Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations

Abstract

[1] We upscaled FLUXNET observations of carbon dioxide, water, and energy fluxes to the global scale using the machine learning technique, model tree ensembles (MTE). We trained MTE to predict site-level gross primary productivity (GPP), terrestrial ecosystem respiration (TER), net ecosystem exchange (NEE), latent energy (LE), and sensible heat (H) based on remote sensing indices, climate and meteorological data, and information on land use. We applied the trained MTEs to generate global flux fields at a 0.5°× 0.5°spatial resolution and a monthly temporal resolution from 1982 to 2008. Cross-validation analyses revealed good performance of MTE in predicting among-site flux variability with modeling efficiencies (MEf) between 0.64 and 0.84, except for NEE (MEf = 0.32). Performance was also good for predicting seasonal patterns (MEf between 0.84 and 0.89, except for NEE (0.64)). By comparison, predictions of monthly anomalies were not as strong (MEf between 0.29 and 0.52). Improved accounting of disturbance and lagged environmental effects, along with improved characterization of errors in the training data set, would contribute most to further reducing uncertainties. Our global estimates of LE (158 ± 7 J × 10 18 yr −1 ), H (164 ± 15 J × 10 18 yr −1), and GPP (119 ± 6 Pg C yr ) were similar to independent estimates. Our global TER estimate (96 ± 6 Pg C yr −1 ) was likely underestimated by 5-10%. Hot spot regions of interannual variability in carbon fluxes occurred in semiarid to semihumid regions and were controlled by moisture supply. Overall, GPP was more important to interannual variability in NEE than TER. Our empirically derived fluxes may be used for calibration and evaluation of land surface process models and for exploratory and diagnostic assessments of the biosphere.

Spatial variation in annual actual evapotranspiration of terrestrial ecosystems in China: Results from eddy covariance measurements

“…Our analyses confirmed the latitudinal pattern of AET and its significant relationships with MAP, MAT and vegetation types reported in previous studies (Jung et al, 2011;Brümmer et al, 2012;Xiao et al, 2013). Given the pronounced covariation or interactive effects among climatic factors (e.g., MAT vs. R n , MAT vs, VPD, MAP vs. RH) and vegetation attributes (e.g., MAT vs. LAI, MAP vs. LAI) (Law et al, 2002), it is not surprised that the spatial patterns of AET significantly correlated with that of R n , VPD, RH and LAI.…”

“…Previous studies have demonstrated that the spatial variation in LAI could affect the spatial patterns of different components of AET. For example, evaporation of canopy interception depended on LAI (Jung et al, 2011), and the proportion of AET that evaporated from soil surfaces increased as LAI decreased (Law et al, 2002). Thus, it is seemly that LAI regulates the spatial patterns of AET mainly by influencing the proportion of different AET components (i.e., vegetation transpiration, soil evaporation and evaporation of canopy interception).…”

“…The five global ET products included a MODIS ET product (MOD16) (Mu et al, 2011), a merged ET synthesis product (MUE13) (Mueller et al, 2013), a normalized differential vegetation index (NDVI)-based ET product (ZHA10) , a product based on a machine-learning algorithm (i.e., model tree ensemble) (JUN11) (Jung et al, 2011), and a product based on water balance approach (ZEN14) (Zeng et al, 2014). Their brief information was summarized in Table 2.…”

Groundwater-dependent ecosystems: recent insights from satellite and field-based studies

“…phenology, LAI) and function (e.g. ET, gross primary productivity) has become increasingly sophisticated (Glenn et al, 2010;Yuan et al, 2010;Jung et al, 2011;Rossini et al, 2012;Kanniah et al, 2013;Ma et al, 2013;Nagler et al, 2013) and increasingly applied to realworld applications of water resources management (Scott et al, 2008;Glenn et al, 2010;Barron et al, 2014;Doody et al, 2014). Remote sensing (RS) provides a robust and spatially explicit means to assess not only vegetation structure and function but also relationships amongst these and climate variables.…”

Primary estimation of Chinese terrestrial carbon sequestration during 2001–2010

“…These technologies have improved continually with their own appropriate spatiotemporal scales, and researchers have also performed meta-analyses based on multi-source data from different approaches [25,26]. Additionally, comprehensive assessments were conducted on the ecosystem productivity or carbon source/sinks at national, continental and global scales by data-model fusion [7,27,28].…”