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1
Haughton, Ned, Gab Abramowitz, Martin G. De Kauwe, and Andy J. Pitman. "Does predictability of fluxes vary between FLUXNET sites?" Biogeosciences 15, no. 14 (July 25, 2018): 4495–513. http://dx.doi.org/10.5194/bg-15-4495-2018.
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Abstract. The FLUXNET dataset contains eddy covariance measurements from across the globe and represents an invaluable estimate of the fluxes of energy, water, and carbon between the land surface and the atmosphere. While there is an expectation that the broad range of site characteristics in FLUXNET result in a diversity of flux behaviour, there has been little exploration of how predictable site behaviour is across the network. Here, 155 datasets with 30 min temporal resolution from the Tier 1 of FLUXNET 2015 were analysed in a first attempt to assess individual site predictability. We defined site uniqueness as the disparity in performance between multiple empirical models trained globally and locally for each site and used this along with the mean performance as measures of predictability. We then tested how strongly uniqueness was determined by various site characteristics, including climatology, vegetation type, and data quality. The strongest determinant of predictability appeared to be that drier sites tended to be more unique. We found very few other clear predictors of uniqueness across different sites, in particular little evidence that flux behaviour was well discretised by vegetation type. Data length and quality also appeared to have little impact on uniqueness. While this result might relate to our definition of uniqueness, we argue that our approach provides a useful basis for site selection in LSM evaluation, and we invite critique and development of the methodology.
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Zhang, Xiangxiang, Yongjiu Dai, Hongzhi Cui, Robert E. Dickinson, Siguang Zhu, Nan Wei, Binyan Yan, et al. "Evaluating common land model energy fluxes using FLUXNET data." Advances in Atmospheric Sciences 34, no. 9 (August 4, 2017): 1035–46. http://dx.doi.org/10.1007/s00376-017-6251-y.
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Blyth, Eleanor, John Gash, Amanda Lloyd, Matthew Pryor, Graham P. Weedon, and Jim Shuttleworth. "Evaluating the JULES Land Surface Model Energy Fluxes Using FLUXNET Data." Journal of Hydrometeorology 11, no. 2 (April 1, 2010): 509–19. http://dx.doi.org/10.1175/2009jhm1183.1.
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Abstract Surface energy flux measurements from a sample of 10 flux network (FLUXNET) sites selected to represent a range of climate conditions and biome types were used to assess the performance of the Hadley Centre land surface model (Joint U.K. Land Environment Simulator; JULES). Because FLUXNET data are prone systematically to undermeasure surface fluxes, the model was evaluated by its ability to partition incoming radiant energy into evaporation and how such partition varies with atmospheric evaporative demand at annual, seasonal, weekly, and diurnal time scales. The model parameters from the GCM configuration were used. The overall performance was good, although weaknesses in model performance were identified that are associated with the specification of the leaf area index and plant rooting depth, and the representation of soil freezing.
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Delwiche, Kyle B., Sara Helen Knox, Avni Malhotra, Etienne Fluet-Chouinard, Gavin McNicol, Sarah Feron, Zutao Ouyang, et al. "FLUXNET-CH<sub>4</sub>: a global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands." Earth System Science Data 13, no. 7 (July 29, 2021): 3607–89. http://dx.doi.org/10.5194/essd-13-3607-2021.
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Abstract. Methane (CH4) emissions from natural landscapes constitute roughly half of global CH4 contributions to the atmosphere, yet large uncertainties remain in the absolute magnitude and the seasonality of emission quantities and drivers. Eddy covariance (EC) measurements of CH4 flux are ideal for constraining ecosystem-scale CH4 emissions due to quasi-continuous and high-temporal-resolution CH4 flux measurements, coincident carbon dioxide, water, and energy flux measurements, lack of ecosystem disturbance, and increased availability of datasets over the last decade. Here, we (1) describe the newly published dataset, FLUXNET-CH4 Version 1.0, the first open-source global dataset of CH4 EC measurements (available at https://fluxnet.org/data/fluxnet-ch4-community-product/, last access: 7 April 2021). FLUXNET-CH4 includes half-hourly and daily gap-filled and non-gap-filled aggregated CH4 fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we (2) evaluate FLUXNET-CH4 representativeness for freshwater wetland coverage globally because the majority of sites in FLUXNET-CH4 Version 1.0 are freshwater wetlands which are a substantial source of total atmospheric CH4 emissions; and (3) we provide the first global estimates of the seasonal variability and seasonality predictors of freshwater wetland CH4 fluxes. Our representativeness analysis suggests that the freshwater wetland sites in the dataset cover global wetland bioclimatic attributes (encompassing energy, moisture, and vegetation-related parameters) in arctic, boreal, and temperate regions but only sparsely cover humid tropical regions. Seasonality metrics of wetland CH4 emissions vary considerably across latitudinal bands. In freshwater wetlands (except those between 20∘ S to 20∘ N) the spring onset of elevated CH4 emissions starts 3 d earlier, and the CH4 emission season lasts 4 d longer, for each degree Celsius increase in mean annual air temperature. On average, the spring onset of increasing CH4 emissions lags behind soil warming by 1 month, with very few sites experiencing increased CH4 emissions prior to the onset of soil warming. In contrast, roughly half of these sites experience the spring onset of rising CH4 emissions prior to the spring increase in gross primary productivity (GPP). The timing of peak summer CH4 emissions does not correlate with the timing for either peak summer temperature or peak GPP. Our results provide seasonality parameters for CH4 modeling and highlight seasonality metrics that cannot be predicted by temperature or GPP (i.e., seasonality of CH4 peak). FLUXNET-CH4 is a powerful new resource for diagnosing and understanding the role of terrestrial ecosystems and climate drivers in the global CH4 cycle, and future additions of sites in tropical ecosystems and site years of data collection will provide added value to this database. All seasonality parameters are available at https://doi.org/10.5281/zenodo.4672601 (Delwiche et al., 2021). Additionally, raw FLUXNET-CH4 data used to extract seasonality parameters can be downloaded from https://fluxnet.org/data/fluxnet-ch4-community-product/ (last access: 7 April 2021), and a complete list of the 79 individual site data DOIs is provided in Table 2 of this paper.
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van der Horst, Sophie V. J., Andrew J. Pitman, Martin G. De Kauwe, Anna Ukkola, Gab Abramowitz, and Peter Isaac. "How representative are FLUXNET measurements of surface fluxes during temperature extremes?" Biogeosciences 16, no. 8 (April 30, 2019): 1829–44. http://dx.doi.org/10.5194/bg-16-1829-2019.
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Abstract. In response to a warming climate, temperature extremes are changing in many regions of the world. Therefore, understanding how the fluxes of sensible heat, latent heat and net ecosystem exchange respond and contribute to these changes is important. We examined 216 sites from the open access Tier 1 FLUXNET2015 and free fair-use La Thuile data sets, focussing only on observed (non-gap-filled) data periods. We examined the availability of sensible heat, latent heat and net ecosystem exchange observations coincident in time with measured temperature for all temperatures, and separately for the upper and lower tail of the temperature distribution, and expressed this availability as a measurement ratio. We showed that the measurement ratios for both sensible and latent heat fluxes are generally lower (0.79 and 0.73 respectively) than for temperature measurements, and the measurement ratio of net ecosystem exchange measurements are appreciably lower (0.42). However, sites do exist with a high proportion of measured sensible and latent heat fluxes, mostly over the United States, Europe and Australia. Few sites have a high proportion of measured fluxes at the lower tail of the temperature distribution over very cold regions (e.g. Alaska, Russia) or at the upper tail in many warm regions (e.g. Central America and the majority of the Mediterranean region), and many of the world's coldest and hottest regions are not represented in the freely available FLUXNET data at all (e.g. India, the Gulf States, Greenland and Antarctica). However, some sites do provide measured fluxes at extreme temperatures, suggesting an opportunity for the FLUXNET community to share strategies to increase measurement availability at the tails of the temperature distribution. We also highlight a wide discrepancy between the measurement ratios across FLUXNET sites that is not related to the actual temperature or rainfall regimes at the site, which we cannot explain. Our analysis provides guidance to help select eddy covariance sites for researchers interested in understanding and/or modelling responses to temperature extremes.
