Accurate quantification of carbon and water fluxes dynamics in arid and semi-arid ecosystems is a critical scientific challenge for regional carbon neutrality assessments and sustainable water resource management.In t...Accurate quantification of carbon and water fluxes dynamics in arid and semi-arid ecosystems is a critical scientific challenge for regional carbon neutrality assessments and sustainable water resource management.In this study,we developed a multi-flux global sensitivity discriminant index(D_(sen))by integrating the Biome-BGCMuSo model with eddy covariance flux observations.This index was combined with a Bayesian optimization algorithm to conduct parameter optimization.The results demonstrated that:(1)Sensitivity analysis identified 13 highly sensitive parameters affecting carbon and water fluxes.Among these,the canopy light extinction coefficient(k)and the fraction of leaf N in Rubisco(FLNR)exhibited significantly higher sensitivity to carbon fluxes(GPP,NEE,Reco;D_(sen)>10%)compared to water flux(ET).This highlights the strong dependence of carbon cycle simulations on vegetation physiological parameters.(2)The Bayesian optimization framework efficiently converged 30 parameter spaces within 50 iterations,markedly improving carbon fluxes simulation accuracy.The Kling-Gupta efficiency(KGE)values for Gross Primary Production(GPP),Net Ecosystem Exchange(NEE),and Total Respiration(Reco)increased by 44.94%,69.23%and 123%,respectively.The optimization prioritized highly sensitive parameters,underscoring the necessity of parameter sensitivity stratification.(3)The optimized model effectively reproduced carbon sink characteristics in mountain meadows during the growing season(cumulative NEE=-375 g C/m^(2)).It revealed synergistic carbon-water fluxes interactions governed by coupled photosynthesis-stomatal pathways and identified substrate supply limitations on heterotrophic respiration.This study proposes a novel multi-flux sensitivity index and an efficient optimization framework,elucidating the coupling mechanisms between vegetation physiological regulation(k,FLNR)and environmental stressors(VPD,SWD)in carbonwater cycles.The methodology offers a practical approach for arid ecosystem model optimization and provides theoretical insights for grassland management through canopy structure regulation and water-use efficiency enhancement.展开更多
Background:Process-based models are widely used to simulate forest productivity,but complex parameterization and calibration challenge the application and development of these models.Sensitivity analysis of numerous p...Background:Process-based models are widely used to simulate forest productivity,but complex parameterization and calibration challenge the application and development of these models.Sensitivity analysis of numerous parameters is an essential step in model calibration and carbon flux simulation.However,parameters are not dependent on each other,and the results of sensitivity analysis usually vary due to different forest types and regions.Hence,global and representative sensitivity analysis would provide reliable information for simple calibration.Methods:To determine the contributions of input parameters to gross primary productivity(GPP)and net primary productivity(NPP),regression analysis and extended Fourier amplitude sensitivity testing(EFAST)were conducted for Biome-BGCMuSo to calculate the sensitivity index of the parameters at four observation sites under climate gradient from ChinaFLUX.Results:Generally,GPP and NPP were highly sensitive to C:Nleaf(C:N of leaves),Wint(canopy water interception coefficient),k(canopy light extinction coefficient),FLNR(fraction of leaf N in Rubisco),MRpern(coefficient of linear relationship between tissue N and maintenance respiration),VPDf(vapor pressure deficit complete conductance reduction),and SLA1(canopy average specific leaf area in phenological phase 1)at all observation sites.Various sensitive parameters occurred at four observation sites within different climate zones.GPP and NPP were particularly sensitive to FLNR,SLA1 and Wint,and C:Nleaf in temperate,alpine and subtropical zones,respectively.Conclusions:The results indicated that sensitivity parameters of China's forest ecosystems change with climate gradient.We found that parameter calibration should be performed according to plant functional type(PFT),and more attention needs to be paid to the differences in climate and environment.These findings contribute to determining the target parameters in field experiments and model calibration.展开更多
不同草地生态系统碳/水循环对极端气候响应及其机制存在差异。本研究基于生物地球化学循环模型Biome-BGCMuSo(BGC with Multilayer Soil Module)结合涡度相关数据,通过敏感性分析和参数优化,探究了不同草地类型碳/水通量差异及其对极端...不同草地生态系统碳/水循环对极端气候响应及其机制存在差异。本研究基于生物地球化学循环模型Biome-BGCMuSo(BGC with Multilayer Soil Module)结合涡度相关数据,通过敏感性分析和参数优化,探究了不同草地类型碳/水通量差异及其对极端气候的响应。结果表明,碳通量模拟对植被光合生理及碳分配参数敏感,水通量对叶片、根系参数敏感,且不同草地类型存在差异,优化高敏感参数可显著提升碳通量模拟精度,但水通量模拟仍有待进一步提高。生长季热浪期间,3种草地仍为碳汇,温性荒漠草原和山地草甸日间碳汇分别减弱28.7%和29.4%;极端降雨提升了3种草地的碳/水通量;干旱事件使温性荒漠草原碳汇增强62.5%,山地草甸碳汇减弱16.3%。本研究揭示了干旱-半干旱区草地碳/水循环对极端气候的不同响应机制,为预测气候变化下草地生态过程演变提供了科学依据。展开更多
