1. Can Contamination Potential of Ground Water to Pesticides be Identified from Hydrogeological Parameters? Barnali Dixon University of South Florida Funded by USDA-CSREES: 2001- 35102-10830

2. NPS!! The Problem!!

3. Solution: Contamination Potential Mapping DRASTIC Low Moderate Moderately High High 0miles 12

5. Introduction Arkansas has a state management plan (SMP) for pesticide monitoring of ground water The Department of Environmental Quality (DEQ) routinely samples 76 irrigation wells in 5 counties 61 pesticides and degradation products were analyzed by DEQ Pesticide use is primarily for weed control in soybeans, cotton and rice

6. Objective To determine the key hydrogeologic parameters that might play a critical role in contamination of ground water by pesticides using GIS and geostatistical approach). Key words: GIS & Geostatistics

7. Spatial Data Layers Well Location/Contamination Soils Geology Landuse Thickness of the Confining Unit (claycap) Recharge of Ground Water Depth to GW

8. Sources of Spatial Data Well Location/ContaminationDEQ Soils NRCS Geology LGS Landuse CAST Claycap USGS Recharge USGS Depth to GW USGS

9. Number of Contaminated Wells for Each Pesticide Pesticide Number

10. Software GRASS, version 4.2 JMP, version 3.2 GS+, version 3.1 MultiSpec

11. Analyses GIS –Buffer Analysis –Coincidence Statistical –Descriptive –Geostatistics 160 m 320 m 240 m

12. Geology Prairie Complex MRMB1 ARMB1 Alluvium Backswamp VTEW2 VTLW2

13. Presence of Bentazon vs. Geology 0 5 10 15 20 Backswamp MRMB1 UC VTLW2 VTEW2 ARMB1 ARMB2ARMB4 ARMB5 ARMB7 VTEWG1 ARMB6 Geology Number of Wells Non Contaminated Contaminated Bentazon

14. Thickness of the Clay Cap Point data Clay Cap Impact of Vadose Zone Interpolation

15. Thickness of Claycap Non Contaminated Contaminated 23 - 30 15 -23 6 - 9 9 - 12 2 - 3 meters

16. Presence of Bentazon vs. Thickness of Claycap Non Contaminated Contaminated 0 5 10 15 20 25 30 4 - 67 - 910 - 1213 - 15 2 - 3 Thickness of Claycap (m) Number of Wells Bentazon

17. Recharge of Ground Water Site File R Interpolated MODFLOW Net Recharge

18. Recharge of Ground Water Non Contaminated Contaminated 26 - 50 0 - 5 6 - 10 11 - 18 19 - 25 cm/yr

19. Presence of Bentazon vs Recharge 0 10 20 30 40 50 60 0 -56 - 1011 - 18 19 - 25 26 - 50 Recharge (cm/yr) Number of Wells Non Contaminated Contaminated Bentazon

20. Development of Depth to Ground Water Potentiometric surface Elevation D Subtract

21. Depth to Ground Water 0 - 5 5 - 14 14 - 18 18 - 27 > 27 meters

22. Presence of Bentazon vs. Depth to GW Bentazon 0 5 10 15 20 25 30 35 Channel0 - 56 - 13 Depth to Ground Water (meters) Number of Wells Non Contaminated Contaminated

23. Soils STATSGO (1:250,000) –mapping units: soil associations SSURGO (1:24,000) –mapping units: soil series –surface texture –drainage class –permeability class

24. STATSGO Soils Crowley-Stuttgart-Hillemann Calloway-Henry-Grenada Sharkey-Alligator-Tunica Loring-Memphis-Collins Rilla-Herbert-Perry Sacul-Savannah-Sawyer Smithdale-Savannah-Sacul Foley-Jackport-Crowley Perry-Portland-Rilla Non Contaminated Contaminated

25. 0 5 10 15 20 25 Calloway-Henry-Greneda Commerce-Sharkey-Foley Dundee-Sharkey-Bosket Foley-Jackport-Crowley Perry-Portland-Rilla Rilla-Hebert-Perry Sharkey-Alligator-Tunica STATSGO Soils Associations Number of Wells Non Contaminated Contaminated Presence of Bentazon vs. STATSGO Soil Associations

