A Comparison of SWAT Pesticide Simulation Approaches for Ecological Exposure Assessments

Transcription

1 A Comparison of SWAT Pesticide Simulation Approaches for Ecological Exposure Assessments Natalia Peranginangin 1, Michael Winchell 2, Raghavan Srinivasan 3 1 Syngenta Crop Protection, LLC, Greensboro, NC; 2 Stone Environmental, Montpelier, Vermont; 3 Texas A&M University, College Station, Texas ACS National Meeting, Denver, August 29, 2011

2 Contents 1 Background 2 Datasets and Methodology 3 Results of Medium-Resolution (STATSGO) Soils Simulations 4 Results of High-Resolution (SSURGO) Soils Simulations 5 Effects of Model Complexity on Pesticide Simulations 6 Conclusions 2

3 Background 3

4 Background An efficient and effective tool is needed for national or regional scale assessment of pesticide exposure in flowing water bodies within small watersheds (e.g sq miles). In the conterminous US ~ 100,000 watersheds of sq mi ~ 32,700 headwater watersheds of 9-40 sq mi Can the Soil and Water Assessment Tool (SWAT) model be applied efficiently and effectively to predict pesticide exposure in flowing water for a large number of small watersheds? 4

5 Background: Research Objectives Determine if uncalibrated parameterization methodology using readily available datasets can result in satisfactory model performance. Determine if a regionally-based minimal calibration approach can be applied to improve pesticide aquatic exposure simulations. Determine if there is a benefit to using high resolution soils data (SSURGO) over medium resolution soils data (STATSGO). 5

6 Datasets 6

7 Data 20 watersheds, sq. mi ( km 2 ) Pesticide: monitored for 2 to 4 years during growing season; grab samples every 4 days; some sites, auto-sampler* Flow: stage recorded at each site Weather: one station/watershed (precipitation, temperature, wind, solar radiation, relative humidity). NOAA s NCDC (National Climatic Data Center) climatic database was used when local watershed data unavailable. *Some of the watersheds now have additional years of monitoring data with daily auto samples. 7

8 Input Datasets Pesticide application rate & percent treated area: GfK Kynetec (formerly Doane Marketing Research). Pesticide application timing: NASS weekly crop progress or observation. Subbasin delineation: NHDPlus catchments and streams, 1 mi 2 minimum area Topographic data: NHDPlus 30-m DEM Filtered Land Use Land use data: 4-years, NASS Cropland Data Layer ( ) 30-m imagery 8

9 Input Datasets: Soils Dominant soil component within each mapping units represented the entire mapping unit STATSGO Soils (medium resolution) SSURGO Soils (high resolution) Dataset Characteristic STATSGO SSURGO Dataset Scale 1:250,000 1:25,000 Soil Components Per Map Unit Minimum Size of Map Unit (ha) Number of Map Units Across US 78,000 ~ 30,000,000 9

10 Input Datasets: Land Use Create a crop rotation dataset that accurately maintains the acreage of potential treated area for crops of interest each year (corn/sorghum) C = corn S = soybean Combined 10

11 Methodology 11

12 Methodology: Uncalibrated Approach Readily available datasets when possible Consistent methodology for all watersheds Assumed county level pesticide application data apply to watersheds at the sub-county level. Realistic pesticide applications: randomized applications to allow spatial and temporal distribution across Hydrologic Response Units (HRUs) throughout the application season (~ 6 weeks). Random application generator 12

13 Methodology: Minimal Calibration Approach 20 Watersheds were grouped geographically into 5 regions (OH, IN, IL, MO, NE/IA). Four parameters were allowed to vary: - SURLAG: Surface runoff lag time - ESCO: Soil evaporation coefficient - CNCOEFF: Curve number coefficient - DEP_IMP: Depth to impermeable layer All watersheds in a given region had the same parameter values. Adjustments to regional parameter sets were based on evaluation of a nearby watershed with a long term flow record, as well as behavior observed in the local watersheds being assessed. 13

14 Results of Medium-Resolution (STATSGO) Soils Simulations 14

15 STATSGO Models: Uncalibrated vs. Minimally Calibrated Simulations Performance metrics focused on 90 th and 95 th percentiles of Cumulative Distribution Functions (CDFs) of daily pesticide concentration. Mean/median absolute error ( obs sim ) represents the mean/median of the 90 th and 95 th percentile values over the 20 watersheds. Watersheds were ranked based on their 90 th and 95 th percentile daily concentration. R 2 of absolute rank indicates the strength of the relationship between observed and simulated ranking. Calibrated simulations resulted in improved model performance. Statistic 90th %-ile 95th %-ile STATSGO no cal STATSGO cal STATSGO no cal STATSGO cal Mean Abs Err (ppb) Median Abs Err (ppb) R 2 of Absolute Rank

16 Results of High-Resolution (SSURGO) Soils Simulations 16

17 STATSGO vs. SSURGO Model Performance: STATSGO Calibration Parameter Values Used same watersheds, input datasets and calibrated parameter values from the STATSGO simulations. *STATSGO calibration parameter values * 95th %-ile* * Statistic 90th %-ile* STATSGO cal SSURGO cal STATSGO cal SSURGO cal Mean Abs Err (ppb) Median Abs Err (ppb) R 2 of Absolute Rank The SSURGO-based simulations using STATSGO calibrated parameter values did not show a strong improvement over the STATSGO-based simulations. The calibrated parameter values identified using the STATSGO soils inputs may not be ideal for SSURGO-based models. 17

