Title ASPRS 2008 Annual Conference Portland, Oregon ♦ April 28 -
May 2, 2008
THE BIGGER THE BETTER? HOW SPATIAL RESOLUTION AFFECT RUNOFF
MODELING AND WATERSHED DELINEATION
David Alvarez
Tampa, FL 33607 email:
[email protected]
Barrett Goodwin
Dallas, Texas 75231 e-mail:
[email protected]
ABSTRACT The modeling of Hydraulic Systems has become increasingly
more sophisticated with the advancement in technology. This
technology is becoming more GIS based for use in hydraulic
modeling. The purpose of this study is to understand how different
spatial resolutions in a DEM affect the delineation of watersheds,
stream networks and runoff. This study area is located in the
Dallas County and Collin County portion of the Rowlett Creek
watershed. For this paper HEC-HMS, developed by the Corps of
Engineers, is been use to calculate runoff. HEC- GeoHMS and
ArcHydro are extensions for Arcview which derives the parameters
necessary for the HEC-HMS model. Several terrain models with
different pixels sizes are generated from Lidar data. Each model's
results were compared based on magnitude of differences between
various delineations of the watershed, stream network and runoff.
Understanding how the pixel size affects the model will give us an
understanding if is worth investing in high accuracy data for
simple watershed modeling studies.
INTRODUCTION
When rain or snow falls onto the earth, it just doesn't sit there
-- it starts moving according to the laws of gravity. A portion of
the precipitation seeps into the ground to replenish Earth's ground
water. Most of it flows downhill as runoff. Surface runoff is the
volume of excess water that runs off a drainage area. Rainfall is
the primary source of water that runs off the surface of small
rural watersheds. The main Factors affecting the volume of rainfall
that runs off are the kind of soil and the type of vegetation in
the watershed, but Factors that affect the rate at which water runs
off are the watershed is topography and shape along with
conservation practices on a watershed. Runoff is extremely
important in that not only does it keep rivers and lakes full of
water, but it also changes the landscape by the action of
erosion.
Water professionals need to be able to manage surface and
groundwater resources over the scale of an entire watershed. The
effects of land cover, vegetation, soil type, topography, geology,
water quality, and other factors must be considered in order to
make sound management decisions. This decision has been in a way
“simplify” by the use of geographic information system (GIS). Put
simply, a GIS is a system of computer software, hardware, and data,
combined with qualified people to assist with manipulation,
analysis, and presentation of information that is tied to a spatial
location.. The GIS user can access and manipulate information
associated with Geographic features and look for spatial and
temporal patterns and relationships. The application of Geographic
Information systems (GIS) in the field of hydrology has grown
significant in the past decade. Advances in the technology
increased professional awareness have greatly improve the accuracy,
functionality and commonality of GIS in water resources management
and related fields
Today in the market there are several applications that can be use
to implement the runoff part of the project. (WMS, HEC-HMS, BASINS,
TRC-55, Mike11 and more). Each of this application has it strength
and weakness. In our case we decided to go with the free side of
the market The Hydrologic Modeling System (HEC-HMS). For the GIS
part we used ArcGIS Desktop 9.2 with the extensions (Spatial
Analyst, Geostatistical and 3D Analyst).
LOCATION
The majority of the Rowlett Creek watershed is within Collin County
Texas with a small portion in Dallas County Texas as shown in
figure 1. Collin County is located in northeastern Texas,
approximately thirty miles south of the Red River. McKinney, the
county seat, is thirty-four miles northeast of Dallas. The county
is bordered by the following counties: Grayson to the north,
Fannin, to the northeast, Hunt to the east, Rockwall to the
southeast, Dallas to the South, and Denton to the west. The county
is approximately 889 square miles in size and has a population of
615,200 in 2004. There are 29 communities within Collin County with
the larger communities located in the southwest portion of the
county. All of the Rowlett Creek watershed is considered urban or
developed. Primary landuse/landcover consist of concrete, housing,
businesses, and industry. The county lies within the Blackland
Prarie region. The surface of the county is generally level to
gently rolling, with an elevation ranging from 450 to 700 feet
above sea level. Deep clayey soils over marl and chalk surface the
central and western part of the county. Dark loamy alluvial soils,
subject to flooding during the rainy season, lie in the eastern
section. Limestone and sand for making cement are the only mineral
resources. Temperatures range from an average high of 96° F in July
to an average low of 34° in January. Rainfall averages just under
thirty-five inches a year, and the growing season extends for 237
days.
