylvania study basins, R2 values tended to
be lower in the summer months, and a few of the regressions
during that period were not even significant. The runoff –
rainfall relationship for a complete basin, which includes
many types of surfaces, should, in fact, not necessarily be
considered constant, and a high degree of temporal variability in a watershed’s runoff response is common for most
regions (Dingman, 1994). Even though season and antecedent moisture conditions were considered in this study,
there were many other controlling factors that were not
included and could have contributed to the data’s scatter.
In particular, rainfall intensity, despite its dominating
influence on runoff generation, was excluded because of
the 24-h nature of the precipitation dataset. The same total
precipitation is capable of generating anywhere from zero
runoff to near the actual depth of precipitation, depending
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S.T. Arthur-Hartranft et al. / Remote Sensing of Environment 86 (2003) 385–400
Table 5
Three runoff – rainfall relationships based on different antecedent moisture
condition regimes
API
(inches)
Runoff =
slope(rainfall) +
intercept
Observations
R2
F-significance
< 0.5
0.5 V API < 3.0
z 3.0
y = 0.009x
y = 0.098x 0.02
y = 0.217x 0.04
24
217
44
0.81
0.42
0.72
< 0.0001
< 0.0001
< 0.0001
Data is for a study basin in southeastern Pennsylvania during the 1980s.
Each equation is the least-squares linear regression trend line from the
relevant runoff vs. rainfall plot. All storm events occurring from June 22
through September 30 were used, but they were stratified based on their
antecedent precipitation index (API) value.
only on the rate at which the rain falls (Smith, 1997). This
factor is most important during the summer months due to
the predominance of convective rainfall events. During this
period, not only does the rainfall intensity tend to vary much
more from event to event, but individual storms can also
exhibit greater spatial variability. Another potential source
of scatter represented in Fig. 4 that was not accounted for,
specifically during the non-summer months, is that of frozen
soil, which can act as an impervious surface. Despite this
omission, these months’ runoff vs. rainfall plots under
typical antecedent moisture conditions were found to be
the most representative of a basin’s land-use patterns. The
plots’ slopes generally displayed an increase from the 1980s
to the 1990s. This increase could be related to changes in
land use, particularly the spread of developed land. Such
decadal changes in the slope were less apparent under dry
conditions, as well as for the summer months in general.
Temporal changes in the plots’ intercepts were also not
obvious, although a seasonal pattern was notable. The
intercept represents the loss in rainfall that must occur
before runoff commences. The particular value will vary
based on such factors as how much rainfall is intercepted by
leaves, branches or other objects, the amount of initial
infiltration, and how much water is stored in surface
depressions. For highly urbanized basins, these initial losses
should be small. Many of the intercepts under dry conditions or during the summer months did not differ significantly from zero. In these situations, the impervious surfaces
directly connected to the drainage system or other everpresent, low-loss source areas probably dominated the runoff response. Many of the remaining pervious areas were
likely not contributing to runoff, since the rainfall was used
for other purposes, such as soil moisture recharge. The
intercepts increased for most of the plots representing
typical non-summer conditions. This was suspected to result
from the additional pervious surfaces, with greater initial
rainfall losses, that were also contributing to the runoff
response.
The rest of this paper focuses on the non-summer, typical
API runoff vs. rainfall slope as a representative index of a
basin’s runoff response. The slope values obtained from
runoff vs. rainfall plots based on individual observations
were compared to those from plots based on yearly totals, a
technique that is typically accepted as a way to reduce
scatter (Bedient & Huber, 1992). The correlation coefficient,
r, between the slopes from these two methods was 0.87. The
slope values obtained from a decade of individual events
also correlated well (0.90) with those slopes obtained using
just the events during the years more closely associated with
the corresponding satellite image. Using a full decade worth
of events helped to reduce the influence of anomalous
periods, and it is believed that the results would not have
differed much if certain years were instead isolated. Additionally, the use of individual observations rather than
annual sums allowed the results to be presented to users
of the final SLEUTH application in more tangible, eventbased terms.
