8day FLUXNET-measured variables:Column1：TEMColumn2:SWColumn3:VPDColumn4:PREColumn5:LAIColumn6:ETColumn7:latitudeColumn8:longitudeColumn9:PTFsSatellite-derived variables for all FLUXNET sites:olumn1:TEMColumn2:RHColumn3:NRColumn4:soil heat fluxColumn5:air densityColumn6:VPDColumn7:PressureColumn8:Water vapor densityColumn9:LAIColumn10:ET3.global annual mean ET during 2001-2014

FLUXNET Canada is a Fluxnet research network comprised of the Fluxnet-Canada Research Network (FCRN) and the Canadian Carbon Program (CCP) operating from 1993 through 2014. It was a national research network of university and government scientists studying the influence of climate and disturbance on carbon cycling along an east-west transect of Canadian forest and peat land ecosystems. The data provided are measured and modeled results as obtained from the site investigators. They were not standardized and quality-controlled. Data include: atmospheric carbon dioxide (CO2) and water vapor fluxes and many ancillary meteorological variables; soil CO2 efflux and soil moisture; stable carbon isotopes; site soil and vegetation characteristics, plus documentation and descriptions for the 32 tower sites across 12 flux research stations. The time period is from 1993 - 2014; most reported data for a site does not cover the entire period.

FLUXNET-CH4 is an initiative led by the Global Carbon Project, in close partnership with AmeriFlux and EuroFlux, to compile a global database of eddy covariance (EC) methane flux measurements. Data are standardized, post-processed (i.e., partitioned and gap-filled), and released as FLUXNET-CH4. FLUXNET-CH4 Version 1.0 includes data from 81 sites, representing freshwater, coastal, upland, natural, and managed ecosystems. The near continuous, high-frequency nature of EC measurements offers significant promise for improving our understanding of ecosystem-scale CH4 flux dynamics.

The FLUXNET-CH4 Community Product is distributed in files separated by sites and by temporal aggregation resolutions (e.g., half-hourly or daily). Version information is also assigned to the file to document changes required for a site. The file naming convention below details these options for each file. Multiple files with different temporal aggregation resolution (same site, same data product) are available for download as a single ZIP file archive. Site information metadata are also provided with a data download. Data variable descriptions can be found here.

The FLUXNET-CH4 Community Product is distributed under the two tiers of the FLUXNET2015 Data Policy. Tower teams chose data policy tiers for their site. Data distributed in both tiers can be accessed from the Data Download page for the FLUXNET-CH4 Dataset. To see a list of site-years of data available for each site, please refer to the list of sites and data availability.

IMPORTANT: In case of a synthesis using both CC-By-4.0 (Tier One) and Tier Two data, all data should be treated as Tier Two. See the FLUXNET 2015 Data Policy for an explanation of data tiers.

This repository is for the datasets and codes for "Partitioning of sensible and latent heat fluxes in different vegetation types and their spatiotemporal variations based on 203 FLUXNET sites". This dataset provides processed FLUXNET data and CLM5.0 simulation results, including sensible heat, latent heat, Bowen ratio, precipitation, temperature, albedo, shortwave radiation, longwave radiation, etc. In all zip files, code.rar provides codes for processing all data and drawing pictures, furgre.rar contains all the figures that appear in the manuscript, peocessed_data.rar provides processed FLUXNET data, including the multi-year annual mean variables in 203 FLUXNET sites, multi-year mean monthly values in 12 vegetation types from 203 FLUXNET sites, variables extracted from the CLM 5.0, etc. CLM_to_2d_12time.nc is listed separately, which provides multi-year (2006-2015) monthly average data of sensible heat, latent heat, and Bowen ratio for 17 pfts with resolution of 0.5°× 0.5° from CLM 5.0. At present, this article has been submitted to JGR for review.

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-NGB NGEE Arctic Barrow. This is the FLUXNET version of the carbon flux data for the site US-NGB NGEE Arctic Barrow produced by applying the standard ONEFlux (1F) software. Site Description - The ecosystem is an Arctic coastal tundra. This site measures greenhouse gasses and meteorological variables at the Barrow Environmental Observatory (BEO) as part of the Next-Generation Ecosystem Experiment - Arctic.

(preliminary) 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.

AmeriFlux [http://ameriflux.lbl.gov/] is part of the larger network Fluxnet [http://daac.ornl.gov/FLUXNET/fluxnet.shtml] which endeavors to connect observations from regional micrometeorological tower sites for global analysis. AmeriFlux towers measure the exchange of energy, water, and carbon through a network of sites with variable vegetation, disturbance records, and climatic conditions. The ecosystems included within the network are temperate and tropical evergreen forests, temperate deciduous forests, woodlands, grasslands, agricultural crops, boreal, and shrublands. Climates in these biomes vary between tundra, temperate, tropical, and arid.

