The Effects of Different Resolution DEMs
in Determining Overland Flow Regimes
Stacy L.
1
Hutchinson ,
J.M. Shawn
2
Hutchinson ,
Ik-Jae
1
Kim , and
3
Woodford
Philip
1Department
of Biological and Agricultural Engineering and 2Department of Geography, Kansas State University, Manhattan, Kansas
3Integrated Training Area Management, Fort Riley, Kansas
Abstract
1.0
A gully head is a unique landscape feature where concentrated overland flow begins to cause
significant erosion. The impacts of four different resolution digital elevation models (DEMs), three (3, 10,
and 30 m) developed using a differential global positioning system (GPS) survey and the USGS 30 m
DEM, were used to identify transitional flow areas on a grassland hillslope. A simple erosion model,
nLS, based on Manning’s kinematic wave theory, was used to determine where overland flow
transitioned from sheet flow to concentrated flow. The accumulated erosive energy was estimated
using the nLS model, where, n is Manning’s coefficient, L is the overland flow length, and S is the slope.
In addition to the DEMs, spatial analyses for soil (SSURGO) and land cover (Kansas GAP) were
conducted in a geographic information system (GIS). First order streams were delineated using each
resolution DEM (contributing area: 900 m2) and overlaid with the concentrated flow data obtained from
the nLS model results. The intersected area was buffered by 3, 6, 10, or 15 m, depending on the DEM
resolution. Results showed that average topographic and hydrologic variables varied between the
different DEM resolutions. The 3 m DEM produced the best model accuracy, predicting two gully head
locations. The recommended buffer radius was found to be 6 m, which is 2 times of the grid size. The
efforts to develop finer data resolution should be supported in assessing reliable erosion potential for
watershed management.
0.8
Uniform Sheet Flow
A=WxL
RMSE for surface elevation
0.898
•GH-3
0.892
Figure 4. RMSE of random sample points (5 %) for
different resolution DEMs using IDW and TIN
interpolation methods. Results show that 10 m DEMs
offer more reliable prediction for hydrologic modeling
than 30 m DEMs. However, the errors in 10 m DEMs
were 2.44 (IDW) or 2.55 (TIN) times greater than in 3
m DEMs. No difference was seen between
interpolation techniques.
0.6
0.2
0.364
0.356
0.4
0.146
0.143
-
•GH-2
3m
(n = 786)
10 m
(n = 745)
30 m
(n = 650)
IDW
•GH-1
3m
(n = 768)
10 m
(n = 742)
30 m
(n = 650)
TIN
Figure 2. The study site, 8 ha
grass-hillslope area on Fort Riley
with GPS points (n > 15,000) and
three gully head locations (red).
Two areas on the upslope and one
at the downslope were excluded
from the survey to avoid field
experiments and dense vegetation.
Three gully locations (GH-1, 2,
and 3) were surveyed.
Concentrated Flow
A=WxLxβ
β = A – Ineffective Area
L
Ground survey
(Differential GPS,
gully head locations)
Land cover
(Kansas GAP)
IDW &
TIN interpolations
W
Manning’s
coefficient
Creating 3, 10, and 30 m DEMs
Slope
Flow length
USGS 30 m DEM
nLS
nLS
Seven nLS layers
Delineating the 1st order stream
(C.A. = 900 m2)
Reclassifying nLS
(-1.0σ < μ <1.0σ)
A gully erosion area (GH-3) on the study site in figure 2.
(Taken on Oct. 15, 2005 by IJ Kim)
intersecting
Figure 1. Illustrates the key differences between sheet and concentrated flows for
controlling overland flow transport. Unless proper management practices are implemented,
permanent gully erosion may develop.
Data and Methods
Three different resolution (3, 10, and 30 m) digital elevation models (DEMs) were developed using a
differential GPS with post-processing. Two interpolation techniques (inverse distance weighted, (IDW)
and triangulated irregular network, (TIN)) were used for converting from GPS point data to raster format
files for each resolution. The vertical and horizontal errors were assessed using root mean square error
(RMSE) on 5 % randomly selected points and two benchmarks. The USGS National Elevation Dataset
(NED) 30m DEM was also used to develop accumulated erosive energy layers within ESRI ArcGIS
using the nLS model. Input variables (slope and flow length) were calculated using the deterministic
eight direction method (D8). Flow direction was estimated without the ‘pit’ removal process. This
artificial process may alter the effect of accumulating overland flow energy for identifying the flow
transition from sheet to concentrated flow. Flow length for each cell was determined by multiplying the
flow accumulation values by the DEM resolution. Kansas GAP landcover data were used to create
Manning’s coefficient (n) data layers. From the information, a continuous “energy accumulation” grid
was developed using the equation (1). The statistical interval (μ±1.0σ, in English Unit) of mean (μ, 131)
and standard deviation (σ, 22.6) was applied to reclassify the transitional areas (i.e., gully head
locations). It was assumed that gully heads are formed along the 1st order streams. The selected 1st
order stream segments were buffered, using 3 m and 6 m (in radius) for the 3 m DEMs, 10 m for the 10
m DEMs, and 15 m for the 30 m DEMs to account for data resolution errors in identifying gully head
points.
Figure 5. Accumulated overland flow energy
calculated using nLS with different resolution
DEMs (left). Potential erosion areas based on
1st order stream networks and acumulated
overland flow energy (μ±1.0σ of nLS) with
buffering using different resolution DEMs (right).
gully heads
Root mean square error
(5 % of surveyed points)
Selected
1st order stream
Intersecting and evaluating
the model accuracy with
modeled area
Buffering the stream lines
(3, 6, 10, or 15 m in radi)
Conclusions and Future Work
Figure 3. A methods data flow diagram for determining transitional erosion
areas in a GIS. (C.A. means the contributing area for delineating 1st order
stream networks using the flow accumulation grids).
Table 1. Root mean square errors (RMSE) for the GPS survey at the two temporary benchmark
points (BM1: east and BM2: west in figure 2).
Benchmark
Location
n
BM1
Key references
BM2
Northing
Easting
Elevation
Northing
Easting
Elevation
15
15
15
15
15
15
0.041
0.079
0.3
0.088
0.096
0.273
Equation (1)
3.3nL
nLS 
S
n: Manning’s coefficient
L: Flow length (m)
S: Slope (m/m)
RMSE
Finer resolution DEMs provided more detailed land analysis than coarser resolution DEMs. Accurate slope
estimation was very important in this study because the lengths of gully head from the upslope are generally
less than the lengths of the hillslope. Errors in slope estimates significantly affected the model performance
when generating the flow direction and flow accumulation grids. Current results suggest that 3 m or finer
DEMs should be used to determine the geographic locations of gully heads in the model. Further analysis
using a large area and or complete watershed is needed to investigate the impact of study area size and
different land covers. The influence of the ‘pit’ removal process should be evaluated. Additionally, the effect
of varying contributing areas for delineating the 1st order stream network using the same fine resolution DEM
should be analyzed.
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Engineering 121 (3):256-266.
Meyer, A. and J.A. Martinez-Casasnovas. 1999. Prediction of existing gully erosion in vineyard parcels of the NE
Spain: a logistic modeling approach. Soil & Tillage Research 50: 319-331.
Acknowledgements
This work is funded through CPSON-03-02 (Characterizing and Monitoring Non-Point Source Runoff from
Military Ranges and Identifying their Impacts to Receiving Water Bodies).