For several cases throughout the year and for various
study basins, storm runoff depth as estimated from the
basin’s representative slope value was compared to the
actual calculation of the runoff depth from a real-time
hydrograph constructed from 15-min USGS data. The
slope-based method was found to be most reliable for
non-summer events. In the summer, the predictability of
the runoff depth was much less consistent. For non-summer
events under typical API conditions (the exact scenario that
was selected as representative of a basin’s general runoff
response), data points tended to lie very close to the line
representing 1:1 correspondence with the real-time hydrograph analysis.
In general, however, the slope-based method should not
be applied to flood stage events or those events producing
an anomalously large amount of runoff. This result was to
be expected since the runoff vs. rainfall slopes were determined from the method of least-squares linear regression in
the absence of a large number of influential flood events.
The general trend for runoff production during the more
frequent, smaller rain events does not extrapolate linearly to
flood stage flows. Betson (1964) likewise concluded that
different relationships were justified for large and small
events, with mathematical models that relate storm rainfall
to runoff frequently under-predicting large events. A typical
storm list contains a large number of small-to-moderate
events, such that the model is forced to fit this more
common situation. In contrast, a study by Jackson, Ragan,
and Fitch (1977) used only those storms with a return period
greater than 2 years; as a result, their model was ‘‘optimized
for large runoff events’’ and was found to overestimate the
flows resulting from small rainfalls.
For the purposes of this paper, smaller rainfall events are
more important than flood episodes. The runoff vs. rainfall
plots were used to determine an index of runoff that is
responsive to changes in urbanization. Man-made impervious surfaces have the greatest potential to alter the hydrologic balance during the lower rainfall amounts of more
typical storm events (Corbett, Wahl, Porter, Edwards, &
Moise, 1997). Under heavier rain events, land use becomes
somewhat indistinct due to saturation of pervious areas. The
S.T. Arthur-Hartranft et al. / Remote Sensing of Environment 86 (2003) 385–400
395
Fig. 4. Runoff vs. rainfall plots for a southeastern Pennsylvania study basin during the non-summer and summer months in the 1980s. For the non-summer
plots, events occurring from June 22 to September 30 were excluded. For the summer plots, ONLY events occurring from June 22 to September 30 were
included. The ‘‘typical conditions’’ plot is based on events with an API value z 0.5 in., while the ‘‘dry conditions’’ plot is based on events with an API value
< 0.5 in.
purpose of the representative runoff vs. rainfall slope is to
serve as an index that communities can use to determine
what influence future land development may have on the
basin’s runoff response.
The task then was to determine this representative value
in the absence of actual data and, instead, in the presence of
growth scenarios predicted by the SLEUTH urban growth
model. In a first attempt to develop a methodology, each
land use was assigned a particular runoff to rainfall ratio, R.
This area-weighted R-value explained only 35% of the
variance observed in the slopes from the runoff vs. rainfall
plots. Even with the added complexity of a buffering
scheme, which took into account not only the land use but
also its proximity to the waterway, the ability of this method
to predict a basin’s representative runoff vs. rainfall slope
value did not improve. Land-use patterns alone were not
able to clearly distinguish the differences observed in the
various basins’ runoff responses.
The ability to predict the representative runoff response
improved dramatically when not just the land-use patterns,
but also the size of the basin and an index of channel slope
were incorporated into a least-squares multiple linear regression equation. This combination of independent variables
succeeded in explaining approximately 91% of the variance
observed in the actual runoff vs. rainfall slopes. However,
when this equation was applied to basins outside the
immediate study area, certain rather arbitrary restrictions
had to be placed on the use of the equation. Plausible landuse scenarios (i.e., the total proportions summed to 1) were
seen to result in unphysical runoff to rainfall ratios (i.e.,
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S.T. Arthur-Hartranft et al. / Remote Sensing of Environment 86 (2003) 385–400
In the equation, all land uses contribute to increasing the
final runoff vs. rainfall slope, with certain land classes, such
as forests, contributing less than others. In addition, the
larger the basin, the lower the runoff response. Woodruff
and Hewlett (1970) also found that larger basins exhibit
lower runoff to rainfall ratios as calculated from the storm
hydrograph. They attributed this trend to attenuation of the
runoff signal as the stormwaters head downstream, as well
as the fact that larger basins are typically not uniformly
covered by the intense storm cells that can produce strong
runoff signals on small basins. Finally, the greater the
elevation change from the point 80% up the channel to
the outlet, the greater the runoff response. The representativeness parameter serves to pull the final runoff vs. rainfall
slope value down to a reasonable level; for those basins
where the range in elevation is much larger than that in the
channel, the predicted value will not be reduced as much.