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Slevin, Darren, Simon F. B. Tett, Jean-François Exbrayat, A. Anthony Bloom, and Mathew Williams. "Global evaluation of gross primary productivity in the JULES land surface model v3.4.1." Geoscientific Model Development 10, no. 7 (July 11, 2017): 2651–70. http://dx.doi.org/10.5194/gmd-10-2651-2017.
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Abstract. This study evaluates the ability of the JULES land surface model (LSM) to simulate gross primary productivity (GPP) on regional and global scales for 2001–2010. Model simulations, performed at various spatial resolutions and driven with a variety of meteorological datasets (WFDEI-GPCC, WFDEI-CRU and PRINCETON), were compared to the MODIS GPP product, spatially gridded estimates of upscaled GPP from the FLUXNET network (FLUXNET-MTE) and the CARDAMOM terrestrial carbon cycle analysis. Firstly, when JULES was driven with the WFDEI-GPCC dataset (at 0. 5° × 0. 5° spatial resolution), the annual average global GPP simulated by JULES for 2001–2010 was higher than the observation-based estimates (MODIS and FLUXNET-MTE), by 25 and 8 %, respectively, and CARDAMOM estimates by 23 %. JULES was able to simulate the standard deviation of monthly GPP fluxes compared to CARDAMOM and the observation-based estimates on global scales. Secondly, GPP simulated by JULES for various biomes (forests, grasslands and shrubs) on global and regional scales were compared. Differences among JULES, MODIS, FLUXNET-MTE and CARDAMOM on global scales were due to differences in simulated GPP in the tropics. Thirdly, it was shown that spatial resolution (0. 5° × 0. 5°, 1° × 1° and 2° × 2°) had little impact on simulated GPP on these large scales, with global GPP ranging from 140 to 142 PgC year−1. Finally, the sensitivity of JULES to meteorological driving data, a major source of model uncertainty, was examined. Estimates of annual average global GPP were higher when JULES was driven with the PRINCETON meteorological dataset than when driven with the WFDEI-GPCC dataset by 3 PgC year−1. On regional scales, differences between the two were observed, with the WFDEI-GPCC-driven model simulations estimating higher GPP in the tropics (5° N–5° S) and the PRINCETON-driven model simulations estimating higher GPP in the extratropics (30–60° N).
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Wang, Liming, Xuhui Lee, Wei Wang, Xufeng Wang, Zhongwang Wei, Congsheng Fu, Yunqiu Gao, et al. "A Meta-Analysis of Open-Path Eddy Covariance Observations of Apparent CO2 Flux in Cold Conditions in FLUXNET." Journal of Atmospheric and Oceanic Technology 34, no. 11 (November 2017): 2475–87. http://dx.doi.org/10.1175/jtech-d-17-0085.1.
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AbstractOpen-path eddy covariance systems are widely used for measuring the CO2 flux between land and atmosphere. A common problem is that they often yield negative fluxes or physiologically unreasonable CO2 uptake fluxes in the nongrowing season under cold conditions. In this study, a meta-analysis was performed on the eddy flux data from 64 FLUXNET sites and the relationship between the observed CO2 flux and the sensible heat flux was analyzed. In theory, these two fluxes should be independent of each other in cold conditions (air temperature lower than 0°C) when photosynthesis is suppressed. However, the results show that a significant and negative linear relationship existed between these two fluxes at 37 of the sites. The mean linear slope value is −0.008 ± 0.001 µmol m−2 s−1 per W m−2 among the 64 sites analyzed. The slope value was not significantly different among the three gas analyzer models (LI-7500, LI-7500A, IRGASON/EC150) used at these sites, indicating that self-heating may not be the only reason for the apparent wintertime net CO2 uptake. These results suggest a systematic bias toward larger carbon uptakes in the FLUXNET sites that deploy open-path eddy covariance systems.
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Seo, Hocheol, Jeongbin Kim, Hyesun Park, and Yeonjoo Kim. "Estimating Evapotranspiration with the Complementary Relationship at Fluxnet Sites Over Asia." Journal of The Korean Society of Civil Engineers 37, no. 2 (April 1, 2017): 303–10. http://dx.doi.org/10.12652/ksce.2017.37.2.0303.
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Wilson, Kell, Allen Goldstein, Eva Falge, Marc Aubinet, Dennis Baldocchi, Paul Berbigier, Christian Bernhofer, et al. "Energy balance closure at FLUXNET sites." Agricultural and Forest Meteorology 113, no. 1-4 (December 2002): 223–43. http://dx.doi.org/10.1016/s0168-1923(02)00109-0.
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Papale, Dario. "Ideas and perspectives: enhancing the impact of the FLUXNET network of eddy covariance sites." Biogeosciences 17, no. 22 (November 17, 2020): 5587–98. http://dx.doi.org/10.5194/bg-17-5587-2020.
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Abstract. 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.
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Carrer, D., S. Lafont, J. L. Roujean, J. C. Calvet, C. Meurey, P. Le Moigne, and I. F. Trigo. "Incoming Solar and Infrared Radiation Derived from METEOSAT: Impact on the Modeled Land Water and Energy Budget over France." Journal of Hydrometeorology 13, no. 2 (April 1, 2012): 504–20. http://dx.doi.org/10.1175/jhm-d-11-059.1.
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Abstract The Land Surface Analysis Satellite Applications Facility (LSA SAF) project radiation fluxes, derived from the Meteosat Second Generation (MSG) geostationary satellite, were used in the Interactions between Soil, Biosphere, and Atmosphere (ISBA) land surface model (LSM), which is a component of the Surface Externalisée (SURFEX) modeling platform. The Système d’Analyze Fournissant des Renseignements Atmosphériques à la Neige (SAFRAN) atmospheric analysis provides high-resolution atmospheric variables used to drive LSMs over France. The impact of using the incoming solar and infrared radiation fluxes [downwelling surface shortwave (DSSF) and longwave (DSLF), respectively] from either SAFRAN or LSA SAF, in ISBA, was investigated over France for 2006. In situ observations from the Flux Network (FLUXNET) were used for the verification. Daily differences between SAFRAN and LSA SAF radiation fluxes averaged over the whole year 2006 were 3.75 and 2.61 W m−2 for DSSF and DSLF, respectively, representing 2.5% and 0.8% of their average values. The LSA SAF incoming solar radiation presented a better agreement with in situ measurements at six FLUXNET stations than the SAFRAN analysis. The bias and standard deviation of differences were reduced by almost 50%. The added value of the LSA SAF products was assessed with the simulated surface temperature, soil moisture, and the water and energy fluxes. The latter quantities were improved by the use of LSA SAF satellite estimates. As many areas lack a high-resolution meteorological analysis, the LSA SAF radiative products provide new and valuable information.
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Jung, M., M. Reichstein, and A. Bondeau. "Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model." Biogeosciences 6, no. 10 (October 6, 2009): 2001–13. http://dx.doi.org/10.5194/bg-6-2001-2009.