18 SSURGO-Based Model Re-Calibration: Methodology The calibrated parameter values based on the STATSGO models were re-calibrated for each regional watershed group using the SSURGO soils based models and SWAT A modification was made to the SWAT 2009 tile drain algorithm (similar to the SWAT 2000 approach) which is less sensitive to the depth to impermeable layer (DEP_IMP). Watershed Region CNCOEFF ESCO SURLAG DEP_IMP IA N/A IL SOL_ZMAX IN SOL_ZMAX MO SOL_ZMAX NE N/A OH SOL_ZMAX STATSGO calibration parameter values 18

19 SSURGO-Based Model Re-Calibration: Methodology The calibrated parameter values based on the STATSGO models were re-calibrated for each regional watershed group using the SSURGO soils based models and SWAT A modification was made to the SWAT 2009 tile drain algorithm (similar to the SWAT 2000 approach) which is less sensitive to the depth to impermeable layer (DEP_IMP). Watershed Region CNCOEFF ESCO SURLAG DEP_IMP IA N/A IL N/A IN N/A MO N/A NE N/A OH N/A SSURGO calibration parameter values Only ONE parameter (CNCOEFF) varied across the different regions 19

20 SSURGO Re-Calibration: Model Performance The re-calibrated SSURGO models show an improvement over the SSURGO models based on the STATSGO calibrated parameter values. Statistic 90th %-ile 95th %-ile SSURGO cal SSURGO recal SSURGO cal SSURGO recal Mean Abs Err (ppb) Median Abs Err (ppb) R 2 of Absolute Rank SSURGO cal = SSURGO models using STATSGO calibration parameter values SSURGO recal = SSURGO models using SSURGO calibration parameter values The re-calibrated SSURGO models have the advantage of a simpler parameterization (1 parameter varied versus 4). 20

21 Results Summary STATSGO No calibration Min. calibration 4 parameters STATSGO SSURGO SSURGO Min. calibration Recalibration 4 parameters 1 parameter SWAT 2005 SWAT 2009 Statistic STATSGO no-cal STATSGO cal SSURGO cal SSURGO re-cal Mean Abs Err (ppb) Median Abs Err (ppb R 2 of Absolute Rank

22 Effects of Model Complexity on Pesticide Simulations C = corn S = soybean Combined 22

23 Effects of Model Complexity: Can We Simplify without Sacrificing Performance? Simplify sub-basins size (e.g. increased size from 1 to 2 sq. mi to ~5 to 10 sq. mi) Simplify corn land use classes (e.g. from originally 15 down to 1) Simplify HRUs delineation (reduces number of HRUs) - Must maintain enough HRUs to allow a large number of different application dates - Land use/soil threshold of 2/2 was chosen for initial testing For 5 Missouri watersheds Land Use/Soil Area Threshold Subbasins (#) Land Use Input Total HRUs 10%/20% 64 Original %/20% 15 Simplified 98 2%/2% 15 Simplified 541 WS-19 WS-19 WS-7 Original Subs WS-7 Simple Subs 23

24 Percentile Percentile Effects of Simplifying the Model Structure: Missouri, Indiana Results CDFs based on the simplified models were comparable to the original models. Daily Concentration Cumulative Distribution WS-8, Growing Seasons from Daily Concentration Cumulative Distribution WS-11, Growing Seasons from Obs Obs 60 Original Simplified 60 Original Simplified Conc (ppb) Conc (ppb) Note: Simulations being compared are based on SWAT 2009 SSURGO-recalibrated simulations. 24

25 Simulated Conc. (ppb) Effects of Simplifying the Model Structure: Performance Comparison Overall, model performance was very similar between the original models and the simplified models for the sub-group assessed Observed vs. Simulated Concentrations 2nd thru 98th Percentiles (2nd, 5th, 10th,..., 90th, 95th, 98th) 4 Indiana and 3 Missouri Watersheds Pooled (147 points) Original Models; r2 = 0.90; slope = 0.84; int.= 0.64 Simplified Models; r2 = 0.88; slope = 0.86; int. = Observed Conc. (ppb) 25

26 Conclusions Minimally calibrated SWAT ( simpleswat ) is a promising tool for conducting national-scale pesticide exposure assessment in flowing water bodies within small watersheds. - Best results were SSURGO soils calibrated with only 1 parameter. It takes <2 days to run a complete processing for a national assessment (100 watersheds, seconds/watershed/5-yr-daily data, 5 servers, 10 CPUs/server) Current model setup has the potential to be used for higher tier drinking water, ecological and endangered species risk assessments in flowing water bodies for corn herbicides. 26

27 Acknowledgment Valuable support /input on this project has been provided by Paul Hendley, Wenlin Chen, JiSu Bang, Chris Harbourt and colleagues at Waterborne Environmental, Inc. 27

28 Thank you 28