Modeling soil respiration and variations in source components using a multi-factor global climate change experiment

Soil respiration is an important component of the global carbon cycle and is highly responsive to changes in soil temperature and moisture. Accurate prediction of soil respiration and its changes under future climatic conditions requires a clear understanding of the processes involved. Most current empirical soil respiration models incorporate just few of the underlying mechanisms that may influence its response. In this study, a new partially process-based component model that separately treated several source components of soil respiration was tested with data from a climate change experiment that manipulated atmospheric [CO2], air temperature and soil moisture. Results from this model were compared to results from other widely used models with the parameters fitted using experimental data. Using the component model, we were able to estimate the relative proportions of heterotrophic and autotrophic respiration in total soil respiration for each of the different treatments. The value of the Q (10) parameters for temperature response component of all of the models showed sensitivity to soil moisture. Estimated Q (10) parameters were higher for wet treatments and lower for dry treatments compared to the values estimated using either the data from all treatments or from only the control treatments. Our results suggest that process-based models provide a better understanding of soil respiration dynamics under changing environmental conditions, but the extent and contribution of different source components need to be included in mechanistic and process-based soil respiration models at corresponding scales.