Figure 1. Watershed general location.
ASPRS 2008 Annual Conference Portland, Oregon ♦ April 28 - May 2,
2008
ASPRS 2008 Annual Conference Portland, Oregon ♦ April 28 - May 2,
2008
SOFTWARE
Watershed Delineation Hydro was developed by Dr. David Maidment
(University of Texas, Austin – Center for Research for Water
Resources), in collaboration with several prominent universities
(Consortium of Universities for the Advancement of Hydrologic
Sciences) and ESRI, as a mapping software for water resource
professionals. It generates watershed boundaries based on
hydrology, and it allows for geospatial representation of surface
water bodies and integration with hydrologic and hydraulic
modeling.
The Geospatial Hydrologic Modeling Extension (Geo-HMS) is an ArcGIS
application that allows developing a number of hydrologic modeling
inputs. Analyzing digital terrain information, HEC-GeoHMS
transforms the drainage paths and watershed boundaries into a
hydrologic data structure that represents the watershed response to
precipitation. Runoff
HEC-HMS is designed to simulate the precipitation-runoff processes
of dendritic watershed systems. The program is a generalized
modeling system capable of representing many different watersheds.
A model of the watershed is constructed by separating the
hydrologic cycle into manageable pieces and constructing boundaries
around the watershed of interest. Any mass or energy flux in the
cycle can then be represented with a mathematical model. In most
cases, several model choices are available for representing each
flux. Each mathematical model included in the program is suitable
in different environments and under different condition.
INPUTS Creation of the Digital Elevation Model (DEM)
Digital Elevation Models (DEMs) are important tools in hydrologic
research and water resources management owing to the relevance that
geo-morphological features intrinsic in the DEMs have for the
simulation of important water flow processes such as surface
runoff, evaporation and infiltration. Digital For the creation of
the Digital Terrain Model (DEM) we used the Geo-Statistical
extension from ESRI. This extension has several interpolation
methods that can be use to generate surface models. There is a huge
amount of literature that compares the different interpolation and
how it does affect result when the terrain model is created. Each
of the interpolation methods has it advantages and it
drawbacks.
“Irrespective of the landscape morphology and surface area, few
differences existed between the techniques under study provided
that the sampling density was high. This could have been foreseen
since the greater the sampling density is, the lower the impact of
the interpolation technique is, simply due to the mechanical
decrease of space between known values”* This implies that for
lower values of sampling density, the accuracy of height estimation
is more dependent on the choice of interpolation techniques.. This
has been said the use IDW is a easy choices. This is due that it
was a simpler and more accurate interpolation method than kriging
for DEM development, likely due to the high density of the lidar
data points. “The technique takes into account only some adjacent
data points, and thus performs well even for complex landforms if
data density is high”**. Soils
Soil Survey Geographic (SSURGO)."The STATSGO data base was designed
primarily for regional, multi-state, river basin, state and
multi-county resource planning, management, and monitoring”***. The
SSURGO data base provides the most detailed level of information
and was designed primarily for farm and ranch, landowner/user,
township, county, or parish natural resource planning and
management. Using the soil attributes, this data base serves as an
excellent source for determining erodible areas and developing
erosion control practices; reviewing site development proposals and
land use potential; making land use assessments and chemical fate
assessments; and identifying potential wetlands and sand and gravel
aquifer areas.
* Fisher, N.I., Lewis, T., Embleton, B.J.J., 1987. Statistical
Analysis of Spherical Data. Cambridge University Press, Cambridge.