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Abstract. Global, spatially and temporally explicit estimates of carbon and water fluxes derived from empirical up-scaling eddy covariance measurements would constitute a new and possibly powerful data stream to study the variability of the global terrestrial carbon and water cycle. This paper introduces and validates a machine learning approach dedicated to the upscaling of observations from the current global network of eddy covariance towers (FLUXNET). We present a new model TRee Induction ALgorithm (TRIAL) that performs hierarchical stratification of the data set into units where particular multiple regressions for a target variable hold. We propose an ensemble approach (Evolving tRees with RandOm gRowth, ERROR) where the base learning algorithm is perturbed in order to gain a diverse sequence of different model trees which evolves over time. We evaluate the efficiency of the model tree ensemble (MTE) approach using an artificial data set derived from the Lund-Potsdam-Jena managed Land (LPJmL) biosphere model. We aim at reproducing global monthly gross primary production as simulated by LPJmL from 1998–2005 using only locations and months where high quality FLUXNET data exist for the training of the model trees. The model trees are trained with the LPJmL land cover and meteorological input data, climate data, and the fraction of absorbed photosynthetic active radiation simulated by LPJmL. Given that we know the "true result" in the form of global LPJmL simulations we can effectively study the performance of the MTE upscaling and associated problems of extrapolation capacity. We show that MTE is able to explain 92% of the variability of the global LPJmL GPP simulations. The mean spatial pattern and the seasonal variability of GPP that constitute the largest sources of variance are very well reproduced (96% and 94% of variance explained respectively) while the monthly interannual anomalies which occupy much less variance are less well matched (41% of variance explained). We demonstrate the substantially improved accuracy of MTE over individual model trees in particular for the monthly anomalies and for situations of extrapolation. We estimate that roughly one fifth of the domain is subject to extrapolation while MTE is still able to reproduce 73% of the LPJmL GPP variability here. This paper presents for the first time a benchmark for a global FLUXNET upscaling approach that will be employed in future studies. Although the real world FLUXNET upscaling is more complicated than for a noise free and reduced complexity biosphere model as presented here, our results show that an empirical upscaling from the current FLUXNET network with MTE is feasible and able to extract global patterns of carbon flux variability.
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Williams, M., A. D. Richardson, M. Reichstein, P. C. Stoy, P. Peylin, H. Verbeeck, N. Carvalhais, et al. "Improving land surface models with FLUXNET data." Biogeosciences Discussions 6, no. 2 (March 5, 2009): 2785–835. http://dx.doi.org/10.5194/bgd-6-2785-2009.
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Abstract. There is a growing consensus that land surface models (LSMs) that simulate terrestrial biosphere exchanges of matter and energy must be better constrained with data to quantify and address their uncertainties. FLUXNET, an international network of sites that measure the land surface exchanges of carbon, water and energy using the eddy covariance technique, is a prime source of data for model improvement. Here we outline a multi-stage process for fusing LSMs with FLUXNET data to generate better models with quantifiable uncertainty. First, we describe FLUXNET data availability, and its random and systematic biases. We then introduce methods for assessing LSM model runs against FLUXNET observations in temporal and spatial domains. These assessments are a prelude to more formal model-data fusion (MDF). MDF links model to data, based on error weightings. In theory, MDF produces optimal analyses of the modelled system, but there are practical problems. We first discuss how to set model errors and initial conditions. In both cases incorrect assumptions will affect the outcome of the MDF. We then review the problem of equifinality, whereby multiple combinations of parameters can produce similar model output. Fusing multiple independent data provides a means to limit equifinality. We then show how parameter probability density functions (PDFs) from MDF can be used to interpret model process validity, and to propagate errors into model outputs. Posterior parameter distributions are a useful way to assess the success of MDF, combined with a determination of whether model residuals are Gaussian. If the MDF scheme provides evidence for temporal variation in parameters, then that is indicative of a critical missing dynamic process. A comparison of parameter PDFs generated with the same model from multiple FLUXNET sites can provide insights into the concept and validity of plant functional types (PFT) – we would expect similar parameter estimates among sites sharing a single PFT. We conclude by identifying five major model-data fusion challenges for the FLUXNET and LSM communities: 1) to determine appropriate use of current data and to explore the information gained in using longer time series; 2) to avoid confounding effects of missing process representation on parameter estimation; 3) to assimilate more data types, including those from earth observation; 4) to fully quantify uncertainties arising from data bias, model structure, and initial conditions problems; and 5) to carefully test current model concepts (e.g. PFTs) and guide development of new concepts.
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Williams, M., A. D. Richardson, M. Reichstein, P. C. Stoy, P. Peylin, H. Verbeeck, N. Carvalhais, et al. "Improving land surface models with FLUXNET data." Biogeosciences 6, no. 7 (July 30, 2009): 1341–59. http://dx.doi.org/10.5194/bg-6-1341-2009.
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Abstract. There is a growing consensus that land surface models (LSMs) that simulate terrestrial biosphere exchanges of matter and energy must be better constrained with data to quantify and address their uncertainties. FLUXNET, an international network of sites that measure the land surface exchanges of carbon, water and energy using the eddy covariance technique, is a prime source of data for model improvement. Here we outline a multi-stage process for "fusing" (i.e. linking) LSMs with FLUXNET data to generate better models with quantifiable uncertainty. First, we describe FLUXNET data availability, and its random and systematic biases. We then introduce methods for assessing LSM model runs against FLUXNET observations in temporal and spatial domains. These assessments are a prelude to more formal model-data fusion (MDF). MDF links model to data, based on error weightings. In theory, MDF produces optimal analyses of the modelled system, but there are practical problems. We first discuss how to set model errors and initial conditions. In both cases incorrect assumptions will affect the outcome of the MDF. We then review the problem of equifinality, whereby multiple combinations of parameters can produce similar model output. Fusing multiple independent and orthogonal data provides a means to limit equifinality. We then show how parameter probability density functions (PDFs) from MDF can be used to interpret model validity, and to propagate errors into model outputs. Posterior parameter distributions are a useful way to assess the success of MDF, combined with a determination of whether model residuals are Gaussian. If the MDF scheme provides evidence for temporal variation in parameters, then that is indicative of a critical missing dynamic process. A comparison of parameter PDFs generated with the same model from multiple FLUXNET sites can provide insights into the concept and validity of plant functional types (PFT) – we would expect similar parameter estimates among sites sharing a single PFT. We conclude by identifying five major model-data fusion challenges for the FLUXNET and LSM communities: (1) to determine appropriate use of current data and to explore the information gained in using longer time series; (2) to avoid confounding effects of missing process representation on parameter estimation; (3) to assimilate more data types, including those from earth observation; (4) to fully quantify uncertainties arising from data bias, model structure, and initial conditions problems; and (5) to carefully test current model concepts (e.g. PFTs) and guide development of new concepts.
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Slevin, D., S. F. B. Tett, and M. Williams. "Multi-site evaluation of the JULES land surface model using global and local data." Geoscientific Model Development 8, no. 2 (February 13, 2015): 295–316. http://dx.doi.org/10.5194/gmd-8-295-2015.
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Abstract. This study evaluates the ability of the JULES land surface model (LSM) to simulate photosynthesis using local and global data sets at 12 FLUXNET sites. Model parameters include site-specific (local) values for each flux tower site and the default parameters used in the Hadley Centre Global Environmental Model (HadGEM) climate model. Firstly, gross primary productivity (GPP) estimates from driving JULES with data derived from local site measurements were compared to observations from the FLUXNET network. When using local data, the model is biased with total annual GPP underestimated by 16% across all sites compared to observations. Secondly, GPP estimates from driving JULES with data derived from global parameter and atmospheric reanalysis (on scales of 100 km or so) were compared to FLUXNET observations. It was found that model performance decreases further, with total annual GPP underestimated by 30% across all sites compared to observations. When JULES was driven using local parameters and global meteorological data, it was shown that global data could be used in place of FLUXNET data with a 7% reduction in total annual simulated GPP. Thirdly, the global meteorological data sets, WFDEI and PRINCETON, were compared to local data to find that the WFDEI data set more closely matches the local meteorological measurements (FLUXNET). Finally, the JULES phenology model was tested by comparing results from simulations using the default phenology model to those forced with the remote sensing product MODIS leaf area index (LAI). Forcing the model with daily satellite LAI results in only small improvements in predicted GPP at a small number of sites, compared to using the default phenology model.
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Tramontana, Gianluca, Martin Jung, Christopher R. Schwalm, Kazuhito Ichii, Gustau Camps-Valls, Botond Ráduly, Markus Reichstein, et al. "Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms." Biogeosciences 13, no. 14 (July 29, 2016): 4291–313. http://dx.doi.org/10.5194/bg-13-4291-2016.
