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Surface water acidification may be caused or influenced by both natural watershed processes and anthropogenic actions. Empirical models and observational data can be useful for identifying watershed attributes or processes that require further research or that should be considered in the development of process models. This study focuses on the Adirondack region of New York and has two purposes: to (1) develop empirical models that can be used to assess the chemical status of lakes for which no chemistry data exist and (2) determine on a regional scale watershed attributes that account for variability in lake pH and acid-neutralizing capacity (ANC). Headwater lakes, rather than lakes linked to upstream lakes, were selected for initial analysis. The Adirondacks Watershed Data Base (AWDB), part of the Acid Deposition Data Network maintained at Oak Ridge National Laboratory (ORNL), integrates data on physiography, bedrock, soils, land cover, wetlands, disturbances, beaver activity, land use, and atmospheric deposition with the water chemistry and morphology for the watersheds of 463 headwater lakes. The AWD8 facilitates both geographic display and statistical analysis of the data. The report, An Adirondack Watershed Data Base: Attribute and Mapping Information for Regional Acidic Deposition Studies (ORNL/TM--10144), describes the AWDB. Both bivariate (correlations and Wilcoxon and Kruskal-Wallis tests) and multivariate analyses were performed. Fifty-seven watershed attributes were selected as input variables to multiple linear regression and discriminant analysis. For model development -200 lakes for which pH and ANC data exist were randomly subdivided into a specification and a verification data set. Several indices were used to select models for predicting lake pH (31 variables) and ANC (27 variables). Twenty-five variables are common to the pH and ANC models: four lake morphology, nine soil/geology, eight land cover, three disturbance, and one watershed aspect. An atmospheric input variable (H{sup +} or NO{sub 3}{sup -}) explains the greatest amount of variation in the dependent variable (pH and ANC) for both models. The percentage of watershed in conifers is the next strongest predictor variable. For all headwater lakes in the Adirondacks, -60% of the lakes are estimated to have an ANC {le}50 {micro}eq/L, and 40% of the lakes have a pH {le}5.5, levels believed to be detrimental to some fish species

Empirical Relationships Between Watershed Attributes and Headwater Lake Chemistry in the Adirondack Region