Effects of Microtopography and Vegetation Growth
on Nonpoint Source Pollution Control in Tallgrass Buffers
Monte
3
Cales ,
Philip L.
1
Barnes ,,
and Amy K.
1
Good
of Biological and Agricultural Engineering, Kansas State University, Manhattan, Kansas
0.98
120
Clipping
(Oct. 1)
0.96
Training Area Management, Fort Riley, Kansas.
3USDA-NRCS,
Fort Riley, Kansas
0.94
PAR absorption
Abstract
Vegetated buffer systems (VBS) are considered one of the most sustainable BMPs for mitigating NPS
pollution. In this study, the efficiency of tallgrass VBS was evaluated on 3-meter by 20-meter tallgrass buffers
during the summer and early fall. Using data collected with double-ring infiltrometers, infiltration rates were
estimated by the Green-Ampt approximation (GA). Three vegetation parameters, canopy height, dry biomass,
and photosynthetically active radiation (PAR) over leaf area, were monitored during the period of study to
assess the impact of vegetation development on runoff generation and transport. A 60-cm and 100-cm
resolution digital elevation model (DEMs) were developed using a total station survey to investigate the impact
of slope variation and flow length on time of concentration for overland flow using Manning’s kinematic equation
and the Darcy-Weisbach equation. The VBS trapping efficiency was 99 % of TSS, 97 % of T-N, 88% of T-P,
and 87 % of PO4--P on average. While infiltration was overestimated in August, the difference between
estimated infiltration from GA and calculated infiltration using a water balance significantly decreased in
October as vegetation senesced. Switch grass (Panicum virgatum) growth averaged 7.0 mm/day between
August and early September and declined to 0.9 mm/day between September and early October. Vegetation
clipping did not influence the runoff ratio or water quality, indicating that upper vegetation canopy does not
retain significant water. The higher resolution DEM (DEM60) showed more detailed slope variation and flow
direction, particularly for smoother buffer topography. Manning’s kinematic estimation yielded more accurate
times of concentration than the Darcy-Weibach estimation.
60
Mean growth:
0.88
7 mm/day
40
(August)
14
12
9.3
10
8
6
5.8
5.0
3.1
4
20
0.84
3.5
3.0
2
0.6
0.3
DOY
Absorption (FV1)
300 m
Absorption (FV2)
CH (FV1)
DOY
Figure 5. Estimated effective hydrologic conductivity (Ks, cm/hr)
using the Green-Ampt model. A reductionin infiltration was
observed as the vegetation senesced, but rates were still
approxiamtely 3 times reported soil texture-based infiltration
estimations.
1.20
90
80
measured (FV1)
70
measured (FV2)
modeled (FV1)
1.00
modeled (FV2)
60
50
40
30
99.6%
FV1
96.9%
83.5%
FV2
82.7%
0.80
99.7%
98.0%
92.2%
91.7%
0.60
0.40
20
Sediment: 6,000 mg/liter
T-N: 20 mg/liter
T-P: 2 mg/liter
0.20
10
0
210
Switch
230
250
270
290
310
DOY
Pump
Figure 6. Differences between the measured and modeled
infiltration volumes. Measured volumes were calculated by
subtracting generated runoff from total water input. The differences
significantly decreased as vegetation senesced, but runoff volume
may be overestimated during the summer by the model.
W
PVC spreader (1.5 m)
Figure 1. The impact of overland flow concentration on buffer effectiveness. Under concentrated flow, the VBS
efficiency may be significantly reduce due to less effective buffer area.
0.00
TSS
T-N
T-P
PO4--P
Figure 7. The removal efficiency of VBS to incoming contaminated
runoff.
FV2
FV1
Legend
60-cm grids
• Regulator (32 to 36 psi)
• Irrigation nozzle (10 psi)
• Irrigation nozzle (6 psi)
• Rain gauge (d: 10.2 cm at 1.0 m)
• Canopy height & Ceptometer
Figure 8. Differences of slope (%)
and flowpath usingdifferent resolution
elevation data, DEM60 and DEM100.
• Double-ring infiltromter
• Native tallgrass
Total Station
3
Input
Table 1. Vegetated buffer system pollutant removal efficiency for contaminated runoff with different topographic
and vegetation conditions.
Runoff
ROSS
(slope:
20%)
4
1
Data and Methods
Two sites (FV1 and FV2) with three replicate plots of 3 m by 20 m were instrumented in late spring, 2005
(figure 2 and 3). Each site was instrumented with a rainfall simulator (irrigation nozzle-type d = 0.47 cm on 1.8
m risers). Water pressure was maintained at 32 to 36 psi with pressure regulators during operation. Rain
gauges (d = 10.2 cm) were located at 3 locations in each replicate at a height of 100 cm above ground to record
actual rainfall volume each plot. To evaluate the efficiency of VBS for trapping NPS pollutants, soil and two
fertilizers were used as pollutant sources. Using a 1,230- liter water reservoir, polluted overland flow was
discharged through an irrigation nozzle and a plastic spreader (d=1.5 m) at 5.7 liter/min. The pollutant
concentrations of synthesized overland flow for each test were 6,000 mg/l total suspended solids (TSS), 20 mg/l
total nitrogen (T-N), and 2 mg/l total phosphorus (T-P). RunOff Sampling Systems (ROSS) were utilized for
collecting runoff. Measured water depths over time were used to estimate runoff volumes and the times of
concentration for overland flow for each experiment. During each field test, infiltration tests were performed
using three double-ring infiltrometers (34cm D x 18cm H, outer ring; 20cm D x 46cm H, inner ring). The
infiltrometers were installed at the top, middle, and bottom slope at each site. Observed infiltration rates were
modeled using the Green-Ampt (GA) equation (Haan et al., 1994; Rawls et al, 1983) to account for infiltrated
volume and water balance within the plots. Measured soil water contents at the depths of 7.6 cm and 15 cm
were used as initial soil water content. Canopy height and PAR (photosynthetically active radiation) was
monitored at five locations in each plot prior to each runoff event. The vegetation was clipped at 15 cm above
ground on October 1, 2005 to investigate the effect of leaf area on runoff generation.
