King County, Department of Natural Resources and Parks, has developed a set of continuous hydrologic/hydrodynamic water quantity/quality 1-D, 2-D, and 3-D numerical models for the Green River and Greater Lake Washington watersheds. The generation, transport and fate of pollutants in these watersheds are calibrated using existing local water quality data and published literature values. However, these data sources lack the characterization of variability necessary to provide calibration/validation of the models beyond a seasonal or annual loading calculation. Furthermore, the Puget Sound region is experiencing rapid population growth, causing landscape changes to occur at a rate that can induce a trend in pollutant loading estimates. This, along with inherent inter- and intra-annual variability, confounds attributing distinct loadings from specific land cover types. Therefore, loading coefficients were developed using site-specific data collected from a program designed to characterize runoff loadings.
Loading estimates for fecal coliform bacteria, E. coli, total phosphorus, orthophosphate phosphorus, nitrate+nitrite nitrogen, ammonia nitrogen, total suspended solids, and total and dissolved copper, mercury, zinc and iron, were developed for different land cover types in the Green River watershed. Loadings were estimated from a 3-year monitoring program (2001-2003) involving storm sequential samples (6-10 discrete samples at 4-hour intervals) and flow weighted composites , and from discrete baseflow samples (3 discrete samples at 8-hour intervals). Monitoring location drainage areas ranged in scale from a few hundred acres to tens of square miles. The smaller drainage areas were selected for relatively homogenous land cover types to further isolate measured pollutant loading rates. Analyses performed on the data included the following:
- a comparison of the routine and project-specific sampling approaches,
- a comparison of water quality data for base flow and storm flow,
- a hysteresis analysis for TSS and alkalinity,
- a correlation analysis among water quality parameters,
- a correlation analysis between water quality and hydrologic parameters,
- a principal component analysis, and
- a correlation analysis between constituent loading and land use/cover categories.
With each analysis, it was apparent that patterns in the data were strongly influenced by land use/cover patterns. The benefit of developing these loading coefficients is the ability to not only evaluate and prioritize significant non-point sources of pollutant generation, but also to evaluate future conditions and any theoretical efficacies using BMPs.
