准确估算陆地生态系统呼吸(Ecosystem respiration,RE)对全球陆地生态系统碳收支研究具有重要意义.模型模拟是估算陆地RE变化的一种常用手段.然而目前陆地生态系统过程模型的RE模拟尚未得到充分验证.基于耦合模式比较计划第五阶段(Coupl...准确估算陆地生态系统呼吸(Ecosystem respiration,RE)对全球陆地生态系统碳收支研究具有重要意义.模型模拟是估算陆地RE变化的一种常用手段.然而目前陆地生态系统过程模型的RE模拟尚未得到充分验证.基于耦合模式比较计划第五阶段(Coupled Model Intercomparison Project Phase 5,CMIP5)的通用陆面模型(Community land model,CLM)RE模拟结果和全球通量网(FLUXNET)66个站点的涡度相关通量观测数据(277条站点年数据)评估CLM模型对RE的模拟效果.结果表明:(1)在空间尺度上,CLM低估了高纬度站点RE,高估了低纬度站点RE,但高纬度低估量更大导致空间格局整体低估(相对误差为-3.56%).(2)在时间尺度上,CLM模型基本捕捉了RE的年际和季节变化,相关系数分别为0.60(P < 0.001)和0.63(P < 0.001);CLM低估年尺度和月尺度的RE(以C计),绝对误差分别是-182.21 g m-2 a-1、-120.16 g m-2 mon-1,相对误差分别是-17.84%、-10.60%.(3)CLM模型对不同植被功能型的RE模拟效果不同,由优及差依次为混交林、常绿针叶林、草地、农田、落叶阔叶林、常绿阔叶林.本研究在时空尺度上量化了CLM模型的生态系统呼吸模拟误差,并分析了土壤呼吸Q10和MRbase参数以及土壤碳库模拟等因素的影响,可为CLM模型的生态系统呼吸模块参数优化提供依据,进而提升其模拟精度.展开更多
Given the crucial role of land surface processes in global and regional climates, there is a pressing need to test and verify the performance of land surface models via comparisons to observations. In this study, the ...Given the crucial role of land surface processes in global and regional climates, there is a pressing need to test and verify the performance of land surface models via comparisons to observations. In this study, the eddy covariance measurements from 20 FLUXNET sites spanning more than 100 site-years were utilized to evaluate the performance of the Common Land Model （CoLM） over different vegetation types in various climate zones. A decomposition method was employed to separate both the observed and simulated energy fluxes, i.e., the sensible heat flux, latent heat flux, net radiation, and ground heat flux, at three timescales ranging from stepwise （30 rain） to monthly. A comparison between the simulations and observations indicated that CoLM produced satisfactory simulations of all four energy fluxes, although the different indexes did not exhibit consistent results among the different fluxes, A strong agreement between the simulations and observations was found for the seasonal cycles at the 20 sites, whereas CoLM underestimated the latent heat flux at the sites with distinct dry and wet seasons, which might be associated with its weakness in simulating soil water during the dry season. CoLM cannot explicitly simulate the midday depression of leaf gas exchange, which may explain why CoLM also has a maximum diurnal bias at noon in the summer. Of the eight selected vegetation types analyzed, CoLM performs best for evergreen broadleaf forests and worst for croplands and wetlands.展开更多
模式评估是模式发展中的重要一环。本文利用来自FLUXNET2015数据集的30个站点的涡动相关系统观测数据,重点关注能量通量,对通用陆面模式(Common Land Model version 2014,CoLM2014)在不同典型下垫面的模拟能力进行评估。结果表明,模式...模式评估是模式发展中的重要一环。本文利用来自FLUXNET2015数据集的30个站点的涡动相关系统观测数据,重点关注能量通量,对通用陆面模式(Common Land Model version 2014,CoLM2014)在不同典型下垫面的模拟能力进行评估。结果表明,模式总体上能抓住感热、潜热和净辐射通量在日、季节和年平均等不同时间尺度上的变化特征,对感热、潜热和净辐射通量都有较好的模拟能力,净辐射的模拟效果最好,潜热通量次之。季节变化模拟中,感热、潜热通量在夏季不同植被型下站点的空间离散程度大于冬季,不同站点间模拟效果相差较大,净辐射多站点标准差变化幅度要小于感热、潜热,不同站点间模拟效果偏差较小。CoLM在常绿针叶林、稀树林地、草地、农田模拟感热、潜热通量的效果相对较好,在永久湿地、落叶阔叶林下模拟感热通量较差。本研究对CoLM2014在未来的改进和发展中提供了有用的参考。展开更多
Surface-latent heat(LE)and sensible heat(SH)fluxes play a pivotal role in governing hydrological,biological,geochemical,and ecological processes on the land surface in the Tibetan Plateau.However,to accurately assess ...Surface-latent heat(LE)and sensible heat(SH)fluxes play a pivotal role in governing hydrological,biological,geochemical,and ecological processes on the land surface in the Tibetan Plateau.However,to accurately assess and understand the spatial distribution of LE and SH fluxes across different underlying surfaces,it is crucial to verify the validity and reliability of ERA-5,GLDAS,and MODIS data against ground measurements obtained from the Flux Net micrometeorological tower network.This study analyzed the spatial patterns of LE and SH over the Tibetan Plateau using data from ERA-5,GLDAS,and MODIS.The results were compared with ground measurements from Flux Net tower observations on different underlying surfaces,and five statistical parameters(Pearson's r,LR slope,RMSE,MBE,and MAE)were used to validate the data.The results showed that:(1)MODIS LE data and ERA-5 SH data exhibited the closest agreement with ground observations,as indicated by their lowest root mean square error and mean bias area values.