Before the first experiment was performed, 60 cm grids were installed at each site using nylon string. A
survey-grade total station was used to measure elevation data. Two benchmarks were set to convert an
imaginary coordinate system to UTM coordinate system using a differential GPS. The inverse distance weighted
(IDW) interpolation method was used to developed 60 cm and 100 cm resolution DEMs (DEM60 and DEM100).
Equations (1) (Manning’s formula) and (2) (Darcy-Weisbach formula) were applied to calculate the times of
concentration (Tc) using the DEM products (slope and longest flow length).
Output
20 m
3m
Figure 3. Schematic of field instrumentation for
simulating overland flow processes and NPS pollution
reduction.
DEM60
DEM100
DEM60
DEM100
Conclusions and Future Work
Water input (hr)
Site
FV1
FV2
mean
288
280
253
246
FV2
CH (FV2)
100
3
(1,230 liter)
FV1
Figure 4. Observed leaf area index (PAR) and canopy height from
August to October. The mean canopy height at FV2 was taller
than the canopy height at FV1. Clipping the vegetation cover to 15
cm above ground resulted in a decrease in PAR of 3 % at FV1 and
6 % at FV2. Standing stems and surface litter continued to absorb
rainfall energy and intercept water.
Infiltration (m )
Figure 2. Locations of VBS field sites at Ft. Riley. Water lines were
connected from a fire hydrant. A benchmark was set at each site.
The major vegetation group was switch grass. Soils were silty clay
loam (avg. hill slope: 3.9 %).
228
280
224
260
mean
240
287
220
281
200
252
180
0
300
245
0.82
229
0
Runoff reservoir
L
0.90
0.86
Concentrated Flow
A=WxLxβ
β = A – Ineffective Area
Uniform Sheet Flow
A=WxL
80
0.92
16
221
2Integrated
100
18
Effective Ks (cm/hr)
Philip
2
Woodford ,
Removal Efficiency (%)
1Department
Ik-Jae
1
Kim ,
Canopy height (CH, cm)
Stacy L.
1
Hutchinson ,
Dates
DOY
Aug. 09 05
Aug. 17 05
Sep. 02 05
Sep. 09 05
Oct. 08 05
Oct. 14 05
Aug. 12 05
Aug. 16 05
Sep. 03 05
Sep. 10 05
Oct. 07 05
Oct. 15 05
221
229
245
252
281
287
224
228
246
253
280
288
Rainfall duration Overland flow
(water depth, cm) (volume, liter)
3 (5.7)
6 (1,961)
1 (1.9)
6 (1,961)
1.3 (2.5)
6 (1,961)
1.3 (2.5)
6 (1,961)
0.83 (1.9)
1.3 (435)
0.83 (1.9)
1.3 (435)
3 (5.7)
6 (1,961)
1 (1.9)
6 (1,961)
1.3 (2.5)
6 (1,961)
1.3 (2.5)
6 (1,961)
0.83 (1.9)
1.3 (435)
0.83 (1.9)
1.3 (435)
Table 2. Operating condition for each experiment on VBS plots.
Contributing
area ratio
Designed
runoff rate, %
50:1
50:1
50:1
50:1
25:1
25:1
50:1
50:1
50:1
50:1
25:1
25:1
6
2
5
5
2
2
6
2
5
5
2
2
The goals of this study were to evaluate the performance of tallgrass buffer systems for mitigating NPS pollution and
to identify impact of buffer topography on hydrologic responses of overland flowpaths using high resolution DEMs. The
vegetation, predominately Switch grass, played a significant role in controlling runoff generation in the rainfallinfiltration-runoff processes. There was a clear distinction between measured and simulated infiltration rates from
August to October. Indicating further research is needed to better understand the impact of vegetation on infiltration,
the primary water and pollutant removal mechanism. Vegetation leaf area did not influence runoff generation or water
quality. In general, the high pollutant removal efficiency of VBS was maintained for varying storm lengths and
contributing areas. Results from the spatial analysis showed that finer resolution DEMs provided more detailed slope
variation, and flow direction calculations were affected by DEM resolution. Manning’s kinematic formula to compute
times of concentration for overland flow yielded more accurate estimates than the Darcy-Weisbach formula. Therefore,
surface roughness should be considered when assessing the function of VBS. More detailed Manning’s coefficients
with respect to vegetation growth may improve the prediction of times of concentration. Further comparisons over
larger buffer areas, especially greater widths, are needed to better assess the impact of DEM resolution on hydrologic
calculations.
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). The authors also acknowledged the field
instrumentation and survey team in Summer, 2005: Curtis R. Trecek, Peter A. Clark, Dr. Gary Clark, Adam C. Madison,
Reid D. Christianson, Ashley E. Clark, Tracy Lee, and Byung J. Ahn