Protection of forest ecosystems in the eastern United States from elevated atmospheric deposition of sulfur and nitrogen

Abstract

Critical loads (CLs) and target loads (TLs) of atmospheric deposition of sulfur (S) and nitrogen (N) specify the thresholds of air pollution above which damage to ecosystems is expected to occur and are used to inform environmental regulation and natural resource management. Model estimates of CL and TL can vary for a given location, and these differences can be important for characterization of ecosystem effects from elevated S and N deposition. Moreover, TLs are used to evaluate associated timeframes of ecosystem recovery. We compared published CLs and TLs based on soil acidity criteria derived from steady-state versus dynamic models for terrestrial ecosystems. We examined the magnitude of differences in the CL/TL results from the two types of models for the same regions in the Eastern U.S. Results showed that CLs/TLs from dynamic models (or from steady state modeling using soil base cation weathering estimates from dynamic models) generally produce a broader range of values of acid-sensitivity, including lower CLs/TLs, as compared with a steady-state approach. Applications of dynamic biogeochemical models capable of developing CLs/TLs are relatively data intensive and typically limited to locations where measured soil and soil solution (or nearby stream water) chemistry are available for model parameterization, calibration, and testing. We recommend that CLs/TLs derived from dynamic models be used, where data permit, as they are likely more accurate and allow for evaluation of time-dependent phenomena and period needed for recovery. However, CLs derived from steady-state models remain a useful tool for understanding broad spatial patterns in soil acid-sensitivity throughout the U.S. Future work should focus on the development of more reliable model input parameters, particularly soil base cation weathering, and the extent to which CLs and TLs at a given location may vary and be altered with anticipated future climate change. In addition, dynamic models could be further developed to estimate CLs/TLs for nutrient N.