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Abstract. Spatio-temporal fields of land–atmosphere fluxes derived from data-driven models can complement simulations by process-based land surface models. While a number of strategies for empirical models with eddy-covariance flux data have been applied, a systematic intercomparison of these methods has been missing so far. In this study, we performed a cross-validation experiment for predicting carbon dioxide, latent heat, sensible heat and net radiation fluxes across different ecosystem types with 11 machine learning (ML) methods from four different classes (kernel methods, neural networks, tree methods, and regression splines). We applied two complementary setups: (1) 8-day average fluxes based on remotely sensed data and (2) daily mean fluxes based on meteorological data and a mean seasonal cycle of remotely sensed variables. The patterns of predictions from different ML and experimental setups were highly consistent. There were systematic differences in performance among the fluxes, with the following ascending order: net ecosystem exchange (R2 < 0.5), ecosystem respiration (R2 > 0.6), gross primary production (R2> 0.7), latent heat (R2 > 0.7), sensible heat (R2 > 0.7), and net radiation (R2 > 0.8). The ML methods predicted the across-site variability and the mean seasonal cycle of the observed fluxes very well (R2 > 0.7), while the 8-day deviations from the mean seasonal cycle were not well predicted (R2 < 0.5). Fluxes were better predicted at forested and temperate climate sites than at sites in extreme climates or less represented by training data (e.g., the tropics). The evaluated large ensemble of ML-based models will be the basis of new global flux products.
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Chu, Housen, Dennis D. Baldocchi, Ranjeet John, Sebastian Wolf, and Markus Reichstein. "Fluxes all of the time? A primer on the temporal representativeness of FLUXNET." Journal of Geophysical Research: Biogeosciences 122, no. 2 (February 2017): 289–307. http://dx.doi.org/10.1002/2016jg003576.
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Hilton, T. W., K. J. Davis, and K. Keller. "Evaluating terrestrial CO<sub>2</sub> flux diagnoses and uncertainties from a simple land surface model and its residuals." Biogeosciences 11, no. 2 (January 21, 2014): 217–35. http://dx.doi.org/10.5194/bg-11-217-2014.
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Abstract. Global terrestrial atmosphere–ecosystem carbon dioxide fluxes are well constrained by the concentration and isotopic composition of atmospheric carbon dioxide. In contrast, considerable uncertainty persists surrounding regional contributions to the net global flux as well as the impacts of atmospheric and biological processes that drive the net flux. These uncertainties severely limit our ability to make confident predictions of future terrestrial biological carbon fluxes. Here we use a simple light-use efficiency land surface model (the Vegetation Photosynthesis Respiration Model, VPRM) driven by remotely sensed temperature, moisture, and phenology to diagnose North American gross ecosystem exchange (GEE), ecosystem respiration, and net ecosystem exchange (NEE) for the period 2001 to 2006. We optimize VPRM parameters to eddy covariance (EC) NEE observations from 65 North American FluxNet sites. We use a separate set of 27 cross-validation FluxNet sites to evaluate a range of spatial and temporal resolutions for parameter estimation. With these results we demonstrate that different spatial and temporal groupings of EC sites for parameter estimation achieve similar sum of squared residuals values through radically different spatial patterns of NEE. We also derive a regression model to estimate observed VPRM errors as a function of VPRM NEE, temperature, and precipitation. Because this estimate is based on model-observation residuals it is comprehensive of all the error sources present in modeled fluxes. We find that 1 km interannual variability in VPRM NEE is of similar magnitude to estimated 1 km VPRM NEE errors.
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Hilton, T. W., K. J. Davis, and K. Keller. "Evaluating terrestrial CO<sub>2</sub> flux diagnoses and uncertainties from a simple land surface model and its residuals." Biogeosciences Discussions 10, no. 8 (August 22, 2013): 13753–802. http://dx.doi.org/10.5194/bgd-10-13753-2013.
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Abstract. Global terrestrial atmosphere-ecosystem carbon dioxide fluxes are well-constrained by the concentration and isotopic composition of atmospheric carbon dioxide. In contrast, considerable uncertainty persists surrounding regional contributions to the net global flux as well as the impacts of atmospheric and biological processes that drive the net flux. These uncertainties severely limit our ability to make confident predictions of future terrestrial biological carbon fluxes. Here we use a simple light-use efficiency ecosystem model (the Vegetation Photosynthesis Respiration Model, VPRM) driven by remotely-sensed temperature, moisture, and phenology to diagnose North American gross ecosystem exchange (GEE), ecosystem respiration, and net ecosystem exchange (NEE) for the period 2001 to 2006. We optimize VPRM parameters to eddy covariance (EC) NEE observations from 65 North American FluxNet sites. We use a separate set of 27 cross-validation FluxNet sites to evaluate a range of spatial and temporal resolutions for parameter estimation. With these results we demonstrate that different spatial and temporal groupings of EC sites for parameter estimation achieve similar sum of squared residuals values through radically different spatial patterns of NEE. We also derive a regression model to estimate observed VPRM errors as a function of VPRM NEE, temperature, and precipitation. Because this estimate is based on model-observation residuals it is comprehensive of all of the error sources present in modeled fluxes. We find that 1 km interannual variability in VPRM NEE is of similar magnitude to estimated 1 km VPRM NEE errors.
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Jung, M., M. Reichstein, and A. Bondeau. "Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model." Biogeosciences Discussions 6, no. 3 (May 26, 2009): 5271–304. http://dx.doi.org/10.5194/bgd-6-5271-2009.
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Abstract. Global, spatially and temporally explicit estimates of carbon and water fluxes derived from empirical up-scaling eddy covariance measurements would constitute a new and possibly powerful data stream to study the variability of the global terrestrial carbon and water cycle. This paper introduces and validates a machine learning approach dedicated to the upscaling of observations from the current global network of eddy covariance towers (FLUXNET). We present a new model TRee Induction ALgorithm (TRIAL) that performs hierarchical stratification of the data set into units where particular multiple regressions for a target variable hold. We propose an ensemble approach (Evolving tRees with RandOm gRowth, ERROR) where the base learning algorithm is perturbed in order to gain a diverse sequence of different model trees which evolves over time. We evaluate the efficiency of the model tree ensemble approach using an artificial data set derived from the the Lund-Potsdam-Jena managed Land (LPJmL) biosphere model. We aim at reproducing global monthly gross primary production as simulated by LPJmL from 1998–2005 using only locations and months where high quality FLUXNET data exist for the training of the model trees. The model trees are trained with the LPJmL land cover and meteorological input data, climate data, and the fraction of absorbed photosynthetic active radiation simulated by LPJmL. Given that we know the "true result" in the form of global LPJmL simulations we can effectively study the performance of the model tree ensemble upscaling and associated problems of extrapolation capacity. We show that the model tree ensemble is able to explain 92% of the variability of the global LPJmL GPP simulations. The mean spatial pattern and the seasonal variability of GPP that constitute the largest sources of variance are very well reproduced (96% and 94% of variance explained respectively) while the monthly interannual anomalies which occupy much less variance are less well matched (41% of variance explained). We demonstrate the substantially improved accuracy of the model tree ensemble over individual model trees in particular for the monthly anomalies and for situations of extrapolation. We estimate that roughly one fifth of the domain is subject to extrapolation while the model tree ensemble is still able to reproduce 73% of the LPJmL GPP variability here. This paper presents for the first time a benchmark for a global FLUXNET upscaling approach that will be employed in future studies. Although the real world FLUXNET upscaling is more complicated than for a noise free and reduced complexity biosphere model as presented here, our results show that an empirical upscaling from the current FLUXNET network with a model tree ensemble is feasible and able to extract global patterns of carbon flux variability.
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FRIEND, ANDREW D., ALMUT ARNETH, NANCY Y. KIANG, MARK LOMAS, JÉRÔME OGÉE, CHRISTIAN RÖDENBECK, STEVEN W. RUNNING, et al. "FLUXNET and modelling the global carbon cycle." Global Change Biology 13, no. 3 (March 2007): 610–33. http://dx.doi.org/10.1111/j.1365-2486.2006.01223.x.
