In the last 20 years, the FLUXNET network provided unique
measurements of CO2, energy and other greenhouse gas exchanges between
ecosystems and atmosphere measured with the eddy covariance technique. ...These
data have been widely used in different and heterogeneous applications, and
FLUXNET became a reference source of information not only for ecological
studies but also in modeling and remote sensing applications. The data are, in general, collected, processed and shared by regional networks or by single
sites, and for this reason it is difficult for users interested in analyses
involving multiple sites to easily access a coherent and standardized
dataset. For this reason, periodic FLUXNET collections have been released in
the last 15 years, every 5 to 10 years, with data standardized and shared
under the same data use policy. However, the new tools available for data
analysis and the need to constantly monitor the relations between ecosystem
behavior and climate change require a reorganization of FLUXNET in order
to increase the data interoperability, reduce the delay in the data sharing
and facilitate the data use, all this while keeping in mind the great effort made
by the site teams to collect these unique data and respecting the different
regional and national network organizations and data policies. Here a
proposal for a new organization of FLUXNET is presented with the aim of
stimulating a discussion for the needed developments. In this new scheme, the
regional and national networks become the pillars of the global initiative,
organizing clusters and becoming responsible for the processing, preparation
and distribution of datasets that users will be able to access in real time and
with a machine-to-machine tool, obtaining always the most updated collection
possible but keeping a high standardization and common data policy. This will
also lead to an increase in the FAIRness (Findability, Accessibility,
Interoperability and Reusability) of the FLUXNET data that will ensure a
larger impact of the unique data produced and a proper data management and
traceability.
Although a key driver of Earth's climate system, global land-atmosphere energy fluxes are poorly constrained. Here we use machine learning to merge energy flux measurements from FLUXNET eddy ...covariance towers with remote sensing and meteorological data to estimate global gridded net radiation, latent and sensible heat and their uncertainties. The resulting FLUXCOM database comprises 147 products in two setups: (1) 0.0833° resolution using MODIS remote sensing data (RS) and (2) 0.5° resolution using remote sensing and meteorological data (RS + METEO). Within each setup we use a full factorial design across machine learning methods, forcing datasets and energy balance closure corrections. For RS and RS + METEO setups respectively, we estimate 2001-2013 global (±1 s.d.) net radiation as 75.49 ± 1.39 W m
and 77.52 ± 2.43 W m
, sensible heat as 32.39 ± 4.17 W m
and 35.58 ± 4.75 W m
, and latent heat flux as 39.14 ± 6.60 W m
and 39.49 ± 4.51 W m
(as evapotranspiration, 75.6 ± 9.8 × 10
km
yr
and 76 ± 6.8 × 10
km
yr
). FLUXCOM products are suitable to quantify global land-atmosphere interactions and benchmark land surface model simulations.
The accurate quantification of carbon fluxes at continental spatial scale is important for future policy decisions in the context of global climate change. However, many elements contribute to the ...uncertainty of such estimate. In this study, the uncertainties of eight days gross primary production (GPP) predicted by Random Forest (RF) machine learning models were analysed at the site, ecosystem and European spatial scales. At the site level, the uncertainties caused by the missing of key drivers were evaluated. The most accurate predictions of eight days GPP were obtained when all available drivers were used (Pearson's correlation coefficient, ρ~0.84; Root Mean Square Error (RMSE)~1.8g C m−2 d−1). However, when predictions were based on only remotely sensed data the accuracy was close to the optimum (ρ~0.8; RMSE~1.9g C m−2 d−1) and to a commonly used light use efficiency model (MOD17) with parameters optimised for the applied study sites (the MOD17+, ρ~0.79; RMSE~2.04g C m−2 d−1). Remotely sensed data were key drivers for the accurate prediction of GPP in ecosystems with high variability of green biomass over the phenological cycle (e.g., deciduous broad-leaved forests) or highly affected by the human management (e.g. croplands). In contrast, in the ecosystems with low variability of greenness (e.g., evergreen broad-leaved forests), the predictions were poor when meteorological information were not used. At a European spatial scale, when modelled grids of meteorological, land cover and fPAR data were used as inputs, the propagation of their uncertainty, not accounted in the models training, had significant effects on the uncertainty of the mean annual GPP. At this scale, the effects of meteorological uncertainty were higher than the misclassification error. These findings suggested that a strategy based on satellite-measured data could be a favourable improvement for the spatial upscaling of GPP, because avoiding the propagation of the uncertainties of the modelled grids.
•We train 10 Random Forest (RF) to spatial upscale Gross Primary Production (GPP).•RF that uses only remote sensing (RS) data has a performance similar to the best RF.•At European scale the uncertainty of prediction due to modelled drivers is high.•The uncertainty of European GPP is mainly due to the meteorological reanalysis.•Model driven by only measured RS data avoids the uncertainty of modelled drivers.
