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This article in JEQ

  1. Vol. 28 No. 2, p. 378-387
    Received: Dec 17, 1997

    * Corresponding author(s): iris@pangea.stanford.edu


A Type Transfer Function Approach for Regional-Scale Pesticide Leaching Assessments

  1. Iris T. Stewart * and
  2. Keith Loague
  1. Department of Geological and Environmental Sciences, Stanford Univ., Stanford, CA 94305.



The objective of this study was the development of a transfer function protocol to obtain quantitative, regional-scale estimates of groundwater contamination from agricultural chemicals. The Jury transfer function model was extended for the first time to regional scales through the development of ‘type’ transfer functions (TTFs). Type transfer functions are generalized impulse-response functions, that define not only the response of a particular system that is characterized by a particular soil profile and surface-water flux, but give a representative response for sufficiently similar systems. For this study TTFs were developed for the Fresno case study. The Fresno case study is a regional-scale, simulated database of 1,2-dibromo-3-chloropropane (DBCP) leaching for a 1172-km2 study area in east-central Fresno County, California. Simulated TTF concentration time series for each DBCP application in every 1-km2 element of the study area were obtained by scaling the appropriate TTF by the mass of applied solute, the annual recharge, and a correction function accounting for sorption and decay. The results of the TTF simulations were compared with PRZM-2 simulations for the same study area. The comparison showed that the TTF model (i) only required scaling of a known function once the set of TTFs was identified, (ii) yielded quantitative estimates of peak DBCP concentrations within one order of magnitude of the PRZM-2 estimates, and (iii) overestimated DBCP concentrations because of a basic TFM formulation and a conservative modeling strategy. The TTF approach is capable of producing quantitative, regional-scale groundwater vulnerability assessments that compare well to process-based simulations.

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