Watershed Stressors and Stream Biological Quality Indicator Modeling

The basic objective of this study was to improve upon univariate regression relationships previously developed between watershed stressors (e.g. imperviousness) and indicators of biological or geomorphologic condition. The approach was to conduct multivariate regression analysis to determine if more robust regression models exist between biological indicators (e.g. Index of Biotic Integrity), and physical, chemical and habitat stressors. Stream biological and physical habitat data were collected in Fairfax County, Virginia. The available data set contains four types of data: (1) general watershed data, (2) biological data, (3) water quality data, and (4) the stream physical habitat assessment (SPHA) data. The habitat data, infrastructure inventory data, and geomorphologic state data were collected on the Fairfax County Stream Physical Assessment project on about 800 miles of stream countywide in 2002–2003. The biological and water quality data were collected by the County under its Stream Protection Strategy (SPS) program during 1999 and 2001 at about 120 sites. A number of linear and non-linear relations were developed using SPS data for IBI as a function of percent imperviousness. It was observed that the maximum R² was 48%. The statistical study findings indicate that percent imperviousness plays an important role in the prediction of the Index of Biotic Integrity (IBI) and Total Habitat Score (THS). However, overall it is a much stronger predictor of IBI than of THS. It was observed that better explanatory and predictive relationships can be determined by classifying the data with respect to stream gradient, stream order, and physiographic province. Further, it was observed that individual THS metrics have better predictive capability for IBI than the THS. The highest correlation between imperviousness and individual THS metrics was for channel alteration, followed by bank stability and bank vegetative protection.