(2)The accuracy of ERA-5 SH was the highest in meadows and steppes,while GLDAS SH performed optimally in shrublands.Notably,MODIS LE consistently outperformed the other datasets across all vegetation types.(3)The spatial distribution of LE and SH displayed considerable heterogeneity,contingent upon the specific data sources and underlying surfaces.Notably,there was a contrasting trend between GLDAS and ERA-5,as well as MODIS,in terms of SH distribution in the shrubland.In shrublands and meadows,MODIS SH and LE exhibited more pronounced changes than ERA-5 and GLDAS.Additionally,ERA-5 SH demonstrated the opposite variation in meadow and steppe regions compared to GLDAS and MODIS.展开更多
The seasonal variabilities of a latent-heat flux （LHF）, a sensible-heat flux （SHF） and net surface heat flux are examined in the northern South China Sea （NSCS）, including their spatial characteristics, using th...The seasonal variabilities of a latent-heat flux （LHF）, a sensible-heat flux （SHF） and net surface heat flux are examined in the northern South China Sea （NSCS）, including their spatial characteristics, using the in situ data collected by ship from 2006 to 2007. The spatial distribution of LHF in the NSCS is mostly controlled by wind in summer and autumn owing to the lower vertical gradient of air humidity, but is influenced by both wind and near-surface air humidity vertical gradient in spring and winter. The largest area-averaged LHF is in autumn, with the value of 197.25 W/m 2 , followed by that in winter; the third and the forth are in summer and spring, respectively. The net heat flux is positive in spring and summer, so the NSCS absorbs heat; and the solar shortwave radiation plays the most important role in the surface heat budget. In autumn and winter, the net heat flux is negative in most of the observation region, so the NSCS loses heat; and the LHF plays the most important role in the surface heat budget. The net heating is mainly a result of the offsetting between heating due to the shortwave radiation and cooling due to the LHF and the upward （outgoing） long wave radiation, since the role of SHF is negligible. The ratio of the magnitudes of the three terms （shortwave radiation to LHF to long-wave radiation） averaged over the entire year is roughly 3：2：1, and the role of SHF is the smallest.展开更多
As an important product of Moderate Resolution Imaging Spectroradiometer(MODIS), MOD17A2 provides dramatic improvements in our ability to accurately and continuously monitor global terrestrial primary production, whic...As an important product of Moderate Resolution Imaging Spectroradiometer(MODIS), MOD17A2 provides dramatic improvements in our ability to accurately and continuously monitor global terrestrial primary production, which is also significant in effort to advance scientific research and eco-environmental management. Over the past decades, forests have moderated climate change by sequestrating about one-quarter of the carbon emitted by human activities through fossil fuels burning and land use/land cover change. Thus, the carbon uptake by forests reduces the rate at which carbon accumulates in the atmosphere. However, the sensitivity of near real-time MODIS gross primary productivity(GPP) product is directly constrained by uncertainties in the modeling process, especially in complicated forest ecosystems. Although there have been plenty of studies to verify MODIS GPP with ground-based measurements using the eddy covariance(EC) technique, few have comprehensively validated the performance of MODIS estimates(Collection 5) across