Input Parameter and Model Resolution Effects on Predictions of Solute Transport

Model predictions of solute transport using larger scale soil and climatic data sets may be useful for classifying mapping units based on their susceptibility to chemical leaching. However, model predictions based on input data sets with low spatial resolution may not accurately reflect transport processes occurring in situ. The goal of the current study was to compare several modeling approaches that might be applicable for classifying soil mapping units (1:24 000) according to their leaching potential to (i) model results based on detailed site‐specific measurements and (ii) observed data collected at a field site (Borollic Calciorthid) in southwestern Montana. Data from a 2‐yr field study of pentafluorobenzoic acid (PFBA), 2,6‐difluorobenzoic acid (DFBA) and dicamba (3,6‐dichloro‐2‐methoxybenzoic acid) transport in fallow and cropped systems under two water application levels were compared to simulations obtained using the Chemical Movement in Layered Soils (CMLS) and Leaching and Chemistry Estimation (LEACHM) models. The resolution of model input parameters was varied based on sources of data. In Case 1, model inputs were obtained primarily from detailed soil profile characterization and site‐specific measurements of precipitation, irrigation, and pan evaporation. LEACHM predictions were also generated using estimated conductivity and retentivity functions from textural data obtained from the USDA‐NRCS Soil Survey (SSURGO) database (Cases 2 and 3). CMLS predictions were generated using (i) detailed site‐specific measurements (Case 1) and (ii) estimated volumetric water contents from textural data (SSURGO) and estimated daily precipitation and evapotranspiration (ET) from the Weather Generator (WGEN) and Montana Agricultural Potentials (MAPS) climate database (Cases 2 and 3). Comparison of observed and simulated mean solute travel times showed that (i) LEACHM and CMLS performed adequately with high‐resolution model inputs, (ii) model performance declined when field conditions were conducive to preferential flow, (iii) estimated K s values from regression equations based on textural data were problematic for generating adequate predictions using LEACHM, and (iv) CMLS predictions were less sensitive to data input resolution, due in part to the fact that CMLS provides an oversimplified description of transport processes.