Simulation of Daily Pesticide Concentrations from Watershed Characteristics and Monthly Climatic Data

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Abstract

A time-series model was developed to simulate daily pesticide concentrations for streams in the coterminous United States. The model was based on readily available information on pesticide use, climatic variability, and watershed charac­teristics and was used to simulate concentrations for four herbicides [atrazine, ethyldipropylthiocarbamate (EPTC), metolachlor, and trifluralin] and three insecticides (carbofuran, ethoprop, and fonofos) that represent a range of physical and chemical properties, application methods, national application amounts, and areas of use in the United States. The time-series model approximates the probability distributions, seasonal variability, and serial correlation characteristics in daily pesticide concentration data from a national network of monitoring stations.

The probability distribution of concentrations for a particular pesticide and station was estimated using the Watershed Regressions for Pesticides (WARP) model. The WARP model, which was developed in previous studies to estimate the probability distribution, was based on selected nationally available watershed-characteristics data, such as pesticide use and soil characteristics. Normality transformations were used to ensure that the annual percentiles for the simulated concentrations agree closely with the percentiles estimated from the WARP model.

Seasonal variability in the transformed concentrations was maintained by relating the transformed concentration to precipitation and temperature data from the United States Historical Climatology Network. The monthly precipitation and temperature values were estimated for the centroids of each watershed. Highly significant relations existed between the transformed concentrations, concurrent monthly precipitation, and concurrent and lagged monthly temperature. The relations were consistent among the different pesticides and indicated the transformed concentrations generally increased as precipitation increased but the rate of increase depended on a temperature-dependent growing-season effect.

Residual variability of the transformed concentrations, after removal of the effects of precipitation and temperature, was partitioned into a signal (systematic variability that is related from one day to the next) and noise (random variability that is not related from one day to the next). Variograms were used to evaluate measurement error, seasonal variability, and serial correlation of the historical data. The variogram analysis indicated substantial noise resulted, at least in part, from measurement errors (the differences between the actual concen­trations and the laboratory concentrations). The variogram analysis also indicated the presence of a strongly correlated signal, with an exponentially decaying serial correlation function and a correlation time scale (the time required for the correlation to decay to e-1 equals 0.37) that ranged from about 18 to 66 days, depending on the pesticide type.

Simulated daily pesticide concentrations from the time-series model indicated the simulated concentrations for the stations located in the northeastern quadrant of the United States where most of the monitoring stations are located generally were in good agreement with the data. The model neither consistently overestimated or underestimated concentrations for streams that are located in this quadrant and the magnitude and timing of high or low concentrations generally coincided reasonably well with the data. However, further data collection and model development may be necessary to determine whether the model should be used for areas for which few historical data are available.

Contents

Abstract

Introduction

Purpose and Scope

Data Used to Develop Time-Series Model

Development of Time-Series Model

Normality Transformations

Seasonal Structure of Transformed Concentration Data

Serial Correlation Structure of Transformed Concentration Data

Simulation of Daily Pesticide Concentrations

Implications and Limitations of Model

Summary

References

Figures

1–2. Maps showing:

1. Locations of pesticide-monitoring stations used to develop time-series model

2. Locations of selected meteorological observation stations for which data are given in the United States Historical Climatology Network

3–43. Graphs showing:

3. Log-transformed atrazine concentration percentiles for the St. Joseph River near Newville, Indiana, and Lonetree Creek near Greeley, Colorado, stations

4. Log-transformed ethyldipropylthiocarbamate concentration percentiles for the Monocacy River at Bridgeport, Maryland, and Milwaukee River at Milwaukee, Wisconsin, stations

5. Log-transformed fonofos concentration percentiles for the Big Limestone Creek near Limestone, Tennessee, and Sugar Creek at New Palestine, Indiana, stations

6. Transformed atrazine and metolachlor concentrations and lag-0 precipitation

7. Transformed atrazine and metolachlor concentrations and lag-0 temperature

8. Transformed atrazine and metolachlor concentrations and lag-0 minus lag-1 temperature

9. Relation between mean transformed atrazine and metolachlor concentrations and temperature variables

10. Relation between mean transformed atrazine and metolachlor concentrations and temperature and precipitation variables

11. Transformed concentrations for all pesticides and fitted values from Tobit regression model

12. Transformed atrazine concentrations and fitted values from Tobit regression model

13. Transformed metolachlor concentrations and fitted values from Tobit regression model

14. Transformed fonofos concentrations and fitted values from Tobit regression model

15. Transformed trifluralin concentrations and fitted values from Tobit regression model

16. Fitted variograms for residuals from Tobit regression model

17. Generated trace of daily metolachlor concentrations for 1991-2000 for the East Mahantango Creek at Klingerstown, Pennsylvania, station (map number 40) and 5th, 50th, and 95th percentiles computed from 100 generated traces

18. Generated trace of daily trifluralin concentrations for 1991-2000 for the East Mahantango Creek at Klingerstown, Pennsylvania, station (map number 40) and 5th, 50th, and 95th percentiles computed from 100 generated traces

19. Generated trace of daily ethyldipropylthiocarbamate concentrations for 1991-2000 for the East Mahantango Creek at Klingerstown, Pennsylvania, station (map number 40) and 5th, 50th, and 95th percentiles computed from 100 generated traces

