1Key Laboratory of Cenozoic Geology and Environment, Institute of Geology and Geophysics, Chinese Academy of Science, 100029, Beijing, China
2Department of Geology and Soil Science, University of Ghent, Krijgslaan 281, 9000 Ghent, Belgium
Received: 13 Jun 2012 – Discussion started: 16 Jul 2012
Abstract. To accurately estimate past terrestrial carbon pools is the key to understanding the global carbon cycle and its relationship with the climate system. SoilGen2 is a useful tool to obtain aspects of soil properties (including carbon content) by simulating soil formation processes; thus it offers an opportunity for both past soil carbon pool reconstruction and future carbon pool prediction. In order to apply it to various environmental conditions, parameters related to carbon cycle process in SoilGen2 are calibrated based on six soil pedons from two typical loess deposition regions (Belgium and China). Sensitivity analysis using the Morris method shows that decomposition rate of humus (kHUM), fraction of incoming plant material as leaf litter (frecto) and decomposition rate of resistant plant material (kRPM) are the three most sensitive parameters that would cause the greatest uncertainty in simulated change of soil organic carbon in both regions. According to the principle of minimizing the difference between simulated and measured organic carbon by comparing quality indices, the suited values of kHUM, (frecto and kRPM in the model are deduced step by step and validated for independent soil pedons. The difference of calibrated parameters between Belgium and China may be attributed to their different vegetation types and climate conditions. This calibrated model allows more accurate simulation of carbon change in the whole pedon and has potential for future modeling of carbon cycle over long timescales.
Revised: 25 Nov 2012 – Accepted: 06 Dec 2012 – Published: 08 Jan 2013
Yu, Y. Y., Finke, P. A., Wu, H. B., and Guo, Z. T.: Sensitivity analysis and calibration of a soil carbon model (SoilGen2) in two contrasting loess forest soils, Geosci. Model Dev., 6, 29-44, doi:10.5194/gmd-6-29-2013, 2013.