A modeling study on mitigation of N2O emissions and NO3 leaching at different agricultural sites across Europe using LandscapeDNDC

Molina-Herrera, Saúl; Haas, Edwin; Klatt, Steffen; Kraus, David; Augustin, Jürgen; Magliulo, Vincenzo; Tallec, Tiphaine; Ceschia, Éric; Ammann, Christof; Loubet, Benjamin; Skiba, U.; Jones, Stephanie; Brümmer, Christian; Butterbach‐Bahl, Klaus; Kiese, Ralf

doi:10.1016/j.scitotenv.2015.12.099

Cited by 59 publications

(59 citation statements)

References 76 publications

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“…4). This result agrees with a study by Molina-Herrera et al (2016) who found LandscapeDNDC to be capable of simulating agricultural N 2 O emissions. However, in our case, the model overestimates peak emissions before fertilizer applications, which leads to higher mean annual modelled emissions (7.33 kg N 2 O-N ha −1 a −1 ).…”

Section: Modelled N Fluxessupporting

confidence: 92%

Constraining a complex biogeochemical model for CO2 and N2O emission simulations from various land uses by model–data fusion

2017

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Abstract. This study presents the results of a combined measurement and modelling strategy to analyse N 2 O and CO 2 emissions from adjacent arable land, forest and grassland sites in Hesse, Germany. The measured emissions reveal seasonal patterns and management effects, including fertilizer application, tillage, harvest and grazing. The measured annual N 2 O fluxes are 4.5, 0.4 and 0.1 kg N ha −1 a −1 , and the CO 2 fluxes are 20.0, 12.2 and 3.0 t C ha −1 a −1 for the arable land, grassland and forest sites, respectively. An innovative model-data fusion concept based on a multicriteria evaluation (soil moisture at different depths, yield, CO 2 and N 2 O emissions) is used to rigorously test the LandscapeDNDC biogeochemical model. The model is run in a Latin-hypercube-based uncertainty analysis framework to constrain model parameter uncertainty and derive behavioural model runs. The results indicate that the model is generally capable of predicting trace gas emissions, as evaluated with RMSE as the objective function. The model shows a reasonable performance in simulating the ecosystem C and N balances. The model-data fusion concept helps to detect remaining model errors, such as missing (e.g. freeze-thaw cycling) or incomplete model processes (e.g. respiration rates after harvest). This concept further elucidates the identification of missing model input sources (e.g. the uptake of N through shallow groundwater on grassland during the vegetation period) and uncertainty in the measured validation data (e.g. forest N 2 O emissions in winter months). Guidance is provided to improve the model structure and field measurements to further advance landscape-scale model predictions.

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Section: Modelled N Fluxessupporting

confidence: 92%

Constraining a complex biogeochemical model for CO2 and N2O emission simulations from various land uses by model–data fusion

2017

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“…, Molina‐Herrera et al. ) reported in earlier studies for forest, arable, and grassland sites. A model intercomparison study with DNDC, DayCent, and EPIC models even showed smaller agreements between measured and simulated values ( r 2 range: 0.31–0.55; Gaillard et al.…”

Section: Discussionmentioning

confidence: 76%

“…, Molina‐Herrera et al. ), have been used for simulating N 2 O emissions and NO 3 − leaching for various land uses.…”

Section: Introductionmentioning

confidence: 99%

Constraining N cycling in the ecosystem model LandscapeDNDC with the stable isotope model SIMONE

Denk

Kraus

Kiese

et al. 2019

Ecology

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The isotopic composition (ic) of soil nitrogen (N) and, more recently, the intramolecular distribution of 15N in the N2O molecule (site preference, SP) are powerful instruments to identify dominant N turnover processes, and to attribute N2O emissions to their source processes. Despite the process information contained in the ic of N species and the associated potential for model validation, the implementation of isotopes in ecosystem models has lagged behind. To foster the validation of ecosystem models based on the ic of N species, we developed the stable isotope model for nutrient cycles (SIMONE). SIMONE uses fluxes between ecosystem N pools (soil organic N, mineral N, plants, microbes) calculated by biogeochemical models, and literature isotope effects for these processes to calculate the ic of N species. Here, we present the concept of SIMONE, apply it to simulations of the biogeochemical model LandscapeDNDC, and assess the capability of 15N‐N2O and, to our knowledge for the first time, SP, to constrain simulated N fluxes by LandscapeDNDC. LandscapeDNDC successfully simulated N2O emission, soil nitrate, and ammonium, as well as soil environmental conditions of an intensively managed grassland site in Switzerland. Accordingly, the dynamics of 15N‐N2O and SP of soil N2O fluxes as simulated by SIMONE agreed well with measurements, though 15N‐N2O was on average underestimated and SP overestimated (root‐mean‐square error [RMSE] of 8.4‰ and 7.3‰, respectively). Although 15N‐N2O could not constrain the N cycling process descriptions of LandscapeDNDC, the overestimation of SP indicated an overestimation of simulated nitrification rates by 10–59% at low water content, suggesting the revision of the corresponding model parameterization. Our findings show that N isotope modeling in combination with only recently available high‐ frequency measurements of the N2O ic are promising tools to identify and address weaknesses in N cycling of ecosystem models. This will finally contribute to augmenting the development of model‐based strategies for mitigating N pollution.