diverse forest types. Therefore, the present study examined the degree of correspondence between MODIS-derived GPP and EC-measured GPP at seasonal and interannual time scales for the main forest ecosystems, including evergreen broadleaf forest(EBF), evergreen needleleaf forest(ENF), deciduous broadleaf forest(DBF), and mixed forest(MF) relying on 16 flux towers with a total of 68 site-year datasets. Overall, site-specific evaluation of multi-year mean annual GPP estimates indicates that the current MOD17A2 product works highly effectively for MF and DBF, moderately effectively for ENF, and ineffectively for EBF. Except for tropical forest, MODIS estimates could capture the broad trends of GPP at 8-day time scale for all other sites surveyed. On the annual time scale, the best performance was observed in MF, followed by ENF, DBF, and EBF. Trend analyses also revealed the poor performance of MODIS GPP product in EBF and DBF. Thus, improvements in the sensitivity of MOD17A2 to forest productivity require continued efforts.展开更多
Variations in net ecosystem exchange(NEE)of carbon dioxide,and the variables influencing it,at woodland sites over multiple years determine the long term performance of those sites as carbon sinks.In this study,weekly...Variations in net ecosystem exchange(NEE)of carbon dioxide,and the variables influencing it,at woodland sites over multiple years determine the long term performance of those sites as carbon sinks.In this study,weekly-averaged data from two AmeriFlux sites in North America of evergreen woodland,in different climatic zones and with distinct tree and understory species,are evaluated using four multi-linear regression(MLR)and seven machine learning(ML)models.The site data extend over multiple years and conform to the FLUXNET2015 pre-processing pipeline.Twenty influencing variables are considered for site CA-LP1 and sixteen for site US-Mpj.Rigorous k-fold cross validation analysis verifies that all eleven models assessed generate reproducible NEE predictions to varying degrees of accuracy.At both sites,the best performing ML models(support vector regression(SVR),extreme gradient boosting(XGB)and multi-layer perceptron(MLP))substantially outperform the MLR models in terms of their NEE prediction performance.The ML models also generate predicted versus measured NEE distributions that approximate cross-plot trends passing through the origin,confirming that they more realistically capture the actual NEE trend.MLR and ML models assign some level of importance to all influential variables measured but their degree of influence varies between the two sites.For the best performing SVR models,at site CA-LP1,variables air temperature,shortwave radiation outgoing,net radiation,longwave radiation outgoing,shortwave radiation incoming and vapor pressure deficit have the most influence on NEE predictions.At site US-Mpj,variables vapor pressure deficit,shortwave radiation incoming,longwave radiation incoming,air temperature,photosynthetic photon flux density incoming,shortwave radiation outgoing and precipitation exert the most influence on the model solutions.Sensible heat exerts very low influence at both sites.The methodology applied successfully determines the relative importance of influential variables in determining weekly NEE trends at both conifer woodland sites studied.展开更多