329 pp. ** ASPRS Vol 72 No 11 ***
http://www.ftw.nrcs.usda.gov/pdf/ssurgo_db.pdf
Land Cover/Land Use The Land Use file was created form base on
photo-interpretation. Features were rectified to the latest
aerial
photography available for each county. Collin, Dallas, Denton,
Rockwall & Tarrant counties were based on orthos with a
relative accuracy of 2-foot.
RUNOFF MODEL
HMS computes the runoff volume by computing the volume of water
that is intercepted, infiltrated, stored, evaporated or transpired,
and subtracts it from the precipitation****. HEC-HMS is divided in
to three components.
• Basin model - contains the elements of the basin, their
connectivity, and runoff parameters • Meteorologic Model - contains
the rainfall and evapotranspiration data • Control Specifications -
contains the start/stop timing and calculation intervals for the
run.
Infiltration, interception, evaporation, storage, and transpiration
are collectively known as losses in HMS. HMS has the following loss
models to account for cumulative losses:
• Initial/Constant • SCS (Soil Conservation Service) Curve Number •
Deficit/Constants • Green and Ampt • Gridded SCS Curve Number •
Soil Moisture Accounting Method (SMA) • Gridded SMA • No Loss
rate
HMS computes the precipitation loss for each computation time
interval. This loss is subtracted from the total precipitation
depth for that interval*. This gives precipitation excess that is
considered constant over the entire study area and hence gives a
volume of runoff. SCS Curve Number Loss Method
The Soil Conservation Service (SCS) developed the SCS Curve Number
method to compute abstractions from storm rainfall. This model is
used to estimate the excess in the precipitation, as a function of
cumulative precipitation, soil cover, land use and antecedent
moisture. This is defined as:
SIaP IaPPe +−
2)( (1)
where: Pe=Cumulative excess rainfall depth P= Cumulative depth of
precipitation Ia= initial Loss/ abstraction S= potential maximum
retention
SCS also developed an empirical relation for initial abstraction
from an analysis of small watersheds and is defined as:
SIa *2.0= (2)
The following relationship relates the maximum retention, S, to
soil and cover conditions of the watershed to the curve number,
CN:
**** U.S Army Corps of Engineering (2001) “Hydrological Modeling
System, HEC-HMS, User manual * U.S Army Corps of Engineering (2001)
“Hydrological Modeling System, HEC-HMS, User manual”
ASPRS 2008 Annual Conference Portland, Oregon ♦ April 28 - May 2,
2008
101000)( −= CN
inS (3)
CN Calculations
The Soil Conservation Service (SCS) Curve Number was developed as
an index that represents the combination of hydrologic soils group,
land use, and treatment class. The Curve Number defines the
impervious character of a watershed with 100 being impervious and 0
being totally pervious. CN is a function of three factors, which
are soil group, cover complex, and antecedent moisture
conditions.
More that 4000 soils have been classified based on their runoff
potential and have been grouped into four Hydrologic Soils Groups
(HSG). The soils are grouped according to the intake of water when
the soils are thoroughly wet and receive precipitation from long
duration storms. The four hydrologic soil groups are A, B, C and D
as shown in Table 2 and Table 3.
GROUP CHARACTERISTICS
Table 1. HSG and soil characteristics*
Group Characteristics A Having a higher filtration rate (low runoff
potential) when thoroughly wet. These consist
mainly of deep, well drained to excessively drained sand or gravely
sand. These soils have a high rate of water transmission.
B Having a moderate infiltration rate when thoroughly wet. These
consist chiefly of moderately deep or deep, moderately well drained
or well drained soils that have moderately fine texture to
moderately coarse texture. These soils have a moderate rate of
water transmission.
C Having a slow infiltration rate when thoroughly wet. These
consist chiefly of soils having a layer that impedes the downward
movement of water or soils of moderately fine texture or fine
texture. These soils have a slow rate of water transmission.