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Gu, Lianhong, Dennis Baldocchi, and Nancy Kiang. "FLUXNET Evaluates “Breathing patterns” of diverse ecosystems." Eos, Transactions American Geophysical Union 81, no. 47 (2000): 565. http://dx.doi.org/10.1029/eo081i047p00565.
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Knox, Sara H., Robert B. Jackson, Benjamin Poulter, Gavin McNicol, Etienne Fluet-Chouinard, Zhen Zhang, Gustaf Hugelius, et al. "FLUXNET-CH4 Synthesis Activity: Objectives, Observations, and Future Directions." Bulletin of the American Meteorological Society 100, no. 12 (December 1, 2019): 2607–32. http://dx.doi.org/10.1175/bams-d-18-0268.1.
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Abstract This paper describes the formation of, and initial results for, a new FLUXNET coordination network for ecosystem-scale methane (CH4) measurements at 60 sites globally, organized by the Global Carbon Project in partnership with other initiatives and regional flux tower networks. The objectives of the effort are presented along with an overview of the coverage of eddy covariance (EC) CH4 flux measurements globally, initial results comparing CH4 fluxes across the sites, and future research directions and needs. Annual estimates of net CH4 fluxes across sites ranged from −0.2 ± 0.02 g C m–2 yr–1 for an upland forest site to 114.9 ± 13.4 g C m–2 yr–1 for an estuarine freshwater marsh, with fluxes exceeding 40 g C m–2 yr–1 at multiple sites. Average annual soil and air temperatures were found to be the strongest predictor of annual CH4 flux across wetland sites globally. Water table position was positively correlated with annual CH4 emissions, although only for wetland sites that were not consistently inundated throughout the year. The ratio of annual CH4 fluxes to ecosystem respiration increased significantly with mean site temperature. Uncertainties in annual CH4 estimates due to gap-filling and random errors were on average ±1.6 g C m–2 yr–1 at 95% confidence, with the relative error decreasing exponentially with increasing flux magnitude across sites. Through the analysis and synthesis of a growing EC CH4 flux database, the controls on ecosystem CH4 fluxes can be better understood, used to inform and validate Earth system models, and reconcile differences between land surface model- and atmospheric-based estimates of CH4 emissions.
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De Kauwe, Martin G., Belinda E. Medlyn, Jürgen Knauer, and Christopher A. Williams. "Ideas and perspectives: how coupled is the vegetation to the boundary layer?" Biogeosciences 14, no. 19 (October 9, 2017): 4435–53. http://dx.doi.org/10.5194/bg-14-4435-2017.
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Abstract. Understanding the sensitivity of transpiration to stomatal conductance is critical to simulating the water cycle. This sensitivity is a function of the degree of coupling between the vegetation and the atmosphere and is commonly expressed by the decoupling factor. The degree of coupling assumed by models varies considerably and has previously been shown to be a major cause of model disagreement when simulating changes in transpiration in response to elevated CO2. The degree of coupling also offers us insight into how different vegetation types control transpiration fluxes, which is fundamental to our understanding of land–atmosphere interactions. To explore this issue, we combined an extensive literature summary from 41 studies with estimates of the decoupling coefficient estimated from FLUXNET data. We found some notable departures from the values previously reported in single-site studies. There was large variability in estimated decoupling coefficients (range 0.05–0.51) for evergreen needleleaf forests. This is a result that was broadly supported by our literature review but contrasts with the early literature which suggests that evergreen needleleaf forests are generally well coupled. Estimates from FLUXNET indicated that evergreen broadleaved forests were the most tightly coupled, differing from our literature review and instead suggesting that it was evergreen needleleaf forests. We also found that the assumption that grasses would be strongly decoupled (due to vegetation stature) was only true for high precipitation sites. These results were robust to assumptions about aerodynamic conductance and, to a lesser extent, energy balance closure. Thus, these data form a benchmarking metric against which to test model assumptions about coupling. Our results identify a clear need to improve the quantification of the processes involved in scaling from the leaf to the whole ecosystem. Progress could be made with targeted measurement campaigns at flux sites and greater site characteristic information across the FLUXNET network.
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Bórnez, Kevin, Andrew D. Richardson, Aleixandre Verger, Adrià Descals, and Josep Peñuelas. "Evaluation of VEGETATION and PROBA-V Phenology Using PhenoCam and Eddy Covariance Data." Remote Sensing 12, no. 18 (September 19, 2020): 3077. http://dx.doi.org/10.3390/rs12183077.
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High-quality retrieval of land surface phenology (LSP) is increasingly important for understanding the effects of climate change on ecosystem function and biosphere–atmosphere interactions. We analyzed four state-of-the-art phenology methods: threshold, logistic-function, moving-average and first derivative based approaches, and retrieved LSP in the North Hemisphere for the period 1999–2017 from Copernicus Global Land Service (CGLS) SPOT-VEGETATION and PROBA-V leaf area index (LAI) 1 km V2.0 time series. We validated the LSP estimates with near-surface PhenoCam and eddy covariance FLUXNET data over 80 sites of deciduous forests. Results showed a strong correlation (R2 > 0.7) between the satellite LSP and ground-based observations from both PhenoCam and FLUXNET for the timing of the start (SoS) and R2 > 0.5 for the end of season (EoS). The threshold-based method performed the best with a root mean square error of ~9 d with PhenoCam and ~7 d with FLUXNET for the timing of SoS (30th percentile of the annual amplitude), and ~12 d and ~10 d, respectively, for the timing of EoS (40th percentile).
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Li, Jiamin, and Chenghai Wang. "An Evaporation Correction Approach and Its Characteristics." Journal of Hydrometeorology 21, no. 3 (March 2020): 519–32. http://dx.doi.org/10.1175/jhm-d-18-0211.1.
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AbstractEvaporation is a principal factor in the hydrological cycle and energy exchange; however, estimations of evaporation include large uncertainties. In this study, a modified estimation of evaporation based on empirical linearly simplified Penman evaporation (PES) is proposed, soil moisture and precipitation are used to correct the land surface evaporation estimation, and the temporal and spatial characteristics of the corrected evaporation (CE) are investigated globally. The results show that CE is strong at low latitudes and weak at high latitudes. CE has obvious seasonal variation, ranging from 0.2 to 4.0 mm day−1; CE is prominent in summer but feeble in winter. Compared to PES, CE is generally weaker in most regions, especially in arid regions, with differences of more than 9 mm day−1. CE agrees well with evaporation derived from FLUXNET-Model Tree Ensemble (FLUXNET-MTE), MERRA, and GLDAS. In general, the root-mean-square error (RMSE) between annual CE and FLUXNET-MTE is less than 0.2 mm day−1, and CE is about 5%–10% less than the evaporation of FLUXNET-MTE. In the arid regions, the maximum CE almost occurs in the month with the strongest precipitation; in the tropical regions, soil moisture enhances CE only when precipitation is less. In the context of global temperature rise, PES always shows an apparent increasing trend due to the water supply is not considered; however, CE decreases in western Asia, the western United States, the Amazon basin, and Central Africa, but weakly increases in the other study regions from 1984 to 2013. This study provides a method for estimating evaporation considering more restrictive factors on evaporation.
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Key, Jeffrey R., and Axel J. Schweiger. "Tools for atmospheric radiative transfer: Streamer and FluxNet." Computers & Geosciences 24, no. 5 (June 1998): 443–51. http://dx.doi.org/10.1016/s0098-3004(97)00130-1.
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Yu, Rong, Benjamin L. Ruddell, Minseok Kang, Joon Kim, and Dan Childers. "Anticipating global terrestrial ecosystem state change using FLUXNET." Global Change Biology 25, no. 7 (April 24, 2019): 2352–67. http://dx.doi.org/10.1111/gcb.14602.
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Loew, A., J. Peng, and M. Borsche. "High resolution land surface fluxes from satellite data (HOLAPS v1.0): evaluation and uncertainty assessment." Geoscientific Model Development Discussions 8, no. 12 (December 21, 2015): 10783–841. http://dx.doi.org/10.5194/gmdd-8-10783-2015.
