Development of a type transfer function approach for modeling non-point-source vadose-zone pesticide leaching at the regional scale

The objective of this dissertation research was the development and application of a new modeling approach to quantitatively characterize NPS agrochemical leaching for regional-scale groundwater vulnerability assessments. The new model extends the Jury transfer function model to regional scales through the development of type transfer functions (TTFs). TTFs are upscaled travel time probability density functions that describe characteristic vertical leaching behavior for categories with similar soil-hydraulic properties in soil classification systems.; In the first phase of this study empirical soil-order-based TTFs were developed for modeling vadose-zone DBCP (a soil fumigant) transport for a simulated data set in Fresno County (California). In the second phase, a generally-applicable, regional-scale, soil-texture-based TTF model was built with stochastic simulated data sets. Seven sets of TTFs, representing different levels of upscaling, were developed for six soil-textural classes. The concentrations predicted by the TTF model for synthetic test cases were compared to those estimated from process-based simulations. In the third phase, the TTF modeling approach was applied in the San Joaquin Valley (California) to make the first quantitative regional-scale groundwater vulnerability assessment with respect to Atrazine (a common herbicide) leaching. The fourth phase assessed the uncertainty introduced by upscaling TTFs, and TTF model accuracy for predictions beyond the depth of calibration.; The results of this study show that (i) the TTF model is capable of estimating the spatiotemporal distributions of solute concentrations at a depth of interest, while relying only on available soil survey and recharge information, and while minimizing the computational cost, (ii) TTF model performs well with respect to mass-balance in a field-wide sense, (iii) sets of is based on individual soil textures give better predictions than sets that span all textures, (iv) TTF model is able to identify locations with high contaminant concentrations based on soil texture, and (v) using spatially distributed as opposed to spatially uniform recharge estimates is to predicted concentrations above the MCL for a greater portion of the study area. In spite of the simplifying assumptions that underlie the TTF modeling approach, it represents a powerful efficient tool for quantitative, regional-scale groundwater vulnerability assessments.