This Level 4 Dataset accumulated from the AmeriFlux Network focuses on hourly, daily, and monthly observations collected at North American tower sites. The selection of AmeriFlux sites were chosen based on the period of available data, the diversity in vegetation types, the past success of testing land surface models, and the availability of atmospheric forcing for Community Land Models. AmeriFlux Level 4 data has been gap-filled, quality-flagged and includes meteorological variables, CO2 exchange rates, heat fluxes, biogeochemical pools, calculated gross productivity, and ecosystem respiration terms. The period of record varies by individual tower site, but some sites have as much as 15 years of recorded data.

General description

SAPFLUXNET contains a global database of sap flow and environmental data, together with metadata at different levels. SAPFLUXNET is a harmonised database, compiled from contributions from researchers worldwide.

The SAPFLUXNET version 0.1.5 database harbours 202 globally distributed datasets, from 121 geographical locations. SAPFLUXNET contains sap flow data for 2714 individual plants (1584 angiosperms and 1130 gymnosperms), belonging to 174 species (141 angiosperms and 33 gymnosperms), 95 different genera and 45 different families. More information on the database coverage can be found here: http://sapfluxnet.creaf.cat/shiny/sfn_progress_dashboard/.

The SAPFLUXNET project has been developed by researchers at CREAF and other institutions (http://sapfluxnet.creaf.cat/#team), coordinated by Rafael Poyatos (CREAF, http://www.creaf.cat/staff/rafael-poyatos-lopez), and funded by two Spanish Young Researcher's Grants (SAPFLUXNET, CGL2014-55883-JIN; DATAFORUSE, RTI2018-095297-J-I00 ) and an Alexander von Humboldt Research Fellowship for Experienced Researchers).

Changelog

Compared to version 0.1.4, this version includes some changes in the metadata, but all time series data (sap flow, environmental) remain the same.

For all datasets, climate metadata (temperature and precipitation, ‘si_mat’ and ‘si_map’) have been extracted from CHELSA (https://chelsa-climate.org/), replacing the previous climate data obtained with Wordclim. This change has modified the biome classification of the datasets in ‘si_biome’.

In ‘species’ metadata, the percentage of basal area with sap flow measurements for each species (‘sp_basal_area_perc’) is now assigned a value of 0 if species are in the understorey. This affects two datasets: AUS_MAR_UBD and AUS_MAR_UBW, where, previously, the sum of species basal area percentages could add up to more than 100%.

In ‘species’ metadata, the percentage of basal area with sap flow measurements for each species (‘sp_basal_area_perc’) has been corrected for datasets USA_SIL_OAK_POS, USA_SIL_OAK_1PR, USA_SIL_OAK_2PR.

In ‘site’ metadata, the vegetation type (‘si_igbp’) has been changed to SAV for datasets CHN_ARG_GWD and CHN_ARG_GWS.

Variables and units

SAPFLUXNET contains whole-plant sap flow and environmental variables at sub-daily temporal resolution. Both sap flow and environmental time series have accompanying flags in a data frame, one for sap flow and another for environmental variables. These flags store quality issues detected during the quality control process and can be used to add further quality flags.

Metadata contain relevant variables informing about site conditions, stand characteristics, tree and species attributes, sap flow methodology and details on environmental measurements. The description and units of all data and metadata variables can be found here: Metadata and data units.

To learn more about variables, units and data flags please use the functionalities implemented in the sapfluxnetr package (https://github.com/sapfluxnet/sapfluxnetr). In particular, have a look at the package vignettes using R:

remotes::install_github(

'sapfluxnet/sapfluxnetr',

build_opts = c("--no-resave-data", "--no-manual", "--build-vignettes")

)

library(sapfluxnetr)

to list all vignettes

vignette(package='sapfluxnetr')

variables and units

vignette('metadata-and-data-units', package='sapfluxnetr')

data flags

vignette('data-flags', package='sapfluxnetr')

Data formats

SAPFLUXNET data can be found in two formats: 1) RData files belonging to the custom-built 'sfn_data' class and 2) Text files in .csv format. We recommend using the sfn_data objects together with the sapfluxnetr package, although we also provide the text files for convenience. For each dataset, text files are structured in the same way as the slots of sfn_data objects; if working with text files, we recommend that you check the data structure of 'sfn_data' objects in the corresponding vignette.

Working with sfn_data files

To work with SAPFLUXNET data, first they have to be downloaded from Zenodo, maintaining the folder structure. A first level in the folder hierarchy corresponds to file format, either RData files or csv's. A second level corresponds to how sap flow is expressed: per plant, per sapwood area or per leaf area. Please note that interconversions among the magnitudes have been performed whenever possible. Below this level, data have been organised per dataset. In the case of RData files, each dataset is contained in a sfn_data object, which stores all data and metadata in different slots (see the vignette 'sfn-data-classes'). In the case of csv files, each dataset has 9 individual files, corresponding to metadata (5), sap flow and environmental data (2) and their corresponding data flags (2).

After downloading the entire database, the sapfluxnetr package can be used to: - Work with data from a single site: data access, plotting and time aggregation. - Select the subset datasets to work with. - Work with data from multiple sites: data access, plotting and time aggregation.

