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Use of historical assessment for evaluation of process-based model projections of future environmental change: Lake acidification in the Adirondack mountains, New York, USA
Authors:Sullivan T J  Turner R S  Charles D F  Cumming B F  Smol J P  Schofield C L  Driscoll C T  Cosby B J  Birks H J  Uutala A J  Kingston J C  Dixit S S  Bernert J A  Ryan P F  Marmorek D R
Institution:E&S Environmental Chemistry, Inc., PO Box 609, Corvallis, Oregon 97339, USA.
Abstract:Because of the considerable uncertainties associated with modeling complex ecosystem processes, it is essential that every effort be made to test model performance prior to relying on model projections for assessment of future surface water chemical response to environmental perturbation. Unfortunately, long-term chemical data with which to validate model performance are seldom available. The authors present here an evaluation of historical acidification of lake waters in the northeastern United States, and compare historical changes in a set of lakes to hindcasts from the same watershed model (MAGIC) used to estimate future changes in response to acidic deposition. The historical analyses and comparisons with MAGIC model hindcasts and forecasts of acid-base response demonstrate that the acidic and low-ANC lakes in this region are responsive to strong acid inputs. However, the model estimates suggest lakewater chemistry is more responsive to atmospheric inputs of sulfur than do the estimates based on paleolimnological historical analyses. A 'weight-of-evidence approach' that incorporates all available sources of information regarding acid-base response provides a more reasonable estimate of future change than an approach based on model projections alone. The results of these analyses have important implications for predicting future surface water chemical change in response to acidic deposition, establishing critical loads of atmospheric pollutants, and other environmental assessment activities where natural variation often exceeds the trends under investigation (high noise-to-signal ratio). Under these conditions, it is particularly important to evaluate future model projections in light of historical trends data.
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