Development of statistical models for estimating daily nitrate load in Iowa

Jessica R Ayers; Gabriele Villarini; Keith Schilling; Christopher Jones

doi:10.1016/j.scitotenv.2021.146643

There is an ongoing need to increase our understanding of the sources and timing of stream nitrate loads across agricultural watersheds in Iowa as water quality improvement strategies are implemented. The goal of this study was to model the relationship between nitrate load and the two components of streamflow (i.e., baseflow and stormflow) to quantify in-stream nitrate patterns and develop a new method for estimating loads on days when monitoring data are not available. We analyzed eight watersheds in Iowa that had long-term water quality data where grab samples have been collected from 1987 to 2019. Four regression models were developed that related daily nitrate load to daily baseflow, stormflow, and streamflow discharge. The first model considered baseflow as a predictor, the second model used stormflow, the third model included both baseflow and stormflow as two different covariates, and the final model used total streamflow (unseparated). For all eight watersheds, the baseflowstormflow models had the highest correlation coefficients, which indicates that both components are necessary and together improve nitrate load estimates. While baseflow models estimated lower nitrate loads better, stormflow models captured the variability associated with larger loads. In addition, streamflow models tended to overestimate large nitrate loads. This simple modeling framework can be used to calculate daily, monthly and annual nitrate loads. Delineating nitrate loads between stormflow and baseflow can help identify differences in nitrate sources for nutrient reduction and remediation. There is an ongoing need to increase our understanding of the sources and timing of stream nitrate loads across agricultural watersheds in Iowa as water quality improvement strategies are implemented. The goal of this study was to model the relationship between nitrate load and the two components of streamflow (i.e., baseflow and stormflow) to quantify in-stream nitrate patterns and develop a new method for estimating loads on days when monitoring data are not available. We analyzed eight watersheds in Iowa that had long-term water quality data where grab samples have been collected from 1987 to 2019. Four regression models were developed that related daily nitrate load to daily baseflow, stormflow, and streamflow discharge. The first model considered baseflow as a predictor, the second model used stormflow, the third model included both baseflow and stormflow as two different covariates, and the final model used total streamflow (unseparated). For all eight watersheds, the baseflow-stormflow models had the highest correlation coefficients, which indicates that both components are necessary and together improve nitrate load estimates. While baseflow models estimated lower nitrate loads better, stormflow models captured the variability associated with larger loads. In addition, streamflow models tended to overestimate large nitrate loads. This simple modeling framework can be used to calculate daily, monthly and annual nitrate loads. Delineating nitrate loads between stormflow and baseflow can help identify differences in nitrate sources for nutrient reduction and remediation. [Display omitted] •Four models compared baseflow and stormflow discharge to nitrate loads in eight Iowa watersheds.•The baseflow-stormflow models highlight the importance of separating out streamflow.•Baseflow was better at predicting smaller nitrate loads while stormflow captured peak loads.

Development of statistical models for estimating daily nitrate load in Iowa

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