Home > Research > Publications & Outputs > Assessing the performance of three frequently u...

Links

Text available via DOI:

View graph of relations

Assessing the performance of three frequently used biogeochemical models when simulating N2O emissions from a range of soil types and fertiliser treatments

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Published

Standard

Assessing the performance of three frequently used biogeochemical models when simulating N2O emissions from a range of soil types and fertiliser treatments. / Zimmermann, J.; Carolan, R.; Forrestal, P. et al.
In: Geoderma, Vol. 331, 01.12.2018, p. 53-69.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

APA

Vancouver

Zimmermann J, Carolan R, Forrestal P, Harty M, Lanigan G, Richards KG et al. Assessing the performance of three frequently used biogeochemical models when simulating N2O emissions from a range of soil types and fertiliser treatments. Geoderma. 2018 Dec 1;331:53-69. Epub 2018 Jun 27. doi: 10.1016/j.geoderma.2018.06.004

Author

Bibtex

@article{f9a5f0b472a64c9a82933e469ed3ce47,
title = "Assessing the performance of three frequently used biogeochemical models when simulating N2O emissions from a range of soil types and fertiliser treatments",
abstract = "Biogeochemical models have the potential to provide insights into the key drivers of greenhouse gas (GHG) dynamics, and may be used in Tier 2 and 3 GHG emission reporting. Modelling nitrous oxide (N2O) emissions from agriculture, however, is still subject to large uncertainties. In the present study we analysed the performance of the three semi-mechanistic models, DailyDayCent (DayCent), DeNitrification-DeComposition (DNDC 9.4 and 9.5), and ECOSSE when simulating N2O fluxes from two different land uses (simulated grazing and spring barley) under a range of fertiliser types and application rates. Model performance was assessed using linear regression analysis, root mean square error (RMSE), and relative error. Monte Carlo analysis was carried out to assess the sensitivity of DayCent and ECOSSE to changes in the timing of management events. The results show high variability in model performance. The performance of each model was dependent on both site and treatment, with no model showing consistently good performance. When averaged across all sites and treatments DayCent simulations produced the lowest number of significant total and relative errors. When looking at the relationship between modelled and measured N2O fluxes, ECOSSE performed best with a significant relationship in 61.8% of all simulations and an average r2 of 0.2. However, outputs from this model displayed the largest total and relative errors. Performance when simulating cumulative fluxes was generally poor. The Monte Carlo analysis showed that shifts in timing of management events by ±7 days lead to annual N2O fluxes varying by 5.6 ± 7.6% and 2.8 ± 4.2% for grassland and cropland, respectively. However the impact of the timing of single events can lead to much larger responses in N2O emissions. The large variation in model performance suggests that further development and calibration is required before using models in GHG reporting.",
keywords = "Greenhouse gas emissions, Biogeochemical modelling, Nitrous oxide, Land use, Management, Agriculture",
author = "J. Zimmermann and R. Carolan and P. Forrestal and M. Harty and G. Lanigan and K.G. Richards and L. Roche and M.G. Whitfield and M.B. Jones",
year = "2018",
month = dec,
day = "1",
doi = "10.1016/j.geoderma.2018.06.004",
language = "English",
volume = "331",
pages = "53--69",
journal = "Geoderma",
issn = "0016-7061",
publisher = "Elsevier Science B.V.",

}

RIS

TY - JOUR

T1 - Assessing the performance of three frequently used biogeochemical models when simulating N2O emissions from a range of soil types and fertiliser treatments

AU - Zimmermann, J.

AU - Carolan, R.

AU - Forrestal, P.

AU - Harty, M.

AU - Lanigan, G.

AU - Richards, K.G.

AU - Roche, L.

AU - Whitfield, M.G.

AU - Jones, M.B.

PY - 2018/12/1

Y1 - 2018/12/1

N2 - Biogeochemical models have the potential to provide insights into the key drivers of greenhouse gas (GHG) dynamics, and may be used in Tier 2 and 3 GHG emission reporting. Modelling nitrous oxide (N2O) emissions from agriculture, however, is still subject to large uncertainties. In the present study we analysed the performance of the three semi-mechanistic models, DailyDayCent (DayCent), DeNitrification-DeComposition (DNDC 9.4 and 9.5), and ECOSSE when simulating N2O fluxes from two different land uses (simulated grazing and spring barley) under a range of fertiliser types and application rates. Model performance was assessed using linear regression analysis, root mean square error (RMSE), and relative error. Monte Carlo analysis was carried out to assess the sensitivity of DayCent and ECOSSE to changes in the timing of management events. The results show high variability in model performance. The performance of each model was dependent on both site and treatment, with no model showing consistently good performance. When averaged across all sites and treatments DayCent simulations produced the lowest number of significant total and relative errors. When looking at the relationship between modelled and measured N2O fluxes, ECOSSE performed best with a significant relationship in 61.8% of all simulations and an average r2 of 0.2. However, outputs from this model displayed the largest total and relative errors. Performance when simulating cumulative fluxes was generally poor. The Monte Carlo analysis showed that shifts in timing of management events by ±7 days lead to annual N2O fluxes varying by 5.6 ± 7.6% and 2.8 ± 4.2% for grassland and cropland, respectively. However the impact of the timing of single events can lead to much larger responses in N2O emissions. The large variation in model performance suggests that further development and calibration is required before using models in GHG reporting.

AB - Biogeochemical models have the potential to provide insights into the key drivers of greenhouse gas (GHG) dynamics, and may be used in Tier 2 and 3 GHG emission reporting. Modelling nitrous oxide (N2O) emissions from agriculture, however, is still subject to large uncertainties. In the present study we analysed the performance of the three semi-mechanistic models, DailyDayCent (DayCent), DeNitrification-DeComposition (DNDC 9.4 and 9.5), and ECOSSE when simulating N2O fluxes from two different land uses (simulated grazing and spring barley) under a range of fertiliser types and application rates. Model performance was assessed using linear regression analysis, root mean square error (RMSE), and relative error. Monte Carlo analysis was carried out to assess the sensitivity of DayCent and ECOSSE to changes in the timing of management events. The results show high variability in model performance. The performance of each model was dependent on both site and treatment, with no model showing consistently good performance. When averaged across all sites and treatments DayCent simulations produced the lowest number of significant total and relative errors. When looking at the relationship between modelled and measured N2O fluxes, ECOSSE performed best with a significant relationship in 61.8% of all simulations and an average r2 of 0.2. However, outputs from this model displayed the largest total and relative errors. Performance when simulating cumulative fluxes was generally poor. The Monte Carlo analysis showed that shifts in timing of management events by ±7 days lead to annual N2O fluxes varying by 5.6 ± 7.6% and 2.8 ± 4.2% for grassland and cropland, respectively. However the impact of the timing of single events can lead to much larger responses in N2O emissions. The large variation in model performance suggests that further development and calibration is required before using models in GHG reporting.

KW - Greenhouse gas emissions

KW - Biogeochemical modelling

KW - Nitrous oxide

KW - Land use

KW - Management

KW - Agriculture

U2 - 10.1016/j.geoderma.2018.06.004

DO - 10.1016/j.geoderma.2018.06.004

M3 - Journal article

VL - 331

SP - 53

EP - 69

JO - Geoderma

JF - Geoderma

SN - 0016-7061

ER -