For example, one basin was observed to have a large runoff
response, despite a low measure of channel terrain. This
same basin displayed its highest terrain along the southeastern tributaries near the outlet—a fact accounted for by
the final term in the equation.
greater than 1 or less than 0). The equations could only be
used to predict the effects of the same type of land-use
change at another location where the conditions were similar
(Maidment, 1993).
As a result, this initial regression equation was viewed as
an example of successful calibration of the method for these
particular southeastern Pennsylvania basins—an example of
how well the technique can be applied to a particular region.
For transferability to other basins, a more general equation
was developed as given in Table 6. The intercept was forced
through zero, and there were six independent variables: (1)
the total proportion of the basin’s land that was developed,
(2) the total proportion of the land that was in agricultural
use, (3) the total proportion of the land that was forested, (4)
the area of the basin in square miles, (5) the channel’s
elevation change in meters, and (6) the representativeness of
this measure of basin terrain in meters/meters.
The land-use proportions were determined using the
basin’s satellite-derived land-use map, with each value
ranging from 0 to 1. ‘‘Developed’’ meant that the land
classification was some form of urban (commercial, residential, etc.), while agricultural use was defined as land
classified as either bare soil or vegetation. In this way, the
land-use variables should represent, at a minimum, approximately 98% of the basin’s surface area. Depending on how
well the channel can be discerned in the satellite image, the
only remaining land class (water) should not compose more
than about 2% of the basin. The methodology presented
here was based on an ideal 24-h storm discharge response
and is not suitable for basins with large storage areas, such
as lakes. The fifth independent variable, elevation change,
represents the decrease in elevation that occurs from a point
near the headwater source area to a point at the basin outlet.
The point 80% up the main channel was defined as the
headwater reference, while the stream gauge site represented
the point at the basin outlet. Both elevation values should be
in meters. In developing the equations, the USGS Digital
Line Graph hydrography data was used to discern the
stream’s main channel, and a USGS digital elevation model
(DEM) was used to determine elevation values. The final
independent variable, representativeness, is the elevation
change value divided by the range in basin elevation, i.e.,
maximum minimum. All measurements in this final term
should be in meters. The lower this final variable, the less
the channel’s measured elevation decrease represents the
actual terrain potential in the basin.
3.3. Modules for the SLEUTH urban growth model
3.3.1. Microclimate
Module add-ons for the SLEUTH urban growth model
were developed from the climate and hydrology results
presented in this paper. Specific details and steps for implementation are thoroughly described at http://www.essc.
psu.edu/SLEUTH. The web site, designed for both learning
about and downloading the climate and hydrology modules,
provides detailed instructions on the modules’ use, as well as
color examples of their output. Some of the latter will also be
given here in black and white versions as an example of
implementation.
Fig. 5 displays the general land-use patterns of White
Clay Creek, which drains both southeast Pennsylvania and
northeast Delaware before it enters the Delaware Bay
through the Christiana River. Currently, the Pennsylvania
portion of the watershed is predominantly rural, with the
greatest development being in the form of small towns and
suburban clusters. In Delaware, however, the watershed
includes part of the city of Newark, and rampant suburbanization is the region’s main characteristic. Despite large
levels of urban growth, forested areas in the watershed’s
Table 6
Determining a basin’s representative runoff vs. rainfall slope in the absence of rainfall – runoff data
Y = a + b(developed) + c(agricultural) + d(forested) + e(area) + f (elevation change) + g(representativeness)
Runoff – rainfall
a
b
c
d
e
f
g
R2
F-significance
0
0.7497
0.4099
0.0223
0.0007
0.0012
0.3682
0.65
< 0.05
The table presents the results from a multiple linear regression analysis with the intercept forced through zero. The independent variables include total land use
proportions, basin size and two measures relating to the basin’s terrain: ‘‘elevation change’’ and ‘‘representativeness’’ (see text). This new analysis included not
only the southeastern Pennsylvania basins in the 1980s and 1990s, but also two additional basins from central Pennsylvania in the 1990s.