A methodology integrating correlation,regression(MLR),machine learning(ML),and pattern analysis of long-term weekly net ecosystem exchange(NEE)datasets are applied to four deciduous broadleaf forest(DBF)sites forming ...A methodology integrating correlation,regression(MLR),machine learning(ML),and pattern analysis of long-term weekly net ecosystem exchange(NEE)datasets are applied to four deciduous broadleaf forest(DBF)sites forming part of the AmeriFlux(FLUXNET2015)database.Such analysis effectively characterizes and distinguishes those DBF sites for which long-term NEE patterns can be accurately predicted using the recorded environmental variables,from those sites cannot be so delineated.Comparisons of twelve NEE prediction models(5 MLR;7 ML),using multi-fold cross-validation analysis,reveal that support vector regression generates the most accurate and reliable predictions for each site considered,based on fits involving between 16 and 24 available environmental variables.SVR can accurately predict NEE for datasets for DBF sites US-MMS and US-MOz,but fail to reliably do so for sites CA-Cbo and MX-Tes.For the latter two sites the predicted versus recorded NEE weekly data follow a Y≠X pattern and are characterized by rapid fluctuations between low and high NEE values across leaf-on seasonal periods.Variable influences on NEE,determined by their importance to MLR and ML model solutions,identify distinctive sets of the most and least influential variables for each site studied.Such information is valuable for monitoring and modelling the likely impacts of changing climate on the ability of these sites to serve as long-term carbon sinks.The periodically oscillating NEE weekly patterns distinguished for sites CA-Cbo and MX-Tes are not readily explained in terms of the currently recorded environmental variables.More detailed analysis of the biological processes at work in the forest understory and soil at these sites are recommended to determine additional suitable variables to measure that might better explain such fluctuations.展开更多
The eddy covariance technique has emerged as an important tool to directly measure carbon dioxide, water vapor and heat fluxes between the terrestrial ecosystem and the atmosphere after a long history of fundamental r...The eddy covariance technique has emerged as an important tool to directly measure carbon dioxide, water vapor and heat fluxes between the terrestrial ecosystem and the atmosphere after a long history of fundamental research and technological developments. With the realization of regional networks of flux measurements in North American, European, Asia, Brazil, Australia and Africa, a global-scale network of micrometeorological flux measurement (FLUXNET) was established in 1998. FLUXNET has made great progresses in investigating the environmental mechanisms controlling carbon and water cycles, quantifying spatial-temporal patterns of carbon budget and seeking the "missing carbon sink" in global terrestrial ecosystems in the past ten years. The global-scale flux measurement also built a platform for international communication in the fields of resource, ecology and environment sciences. With the continuous development of flux research, FLUXNET will introduce and explore new techniques to extend the application fields of flux measurement and to answer questions in the fields of bio-geography, eco-hydrology, meteorology, climate change, remote sensing and modeling with eddy covariance flux data. As an important part of FLUXNET, ChinaFLUX has made significant progresses in the past three years on the methodology and technique of eddy covariance flux measurement, on the responses of CO2 and H2O exchange between the terrestrial ecosystem and the atmosphere to environmental change, and on flux modeling development. Results showed that the major forests on the North-South Transect of Eastern China (NSTEC) were all carbon sinks during 2003 to 2005, and the alpine meadows on the Tibet Plateau were also small carbon sinks. However, the reserved natural grassland, Leymus chinensis steppe in Inner Mongolia, was a carbon source. On a regional scale, temperature and precipitation are the primary climatic factors that determined the carbon balance in major terrestrial ecosystems in China. Finally, the current research emphasis and future directions of ChinaFLUX were presented. By combining flux network and terrestrial transect, ChinaFLUX will develop integrated research with multi-scale, multi-process, multi-subject observations, placing emphasis on the mechanism and coupling relationships between water, carbon and nitrogen cycles in terrestrial ecosystems.展开更多
