The performance of three statistical methods in predicting maximum m-day rolling averages (m=1, 7, 14, 30, and 60 days) of atrazine using 66 approximately daily atrazine time series from the Atrazine Ecological Monitoring Program (AEMP) (2008-2012) was evaluated.  Potential daily covariates developed for the monitoring data included functions of daily flow, deterministic PRZM-hybrid model time series, and atrazine concentration profiles from hydrologically similar sites.  All covariates were positively correlated with atrazine concentrations, with covariates derived from hydrologically similar sites showing the highest correlation.  For three site-years, the performance of statistical methods in predicting m-day rolling averages using the derived covariates was evaluated.  Methods considered were Gaussian imputation and estimation of ARMA (GIEA) time series, cokriging, and time-series state space or dynamic linear models (DLM).  Method performance in comparison to linear interpolation is described.

Methods for predicting atrazine peak concentrations of non-daily surface water monitoring

248th National Meeting (2014)

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