D Soils have a very slow infiltration rate (high runoff potential)
when thoroughly wet. These consist chiefly of clay that has high
shrink-swell potential, soils that have a permanent high water
table, soils that have a clay pan or clay layer at or near the
surface, and soils that are shallow over nearly impervious
material.
Table 2. HSG and their runoff potential *
Group Infiltration Rate A Greater than 0.30 in/hr B 0.15 -0.30
in/hr C 0.05 - 0.15 in/hr D These soils have a very slow rate of
water transmission (0-0.05 in/hr).
As a result of urbanization, the soil profile may be considerably
altered and the listed soil group classification
may no longer apply. In these circumstances, Table 3 should be used
to determine Hydrologic Soils Groups (HSG) according to the texture
of the new surface soil, provided that significant compaction of
the soil has not occurred:
* http://www.chathamtownship.org/NatRes_5_Soils.pdf
ASPRS 2008 Annual Conference Portland, Oregon ♦ April 28 - May 2,
2008
Table 3. HSG and Texture *
Group Texture A Sand, Loamy Sand or Sandy Loam B Silt Loam or Loam
C Sandy Clay Loam D Loam, Silty Clay Loam, Sandy Clay, Silty Clay
or Clay
An exaggeration in the estimation of the curve numbers may yield a
result that does not accurately represent the
∑ ∑=
where:
CNcomposite= The Composite CN that is used for runoff volume
calculations in HEC-HMS i= an index of watershed subdivisions of
uniform land use and soil type Ai= drainage area of subdivision
i.
Table 4. Cover types, HSGs and their respective numbers
Cover Type Curve Number for HSG
A B C D Residential High 77 85 90 92 Residential Low 53 69 80 85
Agriculture 72 81 88 91 Transportation 98 98 98 98 Forest 25 55 70
77 Poor Wood 45 66 77 83 Commercial 89 92 94 95 Grass 39 61 74 80
Industrial 81 88 91 93
After the soil and land use are merge the CN composite is calculate
for each of the Subbasins in each of the
three models as shown in Table 5.
ASPRS 2008 Annual Conference Portland, Oregon ♦ April 28 - May 2,
2008
Table 5. CN composite by subwatershed for the three models
10 ft Model 30 ft Model 90 ft Model Subbasin ID CN Composite CN
Composite CN Composite
1 81.9 82.1 82.1 2 80.7 80.6 80.6 3 82.9 82.9 78.7 4 79.8 79.7 83.0
5 81.3 78.6 79.7 6 78.6 81.5 81.4 7 81.5 77.0 81.5 8 78.9 81.4 75.1
9 79.4 79.5 79.4
10 81.5 81.5 81.5 11 83.0 83.0 83.0 12 80.4 80.9 80.6 13 78.8 78.7
78.6 14 84.2 84.2 84.2 15 77.7 77.7 84.3 16 84.4 84.4 77.7 17 83.7
83.7 83.8 18 78.9 79.2 79.0 19 75.2 75.2 75.0 20 83.7 83.7 83.7 21
81.3 81.3 81.3 22 80.6 80.5 80.2 23 79.0 79.2 79.2 24 82.9 82.9
81.2 25 81.9 82.1 82.9 26 83.5 83.5 83.5 27 79.5 79.5 79.7 28 76.3
76.4 76.1 29 74.3 77.9 77.9 30 77.9 74.2 74.5 31 71.9 71.4 66.6 32
79.5 79.2 78.7 33 82.0 82.1 81.8
In this case due that each model delineated the subbsains some what
different, the relation between the ID
linking the three models could not be the same. The table below
(table 6) show which subbasin have the same subbasin ID in the
three models. As show on the table the difference are not
significant.