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Abstract. Surface water and energy fluxes are essential components of the Earth system. Surface latent heat fluxes provide major energy input to the atmosphere. Despite the importance of these fluxes, state-of-the-art datasets of surface energy and water fluxes largely differ. The present paper introduces a new framework for the estimation of surface energy and water fluxes at the land surface, which allows for temporally and spatially high resolved flux estimates at the global scale (HOLAPS). The framework maximizes the usage of existing long-term satellite data records and ensures internally consistent estimates of the surface radiation and water fluxes. The manuscript introduces the technical details of the developed framework and provides results of a comprehensive sensitivity and evaluation study. Overall the results indicate very good agreement with in situ observations when compared against 49 FLUXNET stations worldwide. Largest uncertainties of latent heat flux and net radiation were found to result from uncertainties in the global solar radiation flux obtained from satellite data products.
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Yang, Yan, A. Anthony Bloom, Shuang Ma, Paul Levine, Alexander Norton, Nicholas C. Parazoo, John T. Reager, et al. "CARDAMOM-FluxVal version 1.0: a FLUXNET-based validation system for CARDAMOM carbon and water flux estimates." Geoscientific Model Development 15, no. 4 (March 2, 2022): 1789–802. http://dx.doi.org/10.5194/gmd-15-1789-2022.
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Abstract. Land–atmosphere carbon and water exchanges have large uncertainty in terrestrial biosphere models (TBMs). Using observations to reduce TBM structural and parametric errors and uncertainty is a critical priority for both understanding and accurately predicting carbon and water fluxes. Recent implementations of the Bayesian CARbon DAta–MOdel fraMework (CARDAMOM) have yielded key insights into ecosystem carbon and water cycling. CARDAMOM estimates parameters for an associated TBM of intermediate complexity (Data Assimilation Linked Ecosystem Carbon – DALEC). These CARDAMOM analyses – informed by co-located C and H2O flux observations – have exhibited considerable skill in both representing the variability of assimilated observations and predicting withheld observations. CARDAMOM and DALEC have been continuously developed to accommodate new scientific challenges and an expanding variety of observational constraints. However, so far there has been no concerted effort to globally and systematically validate CARDAMOM performance across individual model–data fusion configurations. Here we use the FLUXNET 2015 dataset – an ensemble of 200+ eddy covariance flux tower sites – to formulate a concerted benchmarking framework for CARDAMOM carbon (photosynthesis and net C exchange) and water (evapotranspiration) flux estimates (CARDAMOM-FluxVal version 1.0). We present a concise set of skill metrics to evaluate CARDAMOM performance against both assimilated and withheld FLUXNET 2015 photosynthesis, net CO2 exchange, and evapotranspiration estimates. We further demonstrate the potential for tailored CARDAMOM evaluations by categorizing performance in terms of (i) individual land-cover types, (ii) monthly, annual, and mean fluxes, and (iii) length of assimilation data. The CARDAMOM benchmarking system – along with the CARDAMOM driver files provided – can be readily repeated to support both the intercomparison between existing CARDAMOM model configurations and the formulation, development, and testing of new CARDAMOM model structures.
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Groenendijk, M., M. K. van der Molen, and A. J. Dolman. "Seasonal variation in ecosystem parameters derived from FLUXNET data." Biogeosciences Discussions 6, no. 2 (March 11, 2009): 2863–912. http://dx.doi.org/10.5194/bgd-6-2863-2009.
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Abstract. The carbon dioxide sink is in a complex way related to weather and climate. In order to better understand the relationship and feedbacks, we present a methodology to simulate observed carbon dioxide flux data with a simple vegetation model (5PM) with weekly varying model parameters. The model parameters explain the interaction between vegetation and seasonal climate more general than the flux data. Two parameters (Rref and E0) are related to ecosystem respiration and three parameters (Jm, α and λ) to photosynthesis and transpiration. We quantified the weekly variability of ecosystem parameters as a function of vegetation type and climate region. After statistical quality checks 121 FLUXNET sites were available for analysis of the weekly varying model parameters. The simulations of these sites have high correlation coefficients (r2=0.6 to 0.8) between the observed and simulated carbon and water fluxes. With weekly parameters we determined average seasonal cycles for the different combinations of vegetation type and climate regions (PFTs). The variation between PFTs is large, which provides an excellent dataset to study the differences in ecosystem characteristics. In general we observed that needleleaf forests and grasslands in warmer climates have relatively constant parameter values during the year. Broadleaf forests in all climate regions have large seasonal variation for each of the five parameters. In boreal regions parameter values are always lower than in temperate regions. A large seasonality of the model parameters indicates a strong relation between vegetation and climate. This suggests that climate change will have the largest impact on the terrestrial carbon fluxes in boreal regions and for deciduous forests, and less for grasslands and evergreen forests.
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Soyama, N., K. Muramatsu, M. Daigo, F. Ochiai, and N. Fujiwara. "A STUDY ON PRODUCING HIGHLY RELIABILE REFERENCE DATA SETS FOR GLOBAL LAND COVER VALIDATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 24, 2016): 1207–11. http://dx.doi.org/10.5194/isprs-archives-xli-b8-1207-2016.
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Validating the accuracy of land cover products using a reliable reference dataset is an important task. A reliable reference dataset is produced with information derived from ground truth data. Recently, the amount of ground truth data derived from information collected by volunteers has been increasing globally. The acquisition of volunteer-based reference data demonstrates great potential. However information given by volunteers is limited useful vegetation information to produce a complete reference dataset based on the plant functional type (PFT) with five specialized forest classes. In this study, we examined the availability and applicability of FLUXNET information to produce reference data with higher levels of reliability. FLUXNET information was useful especially for forest classes for interpretation in comparison with the reference dataset using information given by volunteers.
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Soyama, N., K. Muramatsu, M. Daigo, F. Ochiai, and N. Fujiwara. "A STUDY ON PRODUCING HIGHLY RELIABILE REFERENCE DATA SETS FOR GLOBAL LAND COVER VALIDATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 24, 2016): 1207–11. http://dx.doi.org/10.5194/isprsarchives-xli-b8-1207-2016.
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Validating the accuracy of land cover products using a reliable reference dataset is an important task. A reliable reference dataset is produced with information derived from ground truth data. Recently, the amount of ground truth data derived from information collected by volunteers has been increasing globally. The acquisition of volunteer-based reference data demonstrates great potential. However information given by volunteers is limited useful vegetation information to produce a complete reference dataset based on the plant functional type (PFT) with five specialized forest classes. In this study, we examined the availability and applicability of FLUXNET information to produce reference data with higher levels of reliability. FLUXNET information was useful especially for forest classes for interpretation in comparison with the reference dataset using information given by volunteers.
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Vuichard, N., and D. Papale. "Filling the gaps in meteorological continuous data measured at FLUXNET sites with ERA-interim reanalysis." Earth System Science Data Discussions 8, no. 1 (January 20, 2015): 23–55. http://dx.doi.org/10.5194/essdd-8-23-2015.
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Abstract. Exchanges of carbon, water and energy between the land surface and the atmosphere are monitored by eddy covariance technique at the ecosystem level. Currently, the FLUXNET database contains more than 500 sites registered and up to 250 of them sharing data (Free Fair Use dataset). Many modelling groups use the FLUXNET dataset for evaluating ecosystem model's performances but it requires uninterrupted time series for the meteorological variables used as input. Because original in-situ data often contain gaps, from very short (few hours) up to relatively long (some months), we develop a new and robust method for filling the gaps in meteorological data measured at site level. Our approach has the benefit of making use of continuous data available globally (ERA-interim) and high temporal resolution spanning from 1989 to today. These data are however not measured at site level and for this reason a method to downscale and correct the ERA-interim data is needed. We apply this method on the level 4 data (L4) from the LaThuile collection, freely available after registration under a Fair-Use policy. The performances of the developed method vary across sites and are also function of the meteorological variable. On average overall sites, the bias correction leads to cancel from 10 to 36% of the initial mismatch between in-situ and ERA-interim data, depending of the meteorological variable considered. In comparison to the internal variability of the in-situ data, the root mean square error (RMSE) between the in-situ data and the un-biased ERA-I data remains relatively large (on average overall sites, from 27 to 76% of the standard deviation of in-situ data, depending of the meteorological variable considered). The performance of the method remains low for the wind speed field, in particular regarding its capacity to conserve a standard deviation similar to the one measured at FLUXNET stations. The ERA-interim reanalysis data debiased at FLUXNET sites can be downloaded from the PANGAEA data center (http://doi.pangaea.de/10.1594/PANGAEA.838234).
