https://dx.doi.org/10.1016/S0304-3800(97)01953-4">
 

Title

Modeling nitrogen saturation in forest ecosystems in response to land use and atmospheric deposition

Abstract

A generalized, lumped-parameter model of carbon (C), water, and nitrogen (N) interactions in forest ecosystems (PnET-CN) is presented. The model operates at a monthly time step and at the stand-to-watershed scale, and is validated against data on annual net primary productivity, monthly carbon and water balances, annual net N mineralization, nitrification, foliar N concentration and annual and monthly N leaching losses for two sites, Hubbard Brook (West Thornton, NH) and Harvard Forest (Petersham, MA). It is then used to predict transient responses in function resulting from changes in land use and N deposition, as well as the maximum rate of N cycling which can be sustained for any given combination of site, climate and species. Model predictions suggest a very long legacy effect of land use history on N cycling. Even with only one ‘active’ soil organic matter pool, complete recovery from three modest harvests at Hubbard Brook is predicted to require more than two centuries at current N deposition rates. Complete recovery is predicted to take even longer at the Harvard Forest where biomass removals have been more intense. PnET-CN is used to predict maximum sustainable rates of N cycling for 14 sites throughout the northeastern USA. Predicted maximum values were higher, as expected, than measured N mineralization rates for all but one site. The measured fraction of N mineralization nitrified at these 14 sites showed a general relationship with the ratio of measured to maximum net N mineralization. This latter ratio is discussed as a potentially useful indicator of the degree of nitrogen saturation in forest ecosystems. A regional map of predicted maximum N cycling rates is presented based on regressions between model predictions and summary climatic variables.

Publication Date

8-1-1997

Journal Title

Ecological Modelling

Publisher

Elsevier

Digital Object Identifier (DOI)

https://dx.doi.org/10.1016/S0304-3800(97)01953-4

Document Type

Article

Rights

© 1997 Published by Elsevier B.V.

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