Estimation of the direction and magnitude of trends in surface water quality remains a problem of great scientific and practical interest. The Weighted Regressions on Time, Discharge, and Season (WRTDS) method was recently introduced as an exploratory data analysis tool to provide flexible and robust estimates of water quality trends. This paper enhances the WRTDS method through the introduction of the WRTDS Bootstrap Test (WBT), an extension of WRTDS that quantifies the uncertainty in WRTDS-estimates of water quality trends and offers various ways to visualize and communicate these uncertainties. Monte Carlo experiments are applied to estimate the Type I error probabilities for this method. WBT is compared to other water-quality trend-testing methods appropriate for data sets of one to three decades in length with sampling frequencies of 6–24 observations per year. The software to conduct the test is in the EGRETci R-package. The statistical procedures presented here are all based on the Weighted Regressions on Time, Discharge, and Season (WRTDS) approach to water quality data analysis. The WRTDS is implemented in the EGRET (Exploration and Graphics for RivEr Trends), R-package (open source) available from the Comprehensive R Archive Network http://cran.r-project.org/web/packages/. The new software that implements the WRTDS Bootstrap Test (WBT) described in this paper is also an R-package called EGRETci, also available from the Comprehensive R Archive Network. 
A bootstrap method for estimating uncertainty of water quality trends