Evapotranspiration(ET)is a pivotal process for ecosystem water budgets and accounts for a substantial portion of the global energy balance.In this paper,the exited actual ETmain datasets in global scale,and the global...Evapotranspiration(ET)is a pivotal process for ecosystem water budgets and accounts for a substantial portion of the global energy balance.In this paper,the exited actual ETmain datasets in global scale,and the global ET modeling and estimates were focused on discussion.The Source energy balance(SEB)models,empirical models and other process-based models are summarized.Accuracy for ET estimates by SEBmodels highly depends on accurate surface temperature retrieval,and SEB models are hard to apply in large heterogeneous surface.The Penman-Monteith(PM)equations are thought to be with considerable sound mechanism.However,it involves large number of parameters,which are not all global available.A simplified PM equation by Priestley and Taylor(PT)is found to perform well on well-watered surface.For both PM and PT equations in estimating ET,the key is to consider the constraint from surface resistance primarily water stress.Empiricalmodels are simple but the accuracy of which highly depends on training samples.Coupling satellite data into ET models can improve ET estimates with higher resolution spatiotemporal information inputs;However,finding the most proper way to estimate global ET remains problematic.Several reasons for this issue are also analyzed in this review.展开更多
文摘准确估算陆地生态系统呼吸(Ecosystem respiration,RE)对全球陆地生态系统碳收支研究具有重要意义.模型模拟是估算陆地RE变化的一种常用手段.然而目前陆地生态系统过程模型的RE模拟尚未得到充分验证.基于耦合模式比较计划第五阶段(Coupled Model Intercomparison Project Phase 5,CMIP5)的通用陆面模型(Community land model,CLM)RE模拟结果和全球通量网(FLUXNET)66个站点的涡度相关通量观测数据(277条站点年数据)评估CLM模型对RE的模拟效果.结果表明:(1)在空间尺度上,CLM低估了高纬度站点RE,高估了低纬度站点RE,但高纬度低估量更大导致空间格局整体低估(相对误差为-3.56%).(2)在时间尺度上,CLM模型基本捕捉了RE的年际和季节变化,相关系数分别为0.60(P < 0.001)和0.63(P < 0.001);CLM低估年尺度和月尺度的RE(以C计),绝对误差分别是-182.21 g m-2 a-1、-120.16 g m-2 mon-1,相对误差分别是-17.84%、-10.60%.(3)CLM模型对不同植被功能型的RE模拟效果不同,由优及差依次为混交林、常绿针叶林、草地、农田、落叶阔叶林、常绿阔叶林.本研究在时空尺度上量化了CLM模型的生态系统呼吸模拟误差,并分析了土壤呼吸Q10和MRbase参数以及土壤碳库模拟等因素的影响,可为CLM模型的生态系统呼吸模块参数优化提供依据,进而提升其模拟精度.
基金supported by the R&D Special Fund for Nonprofit Industry (Meteorology) (Grant Nos. GYHY200706025, GYHY201206013 and GYHY201306066)
文摘Given the crucial role of land surface processes in global and regional climates, there is a pressing need to test and verify the performance of land surface models via comparisons to observations. In this study, the eddy covariance measurements from 20 FLUXNET sites spanning more than 100 site-years were utilized to evaluate the performance of the Common Land Model （CoLM） over different vegetation types in various climate zones. A decomposition method was employed to separate both the observed and simulated energy fluxes, i.e., the sensible heat flux, latent heat flux, net radiation, and ground heat flux, at three timescales ranging from stepwise （30 rain） to monthly. A comparison between the simulations and observations indicated that CoLM produced satisfactory simulations of all four energy fluxes, although the different indexes did not exhibit consistent results among the different fluxes, A strong agreement between the simulations and observations was found for the seasonal cycles at the 20 sites, whereas CoLM underestimated the latent heat flux at the sites with distinct dry and wet seasons, which might be associated with its weakness in simulating soil water during the dry season. CoLM cannot explicitly simulate the midday depression of leaf gas exchange, which may explain why CoLM also has a maximum diurnal bias at noon in the summer. Of the eight selected vegetation types analyzed, CoLM performs best for evergreen broadleaf forests and worst for croplands and wetlands.
