Multi-model evaluation of nitrous oxide emissions from an intensively managed grassland

Kathrin Fuchs*, Lutz Merbold, Nina Buchmann, Daniel Bretscher, Lorenzo Brilli, Nuala Fitton, CFE Topp, Katja Klumpp, Mark Lieffering, Raphaël Martin, Paul CD Newton, RM Rees, Susanne Rolinski, Pete Smith, Valerie Snow

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
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Abstract

Process-based models are useful for assessing the impact of changing management practices and climate on yields and greenhouse gas (GHG) emissions from agricultural systems such as grasslands. They can be used to construct national GHG inventories using a Tier 3 approach.
However, accurate simulations of nitrous oxide (N2O) fluxes remain challenging. Models are limited by our understanding of soil-plant-microbe interactions and the impact of uncertainty in measured input parameters on simulated outputs. To improve model performance, thorough evaluations against in situ measurements are needed. Experimental data of N2O emissions
under two management practices (control with typical fertilization versus increased clover and no fertilization) were acquired in a Swiss field experiment. We conducted a multi-model evaluation with three commonly-used biogeochemical models (DayCent in two variants, PaSim, APSIM in two variants) comparing four years of data. DayCent was the most accurate model for simulating N2O fluxes on annual timescales, while APSIM was most accurate for daily N2O fluxes. The multi-model ensemble average reduced the error in estimated annual fluxes by 41% compared to an estimate using the IPCC derived method for the Swiss agricultural GHG inventory (IPCC-Swiss), but individual models were not systematically more accurate than IPCC-Swiss. The model ensemble overestimated the N2O mitigation effect of the clover-based treatment (measured: 39-45%; ensemble: 52-57%) but was more accurate than IPCC-Swiss (IPCC-Swiss: 72-81%). These results suggest that multi-model ensembles are valuable for estimating the impact of climate and management on N2O emissions.
Original languageEnglish
Article numbere2019JG005261
JournalJournal of Geophysical Research
Volume125
Issue number1
Early online date18 Dec 2019
DOIs
Publication statusPrint publication - Jan 2020

Bibliographical note

This article is protected by AGU copyright. All rights reserved.

Keywords

  • APSIM
  • DayCent
  • PaSim
  • biogeochemical modeling
  • eddy covariance
  • model validation

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