using gis to prioritize green infrastructure installation
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Using GIS to Prioritize Green Infrastructure Installation Strategies in
an Urbanized Watershed
Lauren Owen
1, Daniel R. Hitchcock
2, David L. White
3, Gene W. Eidson
4
AUTHORS: 1 Graduate Research Assistant, Biosystems Engineering, Clemson University, Clemson, SC 29634, USA. 2Associate Professor, Baruch Institute of Coastal Ecology and Forest Science, Clemson University, Georgetown, SC
29440, USA. 3Clemson Computing and Information Technology, Clemson, SC 29631, USA. 4Director, Institute of
Computational Ecology, Clemson University, Clemson, SC 29634 and Urban Ecology Center, Aiken, SC 29801, USA.
REFERENCE: Proceedings of the 2014 South Carolina Water Resources Conference, held October 15-16, 2014 at the
Columbia Metropolitan Convention Center.
ABSTRACT. A Geographic Information System
(GIS) was utilized to create a digital elevation model
(DEM) and a stormwater systems map in order to
perform spatial hydrologic analyses on watershed
discharges from urban Aiken, SC. Arc Hydro® was used
to create and analyze hydro networks, to determine flow
accumulation, and for subwatershed delineation. The
HEC-HMS preprocessor HEC-GeoHMS was then used
to transform the ArcMap model into a format that can be
imported into watershed model HEC-HMS to create
hydrographs for peak flow and runoff in 10
subwatersheds within the entire urban watershed. Model
calibration is underway based on isolated storm event
data to determine stormwater flow and volume
contributions from respective subwatersheds. By
modeling storm events, hydrologically informed
decisions can be made related to the addition of green
infrastructure-based stormwater control measures
(SCMs) in specific subwatersheds. This modeling effort
is also essential for understanding the infrastructure
connections within the existing city stormwater system.
INTRODUCTION
Urbanized watersheds, specifically those with high
percentages of impervious cover, create increased runoff
volumes, flows, and energy that can result in downstream
erosion and other water quality impacts. These highly
impervious urban landscapes could benefit from the use
of sustainable restoration strategies based on green
infrastructure principles that mimic natural hydrological
and ecological processes. Effective sustainable land use
strategies for highly developed areas require an
optimization at varying spatial and temporal scales: not
only at the watershed level, but also at the scale of the
urban centers and residential neighborhoods. This is
especially the case with respect to selection and design
criteria for stormwater control measures (SCMs) as
related to their individual and collective performance
within an urban watershed as well as to their integration
into the existing landscape. In this paper, we share
results from investigation into a flow simulation
methodology for urban stormwater systems.
PROJECT DESCRIPTION
This project is part of the Institute of Computational
Ecology's Intelligent River® Research Enterprise
monitoring and visualization effort along with being part
of an ongoing project located in an urban center being
retrofit to manage stormwater quantity – the Aiken Green
Infrastructure project – while improving downstream
water quality at the Sand River headwaters through
upstream stormwater volume reduction. The urban
stormwater infrastructure of downtown Aiken, SC (33o
33’ 38”N, 81o43’10”W), has recently been retrofit with
pervious paving materials, bioretention cells, and a
cistern, offering an opportunity for the evaluation of
these SCMs. The modeling exercise reported here will
guide priority locations for future SCM installations.
This project is part of the statewide Intelligent River™
monitoring and data visualization effort, which offers
science-based knowledge to guide decision-making with
respect to water resources management.
A major concern exists at a ten-foot diameter pipe that
discharges most of the urban stormwater into the Sand
River, serving as its headwaters - high energy at the
outlet has produced a large canyon due to erosion. In an
attempt to reduce high energy discharges and thus bank
erosion, green infrastructure based SCMs have been
installed within the upstream urban watershed, with
future new projects being proposed. The next step –
through preliminary work presented here – is to
determine the best locations (subwatersheds) for new
flow and volume reduction strategies based on
hydrologic monitoring and modeling.
METHODS
A major issue with urban stormwater characterization
is that these systems typically consist of underground
infrastructure, therefore difficult to recognize throughout
the Digital Elevation Model (DEM) when creating the
representation. Many studies have been conducted on
methods of displaying the pipe system within a
watershed model, and the most common method
demonstrated is a technique called “burning” in the
streams to the DEM (Gironas et al. 2010, Paz 2007,
Maidment 1996, Maidment 2002, Brock 2006). In this
method, a constant depth is subtracted at all cells that
include a known conduit that is not resolved by the DEM
(Gironas, 2010). Once this is completed, the stormwater
network is accepted as the natural stream element of the
DEM and can be used to delineate watersheds based on
the piping and extract various characteristics. The
ArcHydro extension (Maidment, 2002) of ArcMap 10.1
is used to create a drainage network and catchments
through various tools.