基金jointly funded by the National Natural Science Foundation of China(Grant No.42161024)the Central Financial Forestry and Grassland Science and Technology Extension Demonstration Project(2025)(Grant No.Xin[2025]TG 09)。
文摘Accurate quantification of carbon and water fluxes dynamics in arid and semi-arid ecosystems is a critical scientific challenge for regional carbon neutrality assessments and sustainable water resource management.In this study,we developed a multi-flux global sensitivity discriminant index(D_(sen))by integrating the Biome-BGCMuSo model with eddy covariance flux observations.This index was combined with a Bayesian optimization algorithm to conduct parameter optimization.The results demonstrated that:(1)Sensitivity analysis identified 13 highly sensitive parameters affecting carbon and water fluxes.Among these,the canopy light extinction coefficient(k)and the fraction of leaf N in Rubisco(FLNR)exhibited significantly higher sensitivity to carbon fluxes(GPP,NEE,Reco;D_(sen)>10%)compared to water flux(ET).This highlights the strong dependence of carbon cycle simulations on vegetation physiological parameters.(2)The Bayesian optimization framework efficiently converged 30 parameter spaces within 50 iterations,markedly improving carbon fluxes simulation accuracy.The Kling-Gupta efficiency(KGE)values for Gross Primary Production(GPP),Net Ecosystem Exchange(NEE),and Total Respiration(Reco)increased by 44.94%,69.23%and 123%,respectively.The optimization prioritized highly sensitive parameters,underscoring the necessity of parameter sensitivity stratification.(3)The optimized model effectively reproduced carbon sink characteristics in mountain meadows during the growing season(cumulative NEE=-375 g C/m^(2)).It revealed synergistic carbon-water fluxes interactions governed by coupled photosynthesis-stomatal pathways and identified substrate supply limitations on heterotrophic respiration.This study proposes a novel multi-flux sensitivity index and an efficient optimization framework,elucidating the coupling mechanisms between vegetation physiological regulation(k,FLNR)and environmental stressors(VPD,SWD)in carbonwater cycles.The methodology offers a practical approach for arid ecosystem model optimization and provides theoretical insights for grassland management through canopy structure regulation and water-use efficiency enhancement.
基金This study was funded by the National Natural Science Foundation of China(grant number 41871279 and 41901364).
文摘Background:Process-based models are widely used to simulate forest productivity,but complex parameterization and calibration challenge the application and development of these models.Sensitivity analysis of numerous parameters is an essential step in model calibration and carbon flux simulation.However,parameters are not dependent on each other,and the results of sensitivity analysis usually vary due to different forest types and regions.Hence,global and representative sensitivity analysis would provide reliable information for simple calibration.Methods:To determine the contributions of input parameters to gross primary productivity(GPP)and net primary productivity(NPP),regression analysis and extended Fourier amplitude sensitivity testing(EFAST)were conducted for Biome-BGCMuSo to calculate the sensitivity index of the parameters at four observation sites under climate gradient from ChinaFLUX.Results:Generally,GPP and NPP were highly sensitive to C:Nleaf(C:N of leaves),Wint(canopy water interception coefficient),k(canopy light extinction coefficient),FLNR(fraction of leaf N in Rubisco),MRpern(coefficient of linear relationship between tissue N and maintenance respiration),VPDf(vapor pressure deficit complete conductance reduction),and SLA1(canopy average specific leaf area in phenological phase 1)at all observation sites.Various sensitive parameters occurred at four observation sites within different climate zones.GPP and NPP were particularly sensitive to FLNR,SLA1 and Wint,and C:Nleaf in temperate,alpine and subtropical zones,respectively.Conclusions:The results indicated that sensitivity parameters of China's forest ecosystems change with climate gradient.We found that parameter calibration should be performed according to plant functional type(PFT),and more attention needs to be paid to the differences in climate and environment.These findings contribute to determining the target parameters in field experiments and model calibration.
文摘不同草地生态系统碳/水循环对极端气候响应及其机制存在差异。本研究基于生物地球化学循环模型Biome-BGCMuSo(BGC with Multilayer Soil Module)结合涡度相关数据,通过敏感性分析和参数优化,探究了不同草地类型碳/水通量差异及其对极端气候的响应。结果表明,碳通量模拟对植被光合生理及碳分配参数敏感,水通量对叶片、根系参数敏感,且不同草地类型存在差异,优化高敏感参数可显著提升碳通量模拟精度,但水通量模拟仍有待进一步提高。生长季热浪期间,3种草地仍为碳汇,温性荒漠草原和山地草甸日间碳汇分别减弱28.7%和29.4%;极端降雨提升了3种草地的碳/水通量;干旱事件使温性荒漠草原碳汇增强62.5%,山地草甸碳汇减弱16.3%。本研究揭示了干旱-半干旱区草地碳/水循环对极端气候的不同响应机制,为预测气候变化下草地生态过程演变提供了科学依据。
