Sensitivity analysis of Biome-BGCMuSo for gross and net primary productivity of typical forests in China

Process-based models are widely used to simulate forest productivity, but complex parameterization and calibration challenge the application development of these models. Sensitivity analysis numerous parameters is an essential step in model carbon flux simulation. However, not dependent on each other, results sensitivity usually vary due different types regions. Hence, global representative would provide reliable information for simple calibration. To determine contributions input gross primary productivity (GPP) net (NPP), regression extended Fourier amplitude testing (EFAST) were conducted Biome-BGCMuSo calculate index at four observation sites under climate gradient from ChinaFLUX. Generally, GPP NPP highly sensitive C:Nleaf (C:N leaves), Wint (canopy water interception coefficient), k light extinction FLNR (fraction leaf N Rubisco), MRpern (coefficient linear relationship between tissue maintenance respiration), VPDf (vapor pressure deficit complete conductance reduction), SLA1 average specific area phenological phase 1) all sites. Various occurred within zones. particularly FLNR, Wint, temperate, alpine subtropical zones, respectively. The indicated that China's ecosystems change with gradient. We found parameter should be performed according plant functional type (PFT), more attention needs paid differences environment. These findings contribute determining target field experiments