2002 Denver Annual Meeting (October 27-30, 2002)

Paper No. 8
Presentation Time: 3:15 PM


GALLEGOS, Alan, USDA Forest Service, 1600 Tollhouse Road, Clovis, CA 93667, BERG, Neil, Pacific Southwest Research Station, USDA Forest Service, 800 Buchanan St, West Annex Building, Albany, CA 94710-0011, FRAZIER, James, USDA Forest Service, 19777 Greenley Road, Sonora, CA 95370, PROCTOR, Trent, USDA Forest Service, 900 West Grand Ave, Porterville, CA 93257-2035 and DELL, Tom, Self Employed Mathematical Statistician, 11021 Northshore Dr, Bay Saint Louis, MO 39520, ajgallegos@fs.fed.us

The Pacific Southwest Air Program has initiated a project to monitor the effects of atmospheric pollution in high elevation lakes in the Sierra Nevada of California. The objectives of this project are to: identify lakes that are sensitive to atmospheric pollution and to establish a protocol to monitor lakes for atmospheric pollution. Our hypothesis is that small lakes located in headwater areas with steep slopes, with low watershed area to lake area ratio, with high rock outcrop to soil cover ratio; and located in felsic geology are the most sensitive lakes to atmospheric pollution. The study includes a retrospective analysis of 130 Sierra Nevada lakes that were part of a larger EPA study known as the Western Lakes Survey. Geologic bedrock, surficial geology and the presence of carbonates are important variables for predicting the sensitivity of lakes to atmospheric pollution (acid rain) as measured by the acid neutralization capability (ANC) of lake waters. Bedrock and surficial geology are described in a five-class system by geochemical rank. Our model shows a moderate predictability of ANC using variables that include geochemical rank, presence of carbonates, headwater lake designation, lake area to watershed area ratio, lake perimeter to lake area ratio, and percent vegetation in the watershed. The model uses three equations for different conditions that have r2 values of .76, .59 and .51. A residual analysis of 100 lakes in the Kaiser and Emigrant Wildernesses, where observed ANC values were compared to predicted ANC values, demonstrates that our model will identify the most sensitive lakes for synoptic sampling. Actual observed ANC values from synoptic sampling will be used to identify lakes for long term monitoring in other wildernesses.