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Fecal bacteria peak concentrations and breakthrough times as affected by preferential flow to subsurface (tile) drainage systems following irrigation or rainfall are important when assessing the risk of contamination. Process-based, convective-dispersive modeling of microbial transport through preferential flow has been conducted. Likewise, regression modeling has been used to study solute transport (e.g., nitrate) under agricultural systems and can have advantages over process-based modeling, such as fewer or easier to determine parameters and easier determination of confidence intervals. However, empirical models (e.g., regression) have only rarely been used to investigate microbial transport. In addition, the selection of time response curves to empirically model simple, right skewed, single breakthrough events from field or laboratory data is generally an arbitrary choice and often considers only conventional distribution-shaped response curves, such as lognormal distributions. In this study, we evaluate four rational polynomial functions for modeling bromide and E. coli data from a single breakthrough event from a tile-drained field near Nashua, Iowa. Bromide and liquid swine manure were applied to the plot immediately prior to 42 mm of overhead sprinkler irrigation. E. coli and bromide concentrations were determined in subsurface drainage water samples collected for the next 24 h. Nonlinear iteratively re-weighted least squares regression procedures were used to model the breakthrough data. The maximum event value, time of occurrence, and event total were estimated from the parameters for each model Selection of the best model was based on multiple performance criteria. A simple rational polynomial with a linear factor in the numerator and quadratic form in the denominator was the overall best choice for E. coli (R² = 0.92). A related fractional order form also known as the Gunary model was the best choice for bromide (R² = 0.93). In comparison, the more commonly assumed lognormal distribution described only 78% of the variation in E. coli and 68% of the variation in bromide, with a weighted mean square error 3.0 to 4.6 times larger than each selected rational polynomial model In this experiment, the chosen models clearly tracked E. coli and bromide distribution better than the lognormal model. [ABSTRACT FROM AUTHOR]

Changes in land use and alteration of the ecosystem can significantly affect soil physical, chemical, and biological properties. In this study, changes in soil organic carbon (SOC), root biomass, bulk density (ρb), water stable aggregates (WSA), and infiltration rates were examined in reconstructed prairies varying in age and landscape position. The objective of the study was to determine the potential of landscape position effect on these selected soil properties in reconstructed prairies. Findings show that SOC increased as years since prairie establishment increased and had a positive correlation with infiltration rate and WSA. The opposite was true for ρb, where it decreased as prairie age increased and negatively correlated with SOC. However, the effects of SOC and pb on infiltration rates varied by landscape slope position and age of prairie establishment. Root biomass, SOC, and WSA had decreased, while ρb increased at the midslope compared to the summit and toe-slope positions resulting in lower infiltration rates. Although the summit and the toe-slope positions had similar soil properties, infiltration rates were much greater in the toe-slope position. This was ascribed to the toe-slope position's superior WSA, due to greater SOC concentrations. In general, this study shows that over time, increases in SOC did promote aggregate formation and lower ρb, creating more permeable soil surfaces in these reconstructed prairies. However, better soil conservation practices that reduce soil surface water runoff in the midslope in particular are needed during the first few years of prairie establishment. [ABSTRACT FROM AUTHOR]