ASPRS 2008 Annual Conference Portland, Oregon ♦ April 28 - May 2,
2008
Table 6. Common Subbasins ID between the three models
10 ft Model 30 ft Model 90 ft Model
Subbasin ID CN Composite
Area Km2 CN Composite Area Km2 CN Composite Area Km2
1 81.9 82.1 82.1 2 80.7 80.6 80.6 9 79.4 79.5 79.4
11 83.0 83.0 83.0 14 84.2 84.2 84.2 17 83.7 83.7 83.8 18 78.9 79.2
79.0 19 75.2 75.2 75.0 20 83.7 83.7 83.7 21 81.3 81.3 81.3 22 80.6
80.5 80.2 23 79.0 79.2 79.2 24 82.9 82.9 81.2 25 81.9 82.1 82.9 26
83.5 83.5 83.5 27 79.5 79.5 79.7 28 76.3 76.4 76.1 29 74.3 77.9
77.9 30 77.9 74.2 74.5 31 71.9 71.4 66.6 32 79.5 79.2 78.7 33 82.0
82.1 81.8
DIRECT RUNOFF MODEL (TRANSFORM)
Excess rainfall, after flowing over the watershed surface, becomes
direct runoff at the watershed outlet*.The
difference between the observed total rainfall hyetograph and the
excess rainfall hyetograph are abstractions or losses. Losses
result from water absorption by infiltration. Losses can be
determined from the observed hydrograph. There are six methods to
model the direct runoff in HMS:
• Clark • Kinematic Wave • ModClark Snyder • SCS • User-Specific
S-Graph • User Specific UH Graph All watersheds have some response
time that represents how long it takes for runoff to reach the
main
watershed outlet after it begins to rain. The most commonly used
watershed response time is the basin time of concentration. The
Soil Conservation Service (SCS) has developed a relationship
between the basin lag time and the watershed time of concentration,
which is useful for ungauged watersheds. This is defined as:
ASPRS 2008 Annual Conference Portland, Oregon ♦ April 28 - May 2,
2008
* U.S Army Corps of Engineering (2001) “Hydrological Modeling
System, HEC-HMS, User manual”
cl TT 6.0= where:
Tl: Basin Lag Time (Hrs) Tc: Watershed Time of Concentration
(Hrs)
Time of Concentration
The time of concentration is defined as the time required for a
particle of water to travel from the hydraulically most remote part
of the watershed to the main outlet. Frequently the point where the
time of concentration is measured corresponds to the furthest point
from the watershed outlet*. However, because travel velocities are
a function of watershed slope and roughness, it is possible that
the point where the time of concentration is measured may not be
located the furthest away from the watershed outlet. The Soil
Conservation Service has developed a time of concentration formula
that includes the effect of the watershed runoff characteristics.
This is defined as:
Y SLTC 1900
CN S
TC - Watershed time of concentration (Hrs) L – Hydraulic length of
watershed (length from the hydraulically most remote part of
watershed) (ft) Y – Average watershed slope (%) S - Maximum soil
retention. CN – Curve number
Reach Routing
Routing refers to the process of calculating the passage of a flood
hydrograph through a system. The “reach” element in HMS represents
the flow of water through the stream system. When a runoff
hydrograph enters a channel reach, the shapes of the hydrograph
will most likely change as flow travels along the channel.
Generally speaking, the peak discharge is at the downstream end and
is less than the peak flow at the upstream end. Furthermore, the
time to peak is typically greater for the downstream hydrograph
than for the upstream hydrograph, as shown in Figure 2 and Figure
3.
Figure 2. Hydrograph behavior.
The hydrograph entering the channel is usually called an inflow
hydrograph. The time history of flow at the
downstream end of the channel is typically the outflow hydrograph.
The reduction in peak discharge is call flow attenuation. The
difference in time between peak discharges is usually referred to
as the lag time. If there are no
ASPRS 2008 Annual Conference Portland, Oregon ♦ April 28 - May 2,
2008
flow losses (infiltration) or flow gains (lateral flow) along the
channel length, then the volume of runoff will be the same for both
hydrographs.
Figure 3. Hydrographs. D: Duration of the unit hydrograph Tl: Basin
lag time (Hrs)
The primary reason that the peak discharge is attenuated is due to
storage effects in the channel. The smaller the amount of storage
in the channel, the less the amount of flow attenuation. The major
reason that there is a lag between flow peaks is due to the travel
time through the channel. Shorter channels or channels having high
velocities will have a smaller lag time (see Figure 4).
