TY - JOUR
T1 - Similarities and differences in the sensitivity of soil organic matter (SOM) dynamics to biogeochemical parameters for different vegetation inputs and climates
AU - Ceriotti, G.
AU - Tang, F. H.M.
AU - Maggi, F.
N1 - Funding Information:
The authors greatly acknowledge the support of Alberto Guadagnini and Giovanni Porta in advising and revising the development of this work. This work is supported by the SREI2020 EnviroSphere, the Mid Career Research, and the Sydney Research Accelerator Fellowship (SOAR) of the University of Sydney. The authors acknowledge the Sydney Informatics Hub of The University of Sydney for providing the Artemis high performance computing resources that have contributed to the results reported within this work. The BRTSim solver package can be downloaded at https://sites.google.com/site/thebrtsimproject/home or at the mirror link https://www.dropbox.com/sh/wrfspx9f1dvuspr/AAD5iA9PsteX3ygAJxQDxAy9a?dl=0. G. Ceriotti would like to thank the EU and MIUR for funding in the frame of the collaborative international Consortium (WE-NEED) financed under the ERA-NET WaterWorks2014 Co- funded Call. This ERA-NET is an integral part of the 2015 Joint Activities developed by the Water Challenges for a Changing World Joint Programme Initiative (Water JPI).
Publisher Copyright:
© 2020, Springer-Verlag GmbH Germany, part of Springer Nature.
PY - 2020/12
Y1 - 2020/12
N2 - 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 CO 2 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.
AB - 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 CO 2 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.
KW - Bacterial mortality
KW - Environmental model
KW - Forest
KW - Grassland
KW - Sensitivity analysis
KW - Soil organic matter
UR - http://www.scopus.com/inward/record.url?scp=85090927504&partnerID=8YFLogxK
U2 - 10.1007/s00477-020-01868-z
DO - 10.1007/s00477-020-01868-z
M3 - Article
AN - SCOPUS:85090927504
SN - 1436-3240
VL - 34
SP - 2229
EP - 2244
JO - Stochastic Environmental Research and Risk Assessment
JF - Stochastic Environmental Research and Risk Assessment
IS - 12
ER -