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Data for: Nitrogen deposition in forests: Statistical modeling of total deposition from throughfall loads

Authors: Braun, Sabine; Ahrends, Bernd; Alonso, Rocio; Pihl Karlsson, Gunilla; Karlsson, Per Erik; Thimonier, Anne; García-Gómez, Hector; +3 Authors

Data for: Nitrogen deposition in forests: Statistical modeling of total deposition from throughfall loads

Abstract

Introduction: Nitrogen (N) gradient studies in some cases use N deposition in throughfall as measure of N deposition to forests. For evaluating critical loads of N, however, information on total N deposition is required, i.e., the sum of estimates of dry, wet and occult deposition. Methods: The present paper collects a number of studies in Europe where throughfall and total N deposition were compared in different forest types. From this dataset a function was derived which allows to estimate total N deposition from throughfall N deposition. Results: At low throughfall N deposition values, the proportion of canopy uptake is high and thus the underestimation of total deposition by throughfall N needs to be corrected. At throughfall N deposition values >20 kg N ha-1 yr-1 canopy uptake is getting less important. Conclusions: This work shows that throughfall clearly underestimates total deposition of nitrogen. With the present data set covering large parts of Europe it is possible to derive a critical load estimate from gradient studies using throughfall data.

Rmd file can be accessed using R and reproduces the commands used to analyse the data. The PDF file gives a description to the steps involved. The two files belong together.Funding provided by: Federal Office for the Environment, Bern, Switzerland*Crossref Funder Registry ID: Award Number:

This is a meta analysis of various datasets collected in European forests, with the author of the study given in the table.

Keywords

throughfall, Forests, critical loads of nitrogen, Total nitrogen deposition

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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