Modeling N2O emissions of complex cropland management in Western Europe using DayCent: Performance and scope for improvement

Martins, Marcio dos Reis; Necpálová, Magdaléna; Ammann, Christof; Buchmann, Nina; Calanca, Pierluigi; Fléchard, Christophe; Hartman, Melannie D.; Krauss, Maike; Roy, Philippe Le; Mäder, Paul; Maier, Regine; Morvan, Thierry; Nicolardot, Bernard; Skinner, Colin; Six, Johan; Keel, Sonja G.

doi:10.1016/j.eja.2022.126613

Cited by 8 publications

(4 citation statements)

References 44 publications

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“…In the absence of any information on this, we decided however, to not alter the model settings to improve the model results; we wanted to avoid an after the fact fine tuning that would give overly optimistic results. In fact, the model performance is already very good and in the range of what many modelling approaches achieve after a calibration for the evaluation dataset (e.g., Lee et al, 2012;Necpálová et al, 2015;Necpalova et al, 2018;Gurung et al, 2020;dos Reis Martins et al, 2022;Laub et al, 2023).…”

Section: Sensitivity Of Zonalcent To Different Agroforestry System Co...mentioning

confidence: 97%

A novel approach to use the DayCent model for simulating agroforestry systems with multiple components

Laub,

Goff,

Prébandier

et al. 2024

Preprint

Self Cite

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Agroforestry can offer carbon sequestration, higher system productivity and biodiversity. However, a limited number of field trials exist to study their feasibility and trade-offs for large scale deployment. Agroecosystem models could represent a valuable tool for their ex ante assessment. Here, we present ZonalCent, a novel approach to use the DayCent model to simulate multi-component agroforestry systems by splitting them into several independent zones, and simulating each zone individually. We used six agroforestry sites in France to evaluate how well ZonalCent represented carbon sequestration in tree biomass, soil organic carbon stocks and in the total system. This proved promising because with the default parameter set of DayCent, ZonalCent was highly suitable to represent tree carbon sequestration (Nash-Sutcliffe modelling efficiency; NSE of 0.82), and for total system carbon sequestration potential (NSE of 0.67), despite a tendency to overestimate SOC stocks (NSE of 0.31). Only one site had yield data and there, ZonalCent approach could approximate the mean yield reduction - yet more detailed evaluation is necessary. Negative correlations with yield showed that yield was most strongly affected by a) shading by mature trees and b) the loss of arable area due to grass strips. While more detailed models may be needed for a detailed process understanding, ZonalCent includes the most important interactions (light, water, nutrients, temperature) in a simple but effective way and can be readily used - because it is based on DayCent - to estimate the potential carbon sequestration of agroforestry systems at larger scales.

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Section: Sensitivity Of Zonalcent To Different Agroforestry System Co...mentioning

confidence: 97%

A novel approach to use the DayCent model for simulating agroforestry systems with multiple components

Laub,

Goff,

Prébandier

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Long-term changes in N 2 O emissions and tropospheric aN 2 O have been studied and reported for glacial-interglacial variations (Joos et al, 2020;Rubino et al, 2019;Schilt et al, 2010). For the modern period, the N 2 O emissions from terrestrial ecosystems and the oceans have been investigated progressively across scales with site observations (e.g., Kock & Bange, 2015;Pastorello et al, 2020), field experiments (e.g., Breider et al, 2019;Dijkstra et al, 2012), as well as modeling (e.g., Martins et al, 2022;Landolfi et al, 2017;Xu-Ri & Prentice, 2008;Manizza et al, 2012). However, due to the complex processes of N 2 O production in soils, inland waters and oceans (e.g., Battaglia & Joos, 2018;Ma et al, 2022;Hutchins & Capone, 2022) and loss in the stratosphere (Mueller, 2021), there is still poor understanding of the controls on tropospheric aN 2 O and its seasonal and inter-annual variations.…”

