Accounting for variability within map units when linking a pesticide fate model to soil survey

Abstract

The aim of this study was to estimate the probability of exceedance (POE) of the USEPA health advisory level for particular pesticides in groundwater beneath citrus groves in southwestern Florida. The approach included bootstrapping to assess the uncertainty of the model output due to the variability of soil input data; a weather generator to provide daily rainfall amounts; daily evapotranspiration by the Blaney-Criddle (FAO) method; and a program written to compute POE values. Bootstrapping enabled us to assess the uncertainty of soils inputs by generating pseudo-profiles of soils from pedon characterization data. These pseudo-profiles were used in Monte Carlo simulations that captured the variance of selected soil parameters within soil taxonomic units. Single-name map units (i.e., consociations) were represented by no fewer than three actual pedon characterization data sets for the named soil and/or closely similar soil(s). In the case of a multiple-name map unit (i.e., soil association or soil complex), no fewer than three actual pedons of named and/or similar soils were used to generate pseudo-profiles for each of the named soils in the map unit. Inputs were linked to the pesticide fate model Chemical Movement in Layered Soil, CMLS (Nofziger and Hornsby) to produce cumulative probability curves showing the fraction of applied pesticide leaching below the 1-m depth. This curve was then used to determine the POE for various soil delineations in the groves within the watershed. Multiple POEs were generated for multiple-named map units; the rule was to select the higher(est) POE value as an estimator of environmental risk for each such map unit. Thematic maps are then created using soil and land use coverages and POE's for various pesticides evaluated. Our approach suggests that the use of cumulative probability functions and probabilities of exceedance of health standards (USEPA HAL) provides needed integration of the uncertainties associated with input parameters, the significance of which can be easily grasped by decision officials. The use of environmental fate models to predict pesticide behavior in soils throughout a landscape has brought about intense scrutiny of soil attribute data and map unit delineations far beyond that envisioned for the present progressive soil survey. The authors also recognize that uncertainties in model formulation, pesticide fate parameters and toxicity further complicate assessments of this nature.