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Application of four watershed acidification models to Batchawana Watershed, Canada
Authors:Booty W G  Bobba A G  Lam D C  Jeffries D S
Institution:Environment Canada, National Water Research Institute, Rivers Research Branch, 867 Lakeshore Road, Burlington, Ontario, Canada.
Abstract:Four watershed acidification models (TMWAM, ETD, ILWAS, and RAINS) are reviewed and a comparison of model performance is presented for a common watershed. The models have been used to simulate the dynamics of water quantity and quality at Batchawana Watershed, Canada, a sub-basin of the Turkey Lakes Watershed. The computed results are compared with observed data for a four-year period (Jan. 1981-Dec. 1984). The models exhibit a significant range in the ability to simulate the daily, monthly and seasonal changes present in the observed data. Monthly watershed outflows and lake chemistry predictions are compared to observed data. pH and ANC are the only two chemical parameters common to all four models. Coefficient of efficiency (E), linear (r) and rank (R) correlation coefficients, and regression slope (s) are used to compare the goodness of fit of the simulated with the observed data. The ILWAS, TMWAM and RAINS models performed very well in predicting the monthly flows, with values of r and R of approximately 0.98. The ETD model also showed strong correlations with linear (r) and rank (R) correlation coefficients of 0.896 and 0.892, respectively. The results of the analyses showed that TMWAM provided the best simulation of pH (E=0.264, r=0.648), which is slightly better than ETD (E=0.240, r=0.549), and much better than ILWAS (E=-2.965, r=0.293), and RAINS (E=-4.004, r=0.473). ETD was found to be superior in predicting ANC (E=0.608, r=0.781) as compared to TMWAM (E=0.340, r=0.598), ILWAS (E=0.275, r=0.442), and RAINS (E=-1.048, r=0.356). The TMWAM model adequately simulated SO4 over the four-year period (E=0.423, r=0.682) but the ETD (E=-0.904, r=0.274), ILWAS (E=-4.314, r=0.488), and RAINS (E=-6.479, r=0.126) models all performed poorer than the benchmark model (mean observed value).
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