文摘模式评估是模式发展中的重要一环。本文利用来自FLUXNET2015数据集的30个站点的涡动相关系统观测数据,重点关注能量通量,对通用陆面模式(Common Land Model version 2014,CoLM2014)在不同典型下垫面的模拟能力进行评估。结果表明,模式总体上能抓住感热、潜热和净辐射通量在日、季节和年平均等不同时间尺度上的变化特征,对感热、潜热和净辐射通量都有较好的模拟能力,净辐射的模拟效果最好,潜热通量次之。季节变化模拟中,感热、潜热通量在夏季不同植被型下站点的空间离散程度大于冬季,不同站点间模拟效果相差较大,净辐射多站点标准差变化幅度要小于感热、潜热,不同站点间模拟效果偏差较小。CoLM在常绿针叶林、稀树林地、草地、农田模拟感热、潜热通量的效果相对较好,在永久湿地、落叶阔叶林下模拟感热通量较差。本研究对CoLM2014在未来的改进和发展中提供了有用的参考。
基金funded by the West Light Scholar of the Chinese Academy of Sciences(xbzg-zdsys-202202)the Natural Science Foundation of Henan(Grant No.232300420165)Integrated Scientific Investigation of the North-South Transitional Zone of China(2017FY100900)。
文摘Surface-latent heat(LE)and sensible heat(SH)fluxes play a pivotal role in governing hydrological,biological,geochemical,and ecological processes on the land surface in the Tibetan Plateau.However,to accurately assess and understand the spatial distribution of LE and SH fluxes across different underlying surfaces,it is crucial to verify the validity and reliability of ERA-5,GLDAS,and MODIS data against ground measurements obtained from the Flux Net micrometeorological tower network.This study analyzed the spatial patterns of LE and SH over the Tibetan Plateau using data from ERA-5,GLDAS,and MODIS.The results were compared with ground measurements from Flux Net tower observations on different underlying surfaces,and five statistical parameters(Pearson's r,LR slope,RMSE,MBE,and MAE)were used to validate the data.The results showed that:(1)MODIS LE data and ERA-5 SH data exhibited the closest agreement with ground observations,as indicated by their lowest root mean square error and mean bias area values.(2)The accuracy of ERA-5 SH was the highest in meadows and steppes,while GLDAS SH performed optimally in shrublands.Notably,MODIS LE consistently outperformed the other datasets across all vegetation types.(3)The spatial distribution of LE and SH displayed considerable heterogeneity,contingent upon the specific data sources and underlying surfaces.Notably,there was a contrasting trend between GLDAS and ERA-5,as well as MODIS,in terms of SH distribution in the shrubland.In shrublands and meadows,MODIS SH and LE exhibited more pronounced changes than ERA-5 and GLDAS.Additionally,ERA-5 SH demonstrated the opposite variation in meadow and steppe regions compared to GLDAS and MODIS.
基金the National Natural Basic Research Program (" 973" Program) of China, under contract No. 2011CB403504 and No. 2011CB403501the Marine Science Foundation for Young Scientists of State Oceanic Administration of China No. 2012221+1 种基金the National Natural Science Foundation of China under contract No. 40806003the Knowledge Innovation Project for Distinguished Young Scholar of the Chinese Academy of Sciences of China under contract No.KZCX2-EW-QN203
文摘The seasonal variabilities of a latent-heat flux （LHF）, a sensible-heat flux （SHF） and net surface heat flux are examined in the northern South China Sea （NSCS）, including their spatial characteristics, using the in situ data collected by ship from 2006 to 2007. The spatial distribution of LHF in the NSCS is mostly controlled by wind in summer and autumn owing to the lower vertical gradient of air humidity, but is influenced by both wind and near-surface air humidity vertical gradient in spring and winter. The largest area-averaged LHF is in autumn, with the value of 197.25 W/m 2 , followed by that in winter; the third and the forth are in summer and spring, respectively. The net heat flux is positive in spring and summer, so the NSCS absorbs heat; and the solar shortwave radiation plays the most important role in the surface heat budget. In autumn and winter, the net heat flux is negative in most of the observation region, so the NSCS loses heat; and the LHF plays the most important role in the surface heat budget. The net heating is mainly a result of the offsetting between heating due to the shortwave radiation and cooling due to the LHF and the upward （outgoing） long wave radiation, since the role of SHF is negligible. The ratio of the magnitudes of the three terms （shortwave radiation to LHF to long-wave radiation） averaged over the entire year is roughly 3：2：1, and the role of SHF is the smallest.