S.T. Arthur-Hartranft et al. / Remote Sensing of Environment 86 (2003) 385–400
397
Fig. 5. General land use for the White Clay Creek watershed in southeastern Pennsylvania and northeastern Delaware. The left image was derived from a 1996
Landsat TM satellite image, while the right image is the SLEUTH predicted land use for the year 2025. These results are not intended as a verified study of the
area. White Clay Creek was chosen merely to serve as an example of implementation.
southwest region have remained largely intact. A natural
preserve area managed jointly by Pennsylvania and Delaware and Delaware’s White Clay Creek State Park provides
approximately 7 total miles of riparian corridor along the
creek. White Clay Creek has been categorized as ‘‘one of
only a few relatively intact, unspoiled and ecologically
functioning river systems’’ remaining in the highly congested corridor that links Philadelphia, PA with Newark, DE
(White Clay Creek Wild and Scenic River Study Task
Force, 1998). The site thus serves as an excellent location
for application of the ideas in this paper; however, results
should be viewed merely as an example of implementation,
or proof of concept, not model verification.
In Fig. 5, both the 1996 satellite-derived land-use map
and the SLEUTH prediction generated for the year 2025 can
be seen. Using the techniques of Section 3.1, the climate
module generates climate layers for each year that the
SLEUTH model generates a forecasted land-use map. The
scaled surface temperature (T*), fractional vegetation cover
(Fr), and evapotranspiration fraction (ET/Rn) are estimated
for each 1-km2 land parcel. As parcels experience urban
growth over time, their climate variables are updated. The
user of the module is able to establish the type of development that occurs. Choices range from a large decrease in the
local vegetation to the most ‘friendly’ form of development,
i.e., that which actually increases the vegetation. The
module outputs each land parcel’s predicted climate values
in an ascii file, as well as in grayscale images for visualization purposes.
Fig. 6 displays a pair of these images for T*, corresponding to the time period of the land-use patterns presented in
Fig. 5. To generate this output, the ‘‘high impact’’ option
was chosen as the form of development. These images have
been scaled so that, in both, white represents the maximum
possible T* value. Areas of warming can be seen where both
new development and ‘‘filling in’’ occurred—for example,
in northern regions and in the east. The climate module
creates similar output for the vegetation (Fr) and the surface
moisture (ET/Rn) variables. For quantitative analyses, the
images should be used in conjunction with the ascii files that
contain the raw data.
The SLEUTH growth model has the capability to model
non-urban changes in land use. This additional information
is not actively employed in the climate module since the
driving force for the generation of the module’s equations
was urbanization. Thus, for non-urbanizing parcels of land,
any other land-use transitions are ignored in the actual
calculations, even though the model’s full land-use change
capability is still employed. For example, if a parcel of land
is initialized as completely agricultural and then never
experiences development but rather, transitions back to
forest, this does not influence the climate variables used in
the model. Until a parcel experiences urban growth, its
climate remains in its initial state.
3.3.2. Runoff response module (basin-scale)
The basic hydrology module uses the techniques presented in Section 3.2 to quantify the effects of urbanization
on the surface hydrology at a watershed scale. The runoff to
rainfall ratio (based on the representative runoff vs. rainfall
slope value) is estimated for each year that the SLEUTH
model generates a forecasted land-use map. The module
output is in the form of a text file that summarizes each
year’s land-use patterns and the runoff to rainfall ratio.
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S.T. Arthur-Hartranft et al. / Remote Sensing of Environment 86 (2003) 385–400
Fig. 6. Scaled radiant surface temperature images (T*) for the same area and time period as Fig. 5. The 1996 image is based on the Landsat TM-derived T*
values averaged at 1-km resolution. The image on the right is for the predicted temperature field in 2025. Note that the output from the climate module is at
1-km resolution, while the original land use maps (Fig. 5) are at 25-m resolution.
Limitations for application of this module are given in the
user’s guide on the web site. Warnings are output if any of
the basin’s characteristics are out of range. For example, the
basin may be too steep or too large, one particular land use
may dominate the basin, or water may comprise more than
2% of the basin. However, even if a basin’s initial land-use
patterns are suitable for the module, in future years, as the
land-use patterns of the basin change, some of the land
proportions may exceed their usable range. The module
keeps track of the specific requirements and notes when the
runoff to rainfall ratio can no longer be reliably estimated.