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“…A better link between N 2 O emissions and changes in management and environmental conditions can be obtained by the use of biogeochemistry models, which are often run and/or calibrated against measured data at site level [39,40] and spatially upscaled by the use of meta-models [22,41]. However, the large variability in environmental conditions poses a challenge for process-based models if the number of experimental data used for their calibration is insufficient and do not cover the whole range of environmental conditions in the model application area [42].…”

Section: Discussionmentioning

confidence: 99%

Complementing the topsoil information of the Land Use/Land Cover Area Frame Survey (LUCAS) with modelled N2O emissions

et al. 2017

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Two objectives of the Common Agricultural Policy post-2013 (CAP, 2014–2020) in the European Union (EU) are the sustainable management of natural resources and climate smart agriculture. To understand the CAP impact on these priorities, the Land Use/Cover statistical Area frame Survey (LUCAS) employs direct field observations and soil sub-sampling across the EU. While a huge amount of information can be retrieved from LUCAS points for monitoring the environmental status of agroecosystems and assessing soil carbon sequestration, a fundamental aspect relating to climate change action is missing, namely nitrous oxide (N2O) soil emissions. To fill this gap, we ran the DayCent biogeochemistry model for more than 11’000 LUCAS sampling points under agricultural use, assessing also the model uncertainty. The results showed that current annual N2O emissions followed a skewed distribution with a mean and median values of 2.27 and 1.71 kg N ha-1 yr-1, respectively. Using a Random Forest regression for upscaling the modelled results to the EU level, we estimated direct soil emissions of N2O in the range of 171–195 Tg yr-1 of CO2eq. Moreover, the direct regional upscaling using modelled N2O emissions in LUCAS points was on average 0.95 Mg yr-1 of CO2eq. per hectare, which was within the range of the meta-model upscaling (0.92–1.05 Mg ha-1 yr-1 of CO2eq). We concluded that, if information on management practices would be made available and model bias further reduced by N2O flux measurement at representative LUCAS points, the combination of the land use/soil survey with a well calibrated biogeochemistry model may become a reference tool to support agricultural, environmental and climate policies.

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A modeling study on mitigation of N2O emissions and NO3 leaching at different agricultural sites across Europe using LandscapeDNDC

Cited by 59 publications

References 76 publications

Constraining a complex biogeochemical model for CO<sub>2</sub> and N<sub>2</sub>O emission simulations from various land uses by model–data fusion

Constraining a complex biogeochemical model for CO<sub>2</sub> and N<sub>2</sub>O emission simulations from various land uses by model–data fusion

Constraining N cycling in the ecosystem model LandscapeDNDC with the stable isotope model SIMONE

Complementing the topsoil information of the Land Use/Land Cover Area Frame Survey (LUCAS) with modelled N2O emissions

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A modeling study on mitigation of N2O emissions and NO3 leaching at different agricultural sites across Europe using LandscapeDNDC

Cited by 59 publications

References 76 publications

Constraining a complex biogeochemical model for CO&lt;sub&gt;2&lt;/sub&gt; and N&lt;sub&gt;2&lt;/sub&gt;O emission simulations from various land uses by model–data fusion

Constraining a complex biogeochemical model for CO&lt;sub&gt;2&lt;/sub&gt; and N&lt;sub&gt;2&lt;/sub&gt;O emission simulations from various land uses by model–data fusion

Constraining N cycling in the ecosystem model LandscapeDNDC with the stable isotope model SIMONE

Complementing the topsoil information of the Land Use/Land Cover Area Frame Survey (LUCAS) with modelled N2O emissions

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Constraining a complex biogeochemical model for CO<sub>2</sub> and N<sub>2</sub>O emission simulations from various land uses by model–data fusion

Constraining a complex biogeochemical model for CO<sub>2</sub> and N<sub>2</sub>O emission simulations from various land uses by model–data fusion