Figure 4. Lag time Concepts.
HEC-HMS has several different reach routing models that can be used
in the simulation model. Also, HEC-
HMS permits runoff hydrographs to be routed through channels using
the following approaches: • Modified plus method • Muskingum method
• Simple Lag model • Muskingum-Cunge method • Kinematic Wave Method
In this case the simplest method is the Simple Lag model, which is
appropriate in cases where the channels have
little or no storage. This method translates the inflow hydrograph
by a specified amount of time. The outflow hydrograph has exactly
the same runoff values as the inflow hydrograph. The only
difference is that they occur at a time that is equal to the time
at which the inflow occurs, plus the lag time, as shown in Figure
3.
For this project I used the Muskingum-Cunge method, which is based
mostly on the geometry of the channel. In the late 1960’s Cunge
theorized that the Muskingum method was actually a solution to the
kinematic wave problem using a linear scheme. Furthermore, he
asserted that the attenuation of the flood wave was due to a
numerical diffusion associated with the method itself. The major
difference between the Muskingum and Muskingum-Cunge method is in
the way the coefficients are determined. In the Muskingum method
the routing and the weighting
ASPRS 2008 Annual Conference Portland, Oregon ♦ April 28 - May 2,
2008
coefficients are assumed to remain constant over the duration of
the flood event*. Thus coefficients on the update formula also
remain constant.
DIFFERENCE IN THE DELINEATIONS OF THE WATERSHEDS
First we need to compare the stream network that was generated by
each model. Doing a visual inspection of the stream network for
each of the models we can not differentiate between them. The three
networks look the same as shown in Figure 5. Now if a more detail
visual inspection is done, we can see there is difference between
three stream networks as shown in Figure 6.
Figure 5. Comparison between the stream networks generated from the
10, 30 and 90 ft DEM’s.
* Chow V.T. and others. (1988). “Applied Hydrology”
ASPRS 2008 Annual Conference Portland, Oregon ♦ April 28 - May 2,
2008
Figure 6. Comparison between the stream networks generated from the
10, 30 and 90 ft DEM’s.
This difference can be more drastic in the meandering part of the
river. As shown in Figure 6 and Figure 7 the
Stream Network that was generated from the 90 ft DEM is loosing the
curvature of the streams. But in a general sense the streams follow
the same pattern. This simplification or generalization on the
streams will reduce the Time of concentration for the model. The
increase in resolution will simplified the morphology of the area,
but at the same time the terrain constitution is will define; even
in the ninety feet DEM most of the terrain features are preserved.
Maybe in a flat area this will not be the case. In relation to the
length of the stream the difference between the 10 ft and 30 ft DEM
in not significant enough to conclude that it will affect result of
the analysis. But that different between the 10ft and 90 ft is
something to consider. This difference can be clarified in the
table below. See table 7.
ASPRS 2008 Annual Conference Portland, Oregon ♦ April 28 - May 2,
2008
Figure 7. Profiles between the stream networks generated from the
10, 30 and 90 ft DEM’s.
Table 7. Difference between the three models
DEM resolution Total Length of the streams (ft)
Mean Standard deviation Number of Streams
10 ft 506422.20 15346.127488 11387.49432 33 30 ft 498469.98
15105.151046 11104.46017 33 90 ft 475158.48 14398.742119
10854.621298 33
These changes in length between each model can be associated with
the increase or decrease of flow in each
model, but cannot be directly correlated with the amount of runoff
that passes through the outlet. Now if we compare the watershed
delineation between the 10ft and the 30 ft model, there differences
visually are more notable but can be assume to be minimum as shown
in figure 8 and figure 9. The main differences are located at the
edges of the subbsains and some in the interior subbasin. This
could be due to the difference in the pixel size.
Figure 8. Difference between the 10ft and 30 ft delineations.