20. Generated trace of daily carbofuran concentrations for 1991-2000 for the East Mahantango Creek at Klingerstown, Pennsylvania, station (map number 40) and 5th, 50th, and 95th percentiles computed from 100 generated traces

21. Generated trace of daily fonofos concentrations for 1991-2000 for the East Mahantango Creek at Klingerstown, Pennsylvania, station (map number 40) and 5th, 50th, and 95th percentiles computed from 100 generated traces

22. Generated trace of daily metolachlor concentrations for 1991-2000 for the Sugar Creek at New Palestine, Indiana, station (map number 99) and 5th, 50th, and 95th percentiles computed from 100 generated traces

23. Generated trace of daily trifluralin concentrations for 1991-2000 for the Sugar Creek at New Palestine, Indiana, station (map number 99) and 5th, 50th, and 95th percentiles computed from 100 generated traces

24. Generated trace of daily ethyldipropylthiocarbamate concentrations for 1991-2000 for the Sugar Creek at New Palestine, Indiana, station (map number 99) and 5th, 50th, and 95th percentiles computed from 100 generated traces

25. Generated trace of daily carbofuran concentrations for 1991-2000 for the Sugar Creek at New Palestine, Indiana, station (map number 99) and 5th, 50th, and 95th percentiles computed from 100 generated traces

26. Generated trace of daily fonofos concentrations for 1991-2000 for the Sugar Creek at New Palestine, Indiana, station (map number 99) and 5th, 50th, and 95th percentiles computed from 100 generated traces

27. Generated trace of daily fonofos concentrations for 1991-2000 for the White River at Hazelton, Indiana, station (map number 97) and 5th, 50th, and 95th percentiles computed from 100 generated traces

28. Generated trace of daily metolachlor concentrations for 1991-2000 for the Iowa River at Wapello, Iowa, station (map number 22) and 5th, 50th, and 95th percentiles computed from 100 generated traces

29. Generated trace of daily ethyldipropylthiocarbamate concentrations for1991-2000 for the Iowa River at Wapello, Iowa, station (map number 22) and 5th, 50th, and 95th percentiles computed from 100 generated traces

30. Generated trace of daily fonofos concentrations for 1991-2000 for the Iowa River at Wapello, Iowa, station (map number 22) and 5th, 50th, and 95th percentiles computed from 100 generated traces

31. Generated trace of daily metolachlor concentrations for 1991-2000 for the Bogue Phalia near Leland, Mississippi, station (map number 47) and 5th, 50th, and 95th percentiles computed from 100 generated traces

32. Generated trace of daily trifluralin concentrations for 1991-2000 for the Bogue Phalia near Leland, Mississippi, station (map number 47) and 5th, 50th, and 95th percentiles computed from 100 generated traces

33. Generated trace of daily fonofos concentrations for 1991-2000 for the Bogue Phalia near Leland, Mississippi, station (map number 47) and 5th, 50th, and 95th percentiles computed from 100 generated traces

34. Generated trace of daily metolachlor concentrations for 1991-2000 for the Withlacoochee River near Quitman, Georgia, station (map number 24) and 5th, 50th, and 95th percentiles computed from 100 generated traces

35. Generated trace of daily trifluralin concentrations for 1991-2000 for the Withlacoochee River near Quitman, Georgia, station (map number 24) and 5th, 50th, and 95th percentiles computed from 100 generated traces

36. Generated trace of daily fonofos concentrations for 1991-2000 for the Withlacoochee River near Quitman, Georgia, station (map number 24) and 5th, 50th, and 95th percentiles computed from 100 generated traces

37. Generated trace of daily ethyldipropylthiocarbamate concentrations for 1991-2000 for the Palouse River at Hooper, Washington, station (map number 12) and 5th, 50th, and 95th percentiles computed from 100 generated traces

38. Generated trace of daily fonofos concentrations for 1991-2000 for the Palouse River at Hooper, Washington, station (map number 12) and 5th, 50th, and 95th percentiles computed from 100 generated traces

39. Generated trace of daily fonofos concentrations for 1991-2000 for the Rock Creek at Twin Falls, Idaho, station (map number 89) and 5th, 50th, and 95th percentiles computed from 100 generated traces

40. Generated trace of daily metolachlor concentrations for 1991-2000 for the San Joaquin River near Vernalis, California, station (map number 66) and 5th, 50th, and 95th percentiles computed from 100 generated traces

41. Generated trace of daily ethyldipropylthiocarbamate concentrations for 1991-2000 for the San Joaquin River near Vernalis, California, station (map number 66) and 5th, 50th, and 95th percentiles computed from 100 generated traces

42. Generated trace of daily trifluralin concentrations for 1991-2000 for the San Joaquin River near Vernalis, California, station (map number 66) and 5th, 50th, and 95th percentiles computed from 100 generated traces

43. Generated trace of daily fonofos concentrations for 1991-2000 for the San Joaquin River near Vernalis, California, station (map number 66) and 5th, 50th, and 95th percentiles computed from 100 generated traces

Tables

1. Pesticide-monitoring stations used to develop time-series model

2. Transformation equations for percentiles estimated from Watershed Regressions for Pesticides model

3. Fitted variograms for residuals from Tobit regression model for transformed concentrations