基金Under the auspices of National Natural Science Foundation of China(No.41401221,41271500,41201496)Opening Fund of Key Laboratory of Poyang Lake Wetland and Watershed Research(Jiangxi Normal University),Ministry of Education,China(No.PK2014002)
文摘As an important product of Moderate Resolution Imaging Spectroradiometer(MODIS), MOD17A2 provides dramatic improvements in our ability to accurately and continuously monitor global terrestrial primary production, which is also significant in effort to advance scientific research and eco-environmental management. Over the past decades, forests have moderated climate change by sequestrating about one-quarter of the carbon emitted by human activities through fossil fuels burning and land use/land cover change. Thus, the carbon uptake by forests reduces the rate at which carbon accumulates in the atmosphere. However, the sensitivity of near real-time MODIS gross primary productivity(GPP) product is directly constrained by uncertainties in the modeling process, especially in complicated forest ecosystems. Although there have been plenty of studies to verify MODIS GPP with ground-based measurements using the eddy covariance(EC) technique, few have comprehensively validated the performance of MODIS estimates(Collection 5) across diverse forest types. Therefore, the present study examined the degree of correspondence between MODIS-derived GPP and EC-measured GPP at seasonal and interannual time scales for the main forest ecosystems, including evergreen broadleaf forest(EBF), evergreen needleleaf forest(ENF), deciduous broadleaf forest(DBF), and mixed forest(MF) relying on 16 flux towers with a total of 68 site-year datasets. Overall, site-specific evaluation of multi-year mean annual GPP estimates indicates that the current MOD17A2 product works highly effectively for MF and DBF, moderately effectively for ENF, and ineffectively for EBF. Except for tropical forest, MODIS estimates could capture the broad trends of GPP at 8-day time scale for all other sites surveyed. On the annual time scale, the best performance was observed in MF, followed by ENF, DBF, and EBF. Trend analyses also revealed the poor performance of MODIS GPP product in EBF and DBF. Thus, improvements in the sensitivity of MOD17A2 to forest productivity require continued efforts.
文摘Variations in net ecosystem exchange(NEE)of carbon dioxide,and the variables influencing it,at woodland sites over multiple years determine the long term performance of those sites as carbon sinks.In this study,weekly-averaged data from two AmeriFlux sites in North America of evergreen woodland,in different climatic zones and with distinct tree and understory species,are evaluated using four multi-linear regression(MLR)and seven machine learning(ML)models.The site data extend over multiple years and conform to the FLUXNET2015 pre-processing pipeline.Twenty influencing variables are considered for site CA-LP1 and sixteen for site US-Mpj.Rigorous k-fold cross validation analysis verifies that all eleven models assessed generate reproducible NEE predictions to varying degrees of accuracy.At both sites,the best performing ML models(support vector regression(SVR),extreme gradient boosting(XGB)and multi-layer perceptron(MLP))substantially outperform the MLR models in terms of their NEE prediction performance.The ML models also generate predicted versus measured NEE distributions that approximate cross-plot trends passing through the origin,confirming that they more realistically capture the actual NEE trend.MLR and ML models assign some level of importance to all influential variables measured but their degree of influence varies between the two sites.For the best performing SVR models,at site CA-LP1,variables air temperature,shortwave radiation outgoing,net radiation,longwave radiation outgoing,shortwave radiation incoming and vapor pressure deficit have the most influence on NEE predictions.At site US-Mpj,variables vapor pressure deficit,shortwave radiation incoming,longwave radiation incoming,air temperature,photosynthetic photon flux density incoming,shortwave radiation outgoing and precipitation exert the most influence on the model solutions.Sensible heat exerts very low influence at both sites.The methodology applied successfully determines the relative importance of influential variables in determining weekly NEE trends at both conifer woodland sites studied.
