Although the objective of the Clean Water Act is to maintain the “physical, chemical, and biological integrity of the nation’s waters,” the historical focus of many regulatory agencies has been on compliance with chemical water quality criteria to protect various uses, including aquatic life. More recently, regulations are requiring water quality managers to account for chemistry and habitat even though no method had been developed to quantitatively incorporate the significance of habitat limitations on biological communities. The purpose of this project was to develop a method that would incorporate the effects of both chemical and physical parameters on aquatic life. The resulting method, termed the Integrated Impact Analysis method, uses existing analytical methods, including Principle Components Analysis, All Possible Regressions, and Chi-Square Automatic Interaction Detection (CHAID), to identify key variables. Non-linear interactions between the key variables are identified using a three-dimensional modeling program. After the method was refined using three datasets (Santa Ana River in California, South Platte River in Colorado, and Cuyahoga River in Ohio), a step-by-step User Guidance Document (User Guide) was developed. As a validation step, the User Guide was employed by a WERF subscriber and prospective user to analyze data from Fountain Creek in Colorado. Suggestions and comments from this validation step were incorporated into the User Guide. The next step for this method is its use in a real world situation. From discussions with scientists and biologists, there are a number of ways in which this habitat versus chemistry method could be applied, including: identifying the most limiting stressors, whether chemical or physical, on a stream system; predicting how changes in effluent will affect a stream; developing total maximum daily loads (TMDLs); deciding how to spend improvement money – in chemical or habitat improvements; developing site-specific criteria; and determining how much, and what type of, data must be collected to lead to meaningful conclusions.