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Response of terrestrial soil carbon to hydrological physical processes in a coupled water-carbon cycle model
Guodong Sun1 and Mu Mu 2
1 State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences 2 Institute of Atmospheric Sciences, Fudan University
"Soil carbon is the largest carbon pool in the terrestrial biosphere. Large uncertainty exists in model projections of the soil carbon due to inaccurate hydrological processes. In this study, the upper limit of uncertainty in the modeled soil carbon, which is caused by errors in the physical parameters of hydrological processes, was estimated in China under four different climate backgrounds. To accomplish this, an approach of the conditional nonlinear optimal perturbation related to parameters (CNOP-P) and a coupled water-carbon cycle model (the Lund-Potsdam-Jena Wetland Hydrology and Methane Dynamic Global Vegetation Model, LPJ-WHyMe) were employed. It was found that the uncertainties in the hydrological processes resulted in the largest error (2.73 kg C m-2 yr-1, 20.2%) in the modeled soil carbon in the arid and semiarid region of northern China, with errors of 1.20 kg C m-2 yr-1 (6.1%) in northeastern China, 0.45 kg C m-2 yr-1 (3.3%) in southern China, and -1.71 kg C m-2 yr-1 (13.7%) in the semi-humid region of northern China. By analyzing the three components of soil carbon, the fast soil carbon pool was the main cause of the uncertainties in the modeled soil carbon in the four regions of China. Moreover, the below-ground litter was another reason causing uncertainties in the modeled soil carbon in northeastern China and in the semi-humid region of northern China. Further numerical results indicated that the simulation ability and prediction skill of the soil carbon could be improved to reduce all parameter errors in the hydrological processes through observation or targeted observation. The parameter sensitivity test showed that the benefits of modeling soil carbon when reducing the errors of the relatively sensitive hydrological parameter subset are comparable to those when reducing all hydrological parameters. "