To use a complex, process oriented site model for predicting broad-scale environmental effects assumes the availability of a large, quality data base for calibrating the model and the validity of the process of extrapolating site-specific results to a larger region. The greater the level of extrapolation the more error prone the model results may become. A simplified version of a complex process oriented RAINS Lake Model (RLM) was developed by regression methods to quantify these extrapolation errors. RLM variables that explain more than 1% of the variation in the model output (pH of 1980) were selected and calibrated to the measured pH distribution in 1980 in the southern region of Finland from 1920 to 1980. An iterative procedure was then used to select the minimum number of variables which best represented the behavior of the RLM. The results show that extreme regional characteristics may necessitate adaptation of a simplified model that has been calibrated in a less extreme regional context.