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Weighted Regressions on Time,Discharge, and Season (WRTDS), with an Application to Chesapeake Bay River Inputs1
Authors:Robert M Hirsch  Douglas L Moyer  Stacey A Archfield
Institution:1. Respectively, Research Hydrologist, U.S. Geological Survey, 432 National Center, Reston, Virginia 20192;2. Hydrologist, U.S. Geological Survey, Richmond, Virginia 23228;3. Research Hydrologist, U.S. Geological Survey, Northborough, Massachusetts 01532.
Abstract:Hirsch, Robert M., Douglas L. Moyer, and Stacey A. Archfield, 2010. Weighted Regressions on Time, Discharge, and Season (WRTDS), With an Application to Chesapeake Bay River Inputs. Journal of the American Water Resources Association (JAWRA) 46(5):857-880. DOI: 10.1111/j.1752-1688.2010.00482.x Abstract: A new approach to the analysis of long-term surface water-quality data is proposed and implemented. The goal of this approach is to increase the amount of information that is extracted from the types of rich water-quality datasets that now exist. The method is formulated to allow for maximum flexibility in representations of the long-term trend, seasonal components, and discharge-related components of the behavior of the water-quality variable of interest. It is designed to provide internally consistent estimates of the actual history of concentrations and fluxes as well as histories that eliminate the influence of year-to-year variations in streamflow. The method employs the use of weighted regressions of concentrations on time, discharge, and season. Finally, the method is designed to be useful as a diagnostic tool regarding the kinds of changes that are taking place in the watershed related to point sources, groundwater sources, and surface-water nonpoint sources. The method is applied to datasets for the nine large tributaries of Chesapeake Bay from 1978 to 2008. The results show a wide range of patterns of change in total phosphorus and in dissolved nitrate plus nitrite. These results should prove useful in further examination of the causes of changes, or lack of changes, and may help inform decisions about future actions to reduce nutrient enrichment in the Chesapeake Bay and its watershed.
Keywords:monitoring  computational methods  statistics  time series analysis  nonpoint-source pollution  nutrients  point-source pollution
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