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Review of nitrogen fate models applicable to forest landscapes in the southern U.S.

D. M. Amatya, C. G. Rossi, A. Saleh, Z. Dai, M. A. Youssef, R. G. Williams, D. D. Bosch, G. M. Chescheir, G. Sun, R. W. Skaggs, C. C. Trettin, E. D. Vance, J. E. Nettles, S. Tian
Transactions of the ASABE 2013 v.56 no.5 pp. 1731-1757
environmental assessment, environmental fate, environmental impact, forest stands, forests, hydrologic models, landscapes, model validation, nitrogen, nitrogen fertilizers, prediction, uncertainty analysis, Southeastern United States
Assessing the environmental impacts of fertilizer nitrogen (N) used to increase productivity in managed forests is complex due to a wide range of abiotic and biotic factors affecting its forms and movement. We review the applicability of five commonly used eco-hydrologic models (APEX, MIKESHE-DNDC, DRAINMOD-FOREST, REMM, and SWAT) in assessing N fate and transport in southern forest landscapes (<50 km2) because of their comprehensiveness and multi-scale predictions. The field-scale models DRAINMOD-FOREST and REMM contain process-level components characterizing hydrology, forest growth and N dynamics. APEX can describe N fate processes, forest growth, and plant competition at the landscape and small watershed scales. The SWAT model is best suited to hydrologic simulations at watershed scale > 50 km2 although N routing below the subbasin level does not yet exist. Similarly, the distributed MIKESHE-DNDC model has been used to assess N cycles across different spatial scales, on both uplands and lowlands, but was not intended to model lateral N transport. However, MIKESHE alone is capable of describing the hydrology and N transport. The strengths of each of the models reflect their original design and scope intent. Based on this review, none of the five models that we considered is independently adequate to address the fate of N fertilizers applied to forest stands at both small and large scales. While efforts are underway to extend their capabilities and address their various limitations, the models must be validated using experimental data before using their outputs, together with uncertainty analysis, for developing fertilization guidelines.