Predicting In-stream Water Quality from Watershed Characteristics.
In-stream water quality studies are labour intensive and consume a significant amount of time and money. They are usually conducted on larger streams and rivers or those water courses where severe quality problems have been identified. In addition, current modelling efforts are at such a large scale that they do not provide information necessary for analysing the contribution of local watersheds to in-stream contaminant loadings. Non-point source (NPS) pollution originates in smaller watersheds along tributaries to larger streams. If NPS pollution problems are to be substantially mitigated, it is critical to identify problem watersheds and the management practices needed to reduce their impact on receiving water quality. Identification and control of NPS pollution must be accomplished in these smaller watersheds without the resource intensive investigations that are currently the state-of-the-art.
This chapter presents a technique applied to watersheds in the Ridge and Valley Physiographic Province of central Pennsylvania. Although the expressions shown may not be directly applicable to watersheds outside this region, the methodology used should be transferable. Two types of multiple linear regression expressions are shown. The first uses watershed properties, such as area, slope, U. S. Soil Conservation Service (SCS) curve number (CN) (Soil Conservation Service, 1986), hydrologic soil group, time of concentration, and percent of watershed covered by forest, agriculture, or urban area, to estimate the concentrations of water quality measures like pH, alkalinity, conductivity, nitrate-nitrogen, and water temperature. The second uses several of these water quality estimates to predict measures such as the concentration of ammonia-nitrogen and orthophosphate. Some of the expressions, however, represent weak causal relationships, for example, the expression for dissolved oxygen concentrations.
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