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Description:
To monitor the effect of current nitrogen emissions and mitigation strategies, total (wet+dry) atmospheric nitrogen deposition to forests is commonly estimated using chemical transport models or canopy budget models in combination with throughfall measurements. Since flux measurements of reactive nitrogen (Nr) compounds are scarce, dry deposition process descriptions as well as the calculated flux estimates and annual budgets are subject to considerable uncertainties. In this study, we compared four different approaches to quantify annual dry deposition budgets of total reactive nitrogen (SNr) at a mixed forest site situated in the Bavarian Forest National Park, Germany. Dry deposition budgets were quantified based on (I) 2.5 years of eddy covariance flux measurements with the Total Reactive Atmospheric Nitrogen Converter (TRANC); (II) an in situ application of the bidirectional inferential flux model DEPAC (Deposition of Acidifying Compounds), here called DEPAC-1D; (III) a simulation with the chemical transport model LOTOS-EUROS (Long-Term Ozone Simulation – European Operational Smog) v2.0, using DEPAC as dry deposition module; and (IV) a canopy budget technique (CBT). Averaged annual SNr dry deposition estimates determined from TRANC measurements were 4.7±0.2 and 4.3±0.4kg N ha-1a-1, depending on the gap-filling approach. DEPAC-1D-modeled dry deposition, using concentrations and meteorological drivers measured at the site, was 5.8±0.1kg N ha-1a-1. In comparison to TRANC fluxes, DEPAC-1D estimates were systematically higher during summer and in close agreement in winter. Modeled SNr deposition velocities (vd) of DEPAC-1D were found to increase with lower temperatures and higher relative humidity and in the presence of wet leaf surfaces, particularly from May to September. This observation was contrary to TRANC-observed fluxes. LOTOS-EUROS-modeled annual dry deposition was 6.5±0.3kg N ha-1a-1 for the site-specific weighting of land-use classes within the site's grid cell. LOTOS-EUROS showed substantial ...