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https://hdl.handle.net/1959.11/45146
Title: | Similarities and differences in the sensitivity of soil organic matter (SOM) dynamics to biogeochemical parameters for different vegetation inputs and climates | Contributor(s): | Ceriotti, G (author); Tang, F H M (author); Maggi, F (author) | Publication Date: | 2020-12 | Early Online Version: | 2020-09-10 | DOI: | 10.1007/s00477-020-01868-z | Handle Link: | https://hdl.handle.net/1959.11/45146 | Abstract: | The biogeochemical complexity of environmental models is increasing continuously and model reliability must be reanalysed when new implementations are brought about. This work aims to identify influential biogeochemical parameters that control the Soil Organic Matter (SOM) dynamics and greenhouse gas emissions in different ecosystems and climates predicted by a physically-based mechanistic model. This explicitly accounts for four pools of organic polymers, seven pools of organic monomers, five microbial functional groups, and inorganic N and C species. We first benchmarked our model against vertical SOM profiles measured in a temperate forest in North-Eastern Bavaria, Germany (Staudt and Foken in Documentation of reference data for the experimental areas of the Bayreuth Centre for Ecology and Environmental Research (BayCEER) at the Waldstein site. Univ, Bayreuth, Department of Micrometeorology, 2007). Next, we conducted a sensitivity analysis to biogeochemical parameters using modified Morris indices for target SOM pools and gas emissions from a tropical, a temperate, and a semi-arid grassland in Australia. We found that greenhouse gas emissions, the SOM stock, and the fungi-to-bacteria ratio in the top soil were more sensitive to the mortality of aerobic bacteria than other biogeochemical parameters. The larger CO2 emission rates in forests than in grasslands were explained by a greater dissolved SOM content. Finally, we found that the soil N availability was largely controlled by vegetation inputs in forests and by atmospheric fixation in grasslands. | Publication Type: | Journal Article | Source of Publication: | Stochastic Environmental Research and Risk Assessment, 34(12), p. 2229-2244 | Publisher: | Springer | Place of Publication: | Germany | ISSN: | 1436-3259 1436-3240 |
Fields of Research (FoR) 2020: | 410601 Land capability and soil productivity 410604 Soil chemistry and soil carbon sequestration (excl. carbon sequestration science) |
Socio-Economic Objective (SEO) 2020: | 190501 Climate change models | Peer Reviewed: | Yes | HERDC Category Description: | C1 Refereed Article in a Scholarly Journal |
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Appears in Collections: | Journal Article School of Environmental and Rural Science |
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