The relative importance of nonpoint-source (NPS) pollution in the degradation of water quality has<br>increased in the last two decades due to the control of pollutant releases from point sources (Sharpley and<br>Meyer 1994). The most important source of NPS pollutants are agriculture and urban areas, which impact<br>water quality in rivers, lakes, estuaries and groundwaters through the release of eroded sediments, fertilizers,<br>pesticides, and municipal sewage sludge. Because of this, NPS pollution is an important environmental<br>concern at state and national levels.<br>Several transport processes control the dispersal of NPS pollutants, including leaching to groundwater,<br>surface runoff (Pereira and Rostad 1990), and aerial transport and deposition (Glotfelty et al. 1984). Once<br>in groundwater, these contaminants can impact surface water during stream recharge. While the losses of<br>NPS pollutants from agricultural fields or urban areas can be small as a percentage of the total amount<br>released, the cumulative additions to river systems from large drainage areas can be significant.<br>The development of a reliable contaminant transport model for tracing the dispersal of NPS pollutants<br>through a heterogeneous aquifer requires knowledge of the spatial distribution of hydrologic parameters such<br>as hydraulic conductivity or permeability (Zheng and Bennett 1995). In the commonly used stochastic<br>approach (Dagan 1989), these distributions are treated as spatial random functions whose variance and<br>correlation length scale are determined from hydrological information collected by head measurements, pump<br>and tracer tests, and soil core analyses. Point estimates of the hydraulic conductivity or permeability are<br>determined by kriging, a geostatistical interpolation procedure which estimates unknown random functions<br>based on spatial correlations between point observations.<br>Noninvasive geophysical techniques are becoming an increasingly popular component of hydrogeological<br>studies since many geophysical data are sensitive to spatial variations in hydraulic conductivity and permeability.<br>In addition, the cost of surface exploration is only a fraction of the cost of drilling and a wide area1<br>coverage is readily obtained. Controlled source electromagnetic (CSEM) methods provide maps of electrical<br>conductivity and are an appropriate choice if the depth scale of investigation is on the order of 10 m-l km.<br>We are investigating the possibility of placing electrical constraints on the subsurface permeability as<br>part of a larger, integrated study to determine the fate of agricultural chemicals introduced at a research<br>site near the Brazos River. The research involves a joint analysis of the electrical conductivity structure, the<br>available soil cores, and other hydrologic data. A Bayesian approach is taken, following Copty et al. (1993),<br>in which geophysical data are used to update the variance and correlation length scale of a hydrologicallyderived,<br>random permeability field. In this paper we will describe the underlying theory and demonstrate an example using synthetic CSEM data that have been generated from a simulated geoelectrical section of our study site.<br>The incorporation of CSEM data into a determination of subsurface permeability is made more difficult<br>by the presence of man-made electrical conductors at the site. These include an aluminum pipeline and an<br>overhead power line. Such artifacts are characteristic of the human impact at many environmental sites. We<br>are presently applying the integral equation code of Qian and Boerner (1995) to account for the effect of the<br>aluminum pipeline on the CSEM data.


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