| Ecophysiological parameters are mathematical simplifications of plant traits in ecosystem process models.The calculation methods and computational scales of ecophysiological parameters have not yet been taken into account by model researchers.Meanwhile,most of the large-scale forest productivity datasets are currently driven by remote sensing data,and forest productivity datasets driven by actual measured plant traits are rarely reported.Based on the above issues,this study investigated the effects of different methods of calculating ecophysiological parameters on the simulation results of an ecosystem process model(Biome-BGC model)based on community surveys and plant trait measurements at nine typical forest sites(Huzhong,Liangshui,Changbaishan,Donglingshan,Taiyueshan,Shennongjia,Jiulianshan,Dinghushan,Jianfengling)in the North-South Transect in Eastern China.Meanwhile,it was found that the Biome-BGC model simulates a "steep drop" in productivity(including GPP and NEP)in the Changbaishan forest during a continuous dry growing season.This problem is due to the large gap between the simulated water cycle in the model and the real situation,and the water stress on photosynthesis of plants.In order to solve this problem,the stomatal conductance and outflow modules of the model were improved in this study.Afterwards,the sensitivity of the Biome-BGC model to ecophysiological parameters in Changbaishan and Dinghushan forests was Chineseized and calibrated based on our actual measured trait values and the PEST method,and the sensitivity parameters with low spatial variability were extended from Changbaishan and Dinghushan to other similar forest types in the North-South Transect in Eastern China.Finally,this study combined the improved model,the Chinese calibration of the physiological and ecological parameters and the construction of the model regionalization method to simulate the spatial and temporal changes in gross primary productivity(GPP),net primary productivity(NPP)and net ecosystem productivity(NEP)in the forests of eastern China over a 30-year period(1989-2018).The simulation results were then compared to three mainstream productivity products(BEPS product,MODIS product,and VOD dataset)for spatial and temporal trends in this study.The main findings of this study are summarised below:(1)Of the nine forest sites in this study,four(Dinghushan,Shennongjia,Changbaishan and Huzhong)showed significant differences between the simulation results of the Biome-BGC model under different parameterization methods.This suggests that differences in ecophysiological parameterisation may have a significant effect on model simulation results,and that this effect is uncertain in terms of spatial patterns,which do not show a pattern with respect to forest,climate type and species composition.Therefore,the choice of ecophysiological parameterisation method is important when driving ecosystem process models for forest ecosystem simulations and it is hoped that this finding will be of interest to model researchers and users.(2)With only some of the important ecophysiological parameters parameterised and only explored at the methodological level,the differences between the model simulation results under different parameterisation methods and those under AP＿BW were compared and analysed by means of Taylor diagrams.This study found that the simulation results of the all-species biomass weighted average method(AT＿BW)for all tree species in the community were the closest to those of the AP＿BW method,suggesting that AT＿BW can be used as an alternative to the AP＿BW method when conditions are constrained.(3)By improving the stomatal conductance and outflow modules of the Biome-BGC model in this study,the problem of "steep drop" in GPP and NEP during the continuous dry period of the growing season in the simulation of Changbaishan forest by the Biome-BGC model can not only be fixed,but also effectively improve the simulation accuracy of GPP and NEP in Changbaishan forest and Dinghushan forest.The simulation accuracy of GPP and NEP in Changbaishan and Dinghushan forests was effectively improved.In addition,based on the improved Biome-BGC model,the actual measured plant trait values,the PEST method and the flux observation data,the optimal values of the unmeasured model sensitivity parameters were calibrated for the Changbaishan and Dinghushan forests.After comparison,the model improvements and parameter optimisation in this study significantly improved the simulation accuracy of the Biome-BGC model for GPP and NEP in Changbaishan and Dinghushan forests.(4)Based on the improvement of the Biome-BGC model,calibration of sensitivity parameters,spatial expansion and the regionalisation approach of the model,the results of the spatial and temporal changes in GPP,NPP and NEP of the eastern forests over 30 years(1989-2018)are as follows:In terms of temporal trends,the GPP,NPP and NEP of eastern forests in China showed an overall increasing trend over the 30-year period(1989-2018).The regions with significant and largest increases in GPP in eastern forests over the 30-year period were the northern Liangshui,northern Shennongjia and northeastern Jiulianshan.NPP increased significantly and to the greatest extent in northern Liangshui and northern Shennongjia,and NEP increased significantly and to the greatest extent in southern Huzhong and northern Shennongjia.In terms of spatial patterns,the GPP and NPP of eastern forests in China showed an overall pattern of gradual increase from north to south over the 30-year period(1989-2018),and the NEP showed a pattern of clustering according to forest type subregions.In the eastern forests,the area with the lowest annual mean GPP is the Huzhong’s deciduous coniferous forest in the northern part of the study area,and the area with the highest annual mean GPP is the tropical monsoon forest in the southern part of the study area,the Jianfengling.The areas with the lowest annual mean NPP values are the northern region of Huzhong,the area near Shennongjia and the scattered areas in the northeastern part of the Jiulianshan,while the areas with the highest annual mean NPP values are the areas near Dinghushan and the Jiulianshan.The highest annual mean NEP values in the eastern forests are in the mixed coniferous forests north of Liangshui,the northeastern part of the Jiu Lian Mountains and the southern scattered areas of Shennongjia,while the lowest annual mean NEP values are in the scattered areas north of Huzhong and the area near Shennongjia.(5)The results of this study are more consistent with the simulated values of GPP and NPP for the northern and central regions of the study area with the other three mainstream productivity products(BEPS product,MODIS product,and VOD dataset),and the simulated values of GPP and NPP for the southern part of the eastern forest are more different from each other.This suggests that forest productivity in the southern part of the study area is a difficult area to estimate at present,and that the uncertainty in productivity estimates for this area is greater for a variety of products,including the results of this study.(6)The GPP,NPP and NEP results for the eastern forests modelled in this study over a 30-year period are less consistent with the temporal and spatial spatial trends of other productivity products in that order.This suggests that the more complex the forest ecosystem processes involved,the less likely it is that productivity can be estimated accurately.The uncertainty in the estimation of GPP,NPP and NEP in eastern forests increases for several sets of productivity products,including the results of this study.By comparing model simulation results between nine forest sites under different parameterization methods,this study reveals that the effects of parameterization methods on ecosystem process model results are spatially fraught with uncertainty and unpredictability,calling model researchers’ attention to the calculation methods of model physiological parameters.In addition,this study improves the simulation accuracy of the Biome-BGC model for the forest productivity of Changbaishan and Dinghushan in the North-South Transect in Eastern China through model improvement and parameter optimization.Finally,based on the Chineseized ecophysiological parameters calibrated on the eastern forests in this study and the model’s regionalization approach,an annual multiple productivity dataset(1998-2018)for the forests of eastern China driven by measured plant traits was produced as a complement to the current forest productivity products. |
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