The application of a parametric time series model to a water resources problem involves selecting a model and estimating its parameters, both steps adding uncertainty to the analysis. The moving blocks bootstrap is a simple resampling algorithm which can replace parametric time series models, avoiding model selection and only requiring an estimate of the moving block length. The moving blocks bootstrap resamples the observed time series using approximately independent moving blocks. A Monte Carlo experiment is performed involving the use of a time series model to estimate the storage capacity S of a surface water reservoir. Our results document that the bootstrap always produced storage estimates with lower root‐mean‐square‐error than a parametric alternative, even when no model error is introduced into the parametric scheme. These results suggest that the moving blocks bootstrap can provide a simple and attractive alternative to more complex multivariate ARMA models.

Urban stormwater quality data collected over the past 20 years for several large government-sponsored sampling programs in the United States were assembled and analyzed to develop new nationwide estimators and statistics for urban storm water quality. We believe that this is the first attempt to assemble and analyze these major storm water quality data sets for this purpose. In this paper, the first public report of our work to-date, we present the results of the data acquisition, data base assembly, quality assurance, computation of new stormwater event mean concentrations and associated statistics, and comparisons with the original U.S. Environmental Protection Agency's Nationwide Urban Runoff Program (NURP) results. The differences between the pooled means and those estimated from our analysis of the NURP data range from a 79% lower estimate for Copper to a 36% higher estimate for Biochemical Oxygen Demand. It is concluded that the variations between the NURP results and those developed here from the pooling of the three national data bases are important and that future work may provide a basis for differentiating Event Mean Concentrations among urban land uses, geographic region and seasons.

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