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Uncertainty in Predicting Pesticide Surface Runoff Reduction with Vegetative Filter Strips
Garey A. Fox, Ph.D., P.E. - Oklahoma State UniversityRafael Munoz-Carpena, Ph.D. – University of Florida
George Sabbagh, Ph.D. – Bayer CropScience
Organization of Presentations• Introduction to Vegetative Filter Strips (VFS)
– Predicting flow, sediment, and pesticide mass reduction
• Development of an integrated modeling tool for VFS (VFSMOD-W)• Need for understanding parameter importance and uncertainty• Global sensitivity and uncertainty analyses applied to VFSMOD-W
– Uniform flow studies - Sabbagh et al., 2009; Munoz-Carpena et al., 2010
– Uniform vs. Concentrated Flow - Poletika et al., 2009; Fox et al., 2010
Vegetative Filter Strips (VFS)
http://tti.tamu.edu/publications/researcher/v41n1/images/roadway_grass.gif
• Also known as riparian buffers and grassed waterways
• Take home message:One size does not fit all!
VFS ProcessesIncrease in hydraulic resistance Increase in hydraulic resistance
to flow and soil infiltrationto flow and soil infiltration
Overland flow (and dissolved Overland flow (and dissolved pollutants) reduction pollutants) reduction
(infiltration) and delay(infiltration) and delay
Decrease in sediment/particles Decrease in sediment/particles transport capacity of flowtransport capacity of flow
Sediment/particles deposition Sediment/particles deposition (and pollutants bonded) in filter(and pollutants bonded) in filter
VFS - Complex and Dynamic Systems
Liu, X., X. Zhang, and M. Zhang. 2008. Major factors influencing the efficacy of vegetated buffers on sediment trapping: A review and analysis. J. Environ. Qual. 37:1667–1674.Fox, G.A.; Sabbagh, G.J. Comment on “Major Factors Influencing the Efficacy of Vegetated Buffers on Sediment Trapping: A Review and Analysis”. J. Environ. Qual. 2009, 38 (1), 1-3.
• VFS efficacy is difficult to predict
• Variability cannot be explained by buffer width or buffer slope alone
• Large number of parameters and uncertainties need to be taken into account
VFS - Complex and Dynamic Systems
Predictions with simple empirical equation (SWAT)
Lack of relationship with Koc
Sabbagh, G.J.; Fox, G.A.; Kamanzi, A.; Roepke, B.; Tang, J.Z. Effectiveness of vegetative filter strips in reducing pesticide loading: Quantifying pesticide trapping efficiency. J. Environ. Qual. 2009, 38 (2), 762-771.
VFS - Complex and Dynamic Systems• Limited prediction equations available for pesticide
reduction (P):
Calibration Validation
Sabbagh, G.J.; Fox, G.A.; Kamanzi, A.; Roepke, B.; Tang, J.Z. Effectiveness of vegetative filter strips in reducing pesticide loading: Quantifying pesticide trapping efficiency. J. Environ. Qual. 2009, 38 (2), 762-771.
R2=0.86, adjusted R2=0.84standard error of estimate of 8.43, P-value< 0.001
Pesticide Reduction Equation for VFS
Linking Empirical Equation with VFSMOD-W
• Parameters for estimating P, such as Q and E, not easily predicted
• Uncalibrated VFS model that predicts Q and E– Vegetative Filter Strip Modeling System, VFSMOD– Finite-element, field-scale, storm-based model
• Routes incoming hydrograph and sedigraph
• Infiltration - Green-Ampt• Sediment trapping -
GRASSF
VFSMOD-W Performance
Q and E P
Sabbagh, G.J.; Fox, G.A.; Kamanzi, A.; Roepke, B.; Tang, J.Z. Effectiveness of vegetative filter strips in reducing pesticide loading: Quantifying pesticide trapping efficiency. J. Environ. Qual. 2009, 38 (2), 762-771.
Poletika, N.N.; Coody, P.N.; Fox, G.A.; Sabbagh, G.J.; Dolder, S.C.; White, J. Chlorpyrifos and atrazine removal from runoff by vegetated filter strips: Experiments and predictive modeling. J. Environ. Qual. 2009, 38 (3), 1042-1052.
Effect of Concentrated Flow
All
Block means
Atrazine
Chlorpyrifos
Effect of Concentrated Flow
Muñoz-Carpena, R., G.A. Fox and G.J. Sabbagh. 2010. Parameter importance and uncertainty in predicting runoff pesticide reduction with filter strips. J. Environ. Qual. 39(1):1-12
Mathematical Model with 18 Input Parameters
So how to handle this complexity?• Key Drivers: Hydrologic response• So what do we really know?
– Mathematical Models Built in Presence of UNCERTAINTY
– Input factors (uncertainty sources): input variables, parameters, equations, calibration data, scale, model structure
Uncertainty Analysis (UA)
• Propagates all these uncertainties, using the model, onto the model output of interest.
