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A stochastic analysis of the influence of soil and climatic variability on the estimate of pesticide groundwater pollution potential
Author(s) -
Jury William A.,
Gruber Joachim
Publication year - 1989
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/wr025i012p02465
Subject(s) - environmental science , leaching (pedology) , soil science , groundwater , residual , pollution , hydrology (agriculture) , monte carlo method , pesticide , soil water , agronomy , mathematics , statistics , geology , ecology , geotechnical engineering , biology , algorithm
Soil and climatic variability contribute in an unknown manner to the leaching of pesticides below the surface soil zone where degradation occurs at maximum levels. In this paper we couple the climatic variability model of Eagleson (1978) to the soil variability transport model of Jury (1982) to produce a probability density distribution of residual mass fraction (RMF) remaining after leaching below the surface degradation zone. Estimates of the RMF distribution are shown to be much more sensitive to soil variability than climatic variability, except when the residence time of the chemical is shorter than one year. When soil variability dominates climatic variability, the applied water distribution may be replaced by a constant average water application rate without serious error. Simulations of leaching are run with 10 pesticides in two climates and in two representative soil types with a range of soil variability. Variability in soil or climate act to produce a nonnegligible probability of survival of a small value of residual mass even for relatively immobile compounds which are predicted to degrade completely by a simple model which neglects variability. However, the simpler model may still be useful for screening pesticides for groundwater pollution potential if somewhat larger residual masses of a given compound are tolerated. Monte Carlo simulations of the RMF distribution agreed well with model predictions over a wide range of pesticide properties.