To answer new scientific and ecological questions and monitor multiple forest changes, a fine scale characterization of these ecosystems is needed, and could imply the mapping of specific species, of ...detailed forest types, and of functional composition. This characterization can be now provided by the novel Earth Observation tools. This study aims to contribute to understanding the innovation in forest and ecological research that can be brought in by advanced remote sensing instruments, and proposes the guild mapping approach as a tool to efficiently monitor the varied tropical forest resources. We evaluated, in tropical Ghanaian forests, the ability of airborne hyperspectral and simulated multispectral Sentinel-2 data, and derived vegetation indices and textures, to: distinguish between two different forest types; to discriminate among selected dominant species; and to separate trees species grouped according to their functional guilds: Pioneer, Non Pioneer Light Demanding, and Shade Bearer. We then produced guild classification maps for each area using hyperspectral data. Our results showed that with both hyperspectral and simulated Sentinel-2 data these discrimination tasks can be successfully accomplished. Results also stressed the importance of texture features, especially if using the lower spectral and spatial Sentinel-2 resolution data, and highlighted the important role of the new Sentinel-2 data for ecological monitoring. Classification results showed a statistically significant improvement in overall accuracy using Support Vector Machine, over Maximum Likelihood approach. We proposed the functional guilds mapping as an innovative approach to: (i) monitor compositional changes, especially with respect to the effects of global climate change on forests, and particularly in the tropical biome where the occurrence of hundreds of species prevents mapping activities at species level; (ii) support large-scale forest inventories. The imminent Sentinel-2 data could serve to open the road for the development of new concepts and methods in forestry and ecological research.
•We used hyperspectral and simulated Sentinel-2 data in tropical forests.•We tested data for discrimination of forest types, species, and functional guilds.•Guilds maps were produced using hyperspectral data.•We propose the guild mapping approach as a tool for tropical forest monitoring.•We showed high potential for ecological monitoring of Sentinel-2.
Great advances have been made in the last decade in quantifying and understanding the spatiotemporal patterns of terrestrial gross primary production (GPP) with ground, atmospheric, and space ...observations. However, although global GPP estimates exist, each data set relies upon assumptions and none of the available data are based only on measurements. Consequently, there is no consensus on the global total GPP and large uncertainties exist in its benchmarking. The objective of this review is to assess how the different available data sets predict the spatiotemporal patterns of GPP, identify the differences among data sets, and highlight the main advantages/disadvantages of each data set. We compare GPP estimates for the historical period (1990–2009) from two observation‐based data sets (Model Tree Ensemble and Moderate Resolution Imaging Spectroradiometer) to coupled carbon‐climate models and terrestrial carbon cycle models from the Fifth Climate Model Intercomparison Project and TRENDY projects and to a new hybrid data set (CARBONES). Results show a large range in the mean global GPP estimates. The different data sets broadly agree on GPP seasonal cycle in terms of phasing, while there is still discrepancy on the amplitude. For interannual variability (IAV) and trends, there is a clear separation between the observation‐based data that show little IAV and trend, while the process‐based models have large GPP variability and significant trends. These results suggest that there is an urgent need to improve observation‐based data sets and develop carbon cycle modeling with processes that are currently treated either very simplistically to correctly estimate present GPP and better quantify the future uptake of carbon dioxide by the world's vegetation.
Key Points
At global scale, direct measurements of GPP do not exist
Large uncertainties exist on terrestrial global GPP benchmarking
Models show large variability in mean global GPP estimates
Large interannual variations in the measured growth rate of atmospheric carbon dioxide (CO2) originate primarily from fluctuations in carbon uptake by land ecosystems13. It remains uncertain, ...however, to what extent temperature and water availability control the carbon balance of land ecosystems across spatial and temporal scales314. Here we use empirical models based on eddy covariance data15 and process-based models16,17 to investigate the effect of changes in temperature and water availability on gross primary productivity (GPP), terrestrial ecosystem respiration (TER) and net ecosystem exchange (NEE) at local and global scales. We find that water availability is the dominant driver of the local interannual variability in GPP and TER. To a lesser extent this is true also for NEE at the local scale, but when integrated globally, temporal NEE variability is mostly driven by temperature fluctuations. We suggest that this apparent paradox can be explained by two compensatory water effects. Temporal water-driven GPP and TER variations compensate locally, dampening water-driven NEE variability. Spatial water availability anomalies also compensate, leaving a dominant temperature signal in the year-to-year fluctuations of the land carbon sink. These findings help to reconcile seemingly contradictory reports regarding the importance of temperature and water in controlling the interannual variability of the terrestrial carbon balance36,9,11,12,14. Our study indicates that spatial climate covariation drives the global carbon cycle response.
