Modelling regional response of lakewater chemistry to changes in acidic deposition: the MAGIC model applied to lake surveys in southernmost Norway 1974-1986-1995

Abstract

Two methods for modelling regional responses of lake water quality to changes in acidic deposition in southernmost Norway were examined. Both methods are based upon the MAGIC model but differ in mode of regional application; one uses site-specific while the other uses Monte-Carlo methods for model calibration. The simulations of regional responses from both methods were compared with observed responses based on data from three lake surveys in southernmost Norway conducted in 1974, 1986 and 1995. The regional responses of the two modelling approaches were quite similar and agreed well with the observed regional distributions of lakewater chemistry variables. From 1974 to 1986 the observed data indicated that despite a decline of approximately 10% in sulphate (SO4) deposition, the mean acid neutralizing capacity (ANC) of lakes in southernmost Norway declined by approximately 6 μeq l-1. Both modelling approaches simulated no change or a very small decline in mean ANC for that period. From 1986 to 1995 the observed data indicated that, in response to an approximate 40% decline in SO4 deposition, the mean ANC of lakes in southernmost Norway increased by 11-16 μeq l-1. The modelling approaches simulated increases of 9-10 μeq l-1 in mean ANC for the same period. Both simulations and observations indicate that > 65% of lakes in southernmost Norway were acidic in 1974 and 1995. Both simulation methods predict that >65% of the lakes in southernmost Norway will have positive ANC values within 10 years of reductions of SO4 deposition to 20% of 1974 levels. Of the two regionalization methods the site-specific method appears preferable, because whereas the Monte-Carlo method gives results for a region as a whole, the site-specific method also reveals patterns within the region. The maintenance of a one-to-one correspondence between simulated and observed systems means that simulation results can be mapped for a geographically explicit presentation of model results. The ability to examine geographic patterns of response is becoming increasingly important in regional assessments.