A GIS-Enabled Kinematic Wave Approach for Calculating the Transition between Sheet and Concentrated Flows

1
A GIS-Enabled Kinematic Wave Approach for
Calculatingthe Transition between Sheet and
Concentrated Flows Stacy L. Hutchinson1, J.M.
Shawn Hutchinson2, and Ik-Jae Kim11Department of
Biological and Agricultural Engineering and
2Department of Geography, Kansas State
University, Manhattan, Kansas 66506
Introduction Non-point source (NPS) pollution has
been called the nations largest water quality
problem, and its reduction is a major challenge
facing our society today. As of 1998 over
290,000 miles of river, almost 7,900,000 acres of
lake and 12,500 square miles of estuaries failed
to meet water quality standards. Military
training maneuvers have the potential to
significantly alter land surfaces in a manner
that promotes NPS pollution, resulting in the
inability of military installations to meet water
quality standards and the decline of training
lands. Currently, most efforts to reduce NPS
pollution focus on the use of watershed water
quality models. Identification of overland flow
networks is a vital preprocessing step for these
NPS models. Flow networks are used to determine
transport routes for pollution and optimal
placement of best management practices. One
practice that is widely adopted for reducing NPS
pollution is the vegetated buffer system (VBS).
The primary hydrologic consideration for VBS
design and function is uniform sheet flow. With
time, however, overland flow concentrates and
channelizes, reducing contact time with
vegetation and NPS pollution reduction
efficiency. The kinematic wave approximation is
a useful technique for calculating overland flow
time of concentration within a drainage area.
Digital elevation models (DEMs) are widely used
for determining various landscape variables, as
well as for delineating overland flowpath
networks and drainage area boundaries. Using
topographic variables estimated from DEMs and
applying the kinematic wave theory in a GIS
environment, it is possible to estimate the
length and travel time of overland flow providing
an improved understanding of VBS placement for
maximum water quality benefit, as well as a
reduction in gully erosion caused by concentrated
flow.
Data and Methods USGS National Elevation Dataset
(NED) 30m digital elevation model (DEM) data was
used to develop three raster data layers using
ESRIs ArcGIS 9.1 software Slope, flow
direction, and flow accumulation. Slope (S) was
calculated using the deterministic eight
direction method (D8) in 3 by 3 cells. Unlike
the normal procedure for delineating stream
networks, flow direction was determined without
filling because characteristics of land
curvature affect the transition from sheet flow
to concentrated flow and the potential for gully
erosion. A flow accumulation grid, which
connects the direction of flow from cell to cell
and determines the number of cells accumulating
within a downslope flowpath, was estimated using
standard ArcGIS flow direction tools. The flow
accumulation values (no. of cells) was converted
into a slope length grid by multiple number of
cells by 30, then by 3.208 to determine the
upslope slope length for each cell (L). Kansas
GAP landcover data for the installation were used
to create a grid dataset for Mannings
coefficient (n) data layers. From this
information, a continuous energy accumulation
grid calculated as the product of three separate
data layers representing Mannings coefficient
(n), slope length (L), and the square root of
slope (S-0.5).
tov 0.93(nL)0.6 / i0.4(S0.5)0.6
Figure 3. The Fort Riley installation with grid
surfaces representing (from bottom to top)
Mannings coefficient, flow length, slope, and
nLS-0.5 energy accumulation.
Figure 4. Tables showing reported efficiencies
of vegetated buffer systems and McCuen and
Spiess (1995) Tov calculation with evaluation
criteria.
Shrubs nutrient removal
Zone 1
Zone 3
Zone 2
Grass control runoff, sediment
Trees bank stabilization
Uniform Sheet Flow A W x L
Concentrated Flow A W x L x ß ß A
Ineffective Area
L
W
Figure 5. Energy accumulation grid for a
subwatershed of Cheney Reservoir, near Wichita,
Kansas with actual ephemeral gully locations.
References McCuen, R.H. and J.M. Spiess. 1995.
Assessment of kinematic wave time of
concentration. Journal of Hydrologic 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. Prosser, I.P. and
B. Abernethy. 1996. Predicting the topographic
limits to a gully network using a digital terrain
model and process thresholds. Water Resources
Research 32(7) 2289-2298. Tarboton, D.G., R.L.
Bras, and I. Rodriguez-Iturbe. 1991. On the
extraction of channel networks from digital
elevation model. Hydrological Processes
581-100. 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) and the Kansas Agricultural
Experiment Station. Special thanks to Mr. Phil
Woodford and the Fort Riley Integrated Training
Area Management (ITAM) for assistance with field
site development and data collection.
Preliminary Results The location of ephemeral
gullies were recorded, as part of a separate
project, within a subwatershed of Cheney
Reservoir in south-central Kansas. Gully point
locations were overlayed on top of a continuous
energy accumulation grid calculated as the
product of three separate data layers
representing Mannings coefficient (n), slope
length (L), and the square root of slope (S-0.5).
Energy accumulation values for each gully point
were extracted, as were the values for 500
additional points located randomly within the
subwatershed. The mean values for these two
datasets were then compared using a one-tailed
two sample for means z-test in order to determine
whether they were significantly different. The
alternate hypothesis is that nLS-0.05 energy
values at the gully locations would be greater
than those for the randomly-placed
points. Initial data analysis indicates that the
mean energy accumulation values for the gully and
random point datasets are significantly different
at p 0.21. Mean (standard deviation) nLS-0.05
values for the gully and random points were
32,532.5 (66,334.9) and 26,989.6 (123,110.7),
respectively. Though the p-value is not in the
preferred range of 0.01-0.05, this is an
encouraging result given that very coarse
resolution DEMs, and no soil characteristics,
were used in this study.
30m USGS
30 m
10 m
3 m
Figure 2. Digital elevation models of varying
spatial resolutions with resulting flow networks
superimposed upon a false color composite aerial
photograph of the Fort Riley study site.