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SCHWALM, CHRISTOPHER R., CHRISTOPHER A. WILLIAMS, KEVIN SCHAEFER, ALMUT ARNETH, DAMIEN BONAL, NINA BUCHMANN, JIQUAN CHEN, et al. "Assimilation exceeds respiration sensitivity to drought: A FLUXNET synthesis." Global Change Biology 16, no. 2 (February 2010): 657–70. http://dx.doi.org/10.1111/j.1365-2486.2009.01991.x.
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Stöckli, R., D. M. Lawrence, G. Y. Niu, K. W. Oleson, P. E. Thornton, Z. L. Yang, G. B. Bonan, A. S. Denning, and S. W. Running. "Use of FLUXNET in the Community Land Model development." Journal of Geophysical Research: Biogeosciences 113, G1 (March 2008): n/a. http://dx.doi.org/10.1029/2007jg000562.
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Chen, Jing M., Ajit Govind, Oliver Sonnentag, Yongqin Zhang, Alan Barr, and Brian Amiro. "Leaf area index measurements at Fluxnet-Canada forest sites." Agricultural and Forest Meteorology 140, no. 1-4 (November 2006): 257–68. http://dx.doi.org/10.1016/j.agrformet.2006.08.005.
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Ross, I., L. Misson, S. Rambal, A. Arneth, R. L. Scott, A. Carrara, A. Cescatti, and L. Genesio. "How do more extreme rainfall regimes affect ecosystem fluxes in seasonally water-limited Northern Hemisphere temperate shrublands and forests?" Biogeosciences Discussions 8, no. 5 (September 29, 2011): 9813–45. http://dx.doi.org/10.5194/bgd-8-9813-2011.
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Abstract. As a result of climate change, rainfall regimes became more extreme over the course of the 20th century, characterised by fewer and larger rainfall events. Such changes are expected to continue throughout the current century. The effect of changes in the temporal distribution of rainfall on ecosystem carbon fluxes is poorly understood, with most available information coming from experimental studies of grassland ecosystems. Here, continuous measurements of ecosystem carbon fluxes and precipitation from the worldwide FLUXNET network of eddy-covariance sites are exploited to investigate the effects of differences in rainfall distribution on the carbon balance of seasonally water-limited shrubland and forest sites. Once the strong dependence of ecosystem fluxes on total annual rainfall amount is accounted for, results show that sites with more extreme rainfall distributions have significantly lower gross productivity, slightly lower ecosystem respiration and consequently a smaller net ecosystem productivity.
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Ross, I., L. Misson, S. Rambal, A. Arneth, R. L. Scott, A. Carrara, A. Cescatti, and L. Genesio. "How do variations in the temporal distribution of rainfall events affect ecosystem fluxes in seasonally water-limited Northern Hemisphere shrublands and forests?" Biogeosciences 9, no. 3 (March 13, 2012): 1007–24. http://dx.doi.org/10.5194/bg-9-1007-2012.
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Abstract. Rainfall regimes became more extreme over the course of the 20th century, characterised by fewer and larger rainfall events. Such changes are expected to continue throughout the current century. The effect of changes in the temporal distribution of rainfall on ecosystem carbon fluxes is poorly understood, with most available information coming from experimental studies of grassland ecosystems. Here, continuous measurements of ecosystem carbon fluxes and precipitation from the worldwide FLUXNET network of eddy-covariance sites are exploited to investigate the effects of differences in rainfall distribution on the carbon balance of seasonally water-limited shrubland and forest sites. Once the strong dependence of ecosystem fluxes on total annual rainfall amount is accounted for, results show that sites with rainfall distributions characterised by fewer and larger rainfall events have significantly lower gross primary productivity, slightly lower ecosystem respiration and consequently a smaller net ecosystem productivity.
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Wang, Bin, Xu Yue, Hao Zhou, and Jun Zhu. "Impact of diffuse radiation on evapotranspiration and its coupling to carbon fluxes at global FLUXNET sites." Agricultural and Forest Meteorology 322 (July 2022): 109006. http://dx.doi.org/10.1016/j.agrformet.2022.109006.
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Wißkirchen, K., M. Tum, K. P. Günther, M. Niklaus, C. Eisfelder, and W. Knorr. "Quantifying the carbon uptake by vegetation for Europe on a 1 km<sup>2</sup> resolution using a remote sensing driven vegetation model." Geoscientific Model Development Discussions 6, no. 2 (April 11, 2013): 2457–89. http://dx.doi.org/10.5194/gmdd-6-2457-2013.
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Abstract. In this study we compare monthly gross primary productivity (GPP) time series (2000–2007), computed for Europe with the Biosphere Energy Transfer Hydrology (BETHY/DLR) model with monthly data from the eddy covariance measurements network FLUXNET. BETHY/DLR with a spatial resolution of 1 km2 is designed for regional and continental applications (here Europe) and operated at the German Aerospace Center (DLR). It was adapted from the BETHY scheme to be driven by remote sensing data and meteorology. Time series of Leaf Area Index (LAI) are used to control the development of vegetation. These are taken from the CYCLOPES database. Meteorological time series are used to regulate meteorological seasonality. These comprise daily information on temperature, precipitation, wind-speed and radiation. Additionally, static maps such as land cover, elevation, and soil type are used. To validate our model results we used eddy covariance measurements from the FLUXNET network of 74 towers across Europe. For forest sites we found that our model predicts between 20% and 40% higher annual GPP sums. In contrast, for cropland sites BETHY/DLR results show about 18% less GPP than eddy covariance measurements. For grassland sites, between 10% more and 16% less GPP was calculated with BETHY/DLR. A mean total carbon uptake of 2.5 Pg C yr-1 (±0.17 Pg) was found for Europe. In addition, this study states on risks that arise from the comparison of modeled data to FLUXNET measurements and their interpretation width.
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Vuichard, N., and D. Papale. "Filling the gaps in meteorological continuous data measured at FLUXNET sites with ERA-Interim reanalysis." Earth System Science Data 7, no. 2 (July 13, 2015): 157–71. http://dx.doi.org/10.5194/essd-7-157-2015.
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Abstract. Exchanges of carbon, water and energy between the land surface and the atmosphere are monitored by eddy covariance technique at the ecosystem level. Currently, the FLUXNET database contains more than 500 registered sites, and up to 250 of them share data (free fair-use data set). Many modelling groups use the FLUXNET data set for evaluating ecosystem models' performance, but this requires uninterrupted time series for the meteorological variables used as input. Because original in situ data often contain gaps, from very short (few hours) up to relatively long (some months) ones, we develop a new and robust method for filling the gaps in meteorological data measured at site level. Our approach has the benefit of making use of continuous data available globally (ERA-Interim) and a high temporal resolution spanning from 1989 to today. These data are, however, not measured at site level, and for this reason a method to downscale and correct the ERA-Interim data is needed. We apply this method to the level 4 data (L4) from the La Thuile collection, freely available after registration under a fair-use policy. The performance of the developed method varies across sites and is also function of the meteorological variable. On average over all sites, applying the bias correction method to the ERA-Interim data reduced the mismatch with the in situ data by 10 to 36 %, depending on the meteorological variable considered. In comparison to the internal variability of the in situ data, the root mean square error (RMSE) between the in situ data and the unbiased ERA-I (ERA-Interim) data remains relatively large (on average over all sites, from 27 to 76 % of the standard deviation of in situ data, depending on the meteorological variable considered). The performance of the method remains poor for the wind speed field, in particular regarding its capacity to conserve a standard deviation similar to the one measured at FLUXNET stations. The ERA-Interim reanalysis data de-biased at FLUXNET sites can be downloaded from the PANGAEA data centre (http://doi.pangaea.de/10.1594/PANGAEA.838234).