文摘A methodology integrating correlation,regression(MLR),machine learning(ML),and pattern analysis of long-term weekly net ecosystem exchange(NEE)datasets are applied to four deciduous broadleaf forest(DBF)sites forming part of the AmeriFlux(FLUXNET2015)database.Such analysis effectively characterizes and distinguishes those DBF sites for which long-term NEE patterns can be accurately predicted using the recorded environmental variables,from those sites cannot be so delineated.Comparisons of twelve NEE prediction models(5 MLR;7 ML),using multi-fold cross-validation analysis,reveal that support vector regression generates the most accurate and reliable predictions for each site considered,based on fits involving between 16 and 24 available environmental variables.SVR can accurately predict NEE for datasets for DBF sites US-MMS and US-MOz,but fail to reliably do so for sites CA-Cbo and MX-Tes.For the latter two sites the predicted versus recorded NEE weekly data follow a Y≠X pattern and are characterized by rapid fluctuations between low and high NEE values across leaf-on seasonal periods.Variable influences on NEE,determined by their importance to MLR and ML model solutions,identify distinctive sets of the most and least influential variables for each site studied.Such information is valuable for monitoring and modelling the likely impacts of changing climate on the ability of these sites to serve as long-term carbon sinks.The periodically oscillating NEE weekly patterns distinguished for sites CA-Cbo and MX-Tes are not readily explained in terms of the currently recorded environmental variables.More detailed analysis of the biological processes at work in the forest understory and soil at these sites are recommended to determine additional suitable variables to measure that might better explain such fluctuations.
文摘The eddy covariance technique has emerged as an important tool to directly measure carbon dioxide, water vapor and heat fluxes between the terrestrial ecosystem and the atmosphere after a long history of fundamental research and technological developments. With the realization of regional networks of flux measurements in North American, European, Asia, Brazil, Australia and Africa, a global-scale network of micrometeorological flux measurement (FLUXNET) was established in 1998. FLUXNET has made great progresses in investigating the environmental mechanisms controlling carbon and water cycles, quantifying spatial-temporal patterns of carbon budget and seeking the "missing carbon sink" in global terrestrial ecosystems in the past ten years. The global-scale flux measurement also built a platform for international communication in the fields of resource, ecology and environment sciences. With the continuous development of flux research, FLUXNET will introduce and explore new techniques to extend the application fields of flux measurement and to answer questions in the fields of bio-geography, eco-hydrology, meteorology, climate change, remote sensing and modeling with eddy covariance flux data. As an important part of FLUXNET, ChinaFLUX has made significant progresses in the past three years on the methodology and technique of eddy covariance flux measurement, on the responses of CO2 and H2O exchange between the terrestrial ecosystem and the atmosphere to environmental change, and on flux modeling development. Results showed that the major forests on the North-South Transect of Eastern China (NSTEC) were all carbon sinks during 2003 to 2005, and the alpine meadows on the Tibet Plateau were also small carbon sinks. However, the reserved natural grassland, Leymus chinensis steppe in Inner Mongolia, was a carbon source. On a regional scale, temperature and precipitation are the primary climatic factors that determined the carbon balance in major terrestrial ecosystems in China. Finally, the current research emphasis and future directions of ChinaFLUX were presented. By combining flux network and terrestrial transect, ChinaFLUX will develop integrated research with multi-scale, multi-process, multi-subject observations, placing emphasis on the mechanism and coupling relationships between water, carbon and nitrogen cycles in terrestrial ecosystems.
基金This work was supported by the CAS Strategic Priority Research CAS[No.XDA19030402]National Natural Science Foundation of China[31671585,41871253]+1 种基金This work was funded by the CAS Strategic Priority Research Program(No.XDA19030402)the National Natural Science Foundation of China(No.31671585,41871253).
文摘Evapotranspiration(ET)is a pivotal process for ecosystem water budgets and accounts for a substantial portion of the global energy balance.In this paper,the exited actual ETmain datasets in global scale,and the global ET modeling and estimates were focused on discussion.The Source energy balance(SEB)models,empirical models and other process-based models are summarized.Accuracy for ET estimates by SEBmodels highly depends on accurate surface temperature retrieval,and SEB models are hard to apply in large heterogeneous surface.The Penman-Monteith(PM)equations are thought to be with considerable sound mechanism.However,it involves large number of parameters,which are not all global available.A simplified PM equation by Priestley and Taylor(PT)is found to perform well on well-watered surface.For both PM and PT equations in estimating ET,the key is to consider the constraint from surface resistance primarily water stress.Empiricalmodels are simple but the accuracy of which highly depends on training samples.Coupling satellite data into ET models can improve ET estimates with higher resolution spatiotemporal information inputs;However,finding the most proper way to estimate global ET remains problematic.Several reasons for this issue are also analyzed in this review.
