Hostname: page-component-78c5997874-g7gxr Total loading time: 0 Render date: 2024-11-15T12:36:37.414Z Has data issue: false hasContentIssue false
  • English
  • Français

The Role of Sensitivity Analysis in Groundwater Risk Modeling for Pesticides

Published online by Cambridge University Press:  12 June 2017

Don D. Fontaine ,
Patrick L. Havens ,
Gary E. Blau  and
Patricia M. Tillotson
Don D. Fontaine
Affiliation:
DowElanco, Midland, MI 48641-1706
Patrick L. Havens
Affiliation:
DowElanco, Midland, MI 48641-1706
Gary E. Blau
Affiliation:
DowElanco, 4040 Vincennes Circle, Indianapolis, IN 46268-3030
Patricia M. Tillotson
Affiliation:
DowElanco
Get access Rights & Permissions [Opens in a new window]

Abstract

Two methods were used to obtain the sensitivity of chemical leaching depth to variations in the input parameters of the Pesticide Root Zone Model (PRZM). First a Plackett-Burman (PB) screening design was used to vary 35 PRZM inputs over seven ranges around a nominal value. Six of the seven ranges were approximately 0.1, 0.25, 0.5, 1.0, 5.0, and 15%, the seventh range was chosen to cover a range appropriate for a soybean herbicide applied preemergence in the Midwestern region defined by the USDA–SCS land resource region M. Next, Fourier amplitude sensitivity testing (FAST) was then used to vary from 19 to 25 parameters over four of the ranges previously tested. For the smaller parameter ranges the two methods typically gave equivalent results but the PB method required far fewer simulations. For the simulation of the Midwestern region where some parameter varied by larger amounts the relative magnitudes of the sensitivity coefficients obtained by the two methods were similar but the magnitude of the coefficients obtained using FAST were smaller than those obtained using PB.

Keywords

Soybean, Glycine max. (L.) Merr.Chemical mobility simulationpesticide root zone modelFourier amplitude sensitivity test
Type
Symposium
Information
Weed Technology , Volume 6 , Issue 3 , September 1992 , pp. 716 - 724
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. Bouwer, H. 1991. Simple derivation of the retardation equation and application to preferential flow and macrodispersion. Ground Water 29:41–46.Google Scholar
2. Brandstetter, A. and Buxton, B. E. 1989. The role of geostatistical, sensitivity, and uncertainty analysis in performance assessment. p. 89–110 in Buxton, B. E., ed., Proc. DOE/AECL 1987 Conference on Geostatistical, Sensitivity, and Uncertainty Methods for Ground-water Flow and Radionuclide Transport Modeling. Battelle Press, Columbus, OH.Google Scholar
3. Carsel, R. F., Smith, C. N., Dean, J. D., Jowise, P. P., Mulkey, L. A., and Lorber, M. N. 1984. Pesticide Root Zone Model (PRZM): Release 1. U.S. Environmental Protection Agency, EPA 600/3-84-109.Google Scholar
4. Cukier, R. I., Fortuin, C. M., Shuler, K. E., Petschek, A. G., and Schaibly, J. H. 1973. Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients. J. Chem. Phys. 59:3873–3878.Google Scholar
5. Dean, J. D., Jowise, P. P., and Donigian, A. S. Jr. 1984. Leaching Evaluation of Agricultural Chemicals (LEACH) Handbook. EPA 600/3-84-068.Google Scholar
6. Freeze, R. A. and Cherry, J. A. 1979. Groundwater. Prentice-Hall, Englewood Cliffs, NJ.Google Scholar
7. Iman, R. L., Helton, J. C., and Campbell, J. E. 1981. An approach to sensitivity analysis of computer models: Part II–Ranking of input variables, response surface validation, distribution effect and technique synopsis. J. Q. Technol. 13:232–240.CrossRefGoogle Scholar
8. McRae, G. J., Tilden, J. W., and Seinfeld, J. H. 1982. Global sensitivity analysis–A computational implementation of the fourier amplitude sensitivity test (FAST). Comput. Chem. Eng. 6:15–25.CrossRefGoogle Scholar
9. Plackett, R. L. and Burman, J. P. 1946. The design of optimum multifactorial experiments. Biometrika 33:305–325.Google Scholar
10. Siegel, M. D., Phillips, S. L., Leckie, J. O., and Kelly, W. R. 1989. Development of a methodology of geochemical sensitivity analysis for performance assessment. p. 189–211 in Buxton, B. E., ed. Proc. DOE/AECL 1987 Conference on Geostatistical, Sensitivity, and Uncertainty Methods for Ground-water Flow and Radionuclide Transport Modeling. Battelle Press, Columbus, OH.Google Scholar