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Statistical Nonparametric Model for Natural Salt Estimation
By James R. Prairie, Balaji Rajagopalan, Terrance J. Fulp, and Edith A. Zagona. Journal of Environmental Engineering, 131(1), January 1, 2005.
Abstract: Many rivers in the Western U.S. suffer from high salinity content due to both natural and human-induced causes. Computer simulation models are often used to estimate future salinity levels and identify mitigation needs. To date, estimation of future natural salt loading has utilized linear relationships between natural flow and natural salt. We develop a nonparametric regression technique to fit a functional relationship between natural flow and natural salt. The main advantages of the nonparametric technique are: (1) No prior assumptions have to be made as to the underlying form of the relationship and (2) any arbitrary relationship (linear or nonlinear) can be modeled. In addition, we develop a resampling scheme to provide confidence intervals of the natural salt estimates from the nonparametric model. We apply this model to data from a stream gauge at Glenwood Springs, Colo., on the Colorado River. We show that the new natural salt model reduces the average overprediction of salt mass shown in the existing natural salt model for the period 1941–1995 by approximately 15% (78,000 metric tons).