Proceedings of the 2022 Mississippi Water Resources Conference

Year: 2022 Authors: Hill M., Ramirez Avila J.J., Baker B., Evans K.O.

Recent efforts to document the efficacy of suites of conservation practice impacts on water quality have utilized automated edge-of-field water sampling units. Automated sampling procedures involve the collection of composite samples to estimate event mean concentrations which are multiplied by measured discharge to ascertain stream nutrient loading from agricultural landscapes. However, a central and critical step in sample collection procedures is the programmed automated trigger to engage sampling of a runoff event which is designed to represent nutrient concentrations across a runoff hydrograph. However, given the variability in intensity and duration of runoff events, program conditions could have an impact on measured nutrient concentrations and thus estimations of nutrient transport. This study aims to understand the tradeoffs related to sampling protocol by examining differences in nutrient concentrations collected with three different sampling regimes: time-series, flow-weighted by volume, and flow-weighted by change in flow rate. Samplers were installed side-by-side to test the sampling protocols during individual runoff events, the side-by-side trials were replicated in three row-crop agricultural fields in the Mississippi Alluvial Valley from May 2021 through May 2022. Water samples were analyzed for non-point source pollutants of concern: Total Nitrogen, Nitrite-Nitrate -N, Total Kjeldahl Nitrogen, Total Inorganic Phosphorus, Turbidity, and Total Suspended Solids. A multivariate approach is used to parse the influence sampling protocol, season, farm and sensor configuration are having on water quality indicators. Results will help us understand the relative accuracy of nutrient transport estimations in studies that utilize edge-of-field monitoring to examine conservation practice efficacy in agricultural landscapes. Further, results may indicate which sampling protocol is most effective in the widest range of magnitudes of runoff events, providing practical guidance for monitoring efforts.