Once the drainage network is completed, the
preprocessor for HEC-HMS, HEC-GeoHMS, can be used
to transform the ArcMap model into a format that HEC-
HMS accepts. This is done in several studies including,
but not limited to, Verma 2009, Beighley 2003, Du 2012,
Emerson 2003, McColl 2007, Knebl 2005, Al-Abed
2005, Chen at al. 2009, and Ali et al. 2011. The drainage
network and catchments created in using ArcHydro are
then used to create subbasins and a river network that is
accepted by HEC-HMS. It executes this function through
a series of steps collectively known as terrain
preprocessing, implying the utilization of the surface
topography as the origin of the stream network (Knebl et
al., 2005). It is also used to derive several basin and river
characteristics using the input data from the DEM,
drainage network, soil data, and land use data. HEC-
GeoHMS outputs a basin model, meteorological model,
and river model ready to use in HEC-HMS. These main
components focus on determining runoff hydrographs
from sub-basins and routing the hydrographs through
channels to the study area outlet (Beighley, 2003). This
output data can then be used to compare to the volume
and flow data from the Sontek IQ Pipe sensors and
calibrate the modeling. The model can then be used to
predict subwatershed discharges during future storms and
in what subwatershed the storm will have the greatest
impact.
The first step in creating the watershed model in
ArcMap 10.1 was creating a high definition DEM from
Light Detection and Ranging (LiDAR) data. It is
demonstrated that a LiDAR-derived DEM with high
accuracy and high resolution offers the capability of
improving the quality of hydrological features extracted
from DEMs (X, 2005). The DEM was created using
various toolboxes inside ArcMap 10.1 to convert the
LiDAR file to a raster. After the DEM is created, the
stormwater pipe system is “burned” into the DEM
following Method 2 of Gironas et al. of burning all pipes
to the same artificial elevation below the DEM. This
creates the illusion that the pipe system is the natural
stream element of the watershed. Terrain preprocessing is
then completed and the DEM is filled and the flow
direction and flow accumulation grids are created
following Maidment’s methods in ArcHydro: GIS for
Water Resources. Catchments, an adjoint catchment, and
a watershed for the 10 foot pipe outlet, along with
subwatersheds for each monitoring location are created.
Following Merwade’s tutorial from Purdue
University, the HEC-GeoHMS toolbar is used to convert
the ArcMap 10.1 model into an acceptable format for
input into HEC-HMS. This is conducted by inputting the
output files from the ArcHydro toolbox including the
RawDEM (burned DEM), the HydroDEM (filled DEM),
flow direction, flow accumulation, stream grid, stream
link grid, catchment grid, catchment polygon, drainage
line polygon, and adjoint catchment polygon to output a
subbasin shapefile and a river shapefile. All subbasins
were merged to create subbasins that matched the
subwatersheds derived within ArcMap 10.1 using the
basin merge tool in HEC-GeoHMS. Basin processing is
completed to extract subbasin characteristics necessary
for input into HEC-HMS. An HMS schematic is then
created using the HMS schematic tool and inputting the
project point, centroid, river, and subbasin files to output
HMSlink and HMSnode files. This shows the schematic
that will be demonstrated in HEC-HMS along with the
subbasin background shapefile. It is important to note
that an impervious surface grid was clipped to the area of
study and soil groups and land use data were merged to
create a curve number (CN) lookup table within ArcMap
10.1 for use in HEC-GeoHMS. The CN lookup table and
the soil/land use polygon were then used to create a CN
grid, which was input into HEC-GeoHMS along with the
impervious surface grid to calculate subbasin
characteristics such as CN, lag time, and impervious
percent per subbasin. SSURGO soil data from NRCS
was used along with NLCD land cover and NLCD
percent impervious data, along with Microsoft Access,
and input into ArcMap 10.1.
Once opened in HEC-HMS, the only thing that has not
been previously calculated in HEC-GeoHMS is lag time
(minutes) per reach. This was calculated by extracting
elevations and lengths to calculate slope from ArcMap
10.1 and inputting them into equation 1:
( ) (
)
(hours)
with L representing hydraulic length (km), CN
representing the average CN, and Y representing percent
slope (Costache, 2014). The SCS storm method was
chosen for the meteorological model and the storm depth
in inches is input into HEC-HMS. A simulation run is
then generated and computed to output a global summary
of volumes and flow rates per reach and subbasin along
with hydrographs for each option. These outputs can then
be used to compare to the sensor data calculated volumes
and flow rates to calibrate and improve accuracy of the
hydrologic model. The model can then be used to predict
the outcome of a given storm of a certain depth of
rainfall.
Ten Sontek® IQ Pipe™ sensors have been installed to
monitor the stormwater and baseflow volumes and flows
travelling through key stormwater pipes throughout the
city and surrounding watershed. These data will be
compared to predicted subwatershed stormwater flows
and volumes.
