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Approaches and concepts of modelling denitrification: increased process understanding using observational data can reduce uncertainties

Del Grosso, Stephen J.; Smith, Ward; Kraus, David; Massad, Raia S.; Vogeler, Iris; Fuchs, Kathrin

Abstract:
Denitrification is a key but poorly quantified component of the Ncycle. Because it is difficult to measure the gaseous (NO$_{x}$, N$_{2}$O, N$_{2}$)and soluble (NO$_{3}$) components of denitrification with sufficientintensity, models of varying scope and complexity have beendeveloped and applied to estimate how vegetation cover, landmanagement and environmental factors such as soil type andweather interact to control these variables. In this paper we assessthe strengths and limitations of different modeling approaches,highlight major uncertainties, and suggest how differentobservational methods and process-based understanding can becombined to better quantify N cycling. Representation of howbiogeochemical (e.g. org. C., pH) and physical (e.g. soil structure)factors influence denitrification rates and product ratios combinedwith ensemble approaches may increase accuracy withoutrequiring additional site level model inputs.

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Verlagsausgabe §
DOI: 10.5445/IR/1000129647
Veröffentlicht am 12.02.2021
Originalveröffentlichung
DOI: 10.1016/j.cosust.2020.07.003
Scopus
Zitationen: 1
Web of Science
Zitationen: 1
Cover der Publikation
Zugehörige Institution(en) am KIT KIT-Zentrum Mathematik in den Natur-, Ingenieur- und Wirtschaftswissenschaften (KIT-Zentrum MathSEE)
Institut für Meteorologie und Klimaforschung - Atmosphärische Umweltforschung (IMK-IFU)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 12.2020
Sprache Englisch
Identifikator ISSN: 1877-3435
KITopen-ID: 1000129647
HGF-Programm 12.02.01 (POF III, LK 01) Effects of land use and climate change
Erschienen in Current opinion in environmental sustainability
Verlag Elsevier
Band 47
Seiten 37–45
Nachgewiesen in Scopus
Web of Science
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