Two forms of the module are available—one for use with
basins in southeastern Pennsylvania and another for more
general use. The southeastern Pennsylvania version allows
the user to separate the urban growth based on its proximity
to the waterway, as well as predict the runoff vs. rainfall
slope under dry antecedent moisture conditions. The second,
more general version of the module, does not include the
option to buffer the waterway and only predicts the runoff
response for typical conditions.
4. Conclusions
It has been demonstrated that development can induce
predictable changes in the local scaled radiant surface
temperature and evapotranspiration fraction. Specifically,
the changes in vegetation accompanying the development
must be known, as well as the initial climatic state of the
land parcel. For example, development of a landscaped
subdivision on dry, bare fields will have a different climatic
response than the development of a commercial shopping
center on land that was previously forested. These ideas
were examined using multiple regression analyses based on
satellite-derived land cover and climate parameters in order
to develop a quantitative climate module that can be used
with the SLEUTH urban growth model.
Additionally, it was shown that plots of runoff vs. rainfall
for a gauged basin could be interpreted in terms of the
proportion of the basin contributing to a storm event’s
runoff signal. For a particular basin, four distinct runoff
responses, separated by season and antecedent moisture
conditions, were noted. The physical premise behind this
separation of the runoff vs. rainfall plots included such
factors as potential and actual evapotranspiration, infiltration capacity and the soil moisture deficit. The runoff
response was characterized by the slope of the plot’s bestfit linear regression trend line. The slope for the nonsummer months under typical antecedent moisture conditions was seen to be most representative of and responsive
to land-use patterns.
Using a satellite-derived land cover map, this particular
slope value was associated with the proportion of developed, agricultural and forested land, as well as the size of
the basin and several measures of the basin’s terrain. The
resulting multiple linear regression equation was used to
develop a module for estimating the representative runoff
response for each year of predicted land-use change from
the SLEUTH urban growth model. The resulting index of
runoff response can be used by communities to model
increases in the loss of a basin’s precipitation input due to
storm runoff from the basin outlet within 24 h of typical
storms. This loss of water from the system is a reduction in
potential soil moisture, which is necessary both for vegetation and a cooler, more comfortable microclimate. It has the
potential to lead to lower stream flows during dry periods,
increased water shortages and a greater reliance on imported
sources.
S.T. Arthur-Hartranft et al. / Remote Sensing of Environment 86 (2003) 385–400
Overall, this work provides a strategy that allows communities to determine when their water and climate resources
might become unacceptably impacted by urban growth. A
working model has been developed but remains to be actively
applied and tested in a community faced with answering realworld planning questions. For example, in the White Clay
Creek basin, the natural preserve area in the west was part of
the SLEUTH model’s excluded layer, which prevented the
land from becoming developed. This area could be stripped
of its exclusion privileges and the impacts of this planning
decision explored. Additionally, the climate module can be
run successively with different selections for the form of
development. In this way, the role that vegetation plays in
determining how development impacts the local microclimate could be observed, especially for a particular parcel of
land that is about to be developed. Climate layers can also be
generated for each year of the SLEUTH model output and
animations created. Such possibilities as these lead to a new
exploratory tool for communities—a tool which helps to
combine satellite, hydrologic and climate data with GIS
analyses and urban studies.
Acknowledgements
This work was supported primarily through the Pennsylvania State University Meteorology Department. Thanks
are given to David Ripley for his invaluable technical
assistance, as well as to Drs. John Wyngaard, David
DeWalle and Donna Peuqeut for their important input,
feedback and support. The reviewers of this paper are also
acknowledged for their helpful comments. We would like to
thank the U.S. Department of Agriculture, specifically
Thomas J. Schmugge of the Hydrology Laboratory, Beltsville, MD (contract USDA 58-1270-8-0560), and the
Pennsylvania Department of Transportation (PennDOT
Grant 259704 #69), specifically Bert Kisner, for their
assistance in supporting this research. Satellite imagery
provided by NASA is also appreciated.
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