Recently flux tower data have become available for a variety of ecosystems under different climatic and edaphic conditions. Although Flux tower data represent point measurements with a footprint of ...typically 1 km × 1 km they can be used to validate models and to spatialize biospheric fluxes at regional and continental scales. In this paper we present a study where biospheric flux data collected in the EUROFLUX project were used to train a neural network simulator to provide spatial (1 km × 1 km) and temporal (weekly) estimates of carbon fluxes of European forests at continental scale. The novelty of the approach is that flux data were used to constrain and parameterize the neural network structure using a limited number of input driving variables. The overall European carbon uptake from this analysis was 0.47 Gt C yr−1 with distinctive differences between boreal and temperate regions. The length of the growing season is longer in the south of Europe (about 32 weeks), compared with north and central Europe, which have a similar length‐growing season (about 27 weeks). A peak in respiration was depicted in spring at continental scale as a coherent signal which parallel the construction respiration increase at the onset of the season as usually shown by leaf level measurements.
The global land surface absorbs about a third of anthropogenic emissions each year, due to the difference between two key processes: ecosystem photosynthesis and respiration. Despite the importance ...of these two processes, it is not possible to measure either at the ecosystem scale during the daytime. Eddy-covariance measurements are widely used as the closest 'quasi-direct' ecosystem-scale observation from which to estimate ecosystem photosynthesis and respiration. Recent research, however, suggests that current estimates may be biased by up to 25%, due to a previously unaccounted for process: the inhibition of leaf respiration in the light. Yet the extent of inhibition remains debated, and implications for estimates of ecosystem-scale respiration and photosynthesis remain unquantified. Here, we quantify an apparent inhibition of daytime ecosystem respiration across the global FLUXNET eddy-covariance network and identify a pervasive influence that varies by season and ecosystem type. We develop partitioning methods that can detect an apparent ecosystem-scale inhibition of daytime respiration and find that diurnal patterns of ecosystem respiration might be markedly different than previously thought. The results call for the re-evaluation of global terrestrial carbon cycle models and also suggest that current global estimates of photosynthesis and respiration may be biased, some on the order of magnitude of anthropogenic fossil fuel emissions.
More than half of the solar energy absorbed by land surfaces is currently used to evaporate water. Climate change is expected to intensify the hydrological cycle and to alter evapotranspiration, with ...implications for ecosystem services and feedback to regional and global climate. Evapotranspiration changes may already be under way, but direct observational constraints are lacking at the global scale. Until such evidence is available, changes in the water cycle on land−a key diagnostic criterion of the effects of climate change and variability−remain uncertain. Here we provide a data-driven estimate of global land evapotranspiration from 1982 to 2008, compiled using a global monitoring network, meteorological and remote-sensing observations, and a machine-learning algorithm. In addition, we have assessed evapotranspiration variations over the same time period using an ensemble of process-based land-surface models. Our results suggest that global annual evapotranspiration increased on average by 7.1 ± 1.0 millimetres per year per decade from 1982 to 1997. After that, coincident with the last major El Niño event in 1998, the global evapotranspiration increase seems to have ceased until 2008. This change was driven primarily by moisture limitation in the Southern Hemisphere, particularly Africa and Australia. In these regions, microwave satellite observations indicate that soil moisture decreased from 1998 to 2008. Hence, increasing soil-moisture limitations on evapotranspiration largely explain the recent decline of the global land-evapotranspiration trend. Whether the changing behaviour of evapotranspiration is representative of natural climate variability or reflects a more permanent reorganization of the land water cycle is a key question for earth system science.
The Budyko framework elegantly reduces the complex spatial patterns of actual evapotranspiration and runoff to a general function of two variables: mean annual precipitation (MAP) and net radiation. ...While the methodology has first‐order skill, departures from a globally averaged curve can be significant and may be usefully attributed to additional controls such as vegetation type. This paper explores the magnitude of such departures as detected from flux tower measurements of ecosystem‐scale evapotranspiration, and investigates their attribution to site characteristics (biome, seasonal rainfall distribution, and frozen precipitation). The global synthesis (based on 167 sites with 764 tower‐years) shows smooth transition from water‐limited to energy‐limited control, broadly consistent with catchment‐scale relations and explaining 62% of the across site variation in evaporative index (the fraction of MAP consumed by evapotranspiration). Climate and vegetation types act as additional controls, combining to explain an additional 13% of the variation in evaporative index. Warm temperate winter wet sites (Mediterranean) exhibit a reduced evaporative index, 9% lower than the average value expected based on dryness index, implying elevated runoff. Seasonal hydrologic surplus explains a small but significant fraction of variance in departures of evaporative index from that expected for a given dryness index. Surprisingly, grasslands on average have a higher evaporative index than forested landscapes, with 9% more annual precipitation consumed by annual evapotranspiration compared to forests. In sum, the simple framework of supply‐ or demand‐limited evapotranspiration is supported by global FLUXNET observations but climate type and vegetation type are seen to exert sizeable additional controls.
Key Points
Global FLUXNET data support Budyko hypothesis of surface water balance controls
Climate type, vegetation type exert additional control on evapotranspiration
Grasslands exhibit higher evaporative index than forests
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