26. SSURGO: Surface Soil Texture Clay Silty clay Silt loam Silty clay Fine sandy loam

27. Presence of Bentazon vs. Surface Soil Texture 0 5 10 15 20 25 30 35 ClayFine sandy loam Silt loamSilty ClaySSURGO N/A Surface Textural Classes Number of Wells Non Contaminated Contaminated Bentazon

28. SSURGO: Soil Drainage Classes Poor Somewhat poor Very poor Moderately well

29. Presence of Bentazon vs. Soil Drainage Class 0 5 10 15 20 25 30 Moderately well PoorSomewhat Poor WellSSURGO N/A Drainage Classes Number of Wells Non Contaminated Contaminated Bentazon

30. SSURGO: Soil Permeability Classes 1.5 - 5.0 0 -.15 0 - 5 0.15 - 0.5 0.5 - 1.5 5 - 15 15 - 50 Water cm/hr

31. Presence of Bentazon vs. Soil Permeability Class 0 5 10 15 20 25 30 35 0.0 - 0.150.15 - 0.50.5 - 5.01.5 - 5.05.0 - 15.0SSURGO N/A Permeability Classes (cm/hr) Number of Wells Non Contaminated Contaminated Bentazon

32. 2000 Landuse Landsat TM Spring Summer

33. Spring Landuse Urban Bare Soil Barren Forest Flooded Water Non Contaminated Contaminated

34. 0 5 10 15 20 25 30 35 40 45 50 Baldcypress Bare Soil Forest Unclassified Herbaceous/Pasture Wheat/Oats Willow Oak Spring Landuse Number of Wells Non Contaminated Contaminated Presence of Bentazon vs. Spring Landuse

35. Summer Landuse Urban Cotton Soybeans Forest Rice Water Non Contaminated Contaminated

36. 0 5 10 15 20 25 30 Baldcypress Bare Soil Cotton Forest Unclassified Herbaceous/Pasture Rice Sorghum/Corn Soybeans Willow Oak Summer Landuse Number of Wells Non Contaminated Contaminated Presence of Bentazon vs. Summer Landuse

37. Presence of Bentazon vs. critical parameters within buffer zone Geology Thickness of claycapRecharge Bentazon contamination CALLOWAY-HENRY-GRENADA COMMERCE-SHARKEY-FLUVAQUENT DUNDEE-SHARKEY-BOSKET FOLEY-JACKPORT-CROWLEY PERRY-PORTLAND-RILLA RILLA-HEBERT-PERRY SHARKEY-ALLIGATOR-TUNICA Bare Soil Pasture Perennial Water Wheat/Oats Bare Soil Cotton Pasture Perennial Water Rice Sorghum/Corn Soybeans 3 - 6 m 6 - 9 m 9 - 12 m 12 - 15 m 2 - 3 m 0 -5 cm/yr 28 - 50 cm/yr 8 - 10 cm/yr 13 - 18 cm/yr 20 - 25 cm/yr Non Contaminated STATSGO Soil Associations Spring Landuse Summer LanduseDepth to GW Backswamp Mississippi River meander belt1 Upland complex Valley train of late Wisconsin 2 Valley train of early Wisconsin glaciation 2 Arkansas River meander belt1 Arkansas River meander belt2 Arkansas River meander belt4 Arkansas River meander belt5 Arkansas River meander belt7 Valley train of early Wisconsin glaciation Arkansas River meander belt6 6 - 13 m Channel 0 - 5 m

38. Geostatistics

39. Conclusions Contamination coincided with –bare soil in spring –cotton and soybeans in summer –mostly coarser-textured soils –depth to GW of 0 - 5 m –backswmp No spatial correlation was found from semivariogram analyses

40. Conclusions cont…. Each of the natural resource parameters has its own spatial distribution which affects spatial variability of well contamination Further studies needed –Data Layers (soil structure, bulk density, Ksat) –Methodology (e.g. neural networks)

41. Courtesy: Nofzinger et al, CLMS model

42. Photomicrograph of a thin section How do we incorporate this layer in the model?? Hydropedology ? New Branch of Study??