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An evaluation of processes regulating spatial and temporal patterns in lake sulfate in the Adirondack region of New York
Author(s) -
Chen Limin,
Driscoll Charles T.
Publication year - 2004
Publication title -
global biogeochemical cycles
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.512
H-Index - 187
eISSN - 1944-9224
pISSN - 0886-6236
DOI - 10.1029/2003gb002169
Subject(s) - biogeochemical cycle , deposition (geology) , environmental science , acid deposition , sulfate , surface water , hydrology (agriculture) , acid rain , atmospheric sciences , environmental chemistry , physical geography , geology , soil science , ecology , chemistry , soil water , sediment , geomorphology , environmental engineering , organic chemistry , geography , geotechnical engineering , biology
As a result of the Clean Air Act Amendments of 1970 and 1990, there have been significant decreases in sulfate (SO 4 2− ) concentrations in surface waters across the northeastern United States. The 37 Direct/Delayed Response Program (DDRP) watersheds in the Adirondacks receive elevated levels of atmospheric S deposition and showed considerable variability in lake SO 4 2− concentrations. In response to decreases in atmospheric S deposition, these sites have generally exhibited relatively uniform decreases in surface water SO 4 2− concentrations. In this study, an integrated biogeochemical model (PnET‐BGC) was used to simulate the response of lake SO 4 2− concentrations at these DDRP sites to recent changes in atmospheric S deposition. Using default parameters and algorithms, the model underpredicted lake SO 4 2− concentrations at sites with high SO 4 2− concentrations and overpredicted at sites with low SO 4 2− concentrations. Initial predictions of lake SO 4 2− were relatively uniform across the region. Initial model simulations also underpredicted decreases in lake SO 4 2− concentrations from 1984 to 2001. We identified seven hypotheses that might explain the discrepancies between model predictions and the measured data. Model inputs, parameters, and algorithms were modified to help test these hypotheses and better understand factors that control spatial and temporal patterns in lake SO 4 2− in this acid‐sensitive region.