1. T. Kevin O’Donnell1, Claire Baffaut2, Stephen H. Anderson1
1 Department of Soil, Environmental and Atmospheric Sciences,
University of Missouri, Columbia, Missouri USA
2 USDA-ARS, Cropping Systems and Water Quality Research Unit,
Columbia, Missouri, USA
1
Evaluation of Water Quality Trends in
Goodwater Creek Experimental Watershed:
Implications for Monitoring Strategies and
Objective Setting

2. Upper Mississippi River Basin:
River Restoration
Limited accountability
for projects
Poor project objective
setting
Poorly designed
monitoring plans
Decreased chances to
demonstrate success
2

3. Study Objectives
Assess BMP Effects in the Goodwater Creek
Experimental Watershed (GCEW)
Atrazine Event Loads
Nitrate Event Loads
Evaluate GCEW Monitoring Network
Goal: “Documenting Impact of Prevailing
Farming Systems on Herbicide Contamination
and Transport”
Future Objective Setting for BMP
Implementation
3

4. Methods
GCEW Monitoring Data: Hydrologic Data
Stream Stage at Three
Weirs
12, 31, 73 km2
Precipitation
Max. 10 rainfall gages
4

5. Methods
GCEW Monitoring Data: Atrazine & Nitrate
Autosamplers
Weir 1
Atrazine (1993 to 2006)
Nitrate (1993 to 2004)
Weir 9 (1993 to April 1997)
Weir 11 (1993 to April 1997)
Weekly Grab Samples
5

6. Methods
GCEW BMP Implementation
6

7. Methods
GCEW BMP Implementation
7

8. Methods
GCEW BMP Implementation
8

9. Methods
Event Identification: Constant Slope Method (1967)
9

10. Methods
Event Identification: Constant Slope Method (1967)
Weir 1 Time of Concentration: 29 hours
10

11. Methods
Event Identification: Constant Slope Method (1967)
Event Delineation
Weir 1: 59 Events; Weir 9: 32 Events; Weir 11: 39 Events
11

12. Methods
Event Identification: Constant Slope Method (1967)
Final Event Identification
Weir 1: 282 Events; Weir 9: 126 Events; Weir 11: 142
Events
12

13. Methods
Gradual Change, Multiple Regression Model
Y = atrazine/nitrate event load (kg)
β0 = intercept
F = 4 streamflow/hydrograph predictors
P = 9 rainfall predictors
BMP = chronology of GCEW area affected by 5
BMP types (ha)
є = residual
εβ ++++= BMPPFY 0
13

14. Methods
Gradual Change, Multiple Regression Model
Step 1: Identify best subset of streamflow/rainfall
predictors by:
R-squared (r2)
Akaike’s Information Criterion (AIC)
Remove temporal autocorrelation of residuals
εβ ++++= BMPPFY 0
14

15. Methods
Gradual Change, Multiple Regression Model
Step 2: Identify/quantify BMP effects on loads:
Akaike’s weights (wi)
All possible combinations of BMP predictors
included in model
εβ ++++= BMPPFY 0
15

16. Methods
Gradual Change, Multiple Regression Model
BMP Predictors
Grass waterways
Terraces drained by grass waterways
Terraces drained by underground outlets
Vegetative filter strips
Permanent vegetation
32 possible model combinations ranked by wi (0-1)
εβ ++++= BMPPFY 0
16

17. Methods
Future Monitoring Needed for Reduction Scenarios
n = number of sampled events needed to show a %
change in mean atrazine/nitrate load
MSE = mean squared error of regression models
t = two tailed, Student’s t value at an α=0.05
D = % change in mean atrazine/nitrate event load
5%, 10%, 20%, and 25% change
2
)*2(
2
**2
D
tMSE
n
knpre −
=
17

18. Results
Event Loads
18

19. Results
Spring Atrazine Regression Models
Flow/Precipitation
Parameter Estimates
Events n ßo
Event
Runoff†
Event
Rainfall
Temporal
Autocorrelation MSE‡ R2
Weir 1
116 78 0.21 0.69 Yes 0.17 0.46
Weir 9
43 27 0.16 0.01 No 0.12 0.44
Weir 11
53 36 0.21 0.01 Yes 0.16 0.36
†Log10 Event Runoff
‡ Mean Squared Error of Regression Model
19

