Effects of Input Uncertainty on VFSMOD Modeling of Water, Sediment, and Pesticide Trapping by Vegetative Filter Strips

Transcription

1 Effects of Input Uncertainty on VFSMOD Modeling of Water, Sediment, and Pesticide Trapping by Vegetative Filter Strips Solomon M. Folle 1, Michael F. Winchell 1, Tammara L. Estes 1, Katie Budreski 1, John Hanzas 1 Roger L. Breton 2 and Paul Whatling 3 1 Stone Env. Inc 2 Intrinsik Env. Sciences 3 Cheminova Inc. ACS National Meeting, Denver Colorado 29 August 2011

2 Contents 1 Introduction and Background 2 Motivation and Objectives 3 Methodology 4 Input Data Organization 5 Results and Discussion 6 Conclusions 2

3 Introduction and Background Vegetative Filter Strip (VFS) A dense grass strip established from natural or planted grass To intercept surface runoff at the down slope of a field Reduce sediment and diffuse contaminant loads To protect receiving water bodies 3

4 Intro. and Background--- cont d Vegetative Filter Strip Model (VFSMOD-W) Field scale, mechanistic and storm based Simulates routing of incoming hydrograph, sediment, and chemical contaminants Has pesticide transport module of Sabbagh et al., (2009) Model simulation outputs: Surface runoff reduction efficiency of the VFS 4 Sediment and contaminant trapping efficiency

5 Intro. and Background--- cont d Why is the issue of VFSMOD Input uncertainty important? Uncertainty refers to lack of precise knowledge of the truth Incomplete knowledge of input parameters appropriate values Partial availability or absence of data Spatial and temporal variability Input uncertainty drives VFSMOD predicted output variance High degree of model uncertainty leads to very conservative assumptions To avoid exposure to risk Implies greater mitigation costs 5

6 VFSMOD simulation study of 27 sites (US and Canada) During analysis, several sensitive input parameters were identified Decision was made to conduct detailed studies of most sensitive three parameters Objectives Motivation and Objectives To analyze the variability in pesticide reduction efficiency due to input parameter uncertainty Provide suggestions on how to enable the effective use of VFSMOD in exposure assessments 6

7 Methodology EPA PRZM/EXAMS simulations to provide inputs to VFSMOD On a study site in Pennsylvania Off-field loadings of runoff, flow, sediment, pesticide, and daily precipitation SCS TR-55: to estimate surface runoff distribution for each rainfall event USDA SPAW program was used to estimate: Ksat and Өsat VFS trapping efficiency was studied on two hypothetical pesticides with different mobility characteristics: Koc of 217 and 2 VFSMOD simulations were made to analyze most sensitive three inputs Rainfall-runoff temporal distribution, soil saturated conductivity and incoming sediment clay concentration 7 Monte Carlo simulation of generated random samples was made to analyze the uncertainty

8 VFSMOD Input Data Organization Hydrologic Inputs Units Value Description FWIDTH M Buffer strip width VL M 5-35 Buffer strip length VKS cm/hr Soil saturated K SAV m Suction at the wetting front OS m 2 /m Saturated water content OI Initial water content VFS Inputs Units Value Description VN s/cm Grass Manning s n SOA m/m 0.03 Slope of each VFS segment SS cm Spacing of grass steams H cm Filter grass height 8

9 Input Data Organization---cont d Sediment Inputs Units Value Description CL g/cm Flow sed. concentration NPART - 6 Incoming sed. particle class DP (d50) cm Particle size diameter POR Porosity COARSE Sediment with D> cm SG g/cm Particle density Pesticide Inputs Kd /0.035 Distribution coefficient Sed. clay content % 20 Initial water content 9

10 10 Results & Discussions

11 Predictive Uncertainty of Rainfall Inputs Fig: Distribution of hourly rainfall using three methods Inaccuracies in rainfall inputs directly compromise model predictions 11

12 Uncertainty of Rainfall ---cont d Variable Sinusoidal Linear Fig: Cumulative runoff for the three different methods 12

13 Uncertainty of Rainfall and its Effects on Runoff and Sediment Loss Reduction RF Distribution method OHR SCS 1hr SCS 6hrs Uni Max Runoff Reduction Max Sediment Reduction Effective Width

14 Uncertainty of Rainfall and its Effects on Pesticide Loss Reduction RF Distribution method OHR SCS 1hr SCS 6hrs Uni Pesticide loss Reduction, % VFS Width, m Note: The result above was made based on a pesticide with Koc of

15 Rainfall and Pesticide Loss Reduction---Cont d Lower Kd values trigger more pesticide losses The increase in Kd from to 3.78 increases the pesticide loss reduction by 2.24% per meter width of VFS 15

16 Effects of the Uncertainty of Soil Saturated conductivity (Ksat) A parameter determining the infiltration property of soils Ksat values are spatially variable in the field Sources for Ksat values can be from: - SSURGO soils database - Field or Lab measurements - Estimated using tools like the USDA SPAW program Based on Texture and OM Consider crack volume and preferential flow 16

17 Effects of Ksat---cont d Base value = Sample µ = Sample σ = Fig: Probability density function of Ksat input data 17

18 Effects of Ksat---cont d Sed loss = 1.91 ksat R.off loss = 4.88 Ksat Pest loss = 3.43 ksat Fig: Effects of Ksat on sediment, runoff and pesticide losses Note: The result above and in my presentation after this slide were made based on a 20 m buffer and a pesticide with Koc of

19 Effects of Ksat---cont d Min = 52.7%, Max = 94.5%, µ = 72.5% σ = Ksat Fig: Probability density function of pesticide loss reduction High reduction rates are associated with high Ksat Probability of the Pesticide reduction to be µ+σ is 64% 19

20 Effects of the Uncertainty of Incoming Sediment Clay Content Sediment clay content is hard to estimate, and quite variable Studies reported that sediments from interrill erosion were enriched in sand (Young & Onstad, 1978; Alberts et al., 1980) Clay, and not sand, was enriched in the eroded sediment (Monke et al., 1977; Alberts et al., 1983) Factors contributing to incoming sediment clay content: Differences in parent soils properties (e.g., texture etc.) Soil surface prior to a rainfall event: surface aggregates, moisture content and size Characteristics of the rainfall event (e.g., intensity, raindrop size and energy, and duration). 20

21 Frequency Cumulative Frequency Effects of Clay--- cont d Log-normally distributed 1000 random samples of % clay µ = 20 and σ = Clay % Fig: Probability density function of incoming sediment clay % 21

22 Effects of Clay--- cont d 20% clay Fig: Probability density function of pesticide loss reduction High reduction rates are associated with lower clay % Range of pesticide loss reduction = 60-87% Probability of the pesticide reduction to be µ+σ is 70% 22

23 Conclusions Models such as VFSMOD are important tools for estimating the effectiveness of vegetated buffer strips at reducing pesticide losses Uncertainty in rainfall, Ksat, and incoming sediment clay content can have significant effects on VFSMOD outputs VFS pesticide reduction efficiency for 20 m buffer: % variability due to rainfall distribution 52-95% variability due to saturated conductivity 60-87% variability due to incoming sediment clay content Standard processes for estimating sensitive inputs and parameters should be developed to maintain consistency in model results across multiple studies 23

24 Contact Info. Solomon Folle (Ph.D.) Senior Environmental Modeler / Hydrologist Ph: sfolle@stone-env.com Stone Environmental, Inc. 24