Parameter optimization of BEPS model based on the flux data of the temperate deciduous broad-leaved forest in Northeast China.

doi:10.13287/j.1001-9332.201605.001

Abstract

Abstract: Keeping other parameters as empirical constants, different numerical combinations of the main photosynthetic parameters V_{c max} and J_max were conducted to estimate daily GPP by using the iteration method in this paper. To optimize V_{c max} and J_max in BEPSHourly model at hourly time steps, simulated daily GPP using different numerical combinations of the parameters were compared with the flux tower data obtained from the temperate deciduous broad-leaved forest of the Maoershan Forest Farm in Northeast China. Comparing the simulated daily GPP with the observed flux data in 2011, the results showed that optimal V_{c max} and J_max for the deciduous broad-leaved forest in Northeast China were 41.1 μmol·m^-2·s^-1 and 82.8 μmol·m^-2·s^-1 respectively with the minimal RMSE and the maximum R² of 1.10 g C·m^-2·d^-1 and 0.95. After V_{c max} and J_max optimization, BEPSHourly model simulated the seasonal variation of GPP better.

LU Wei, FAN Wen-yi, TIAN Tian. Parameter optimization of BEPS model based on the flux data of the temperate deciduous broad-leaved forest in Northeast China.[J]. Chinese Journal of Applied Ecology, 2016, 27(5): 1353-1358.

References

[1] Li S-H (李世华), Niu Z (牛铮), Li B-C (李壁成). Study on remote sensing process model of vegetation NPP. Research of Soil and Water Conservation (水土保持研究), 2005, 12(3): 126-128 (in Chinese)
[2] Potter CS, Randerson JT, Field CB, et al. Terrestrial ecosystem production: A process model based on global satellite and surface data. Global Biogeochemical Cycles, 1993, 7: 811-841
[3] Chen WJ, Chen J, Liu J, et al. Approaches for reducing uncertainties in regional forest carbon balance. Global Biogeochemical Cycles, 2000, 14: 827-838
[4] Liu J, Chen JM, Cihlar J, et al. A process-based boreal ecosystem productivity simulator using remote sensing inputs. Remote Sensing of Environment, 1997, 62: 158-175
[5] Liu J, Chen JM, Cihlar J, et al. Net primary productivity mapped for Canada at 1 km resolution. Global Ecology and Biogeography, 2002, 11: 115-129
[6] Wang P-J (王培娟), Sun R (孙睿), Zhu Q-J (朱启疆), et al. Improvement on the abilities of BEPS under accidented terrain. Journal of Image and Graphics (中国图象图形学报), 2006, 11(7): 1017-1025 (in Chinese)
[7] Wang P-J (王培娟), Xie D-H (谢东辉), Zhang J-H (张佳华), et al. Spatial scaling of net primary productivity based on process model in Changbai Mountain in Natural Reserve. Acta Ecologica Sinica (生态学报), 2007, 27(8): 3215-3223 (in Chinese)
[8] Mao X-G (毛学刚). Study on the Model of Northeast Forest Carbon Cycle at Daily Step and the Integrated Application of Remote Sensing. PhD Thesis. Harbin: Northeast Forest University, 2011 (in Chinese)
[9] Li D-Q (李登秋), Ju W-M (居为民), Zheng G (郑光), et al. Comparison of estimated forest biomass increment rate based on a process-based ecological model and forest inventory data. Ecology and Environmental Sciences (生态环境学报), 2013, 22(10): 1647-1657 (in Chinese)
[10] Zhang Y-M (张彦敏), Zhou G-S (周广胜). Advances in leaf maximum carboxylation rate and its response to environmental factors. Acta Ecologica Sinica (生态学报), 2012, 32(18): 5907-5917 (in Chinese)
[11] Zeng W (曾伟), Jiang Y-L (蒋延玲), Li F (李峰). Responses of Quercus mongolica’s photosynthetic parameters to soil moisture stress. Acta Ecologica Sinica (生态学报), 2008, 28(6): 2504-2510 (in Chinese)
[12] Chen BZ, Chen JM, Ju WM. Remote sensing-based ecosystem-atmosphere simulation scheme (EASS): Model formulation and test with multiple-year data. Ecological Modelling, 2007, 209: 277-300
[13] Ju WM, Chen JM, Thomas AB, et al. Modelling multi-year coupled carbon and water fluxes in a boreal aspen forest. Agricultural and Forest Meteorology, 2006, 140: 136-151
[14] Farquhar GD, Caemmerer SV, Berry JA. A biochemical model of photosynthetic CO₂ assimilation in leaves of C₃ species. Planta, 1980, 149: 78-90
[15] Chen JM, Liu J, Cihlar J, et al. Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications. Ecological Modelling, 1999, 124: 99-119
[16] Collatz GJ, Ball JT, Grivet C, et al. Physiological and environmental regulation of stomatal conductance, photosynthesis and transpiration: A model that includes a laminar boundary layer. Agricultural and Forest Meteoro-logy, 1991, 54: 107-136
[17] Ball JT, Woodrow IE, Berry JA. A model predicting stomatal conductance and its contribution to the control of photosynthesis under different environmental conditions// Biggins J, ed. Progress in Photosynthesis Research. New York: Springer, 1987: 221-224
[18] Chen JM, Chen XY, Ju WM, et al. Distributed hydrological model for mapping evapotranspiration using remote sensing inputs. Journal of Hydrology, 2005, 305: 15-39
[19] Baldocchi D. An analytical solution for coupled leaf photosynthesis andstomatal conductance models. Tree Phy-siology, 1994, 14: 1069-1079
[20] Wullschleger SD. Biochemical limitations to carbon assimilation in C₃ plants: A retrospective analysis of the A/C_i curves from 109 species. Journal of Experimental Botany, 1993, 44: 907-920
[21] Yan S (闫霜), Zhang L (张黎), Jing Y-S (景元书), et al. Variations in the relationship between maximum leaf carboxylation rate and leaf nitrogen concentration. Chinese Journal of Plant Ecology (植物生态学报), 2014, 38(6): 640-652 (in Chinese)
[22] Gorsel EV, Leuning R, Cleugh HA, et al. Nocturnal carbon efflux: Reconciliation of eddy covariance and chamber measurements using an alternative to the u^*-threshold filtering technique. Tellus Series B: Chemical and Physical Meteorology, 2007, 59: 397-403
[23] Lloyd J, Taylor JA. On the temperature dependence of soil respiration. Functional Ecology, 1994, 8: 315-323