20. Results
Spring Atrazine Regression Models
Flow/Precipitation
Parameter Estimates
Events n ßo
Event
Runoff†
Event
Rainfall
Temporal
Autocorrelation MSE‡ R2
Weir 1
116 78 0.21 0.69 Yes 0.17 0.46
Weir 9
43 27 0.16 0.01 No 0.12 0.44
Weir 11
53 36 0.21 0.01 Yes 0.16 0.36
†Log10 Event Runoff
‡ Mean Squared Error of Regression Model
20

21. Results
Spring Nitrate Regression Models
Flow/Precipitation
Parameter Estimates
Events n ßo
Event
Runoff†
10-Day
Rain
Temporal
Autocorrelation MSE‡ R2
Weir 1
104 70 2.33 0.93 -0.002 Yes 0.09 0.75
Weir 9
43 25 1.75 0.99 Yes 0.11 0.75
Weir 11
53 33 1.49 0.94 Yes 0.10 0.78
†Log10 Event Runoff
‡ Mean Squared Error of Regression Model
21

22. Results
GCEW BMP Implementation
75% of area
affected by
BMPs
1% of
watershed area
affected by
BMPs
22

23. Results
Determination of BMP Effects
Predictor Variable
Parameter
Estimate AICc Deltai wi
Weir 1 Spring Atrazine Load
No BMPs - 57.3 0.0 0.94
Filter Strips 0.004 64.4 7.1 0.03
Terraces w/ Grass Waterways -0.002 65.4 8.1 0.02
Weir 1 Spring Nitrate Load
No BMPs - 9.2 0.0 0.95
Filter Strips 0.003 16.6 7.4 0.02
Terraces w/ Grass Waterways 0.001 17.9 8.7 0.01
23

24. Results
Future Monitoring Needed at Weir 1
Monitoring Required to Show % Reduction
Analyte 5% 10% 20% 25%
Sampled Events
Atrazine 2519 659 180 120
Nitrate 1063 280 78 53
24

25. Results
Future Monitoring Needed at Weir 1
Monitoring Required to Show % Reduction
Analyte 5% 10% 20% 25%
Sampled Events
Atrazine 2519 659 180 120
Nitrate 1063 280 78 53
Sampling Years
Atrazine 439 114 31 21
Nitrate 185 49 13 9
25

26. Discussion
No Evidence of BMP Effects: Atrazine
No watershed-scale
objectives
No-till adoption
~30% ↑ in county crop area
20% ↑ in runoff
120% ↑ in atrazine loss
↑ Split-application of
atrazine?
26

27. Discussion
No Evidence of BMP Effects: Nitrate
Unintended effects
of terraces
27

28. Discussion
No Evidence of BMP Effects: Nitrate
Unintended effects
of terraces
Groundwater
leaching?
BMP choice for
reducing nitrate
Winter cover crop
(Blevins et al. 1996)28

29. Discussion
Potential Errors in Event Load Determinations
Delay in 1st
autosample
77% of Weir 1
sampled events
Mean: 8.4 hr delay
Mean: 20% volume
missed
29

30. Discussion
Potential Errors in Event Load Determinations
Delay in 1st
autosample
77% of Weir 1
sampled events
Mean: 8.4 hr delay
Mean: 20% volume
missed
30

31. Discussion
Potential Errors in Event Load Determinations
Delay in 1st
autosample
77% of Weir 1
sampled events
Mean: 8.4 hr delay
Mean: 20% volume
missed
31

32. Discussion
Potential Errors in Event Load Determinations
Delay in 1st
autosample
77% of Weir 1
sampled events
Mean: 8.4 hr delay
Mean: 20% volume
missed
Sample compositing
between events
22% of Weir 1 events
32

33. Discussion
Potential Errors in Event Load Determinations
Delay in 1st
autosample
77% of Weir 1
sampled events
Mean: 8.4 hr delay
Mean: 20% volume
missed
Sample compositing
between events
22% of Weir 1 events
33

34. Discussion
Monitoring Goals: Atrazine vs. Nitrate
Goal: “Documenting Impact of Prevailing Farming
Systems on Herbicide Contamination and Transport”
Nitrate r2: 0.76
Atrazine r2: 0.42
Prevailing Farming Systems are not monitored
Targeted monitoring needed
Decrease potential errors in load by adapting flow-
proportional autosampling?
34

35. Questions?
Email: tkot24@mail.mizzou.edu
35