Hostname: page-component-77c89778f8-vsgnj Total loading time: 0 Render date: 2024-07-17T07:44:31.534Z Has data issue: false hasContentIssue false
  • English
  • Français

Pesticide Runoff Simulations: Long-Term Annual Means vs. Event Extremes?

Published online by Cambridge University Press:  12 June 2017

Ralph A. Leonard ,
Clint C. Truman ,
Walt G. Knisel  and
Frank M. Davis
Ralph A. Leonard
Affiliation:
U.S. Dep. Agric.–Agric. Res. Serv., Southeast Watershed Res. Lab., Tifton, GA
Clint C. Truman
Affiliation:
U.S. Dep. Agric.–Agric. Res. Serv., Southeast Watershed Res. Lab., Tifton, GA
Walt G. Knisel
Affiliation:
Agric. Eng. Dep., Univ. Georgia, Tifton, GA
Frank M. Davis
Affiliation:
U.S. Dep. Agric.–Agric. Res. Serv., Southeast Watershed Res. Lab., Tifton, GA
Get access Rights & Permissions [Opens in a new window]

Abstract

The GLEAMS model (Groundwater Loading Effects of Agricultural Management Systems) is used to illustrate model application in evaluating potential pesticide runoff of two similar pesticides from one soil. This limited application was chosen for simplicity in illustrating relationships between annual means and single events. When using annual totals of simulated pesticide runoff for comparing two pesticides or assessing risks, long-term 50-yr simulations are preferable to short 10-yr simulations. When short-term simulations are performed, care must be exercised in selecting representative climatic periods. For short half-life pesticides, as demonstrated in this study, initial rainfall events on or near the day of application will often contribute most to annual pesticide lost. In these cases, single event analysis may be required. Procedures are demonstrated for expressing annual total pesticide losses and single rainfall event losses probabilistically in terms of expected recurrence intervals.

Keywords

WeatherCREAMSGLEAMSGumbel distributionherbicide
Type
Symposium
Information
Weed Technology , Volume 6 , Issue 3 , September 1992 , pp. 725 - 730
Copyright
Copyright © 1990 by the Weed Science Society of America 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Literature Cited

1. Foster, G. R., Lane, L. J., Nowlin, J. D., Laflen, J. M., and Young, R. A. 1980. A model to estimate sediment yield from field-size areas: development of model in: Knisel, W. G., ed., CREAMS: A Field-Scale Model for Chemicals, Runoff, and Erosion from Agricultural Management Systems. U. S. Dep. Agric., Sci. Educ. Admin., Conserv. Res. Rep. No. 26, p. 36–64.Google Scholar
2. Foster, G. R., Young, R. A., and Neibling, W. H. 1985. Sediment composition for nonpoint source pollution analyses. Trans. Am. Soc. Agric. Eng. 28:133–139, 146.Google Scholar
3. Knisel, W. G., ed. 1980. CREAMS: A Field-Scale Model for Chemicals, Runoff, and Erosion from Agricultural Management Systems. U.S. Dep. Agric., Sci. Educ. Admin., Conserv. Res. Rep. No. 26.Google Scholar
4. Knisel, W. G. and Leonard, R. A. 1990. Representative Climatic Record for Pesticide Runoff and Leaching Simulation. Departmental Pub. No. 2. Agric. Eng. Dep., Univ. Georgia, Coastal Plain Exp. Stn., Tifton, GA. 16 p.Google Scholar
5. Leonard, R. A., Knisel, W. G., and Davis, F. M. 1989. Groundwater Loadings of Pesticides from Chemigation: A GLEAMS Model Simulation. Proc. Am. Soc. Civil Eng., Irrig. and Drainage Nat. Conf., Newark, DE, July 18–20; p. 430–442.Google Scholar
6. Leonard, R. A., Knisel, W. G., and Still, D. A. 1987. GLEAMS: Groundwater Loading Effects of Agricultural Management Systems. Trans. Am. Soc. Agric. Eng. 30:1403–1418.Google Scholar
7. Leonard, R. A. 1990. Movement of pesticides into surface waters. Chapter 9 in Pesticides in the Soil Environment: Processes, Impacts, and Modeling. Cheng, H. H., ed. Soil Sci. Soc. Am. Book Ser.: 2, Soil Sci. Soc. Am., Madison, WI, p. 303–349.Google Scholar
8. Leonard, R. A., Knisel, W. G., and Davis, F. M. 1990. The GLEAMS model—a tool for evaluating agrichemical ground-water loading as affected by chemistry, soils, climate, and management in Transferring Models to Users. Janes, E. B. and Hotchkiss, W. R., eds. Am. Water Resour. Assoc., Bethesda, MD. p. 187–197.Google Scholar
9. Leonard, R. A., Knisel, W. G., Davis, F. M., and Johnson, A. W. 1990. Validating GLEAMS with field data for fenamiphos and its metabolites. J. Irrig. Drainage Eng. 116:24–35.CrossRefGoogle Scholar
10. Potter, W. D. 1949. Simplification of the Gumbel Method for Computing Probability Curves. Soil Conservation Ser. TP-79.Google Scholar
11. Sheridan, J. M., Knisel, W. G., Woody, T. K., and Asmussen, L. E. 1979. Seasonal Variation in Rainfall and Rainfall-Deficient Periods in the Southern Coastal Plain and Flatwoods Regions of Georgia. Res. Bull. 243, Univ. Georgia, Coll. Agric. Exp. Stn., Athens, GA. 73 p.Google Scholar
12. U. S. Department of Agriculture, Soil Conservation Service. 1972. National Engineering Handbook: Section 4, Hydrology. Washington, DC. 548 p.Google Scholar
13. U. S. Department of Commerce. 1980. Seasonal Variation of 10-Square Mile Probable Maximum Precipitation, United States East of 105th Meridian Hydrometerological Rep. No. 53. NOAA and U.S. Nuclear Reg. Com., Silver Spring, MD.Google Scholar
14. Wauchope, R. D. 1978. The pesticide content of surface water drainage from agricultural fields: A review. J. Environ. Q. 7:459–472.Google Scholar
15. Williams, J. R. and Nicks, A. D. 1982. CREAMS Hydrology Model—Option 1. in Proc. Int. Symp. on Rainfall-Runoff Modeling. 69–86. Littleton, CO: Water Resources Publications.Google Scholar
16. Wischmeier, W. H. and Smith, D. D. 1978. Predicting Rainfall Erosion Losses—A Guide to Conservation Planning. Agric. Handb. No. 537, U.S. Dep. Agric., Sci. Educ. Admin. 58 p.Google Scholar