MODEL0
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Bin
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uency
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CDF
UNCERTAINTY ANALYSIS
• Apportions the uncertainty in the output to different sources of uncertainty in model input
B
C
A
SENSITIVITY ANALYSIS
TOTAL OUTPUT VARIANCE
For model with 2 input factors: A, B. Residual variance C
SENSITIVITY ANALISIS
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uency
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CDF
?
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Sensitivity Analysis (SA)
UA/SA Methods• Why is it important?
– Explore model behavior, identify influential parameters, characterize interactions, simplify
• Local vs. global sensitivity:– Local techniques inherently assume models are
monotonic, linear and additive– Parameters are varied over a limited range and about an
assumed central value, one at a time – interactions of parameters are not accounted for
– Global analysis techniques attempt to measure total sensitivity to a parameter
Two-Step Global Process
1. Global SA – Screening with limited number of simulations (Morris Method) - QUALITATIVE RESULTS
2. Global SA and UA - Variance-based method (Extended Fourier Analysis of Sensitivity Test - Extended FAST) - QUANTITATIVE RESULTS
Step 1: Screening w/ Morris Method• Uses few simulations to map relative sensitivity• Identifies a subset of more important parameters for
quantitative analysis• Provides an early indication of the importance of first
order effects vs. interactions
valueSH detentTOPO
a0.00
0.05
0.10
0.15
0.20
0.00 0.05 0.10 0.15 0.20m*
s
minimum
μ* - Importance
σ-
Inte
ract
ions
• Morris Method results in two sensitivity measures: μ* and σ
• Quantifies the direct contribution to variance of each parameter
• Quantifies the total contribution to variance of all the interactions between parameters
• Variance decomposition requires a large number of simulations per parameter, hence the need for initial screening (Morris)
Step 2: Variance-Based Method
Step 2: Variance-Based Method
1 2( ) ... kV Y V V V R
V(Y) – variance of output, Vi – variance due input factor Xi, k – number of uncertain factors, R - residual
V3
V2V1
R
1. Si - first-order sensitivity index: Si = Vi / V(Y)
Quantitative Extended FAST
2. ST(i) - total sensitivity index
STi - Si = higher-order effectsSA
SABSAC
SABC
For model with 3 parameters: A, B, and C:ST(A) = SA + SAB + SAC + SABC ST(A)
Evaluation Framework
Application of Framework to VFS Studies• Uniform Flow Studies:
– Arora et al. (1996), Patzold et al. (2007) and Poletika et al. (2009)
– Input PDFs derived for the model’s 18 input variables– Output variables: Q, E, and P
Uniform Flow Studies – Morris Q
Poletika and Patzold Arora
Q - Poletika et al. (2009)
Absolute Value of Mean Elementary Effects, m*
0 10 20 30 40
Sta
ndar
d D
evia
tion
of
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men
tary
Eff
ects
, s
0
10
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30
40
VKS
FWIDTH OIOS
Q - Arora et al. (1996)
Absolute Value of Mean Elementary Effects, m*
0 5 10 15 20 25 30
Sta
ndar
d D
evia
tion
of
Ele
men
tary
Eff
ects
, s0
5
10
15
20
25
30
VKSVL
OI
OS
SOA
RNA
FWIDTH
Uniform Flow Studies – Morris E
E - Poletika et al. (2009)
Absolute Value of Mean Elementary Effects, m*
0 1 2 3 4 5
Sta
ndar
d D
evia
tion
of
Ele
men
tary
Eff
ects
, s
0
1
2
3
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5
VKS
FWIDTHVL
OS
DP
OI
SAV
SS
E - Arora et al. (1996)
Absolute Value of Mean Elementary Effects, m*
0 2 4 6 8 10
Sta
ndar
d D
evia
tion
of
Ele
men
tary
Eff
ects
, s0
2
4
6
8
10
VKSOI
SSRNA
VNDP
SOA
Poletika and Patzold Arora
Uniform Flow Studies – Morris P
Poletika and Patzold Arora
P - Patzold et al. (2007) - Metolachlor
Absolute Value of Mean Elementary Effects, m*
0 5 10 15 20 25 30 35
Sta
ndar
d D
evia
tion
of
Ele
men
tary
Eff
ects
, s
0
5
10
15
20
25
30
35
VKSOI OS
KOC PCTC
P - Arora et al. (1996) - Metolachlor
Absolute Value of Mean Elementary Effects, m*
0 3 6 9 12 15
Sta
ndar
d D
evia
tion
of
Ele
men
tary
Eff
ects
, s0
3
6
9
12
15
VKSFWIDTH
OI
OS
RNA PCTCVN
SOA
DPKOC
