Comparing an exponential respiration model to alternative models for soil respiration components in a Canadian wildfire chronosequence (FireResp v1.0)

• Published in: Geoscientific Model Development
• Main Authors: Zobitz, John, Aaltonen, Heidi, Zhou, Xuan, Berninger, Frank, Pumpanen, Jukka, Köster, Kajar
• Format: Text
• Language: English
• Published: 2021
• Subjects: Canada; Northwest Territories; Yukon
• Online Access: https://doi.org/10.5194/gmd-14-6605-2021; https://gmd.copernicus.org/articles/14/6605/2021/

Forest fires modify soil organic carbon and suppress soil respiration for many decades after the initial disturbance. The associated changes in soil autotrophic and heterotrophic respiration from the time of the forest fire, however, are less well characterized. The FireResp model predicts soil autotrophic and heterotrophic respiration parameterized with a novel dataset across a fire chronosequence in the Yukon and Northwest Territories of Canada. The dataset consisted of soil incubation experiments and field measurements of soil respiration and soil carbon stocks. The FireResp model contains submodels that consider a Q 10 (exponential) model of respiration compared to models of heterotrophic respiration using Michaelis–Menten kinetics parameterized with soil microbial carbon. For model evaluation we applied the Akaike information criterion and compared predicted patterns in components of soil respiration across the chronosequence. Parameters estimated with data from the 5 cm soil depth had better model–data comparisons than parameters estimated with data from the 10 cm soil depth. The model–data fit was improved by including parameters estimated from soil incubation experiments. Models that incorporated microbial carbon with Michaelis–Menten kinetics reproduced patterns in autotrophic and heterotrophic soil respiration components across the chronosequence. Autotrophic respiration was associated with aboveground tree biomass at more recently burned sites, but this association was less robust at older sites in the chronosequence. Our results provide support for more structured soil respiration models than standard Q 10 exponential models.