An integrated approach coupling water quality computer simulation modeling with a geographic information system (GIS) was used to delineate critical areas of nonpoint source (NPS) pollution at the watershed level. Two simplified pollutant export models were integrated with the Virginia Geographic Information System (VIrGIS) to estimate soil erosion, sediment yield, and phosphorus (1') loading from the Nomini Creek watershed located in Westmoreland County, Virginia.On the basis of selected criteria for soil erosion rate, sediment yield, and P loading, model outputs were used to identily watershed areas which exhibit three categories (low, medium, high) of nonpoint source pollution potentials. The percentage of the watershed area in each category, and the land area with critical pollution problems were also identified. For the 1505-ha Nomini Creek watershed, about 15, 16, and 21 percent of the watershed area were delineated as sources of critical soil erosion, sediment, and phosphorus pollution problems, respectively. In general, the study demonstrated the usefulness of integrating GIS with simulation modeling for nonpoint source pollution control and planning. Such techniques can facilitate making priorities and targeting nonpoint source pollution control programs. (KEY TERMS: nonpoint source pollution; water quality modeling; geographic information system.)

Multiple linear equations to predict selected parameters for the Kentucky watershed model (KWM) are presented. The independent variables consist of easily determinable watershed characteristics. The relationship provides a means by which the KWM can be used to predict streamflows from ungaged drainage basins. Examples are given for five test watersheds. Results are variable. INTRODUCTION include watershed data from other states in the southern re-Competition among users for a relatively fixed supply of gion. water has focused attention on the need for effective means to plan for the optimum use of this vital resource. Parametric models, which lend themselves to computer application and attempt to simulate the hydrologic cycle, have become recognized as useful aids to such planning. One of the better known and more comprehensive parametric models is the Stanford watershed model [Crawford and Linsley, 1966]. Because of its popularity it has undergone numerous modifications [e.g., James, 1965; Claborne and Moore, 1970; $hanholtz et al., 1972; Ligon et al., 1969]. However, each modification is basically a soil moisture accounting procedure in which mathematical expressions are used to define relationships between elements of the hydrologic cycle and the interactions between its components. Numerous parameters are utilized in this process, some of which are derived from historical records, some from climatological data, others from physical watershed characteristics, and still other nonmeasurable entities from estimation by trial-and-adjustment or optimization procedures. The effectiveness of these routines depends on the availability of a streamflow record of sufficient length to calibrate the model. Typically, several different combinations of parameters are utilized before an acceptable match can be found between predicted and recorded streamflows. The need of prior calibration makes it difficult to apply existing watershed models to ungaged watersheds. To circumvent this problem, attempts have been made to correlate model parameters to measurable physical watershed characteristics. James [1972], reporting on work by Ross [1970], discusses linear regression relationships that were developed to relate plant available water capacity (AWC), permeability of the A soil horizon, and overland flow surface slope to selected parameters in the Kentucky watershed model (KWM). Jarboe and Haan [1974] used a multiregression approach to estimate parameters for use in a monthly water yield model. Ambaruch and Simmons [1973] also reported some success with a multiregression approach, using data from several Tennessee Valley Authority watersheds. The study reported herein was undertaken to extend the work of Ross [1970] and Ambaruch and Simmons [1973] to

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The Attenuation Factor (AF), a screening model, was used to evaluate the relative degree of vulnerability of groundwater to pesticide contamination in Louisa County, Virginia. For evaluating the contamination potential of pesticides, three scenarios of pesticide leaching represented by high, moderate, and low cases of degradation and sorption in the soil were considered. Data layers were overlaid within a Geographic Information System (GIS) for spatial computation of AF for the actual and 2m groundwater depths. This spatial database was divided into five contamination potential categories namely high, medium, low, very low, and unlikely, based on the numerical values of the AF for each cell (119 ha). The results for the three most mobile pesticides are presented in this paper. The performance of the AF model was evaluated by comparing its predicted results with the field data from an experimental watershed. The AF model was able to identify most of the frequently detected pesticides in the watershed. A sensitivity analysis was also performed. The results of this study provide information about the potential groundwater threat by pesticides to the citizens ahd decision‐makers in the County and can be used for formulating an appropriate land use management plan to protect the groundwater quality.

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