ASPRS 2008 Annual Conference Portland, Oregon ♦ April 28 - May 2,
2008
Figure 9. Difference between the 10ft and 30 ft delineations.
The difference in areas is shown on table 8. This difference
between the total areas can be translated into an increase of the
value of the runoff. This increase has to be related not only to
the difference is area, but also to the land cover and soil type
that the difference adds.
Table 8. Difference between the subbasin delineations
DEM resolution Total Area (ft2) Mean Standard deviation Number of
Streams
10 ft 3363129200.00 101913006.06 78593963.99 33 30 ft 3359814300.00
101812554.54 78999948.08 33 90 ft 3357927899.99 101755390.90
81296442.56 33
ASPRS 2008 Annual Conference Portland, Oregon ♦ April 28 - May 2,
2008
By examining the hydrograph for each of the models, each one has
very similar behavior, except for the small drop in the 30 and 90
ft models as shown in figures 10, 11 and 12. This strange drop
could be due to the difference in the areas, or even in the length
of the stream network or possible due to the simplification of the
terrain morphology due to the change in pixel size. The only
significant difference will be the amount of flow that each model
reaches at each peak which is significant.
Figure 10. 10 ft DEM Hydrograph.
Figure 11. 30 ft DEM Hydrograph.
ASPRS 2008 Annual Conference Portland, Oregon ♦ April 28 - May 2,
2008
Figure 12. 90 ft DEM Hydrograph.
The total outflow will vary between models, but its variation is
almost insignificant as shown in table 9 and figure 13. This will
provide the understanding that the spatial resolution in this
project does not affect the runoff model. More research is needed.
As a general conclusion, we can infer that the resolution does
affect the end results, but is not the only factor that affects the
runoff. This result has to be quantified to determine how much the
outflow difference is related to the DEM resolution.
Table 9. Outflow variation between the models
Model Total Outflow (in) Peak Outflow (cfs) 10 ft 36.28 1269518 30
ft 36.73 445571 90 ft 36.76 416829
Model vs Outflow
Spatial Resolution
To ta
Figure 13. Outflow variation between the models.
ASPRS 2008 Annual Conference Portland, Oregon ♦ April 28 - May 2,
2008
CONCLUSIONS
In general, the resolution does affect the end result in how the
watershed is delineated and the stream network is determined, but
it is not the only factor that affects the runoff. This result has
to be quantified, i.e., how much of the outflow difference is
related to the DEM resolution. Structures also are a key element to
keep in mind when hydrologic modeling is taking place. Structures
affect the delineation of the watershed and the stream network.
Based on the results of this project, a definitive conclusion based
on the assumption that “the better the resolution the better the
result” is not possible.
REFERENCES Chaplot, Vincent, et al. Accuracy of interpolation
techniques for the derivation of digital elevation models in
relation to landform types and data density, Geomorphology, v. 77
issue 1-2, p. 126-141. Chow V.T. and others, 1988. Applied
Hydrology, McGraw-Hill Education. Fisher, N.I., Lewis, T.,
Embleton, B.J.J., 1987. Statistical Analysis of Spherical Data,
Cambridge University Press,
Cambridge. 329 pp. Handbook of Texas Online
http://www.tshaonline.org/handbook/online/index.html
http://www.hec.usace.army.mil/software/hec-hms/. Su, Jason, et al.,
2006. Influence of vegetation, slope and lidar sampling angle on
DEM accuracy, Photogrammetric
Engineering and Remote Sensing, v72, 11, pp1265-1274. U.S Army
Corps of Engineers, 2001. Hydrological modeling system, HEC-HMS,
User Manual. USGS web page (standards for DEM)
http://geology.er.usgs.gov/eespteam/GISLab/Cyprus/dem_standards.htm.
Vazquez, R.F , Feyen, J. Assessment of effects of DEM gridding on
the predictions of basin runoff using MIKE
SHE and a modeling resolution of 600 m, Journal of Hydrology, 334,
73-87.
ASPRS 2008 Annual Conference Portland, Oregon ♦ April 28 - May 2,
2008