Publication: Predicting acidification recovery at the Hubbard Brook Experimental Forest, New Hampshire, USA: Evaluation of four models
All || By Area || By Year| Title | Predicting acidification recovery at the Hubbard Brook Experimental Forest, New Hampshire, USA: Evaluation of four models | Authors/Editors* | K. Tominaga, J. Aherne, S.A. Watmough, M. Alveteg, B.J. Cosby, C.T. Driscoll, M. Posch, A. Pourmokhtarian |
|---|---|
| Where published* | Environmental Science and Technology |
| How published* | Journal |
| Year* | 2010 |
| Volume | |
| Number | |
| Pages | |
| Publisher | American Chemical Society |
| Keywords | |
| Link | pubs.acs.org/doi/abs/10.1021/es102243j |
| Abstract |
The performance and prediction uncertainty (owing to parameter and structural uncertainties) of four dynamic watershed acidification models (MAGIC, PnET-BGC, SAFE and VSD) were assessed by systematically applying them to data from the Hubbard Brook Experimental Forest (HBEF), New Hampshire, USA, where long-term records of precipitation and stream chemistry were available. In order to facilitate systematic evaluation, Monte Carlo simulation was used to randomly generate common model input data sets (n = 10000) from parameter distributions; input data were subsequently translated among models to retain consistency. The model simulations were objectively calibrated against observed data (stream water: 1963â2004, soil: 1983). The ensemble of calibrated models was used to assess future response of soil and stream chemistry to reduced sulfur deposition at the HBEF. Although both hindcast (1850â1962) and forecast (2005â2100) predictions were qualitatively similar across the four models, the temporal pattern of key indicators of acidification recovery (stream acid neutralizing capacity and soil base saturation) differed substantially. The range in predictions resulted from differences in model structure and their associated posterior parameter distributions. These differences can be accommodated by employing multiple models (ensemble analysis) but have implications for individual model applications. |
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