1. Uncertainty in Predicting Pesticide Surface Runoff Reduction with Vegetative Filter Strips Garey A. Fox, Ph.D., P.E. - Oklahoma State University Rafael Munoz-Carpena, Ph.D. – University of Florida George Sabbagh, Ph.D. – Bayer CropScience

2. 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

3. Vegetative Filter Strips (VFS) http://tti.tamu.edu/publications/resear cher/v41n1/images/roadway_grass.gif Also known as riparian buffers and grassed waterways Take home message: One size does not fit all!

4. VFS Processes Increase in hydraulic resistance to flow and soil infiltration Overland flow (and dissolved pollutants) reduction (infiltration) and delay Decrease in sediment/particles transport capacity of flow Sediment/particles deposition (and pollutants bonded) in filter

5. VFS - Complex and Dynamic Systems

6. 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

7. Predictions with simple empirical equation (SWAT) Lack of relationship with K oc 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):

8. CalibrationValidation 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. R 2 =0.86, adjusted R 2 =0.84 standard error of estimate of 8.43, P-value< 0.001 Pesticide Reduction Equation for VFS

9. 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

10. VFSMOD-W Performance  Q and  E P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.

11. 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

12. All Block means Atrazine Chlorpyrifos Effect of Concentrated Flow

13. 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

14. 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

15. Uncertainty Analysis (UA) Propagates all these uncertainties, using the model, onto the model output of interest. MODEL UNCERTAINTY ANALYSIS

16. Apportions the uncertainty in the output to different sources of uncertainty in model input SENSITIVITY ANALYSIS TOTAL OUTPUT VARIANCE For model with 2 input factors: A, B. Residual variance C SENSITIVITY ANALISIS Sensitivity Analysis (SA)

17. 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

18. 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

19. 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 μ* - Importance σ- Interactions Morris Method results in two sensitivity measures: μ* and σ

20. 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

21. V(Y) – variance of output, V i – variance due input factor X i, k – number of uncertain factors, R - residual

22. 1. S i - first-order sensitivity index: S i = V i / V(Y) Quantitative Extended FAST 2. S T(i) - total sensitivity index S Ti - S i = higher-order effects For model with 3 parameters: A, B, and C: S T(A) = S A + S AB + S AC + S ABC S T(A)

23. Evaluation Framework

24. 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

25. Uniform Flow Studies – Morris  Q Poletika and PatzoldArora

26. Uniform Flow Studies – Morris  E Poletika and PatzoldArora

27. Uniform Flow Studies – Morris  P Poletika and PatzoldArora

28. 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 S i = V i / V(Y)

29. Uniform Flow Studies – Extended FAST Global UA provided ranges in expected  Q,  E, and  P:

30. 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

31. Uniform vs. Concentrated – Morris  Q UniformConcentrated

32. Uniform vs. Concentrated – Morris  E UniformConcentrated

33. Uniform vs. Concentrated – Morris  P UniformConcentrated

34. 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

35. Uniform vs. Concentrated – Extended FAST Global UA provided ranges in expected  Q,  E, and  P:

36. Uniform vs. Concentrated – Extended FAST Global UA provided ranges in expected  Q,  E, and  P:

37. 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

38. Questions? E-mail: garey.fox@okstate.edu

39. Uniform Flow Studies – Extended FAST Global UA provided ranges in expected  Q,  E, and  P: