基于FLUXNET数据集对陆面模式CoLM能量通量的单点评估

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。

Machine Learning and Regression Analysis Reveal Different Patterns of Influence on Net Ecosystem Exchange at Two Conifer Woodland Sites

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.

Machine Learning and Pattern Analysis Identify Distinctive Influences from Long-term Weekly Net Ecosystem Exchange at Four Deciduous Woodland Locations

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.