Section: Introductionmentioning

confidence: 99%

The Modeled Seasonal Cycles of Surface N₂O Fluxes and Atmospheric N₂O

Sun,

Joos,

Lienert

et al. 2024

Global Biogeochemical Cycles

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Nitrous oxide (N2O) is a greenhouse gas and stratospheric ozone‐depleting substance with large and growing anthropogenic emissions. Previous studies identified the influx of N2O‐depleted air from the stratosphere to partly cause the seasonality in tropospheric N2O (aN2O), but other contributions remain unclear. Here, we combine surface fluxes from eight land and four ocean models from phase 2 of the Nitrogen/N2O Model Intercomparison Project with tropospheric transport modeling to simulate aN2O at eight remote air sampling sites for modern and pre‐industrial periods. Models show general agreement on the seasonal phasing of zonal‐average N2O fluxes for most sites, but seasonal peak‐to‐peak amplitudes differ several‐fold across models. The modeled seasonal amplitude of surface aN2O ranges from 0.25 to 0.80 ppb (interquartile ranges 21%–52% of median) for land, 0.14–0.25 ppb (17%–68%) for ocean, and 0.28–0.77 ppb (23%–52%) for combined flux contributions. The observed seasonal amplitude ranges from 0.34 to 1.08 ppb for these sites. The stratospheric contributions to aN2O, inferred by the difference between the surface‐troposphere model and observations, show 16%–126% larger amplitudes and minima delayed by ∼1 month compared to Northern Hemisphere site observations. Land fluxes and their seasonal amplitude have increased since the pre‐industrial era and are projected to grow further under anthropogenic activities. Our results demonstrate the increasing importance of land fluxes for aN2O seasonality. Considering the large model spread, in situ aN2O observations and atmospheric transport‐chemistry models will provide opportunities for constraining terrestrial and oceanic biosphere models, critical for projecting carbon‐nitrogen cycles under ongoing global warming.

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“…The longterm changes in N 2 O emissions and tropospheric aN 2 O have been studied and reported for glacial-interglacial variations [Joos et al, 2020;Rubino et al, 2019;Schilt et al, 2010]. For the modern period, the N 2 O emissions from terrestrial ecosystems and the oceans have been investigated progressively across scales with site observations [e.g., Kock and Bange, 2015;Pastorello et al, 2020], field experiments [e.g., Breider et al, 2019;Dijkstra et al, 2012], as well as modeling [e.g., Martins et al, 2022;Landolfi et al, 2017;Xu-Ri and Prentice, 2008;Manizza et al, 2012]. However, due to the complex processes of N 2 O production in soils, inland waters and oceans [e.g., Battaglia and Joos, 2018;Ma et al, 2022;Hutchins and Capone, 2022] and loss in the stratosphere [Mueller, 2021], there is still poor understanding of the controls on tropospheric aN 2 O and its seasonal and inter-annual variations.…”

Section: Introductionmentioning

confidence: 99%

The modeled seasonal cycles of land biosphere and ocean N2O fluxes and atmospheric N2O

Sun,

Joos,

Lienert

et al. 2023

Preprint

View full text Add to dashboard Cite

Nitrous oxide (N2O) is a greenhouse gas and an ozone-depleting agent with large and growing anthropogenic emissions. Previous studies identified the influx of N2O-depleted air from the stratosphere to partly cause the seasonality in tropospheric N2O (aN2O), but other contributions remain unclear. Here we combine surface fluxes from eight land and four ocean models from phase 2 of the Nitrogen/N2O Model Intercomparison Project with tropospheric transport modeling to simulate aN2O at the air sampling sites: Alert, Barrow, Ragged Point, Samoa, Ascension Island, and Cape Grim for the modern and preindustrial periods. Models show general agreement on the seasonal phasing of zonal-average N2O fluxes for most sites, but, seasonal peak-to-peak amplitudes differ severalfold across models. After transport, the seasonal amplitude of surface aN2O ranges from 0.25 to 0.80 ppb (interquartile ranges 21-52% of median) for land, 0.14 to 0.25 ppb (19-42%) for ocean, and 0.13 to 0.76 ppb (26-52%) for combined flux contributions. The observed range is 0.53 to 1.08 ppb. The stratospheric contributions to aN2O, inferred by the difference between surface-troposphere model and observations, show 36-126% larger amplitudes and minima delayed by ~1 month compared to Northern Hemisphere site observations. Our results demonstrate an increasing importance of land fluxes for aN2O seasonality, with land fluxes and their seasonal amplitude increasing since the preindustrial era and are projected to grow under anthropogenic activities. In situ aN2O observations and atmospheric transport-chemistry models will provide opportunities for constraining terrestrial and oceanic biosphere models, critical for projecting surface N2O sources under ongoing global warming.

show abstract

Modeling N2O emissions of complex cropland management in Western Europe using DayCent: Performance and scope for improvement

Cited by 8 publications

References 44 publications

A novel approach to use the DayCent model for simulating agroforestry systems with multiple components

A novel approach to use the DayCent model for simulating agroforestry systems with multiple components

The Modeled Seasonal Cycles of Surface N₂O Fluxes and Atmospheric N₂O

The modeled seasonal cycles of land biosphere and ocean N2O fluxes and atmospheric N2O

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Modeling N2O emissions of complex cropland management in Western Europe using DayCent: Performance and scope for improvement

Cited by 8 publications

References 44 publications

A novel approach to use the DayCent model for simulating agroforestry systems with multiple components

A novel approach to use the DayCent model for simulating agroforestry systems with multiple components

The Modeled Seasonal Cycles of Surface N2O Fluxes and Atmospheric N2O

The modeled seasonal cycles of land biosphere and ocean N2O fluxes and atmospheric N2O

Contact Info

Product

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The Modeled Seasonal Cycles of Surface N₂O Fluxes and Atmospheric N₂O