VL
Uniform Flow Studies – Extended FAST• Global SA confirmed Morris results:
– Removal efficiencies were not simple and were dominated by interactions and non-linear responses
– VKS single most important input factor (Q and P)
Total Output Variance
Explained by an Input Parameter
= First-Order Index
Si = Vi / V(Y)
Uniform Flow Studies – Extended FAST• Global UA provided ranges in expected Q, E, and P:
Poletika et al. (2009)
0% 20% 40% 60% 80% 100%
Rel
ativ
e F
requ
ency
0%
5%
10%
15%
20%
25%
Q E P - AtrazineP - Chlorpyrifos
Percent Reduction in Runoff (Q), Erosion (E) and Pesticide (P)
Towards Q Towards E
Application of Framework to VFS Studies• Uniform vs. Concentrated Flow:
– Poletika et al. (2009) study included both uniform flow and concentrated flow treatments
– Input PDFs derived for the model’s 18 input variables with varying FWIDTH distributions (4.6 m vs. 0.46 m)
– Output variables: Q, E, and P
Uniform vs. Concentrated – Morris Q
Uniform Concentrated
Q - Uniform Flow
Absolute Value of Mean Elementary Effects, m*
0 10 20 30 40
Sta
ndar
d D
evia
tion
of
Ele
men
tary
Eff
ects
, s
0
10
20
30
40
VKS
FWIDTH OIOS
Q - Concentrated Flow
Absolute Value of Mean Elementary Effects, m*
0 1 2 3 4 5
Sta
ndar
d D
evia
tion
of
Ele
men
tary
Eff
ects
, s0
1
2
3
4
5
VKS
FWIDTHOI
OS
Uniform vs. Concentrated – Morris E
Uniform Concentrated
E - Uniform Flow
Absolute Value of Mean Elementary Effects, m*
0 1 2 3 4 5
Sta
ndar
d D
evia
tion
of
Ele
men
tary
Eff
ects
, s
0
1
2
3
4
5
VKS
FWIDTH
OI OS
DP
VLSS
SAV
E - Concentrated Flow
Absolute Value of Mean Elementary Effects, m*
0 3 6 9 12 15
Sta
ndar
d D
evia
tion
of
Ele
men
tary
Eff
ects
, s0
3
6
9
12
15
VKSFWIDTH
DPSS
VNVL
Uniform vs. Concentrated – Morris P
Uniform Concentrated
P - Atrazine - Uniform Flow
Absolute Value of Mean Elementary Effects, m*
0 5 10 15 20 25
Sta
ndar
d D
evia
tion
of
Ele
men
tary
Eff
ects
, s
0
5
10
15
20
25
VKS
OI OS
PCTCKOC
P - Atrazine - Concentrated Flow
Absolute Value of Mean Elementary Effects, m*
0 1 2 3 4 5 6
Sta
ndar
d D
evia
tion
of
Ele
men
tary
Eff
ects
, s0
1
2
3
4
5
6
VKS
SS PCTCKOC
DPFWIDTH
VNVL
OSOI
Uniform vs. Concentrated – Extended FAST• Global SA results:
– Percent of total output variance explained by first-order effects:• 48-64% for Uniform Flow• 19-21% for Concentrated Flow
– Uniform flow - Q controlled model response under uniform flow with VKS accounting for 46-51% of total output variance
– Concentrated flow – not one input factor explained more than 8% of the total output variance
• Unique processes introduced into VFS during concentrated flow
Uniform vs. Concentrated – Extended FAST• Global UA provided ranges in expected Q, E, and P:
PDF - Uniform Flow
0% 20% 40% 60% 80% 100%
Rel
ativ
e F
requ
ency
0%
5%
10%
15%
20%
25%
30%
Q E P - AtrazineP - Chlorpyrifos
Percent Reduction in Runoff (Q), Erosion (E) and Pesticide (P)
Uniform vs. Concentrated – Extended FAST• Global UA provided ranges in expected Q, E, and P:
PDF - Concentrated Flow
0% 20% 40% 60% 80% 100%
Rel
ativ
e F
requ
ency
0%
5%
10%
15%
20%
25%
30%
Q E P - AtrazineP - Chlorpyrifos
Percent Reduction in Runoff (Q), Erosion (E) and Pesticide (P)
Conclusions• Global SA and UA helped in the analysis of VFS
– Hydraulic conductivity most important input factor for flow– Average particle diameter and conductivity most important for sedimentation– Same parameters most important for pesticide trapping
• Significant interaction effects between variables, especially for concentrated flow• Global UA showed commonly observed reduction in pesticide trapping with
concentrated flow
Questions?
E-mail: garey.fox@okstate.edu
Uniform Flow Studies – Extended FAST• Global UA provided ranges in expected Q, E, and P:
Arora et al. (1996)
0% 20% 40% 60% 80% 100%
Rel
ativ
e F
requ
ency
0%
2%
4%
6%
8%
10%
12%
14%
16%
Q E P - Atrazine, CyanazineP - Metolachlor
Percent Reduction in Runoff (Q), Erosion (E) and Pesticide (P)
Patzold et al. (2007)
0% 20% 40% 60% 80% 100%
Rel
ativ
e F
requ
ency
0%
2%
4%
6%
8%
10%
Q E P - MetolachlorP - PentimethalinP - Terbuthylazine
Percent Reduction in Runoff (Q), Erosion (E) and Pesticide (P)
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