Improved understanding of the drivers of stream nitrate is necessary to improve water quality. This is particularly true for Iowa, a large contributor to Mississippi River Basin nitrate loads. Here, we focus on the Raccoon River at Des Moines, Iowa, and develop statistical models to describe the monthly (from March to August) nitrate concentrations in terms of eight drivers representing monthly climate, monthly hydrology, and yearly cropping practices. We consider six two-parameter distributions, linear and nonlinear dependencies between the predictors, and the distributions' parameters. Model selection was performed by penalizing more complex models. Our results show that the Weibull and Gumbel distributions are the only two selected distributions. Baseflow and the previous year's soybean [Glycine max (L.) Merr.] area were the two predictors most often identified as important. Our modeling results imply that increases in soybean area have led to increasing nitrate concentrations. Moreover, nitrate concentrations are related to baseflow in a nonlinear way, with effects strongest when baseflow is near or below the average condition. Additional relevant predictors were precipitation and, to a lesser extent, temperature. We conclude that best management practices and improved conservation targeting soybean in a corn (Zea mays L.)-soybean rotation will improve water quality in this artificially drained system.
All Science Journal Classification (ASJC) codes
- Environmental Engineering
- Water Science and Technology
- Waste Management and Disposal
- Management, Monitoring, Policy and Law