Factors controlling water chemistry and sensitivity to acidification of lakes in upper Michigan

Nikolaos P. Nikolaidis, University of Iowa

Abstract

Discriminant analysis is a powerful technique in predicting sensitivity of lakes to acidification by using information derived from maps. A set of watershed descriptors (n = 19) was selected and a statistical model developed to classify any lake as to its sensitivity to acidification. Hydrology, bedrock geology, morphometry, and soil chemistry were shown to be the key factors. Sensitive lakes (alkalinity less than 40 ..mu..eq/l) were characterized as seepage or inflow, with sandstone or sandy dolomite bedrock geology, acid soils (soil pH less than 5.5), and small surface areas (less than 50 hectares). Lakes in the eastern end of the upper peninsula of Michigan were found to have a range of alkalinities from -40 to 8800 ..mu..eq/l. Fifteen out of 53 lakes were sensitive with alkalinity values less than 40 ..mu..eq/l and pH from 4.4 to 6.2. An additional eight lakes had alkalinity between 40 and 200 ..mu..eq/l and pH from 5.9 to 6.8. The multivariate statistical model, correctly classified from 62.3 to 86.8% of the lakes according to alkalinity. This range needs further varification. At least twenty lakes with known water quality data should be selected and watershed descriptors determined. The lakes should be classified through the model discriminant functions and cross-validated with alkalinity data. The results of this technique could be further improved as follows: (1) the sample size should be increased three to four times; (2) some of the variables could be changed to better describe watershed chemistry; (3) some of the most important watershed descriptors could be changed to continuous; and (4) on-site verification of the hydrologic and geologic descriptors. Overall, a methodology using cluster and discriminant analysis was developed that has many applications. the technique can be used to design sampling networks or to estimate resources-at-risk from regional synoptic surveys of water quality. 27 references, 6 figures, 14 tables.

 

URL

https://ir.uiowa.edu/cee_pubs/267