A grid-based version of the Soil and Water Assessment Tool (SWAT) model, SWATgrid, was developed to perform simulations on a regularized grid with a modified routing algorithm to allow interaction between grid cells. However, SWATgrid remains largely untested with little understanding of the impact of user-defined grid cell size. Moreover, increases in computation time effectively preclude direct calibration of SWATgrid. To gain insight into defining appropriate strategies for future development and application of SWATgrid, this research considers the simulated differences between commonly-employed hydrologic response unit (HRU)-based and grid-based SWAT models and the implications of resolution on their simulation and calibrated parameter values for a Midwestern, agricultural watershed. Results indicate that: (1) the gridded approach underpredicted simulated streamflow between 5% and 50% relative to the baseline model, depending upon the input spatial resolution and routing algorithm implemented; (2) gridded models generally underpredicted total phosphorous and sediment loads while overpredicting nitrate load; and (3) ranges of values of optimized model parameters remained similar up to 90 m. Results from this analysis should help in defining future applications of the SWATgrid model and the effects of differing spatial resolution of the model input data.

The estimation of pesticide concentrations in surface water bodies with models is a critical component of the environmental and human health risk assessment process. The most recent version of the Soil and Water Assessment Tool (SWAT+) provides new features that are useful for pesticide exposure assessments. This research is the first SWAT+ pesticide simulation study and was conducted to evaluate SWAT+’s new features and to assess its ability to predict pesticide and metabolite concentrations. The evaluation was conducted based upon a comparison of the results from seven different model configurations with high-resolution monitoring data. The results showed that (1) SWAT+ is able to simulate the formation of degradation compounds and predict resulting concentrations in surface water, (2) an accurate representation of transport processes for pesticide exposure assessments is important, and (3) an appropriate degree of realism can be achieved with a rule-based probabilistic pesticide application schedule if information about the annual percent crop treated, a typical application rate, and a typical application window is available. The accuracy of the pesticide concentration simulations with the new features of SWAT+ in the present study demonstrates the model’s ability to provide more accurate estimates with reduced uncertainty compared to SWAT simulations.

Pesticide surface water monitoring data have rarely been used as the only quantitative measure of exposure because the available monitoring data for most pesticides has not been considered robust enough for direct use in pesticide exposure assessments due to infrequent sampling. The cost of daily sample collection and analysis prohibits frequent sampling for most monitoring programs. In this context, a common question raised in assessments is how likely peak concentrations (i.e., annual maxima) may be missed if sampling intervals are longer than daily. The US Geological Survey developed the statistical model “seasonal wave with streamflow adjustment and extended capability” (SEAWAVE–QEX) to address the need to estimate infrequently occurring pesticide concentrations, such as annual maximum daily concentrations, for sites with nondaily monitoring data. This study compares the results of two postprocessing methods and evaluates the capability of SEAWAVE–QEX to estimate annual maximum concentrations of three commonly used herbicides and one metabolite in a catchment in Belgium. The study concludes that the appropriateness of using SEAWAVE–QEX to estimate annual maximum concentrations depends on pesticide characteristics and use and that the model can be particularly sensitive to nonflow correlated exposure events (e.g., point source contributions or drift). Integr Environ Assess Manag 2023;19:513–526. © 2022 Stone Environmental and Bayer AG Crop Science Division. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).