Multivariate adaptive regression splines for estimating riverine constituent concentrations

Abstract

AbstractRegression‐based methods are commonly used for riverine constituent concentration/flux estimation, which is essential for guiding water quality protection practices and environmental decision making. This paper developed a multivariate adaptive regression splines model for estimating riverine constituent concentrations (MARS‐EC). The process, interpretability and flexibility of the MARS‐EC modelling approach, was demonstrated for total nitrogen in the Patuxent River, a major river input to Chesapeake Bay. Model accuracy and uncertainty of the MARS‐EC approach was further analysed using nitrate plus nitrite datasets from eight tributary rivers to Chesapeake Bay. Results showed that the MARS‐EC approach integrated the advantages of both parametric and nonparametric regression methods, and model accuracy was demonstrated to be superior to the traditionally used ESTIMATOR model. MARS‐EC is flexible and allows consideration of auxiliary variables; the variables and interactions can be selected automatically. MARS‐EC does not constrain concentration‐predictor curves to be constant but rather is able to identify shifts in these curves from mathematical expressions and visual graphics. The MARS‐EC approach provides an effective and complementary tool along with existing approaches for estimating riverine constituent concentrations.