Multi-model simulation of soil temperature, soil water content and biomass in Euro-Mediterranean grasslands: Uncertainties and ensemble performance

Sándor, Renata; Barcza, Zoltán; Acutis, Marco; Doro, Luca; Hidy, Dóra; Köchy, Martin; Minet, Julien; Lellei‐Kovács, Eszter; Ma, S.; Perego, Alessia; Rolinski, Susanne; Ruget, Françoise; Sanna, Mattia; Seddaiu, Giovanna; Wu, Lianhai; Bellocchi, Gianni

doi:10.1016/j.eja.2016.06.006

Cited by 70 publications

(47 citation statements)

References 41 publications

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“…The end of the grazing period with 5 g C m −2 is chosen a bit lower than other modeling studies (e.g., for the ORCHIDEE model: 300 kg DM ha −1 = 13.5 g C m −2 ; Chang et al, 2013). Assumptions are necessary also with respect to the specification of the spatial distribution of management activities (Chang et al, 2013;Sándor et al, 2017). Here, we chose to analyze scenarios with spatially homogeneous management variations.…”

Section: Uncertainties and Assumptionsmentioning

confidence: 99%

Modeling vegetation and carbon dynamics of managed grasslands at the global scale with LPJmL 3.6

et al. 2018

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Abstract. Grassland management affects the carbon fluxes of one-third of the global land area and is thus an important factor for the global carbon budget. Nonetheless, this aspect has been largely neglected or underrepresented in global carbon cycle models. We investigate four harvesting schemes for the managed grassland implementation of the dynamic global vegetation model (DGVM) Lund-PotsdamJena managed Land (LPJmL) that facilitate a better representation of actual management systems globally. We describe the model implementation and analyze simulation results with respect to harvest, net primary productivity and soil carbon content and by evaluating them against reported grass yields in Europe. We demonstrate the importance of accounting for differences in grassland management by assessing potential livestock grazing densities as well as the impacts of grazing, grazing intensities and mowing systems on soil carbon stocks. Grazing leads to soil carbon losses in polar or arid regions even at moderate livestock densities ( < 0.4 livestock units per hectare -LSU ha −1 ) but not in temperate regions even at much higher densities (0.4 to 1.2 LSU ha −1 ). Applying LPJmL with the new grassland management options enables assessments of the global grassland production and its impact on the terrestrial biogeochemical cycles but requires a global data set on current grassland management.

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Section: Uncertainties and Assumptionsmentioning

confidence: 99%

Modeling vegetation and carbon dynamics of managed grasslands at the global scale with LPJmL 3.6

et al. 2018

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“…One example of a biogeochemical model is PaSim, which is used to simulate the interaction between greenhouse gas emissions, growth and grazing in managed grasslands [46,47], and other examples are included in an ensemble of models studied by Sándor et al [48] and Ehrhardt et al [49]. More complex examples of biogeochemical modelling are formed by Dynamic Global Vegetation Models (DGVMs; [50]), which simulate possible transitions between PFTs driven by environmental change.…”

Section: Biogeochemical Modellingmentioning

confidence: 99%

Effects of Climate Change on Grassland Biodiversity and Productivity: The Need for a Diversity of Models

2018

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There is increasing evidence that the impact of climate change on the productivity of grasslands will at least partly depend on their biodiversity. A high level of biodiversity may confer stability to grassland ecosystems against environmental change, but there are also direct effects of biodiversity on the quantity and quality of grassland productivity. To explain the manifold interactions, and to predict future climatic responses, models may be used. However, models designed for studying the interaction between biodiversity and productivity tend to be structurally different from models for studying the effects of climatic impacts. Here we review the literature on the impacts of climate change on biodiversity and productivity of grasslands. We first discuss the availability of data for model development. Then we analyse strengths and weaknesses of three types of model: ecological, process-based and integrated. We discuss the merits of this model diversity and the scope for merging different model types.

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“…Model ensembles were used for yield predictions with annual crop monocultures (e.g. maize: Bassu et al., ; rice: Li et al., ; wheat: Ruane et al., ), but to a much lesser extent for crop rotations (Kollas et al., ) and grasslands (Sándor et al., ).…”

Section: Introductionmentioning

confidence: 99%

Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N₂O emissions

Ehrhardt¹,

Soussana²,

Bellocchi³

et al. 2017

Global Change Biology

122

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Simulation models are extensively used to predict agricultural productivity and greenhouse gas emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multi-species agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multi-model ensembles to predict productivity and nitrous oxide (N O) emissions for wheat, maize, rice and temperate grasslands. Using a multi-stage modelling protocol, from blind simulations (stage 1) to partial (stages 2-4) and full calibration (stage 5), 24 process-based biogeochemical models were assessed individually or as an ensemble against long-term experimental data from four temperate grassland and five arable crop rotation sites spanning four continents. Comparisons were performed by reference to the experimental uncertainties of observed yields and N O emissions. Results showed that across sites and crop/grassland types, 23%-40% of the uncalibrated individual models were within two standard deviations (SD) of observed yields, while 42 (rice) to 96% (grasslands) of the models were within 1 SD of observed N O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N O emissions within experimental uncertainties for 44% and 33% of the crop and grassland growth cycles, respectively. Partial model calibration (stages 2-4) markedly reduced prediction errors of the full model ensemble E-median for crop grain yields (from 36% at stage 1 down to 4% on average) and grassland productivity (from 44% to 27%) and to a lesser and more variable extent for N O emissions. Yield-scaled N O emissions (N O emissions divided by crop yields) were ranked accurately by three-model ensembles across crop species and field sites. The potential of using process-based model ensembles to predict jointly productivity and N O emissions at field scale is discussed.

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Multi-model simulation of soil temperature, soil water content and biomass in Euro-Mediterranean grasslands: Uncertainties and ensemble performance

Cited by 70 publications

References 41 publications

Modeling vegetation and carbon dynamics of managed grasslands at the global scale with LPJmL 3.6

Modeling vegetation and carbon dynamics of managed grasslands at the global scale with LPJmL 3.6

Effects of Climate Change on Grassland Biodiversity and Productivity: The Need for a Diversity of Models

Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N₂O emissions

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Multi-model simulation of soil temperature, soil water content and biomass in Euro-Mediterranean grasslands: Uncertainties and ensemble performance

Cited by 70 publications

References 41 publications

Modeling vegetation and carbon dynamics of managed grasslands at the global scale with LPJmL 3.6

Modeling vegetation and carbon dynamics of managed grasslands at the global scale with LPJmL 3.6

Effects of Climate Change on Grassland Biodiversity and Productivity: The Need for a Diversity of Models

Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2O emissions

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Product

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Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N₂O emissions