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

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

doi:10.1016/j.cosust.2020.07.003

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

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

Department of Agroecology - Soil Fertility

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Review › Research › peer-review

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Abstract

Denitrification is a key but poorly quantified component of the N cycle. Because it is difficult to measure the gaseous (NO_x, N₂O, N₂) and soluble (NO₃) components of denitrification with sufficient intensity, models of varying scope and complexity have been developed and applied to estimate how vegetation cover, land management and environmental factors such as soil type and weather interact to control these variables. In this paper we assess the strengths and limitations of different modeling approaches, highlight major uncertainties, and suggest how different observational methods and process-based understanding can be combined to better quantify N cycling. Representation of how biogeochemical (e.g. org. C., pH) and physical (e.g. soil structure) factors influence denitrification rates and product ratios combined with ensemble approaches may increase accuracy without requiring additional site level model inputs.

Original language	English
Journal	Current Opinion in Environmental Sustainability
Volume	47
Pages (from-to)	37-45
Number of pages	9
ISSN	1877-3435
DOIs	https://doi.org/10.1016/j.cosust.2020.07.003
Publication status	Published - Dec 2020

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

abstract = "Denitrification is a key but poorly quantified component of the N cycle. Because it is difficult to measure the gaseous (NOx, N2O, N2) and soluble (NO3) components of denitrification with sufficient intensity, models of varying scope and complexity have been developed and applied to estimate how vegetation cover, land management and environmental factors such as soil type and weather interact to control these variables. In this paper we assess the strengths and limitations of different modeling approaches, highlight major uncertainties, and suggest how different observational methods and process-based understanding can be combined to better quantify N cycling. Representation of how biogeochemical (e.g. org. C., pH) and physical (e.g. soil structure) factors influence denitrification rates and product ratios combined with ensemble approaches may increase accuracy without requiring additional site level model inputs.",

author = "{Del Grosso}, {Stephen J.} and Ward Smith and David Kraus and Massad, {Raia S.} and Iris Vogeler and Kathrin Fuchs",

year = "2020",

month = dec,

doi = "10.1016/j.cosust.2020.07.003",

language = "English",

volume = "47",

pages = "37--45",

journal = "Current Opinion in Environmental Sustainability",

issn = "1877-3435",

publisher = "Elsevier Ltd. * Current Opinion Journals",

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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 et al.
In: Current Opinion in Environmental Sustainability, Vol. 47, 12.2020, p. 37-45.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Review › Research › peer-review

TY - JOUR

T1 - Approaches and concepts of modelling denitrification

T2 - increased process understanding using observational data can reduce uncertainties

AU - Del Grosso, Stephen J.

AU - Smith, Ward

AU - Kraus, David

AU - Massad, Raia S.

AU - Vogeler, Iris

AU - Fuchs, Kathrin

PY - 2020/12

Y1 - 2020/12

N2 - Denitrification is a key but poorly quantified component of the N cycle. Because it is difficult to measure the gaseous (NOx, N2O, N2) and soluble (NO3) components of denitrification with sufficient intensity, models of varying scope and complexity have been developed and applied to estimate how vegetation cover, land management and environmental factors such as soil type and weather interact to control these variables. In this paper we assess the strengths and limitations of different modeling approaches, highlight major uncertainties, and suggest how different observational methods and process-based understanding can be combined to better quantify N cycling. Representation of how biogeochemical (e.g. org. C., pH) and physical (e.g. soil structure) factors influence denitrification rates and product ratios combined with ensemble approaches may increase accuracy without requiring additional site level model inputs.

AB - Denitrification is a key but poorly quantified component of the N cycle. Because it is difficult to measure the gaseous (NOx, N2O, N2) and soluble (NO3) components of denitrification with sufficient intensity, models of varying scope and complexity have been developed and applied to estimate how vegetation cover, land management and environmental factors such as soil type and weather interact to control these variables. In this paper we assess the strengths and limitations of different modeling approaches, highlight major uncertainties, and suggest how different observational methods and process-based understanding can be combined to better quantify N cycling. Representation of how biogeochemical (e.g. org. C., pH) and physical (e.g. soil structure) factors influence denitrification rates and product ratios combined with ensemble approaches may increase accuracy without requiring additional site level model inputs.

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DO - 10.1016/j.cosust.2020.07.003

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VL - 47

SP - 37

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JO - Current Opinion in Environmental Sustainability

JF - Current Opinion in Environmental Sustainability

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

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