RESULTS
The DEM generated from the LiDAR data with
elevations ranging from 219 feet to the highest elevation
of 566 feet, shown in Figure 1 with monitoring locations.
Figure 1: DEM created from LiDAR data and
monitoring locations.
The most important outputs from the ArcHydro toolbox
are the watershed and subwatershed polygons, along with
the drainage line, catchment and adjoint catchment
polygon layers which are demonstrated in Figures 2-5
respectively. Resulting watershed and subwatershed
areas are also provided in Table 1. The delineated total
watershed area as represented by a sum of these
subwatershed areas is 1079 areas while the total area
determined by GIS at the 10-ft. pipe discharge (Station 6)
is 1070 acres.
Figure 2: Subwatersheds per monitoring location.
Total 10 foot pipe watershed area includes all
subwatersheds except that draining at Station 11.
Table 1. Subwatershed output areas. Subwatershed 6
is the entire drainage area at the 10-ft pipe (1079
acres) excluding Subwatershed 11.
Figure 3: Drainage line indicating the natural streams
and inclusion of the stormwater system.
Figure 4: The catchment polygon layer dividing at
each stream segment.
Figure 5: Adjoint catchment polygon layer, in pale
blue, demonstrating the same boundary definition as
the watershed layer.
Figure 6: The river and subbasin file generated in
HEC_GeoHMS for input into HEC-HMS.
Once input into HEC-GeoHMS, these layers along with
several others are transformed into a river shapefile and a
subbasin shapefile as shown in Figure 6.
Table 2 provides the average CN, impervious
percentage (Xian, 2011), and lag time (hours) per
subbasin that are input into HEC-HMS and automatically
calculated through HEC-GeoHMS using the impervious
surface grid, the soil group data (Soil Survey Staff,
2014), and the land use polygon (Jin, 2011).
Table 3 provides sample calculations from a July 19th
storm and demonstrates volume (gal) and peak flow rate
(cms) per subwatershed. Runoff ratios (or the percentage
of rainfall generated as watershed discharge) ranged from
0.37 (Subwatershed 5) to 6.68 (Subwatershed 8).
Table 2: Subbasin characteristics extracted and
calculated using HEC-GeoHMS.
Table 3: HEC-HMS volume and peak flow rate per
watershed for a July 19th
storm of 0.51 inches along
with the rainfall runoff ratio.
DISCUSSION
As mentioned before, the point of “burning” the
stormwater pipe system into the DEM is to force ArcMap
10.1 to accept them as the natural stream element of the
study area. In order to prove this, the watershed polygon
and the adjoint catchment polygon must illustrate the
same boundary outline due to their definitions. A
catchment is defined as a tessellation or subdivision of a
basin into elementary drainage areas defined by a
consistent set of physical rules, while a watershed is
defined as a tessellation or subdivision of a basin into
drainage areas selected for a particular hydrologic
purpose (Maidment, 2002). In other words, a catchment
is defined by the natural hydrologic elements while a
watershed is set specifically by an outlet. This means that
if the stormwater system was accepted by the model as
the natural stream element, then the adjoint catchment
and watershed boundaries should be the same as
demonstrated by Figure 2 showing the total watershed
and Figure 5 which considers adjoints. It is also proven
in Figure 3 which illustrates the drainage line, where the
pipe system is clearly included along with the natural
streams of the area in the shapefile.
The output calculations including percent impervious,
average CN, and lag time per subbasin all demonstrate
reasonable numbers for HEC-HMS input. HEC-HMS is
currently being calibrated to compare with sensor-
collected volume data and flow rate calculations;
however, the model is functioning properly and
generating output hydrographs and volume and flow
calculations. The rainfall runoff ratios were then
calculated, which should be between 0 and 1.
Subwatersheds with ratios greater than 1.0 could be due
to rainfall variability over the entire watershed as well as
baseflow contributions from groundwater, irrigation
within the watershed, and potential water leaks in the
system that were not considered in flow simulations. The
model can then be used to decide which subwatersheds
are contributing the most flow to the stormwater system
and where additional green infrastructure will be the
most efficient.
This work shows that urban stormwater systems can be
modeled and analyzed in various modeling formats
including: ArcMap 10.1, HEC-GeoHMS, and HEC-
HMS. We also demonstrate how these various programs
can be used in unison to create a hydrologic model for
urban stormwater analysis. Other urban or developing
areas can use the skeletal model of this strategy to solve
their existing stormwater runoff issues, as well as, and
more importantly, to prevent these problems from arising
in the first place.
ACKNOWLEDGMENTS
This project is funded through a City of Aiken, SC
research grant to the Institute of Computational Ecology.
I would like to give a huge thank you to the City for their
funding and support. Special thanks to all who have
made amazing efforts to make this project what it is
today including but not limited to: Kelly Kruzner, Alex
Pellett, Ben Smith, and Ron Mitchell.
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