Dynamic simulation of management events for assessing impacts of climate change on pre-alpine grassland productivity

Petersen, Krischan; Kraus, David; Calanca, Pierluigi; Semenov, Mikhail A.; Butterbach‐Bahl, Klaus; Kiese, Ralf

doi:10.1016/j.eja.2021.126306

Cited by 23 publications

(9 citation statements)

References 81 publications

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“…We employed the physiology‐oriented BVOC emission model JJv according to Grote et al. (2014) coupled to the ecosystem module PlaMo x (Kraus et al., 2016; Petersen et al., 2021) that runs on an hourly temporal resolution within the LandscapeDNDC model framework (Haas et al., 2013). The model framework provides dynamic crop growth that is calculated from photosynthesis (Farquhar et al., 1980; Leuning, 1995) and ecosystem respiration (Thornley & Cannell, 2000) in dependence on environmental conditions (including nitrogen supply and water availability).…”

Section: Methodsmentioning

confidence: 99%

Modeling Intra‐ and Interannual Variability of BVOC Emissions From Maize, Oil‐Seed Rape, and Ryegrass

Havermann

Ghirardo

Schnitzler

et al. 2022

J Adv Model Earth Syst

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Vegetation plays a pivotal role in the exchange of trace gases between the land surface and the atmosphere. The emissions of biogenic volatile organic compounds (BVOCs) with a global mean of 760 TgC a −1 (1980-2010), exceed those from anthropogenic sources with around 110 TgC a −1 (AVOCs, e.g., from transport, solvent use, production and storage processes, and combustion processes) by far (Calfapietra et al., 2013;Piccot et al., 1992;Sindelarova et al., 2014). In addition, BVOCs are generally more reactive than AVOCs. Once emitted into the air, BVOCs immediately start to oxidize with the hydroxyl radical (OH), ozone (O 3 ), nitrate radical (NO 3 ), and in coastal areas with chlorine (Cl) atoms, leading to first generation BVOC products (Peñuelas & Staudt, 2010). The reaction of BVOCs with OH is the dominant kind of BVOC decomposition in the troposphere, which is why the total BVOC-OH reactivity is generally used to quantify the emission impact on atmospheric chemistry

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Section: Methodsmentioning

confidence: 99%

Modeling Intra‐ and Interannual Variability of BVOC Emissions From Maize, Oil‐Seed Rape, and Ryegrass

Havermann

Ghirardo

Schnitzler

et al. 2022

J Adv Model Earth Syst

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“…• A cut decision algorithm, which allows the model to simulate management decisions in the absence of such input data. The decision process is based on work by Huguenin-Elie et al (2017) and Petersen et al (2021). • Plant responses to elevated CO 2 conditions: The evapotranspiration (Kruijt et al, 2008) and photosynthetic rates (Kellner et al, 2017;Soltani & Sinclair, 2012) of plants can be modified by the atmospheric CO 2 concentration.…”

Section: Model Extensionsmentioning

confidence: 99%

growR: R Implementation of the Vegetation Model ModVege

Kramer,

Calanca

2024

JOSS

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Grasslands constitute one of Earth's most widespread terrestrial ecosystems (Zhao et al., 2020) and managed grasslands are a core element in global agriculture, providing roughly half the feed inputs for global livestock systems (Herrero et al., 2013). Beside their contribution to global food production, they provide a catalogue of other ecosystem services, such as water flow and erosion regulation, pollination service, carbon sequestration and climate regulation (Zhao et al., 2020). The latter have become particularly important in light of anthropogenic climate change (Bezner Kerr et al., 2022).