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Raoult, Nina, Catherine Ottlé, Philippe Peylin, Vladislav Bastrikov, and Pascal Maugis. "Evaluating and Optimizing Surface Soil Moisture Drydowns in the ORCHIDEE Land Surface Model at In Situ Locations." Journal of Hydrometeorology 22, no. 4 (April 2021): 1025–43. http://dx.doi.org/10.1175/jhm-d-20-0115.1.
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AbstractThe rate at which land surface soils dry following rain events is an important feature of terrestrial models. It determines, for example, the water availability for vegetation, the occurrences of droughts, and the surface heat exchanges. As such, surface soil moisture (SSM) “drydowns,” i.e., the SSM temporal dynamics following a significant rainfall event, are of particular interest when evaluating and calibrating land surface models (LSMs). By investigating drydowns, characterized by an exponential decay time scale τ, we aim to improve the representation of SSM in the ORCHIDEE global LSM. We consider τ calculated over 18 International Soil Moisture Network sites found within the footprint of FLUXNET towers, covering different vegetation types and climates. Using the ORCHIDEE LSM, we compare τ from the modeled SSM time series to values computed from in situ SSM measurements. We then assess the potential of using τ observations to constrain some water, carbon, and energy parameters of ORCHIDEE, selected using a sensitivity analysis, through a standard Bayesian optimization procedure. The impact of the SSM optimization is evaluated using FLUXNET evapotranspiration and gross primary production (GPP) data. We find that the relative drydowns of SSM can be well calibrated using observation-based τ estimates, when there is no need to match the absolute observed and modeled SSM values. When evaluated using independent data, τ-calibration parameters were able to improve drydowns for 73% of the sites. Furthermore, the fit of the model to independent fluxes was only minutely changed. We conclude by considering the potential of global satellite products to scale up the experiment to a global-scale optimization.
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Peylin, Philippe, Cédric Bacour, Natasha MacBean, Sébastien Leonard, Peter Rayner, Sylvain Kuppel, Ernest Koffi, et al. "A new stepwise carbon cycle data assimilation system using multiple data streams to constrain the simulated land surface carbon cycle." Geoscientific Model Development 9, no. 9 (September 20, 2016): 3321–46. http://dx.doi.org/10.5194/gmd-9-3321-2016.
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Abstract. Large uncertainties in land surface models (LSMs) simulations still arise from inaccurate forcing, poor description of land surface heterogeneity (soil and vegetation properties), incorrect model parameter values and incomplete representation of biogeochemical processes. The recent increase in the number and type of carbon cycle-related observations, including both in situ and remote sensing measurements, has opened a new road to optimize model parameters via robust statistical model–data integration techniques, in order to reduce the uncertainties of simulated carbon fluxes and stocks. In this study we present a carbon cycle data assimilation system that assimilates three major data streams, namely the Moderate Resolution Imaging Spectroradiometer (MODIS)-Normalized Difference Vegetation Index (NDVI) observations of vegetation activity, net ecosystem exchange (NEE) and latent heat (LE) flux measurements at more than 70 sites (FLUXNET), as well as atmospheric CO2 concentrations at 53 surface stations, in order to optimize the main parameters (around 180 parameters in total) of the Organizing Carbon and Hydrology in Dynamics Ecosystems (ORCHIDEE) LSM (version 1.9.5 used for the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations). The system relies on a stepwise approach that assimilates each data stream in turn, propagating the information gained on the parameters from one step to the next. Overall, the ORCHIDEE model is able to achieve a consistent fit to all three data streams, which suggests that current LSMs have reached the level of development to assimilate these observations. The assimilation of MODIS-NDVI (step 1) reduced the growing season length in ORCHIDEE for temperate and boreal ecosystems, thus decreasing the global mean annual gross primary production (GPP). Using FLUXNET data (step 2) led to large improvements in the seasonal cycle of the NEE and LE fluxes for all ecosystems (i.e., increased amplitude for temperate ecosystems). The assimilation of atmospheric CO2, using the general circulation model (GCM) of the Laboratoire de Météorologie Dynamique (LMDz; step 3), provides an overall constraint (i.e., constraint on large-scale net CO2 fluxes), resulting in an improvement of the fit to the observed atmospheric CO2 growth rate. Thus, the optimized model predicts a land C (carbon) sink of around 2.2 PgC yr−1 (for the 2000–2009 period), which is more compatible with current estimates from the Global Carbon Project (GCP) than the prior value. The consistency of the stepwise approach is evaluated with back-compatibility checks. The final optimized model (after step 3) does not significantly degrade the fit to MODIS-NDVI and FLUXNET data that were assimilated in the first two steps, suggesting that a stepwise approach can be used instead of the more “challenging” implementation of a simultaneous optimization in which all data streams are assimilated together. Most parameters, including the scalar of the initial soil carbon pool size, changed during the optimization with a large error reduction. This work opens new perspectives for better predictions of the land carbon budgets.
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Misson, Laurent, D. D. Baldocchi, T. A. Black, P. D. Blanken, Y. Brunet, J. Curiel Yuste, J. R. Dorsey, et al. "Partitioning forest carbon fluxes with overstory and understory eddy-covariance measurements: A synthesis based on FLUXNET data." Agricultural and Forest Meteorology 144, no. 1-2 (May 2007): 14–31. http://dx.doi.org/10.1016/j.agrformet.2007.01.006.
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Wolf, Adam, Christopher B. Field, and Joseph A. Berry. "Allometric growth and allocation in forests: a perspective from FLUXNET." Ecological Applications 21, no. 5 (July 2011): 1546–56. http://dx.doi.org/10.1890/10-1201.1.
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Gong, Peng. "Accuracies of Global Land Cover Maps Checked against Fluxnet Sites." Science Foundation in China 16, no. 1 (January 1, 2008): 31–35. http://dx.doi.org/10.1088/1005-0841/16/1/003.
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Ershadi, A., M. F. McCabe, J. P. Evans, N. W. Chaney, and E. F. Wood. "Multi-site evaluation of terrestrial evaporation models using FLUXNET data." Agricultural and Forest Meteorology 187 (April 2014): 46–61. http://dx.doi.org/10.1016/j.agrformet.2013.11.008.
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Gonsamo, Alemu, and Jing M. Chen. "Continuous observation of leaf area index at Fluxnet-Canada sites." Agricultural and Forest Meteorology 189-190 (June 2014): 168–74. http://dx.doi.org/10.1016/j.agrformet.2014.01.016.
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Williams, Karina, Jemma Gornall, Anna Harper, Andy Wiltshire, Debbie Hemming, Tristan Quaife, Tim Arkebauer, and David Scoby. "Evaluation of JULES-crop performance against site observations of irrigated maize from Mead, Nebraska." Geoscientific Model Development 10, no. 3 (March 27, 2017): 1291–320. http://dx.doi.org/10.5194/gmd-10-1291-2017.
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Abstract. The JULES-crop model (Osborne et al., 2015) is a parametrisation of crops within the Joint UK Land Environment Simulator (JULES), which aims to simulate both the impact of weather and climate on crop productivity and the impact of croplands on weather and climate. In this evaluation paper, observations of maize at three FLUXNET sites in Nebraska (US-Ne1, US-Ne2 and US-Ne3) are used to test model assumptions and make appropriate input parameter choices. JULES runs are performed for the irrigated sites (US-Ne1 and US-Ne2) both with the crop model switched off (prescribing leaf area index (LAI) and canopy height) and with the crop model switched on. These are compared against GPP and carbon pool FLUXNET observations. We use the results to point to future priorities for model development and describe how our methodology can be adapted to set up model runs for other sites and crop varieties.