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“…The ecosystem simulation framework LandscapeDNDC (Haas et al, 2013;Kraus et al, 2015) is used for simulating CH 4 and N 2 O emissions from rice fields in Vietnam. LandscapeDNDC allows different combinations of sub-models describing water, carbon and nitrogen cycling and exchange processes with the atmosphere and hydrosphere of forest (Dirnböck et al, 2020;Grote et al, 2020), grassland (Liebermann et al, 2020;Petersen et al, 2021), and cropland ecosystems (Molina-Herrera et al, 2016;Smerald et al, 2022). The model selection that is applied in this study corresponds to Kraus et al (2015Kraus et al ( , 2016Kraus et al ( , 2022…”

Section: Ecosystem Model Landscapedndcmentioning

confidence: 99%

Activity data on crop management define uncertainty of CH₄ and N₂O emission estimates from rice: A case study of Vietnam

Butterbach‐Bahl

Kraus

Kiese

et al. 2022

J. Plant Nutr. Soil Sci.

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Background: Globally, rice systems are a major source of atmospheric CH 4 and for major rice-producing countries, such as Vietnam, CH 4 as well as N 2 O emissions from agricultural land used for rice production may represent about one-fourth of total national anthropogenic greenhouse gas (GHG) emissions. However, national-scale estimates of GHG emissions from rice systems are uncertain with regard to its magnitude, spatial distribution, and seasonality.Aims: Here, we used the biogeochemical model LandscapeDNDC to calculate emissions of CH 4 and N 2 O from rice systems in Vietnam (Tier 3 IPCC approach). Our objectives were to identify hotspot regions of emissions and to assess the contribution of N 2 O to the total non-CO 2 (CH 4 +N 2 O) GHG balance of rice systems as well as the seasonal and interannual variability of fluxes in dependence of uncertain input data on field management . Methods:The biogeochemical model LandscapeDNDC model was linked to publicly available information on climate, soils, and land management (fertilization, irrigation, crop rotation) for calculating a national inventory in daily time steps of CH 4 and N 2 O emissions from rice systems at a spatial resolution of 0.083 • × 0.083 • . Uncertainty in management practices related to fertilization, use of harvest residues or irrigation water, and its effects on simulated CH 4 and N 2 O fluxes was accounted for by Latin Hypercube Sampling of probability distribution functions. Results:Our study shows that CH 4 and N 2 O fluxes from rice systems in Vietnam are highly seasonal, with national CH 4 and N 2 O emissions totaling to about 2600 Gg CH 4 year -1 and 42 Gg N 2 O year -1 , respectively. Highest emissions were simulated for double and triple rice cropping systems in the Mekong Delta region. Yield-scaled emissions varied largely in a range of 300-3000 kg CO 2 -eq Mg -1 year -1 , with CH 4 emissions during the rice season(s) dominating (>82%) the total annual non-CO 2 GHG balance of rice systems. In our study, uncertainty in field management information (nitrogen fertilization, ratio synthetic to organic fertilization, residue management, availability of irrigation water) were major drivers of uncertainty of the national CH 4 and N 2 O emission inventory.

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Dynamic simulation of management events for assessing impacts of climate change on pre-alpine grassland productivity

Cited by 23 publications

References 81 publications

Modeling Intra‐ and Interannual Variability of BVOC Emissions From Maize, Oil‐Seed Rape, and Ryegrass

Modeling Intra‐ and Interannual Variability of BVOC Emissions From Maize, Oil‐Seed Rape, and Ryegrass

growR: R Implementation of the Vegetation Model ModVege

Activity data on crop management define uncertainty of CH₄ and N₂O emission estimates from rice: A case study of Vietnam

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Dynamic simulation of management events for assessing impacts of climate change on pre-alpine grassland productivity

Cited by 23 publications

References 81 publications

Modeling Intra‐ and Interannual Variability of BVOC Emissions From Maize, Oil‐Seed Rape, and Ryegrass

Modeling Intra‐ and Interannual Variability of BVOC Emissions From Maize, Oil‐Seed Rape, and Ryegrass

growR: R Implementation of the Vegetation Model ModVege

Activity data on crop management define uncertainty of CH4 and N2O emission estimates from rice: A case study of Vietnam

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Product

Resources

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Activity data on crop management define uncertainty of CH₄ and N₂O emission estimates from rice: A case study of Vietnam