Please check the following package vignettes to learn more about how to work with sfn_data files:

Quick guide

Metadata and data units

sfn_data classes

Custom aggregation

Memory and parallelization

Working with text files

We recommend to work with sfn_data objects using R and the sapfluxnetr package and we do not currently provide code to work with text files.

Data issues and reporting

Please report any issue you may find in the database by sending us an email: sapfluxnet@creaf.uab.cat.

Temporary data fixes, detected but not yet included in released versions will be published in SAPFLUXNET main web page ('Known data errors').

Data access, use and citation

This version of the SAPFLUXNET database is open access and corresponds to the data paper submitted to Earth System Science Data in August 2020.

When using SAPFLUXNET data in an academic work, please cite the data paper, when available, or alternatively, the Zenodo dataset (see the ‘Cite as’ section on the right panels of this web page).

This dataset includes measured data used for developing hybrid-predictive-modeling (HPM) approach and simulated evapotranspiration and ecosystem respiration data across several Fluxnet sites, SNOTEL sites and East River locations (Chen et al., 2020 in review). Fluxnet sites considered in this study include: CA-OAS, CA-OBS, US-NR1, US-TON, US-VAR, US-SRM, US-WHS, US-WKG. Snotel sites Butte (ER-BT), Porphyry Creek (ER-PK) and Schofield Pass (ER-SP) are included as well 16 east river locations with different vegetation types denoted by evergreen forests (EF), deciduous forests (DF), riparian shrublands (RS) and meadow grassland (MS). This data package includes: 1) A summary spread sheet of the sites and acronyms for data variables. 2) Processed Fluxnet datasets that are obtained from Fluxnet.fluxdata.org with HPM estimated evapotranspiration (ET) and ecosystem respiration (RECO). Trained models and metrics for parameters for the Fluxnet sites were added in an update on November 2, 2020; 3) Processed SNOTEL datasets obtained from www.nrcs.usda.gov/snow/ and HPM estimated ET and RECO; 4) Weather data at east river locations obtained from DAYMET (daymet.ornl.gov) as well as HPM estimated ET and RECO. NDVI datasets are obtained from Landsat 5, 7 and 8 on Google Earth Engine platform. Detailed QA/QC and sampling methods of measured data are available at the original data source.

Matlab code with the novel algorithm designed to automatically detect VE-driven CO2 emissions among the FLUXNET2015 (https://fluxnet.fluxdata.org), AmeriFlux (http://ameriflux.lbl.gov), OzFlux (http://www.ozflux.org.au) and AsiaFlux (http://www.asiaflux.net) databases.

Our algorithm aims at detecting VE over periods when subterranean ventilation may prevail over other processes. The algorithm was designed based on previous research reflected in the following assumptions: (1) atmospheric turbulence must be sufficient to pump CO2-rich air from the vadose zone to the atmosphere; (2) no nocturnal VE happens due to soil re-humidification; (3) the required variability in air temperature and soil moisture must be insufficient to explain variations of net CO2 emissions (i.e. incompatible with an ecophysiological interpretation of fluxes, such as the Birch effect at the end of the dry season).

A temporal window is needed to statistically discriminate subterranean CO2 release due to changes in air temperature (e.g. with the passage of high and low pressure systems and fronts) or soil moisture. Although VE is an abrupt process, considering previous research, a period of five days with sustained high-turbulence, similar mean air temperature and dry conditions was considered as unequivocal to detect VE. Thus, we applied the algorithm to five-day intervals over one-year for each selected site.

In accordance with the above assumptions, we filtered these data according to the following criteria: (1) only daytime [shortwave radiation incoming (SW_IN) or photosynthetic photon flux density incoming (PPFD_IN) > 50 W m-2], (2) positive fluxes [net ecosystem exchange (NEE) or net carbon dioxide flux (Fc) > 0 µmolCO2 m-2 s-1], (3) maximum mean air temperature (TA) absolute difference between days 1 and 5 of 3 °C, (4) maximum mean soil water content (SWC) absolute difference between days 1 and 5 of 1 %, (5) null precipitation [P > 0.00001] and (6) high-turbulence conditions [u* > 0.2 m s-1] data were used. Furthermore, to reduce the potential effect of data availability on the number of VE detected our algorithm, only five-day intervals with a minimum number of data [N>40] and CO2 maximum quality [flag qc = 0] were used. Finally, we are gathered an indicator of data availability for each site and year analyzed (see Table S1). We computed Partial Spearman correlation coefficients over each five-day period filtered to eliminate spurious correlation effects between Fc (µmolCO2 m-2 s-1) and ancillary data. Ancillary variables considered in partial correlation were: air temperature, vapor pressure deficit, soil temperature, atmospheric pressure, soil water content, photosynthetic photon flux density incoming, incoming shortwave radiation and friction velocity. Only five-day intervals with a partial Spearman correlation coefficients between Fc and u* above 0.2 and p < 0.05 (support the evidence of the alternative hypothesis, i.e. non-zero partial correlation, being significantly different from the null hypothesis, i.e. zero partial correlation) were considered as VE events. The software Matlab was used for statistical analyses (Matlab R2017a).

In order to balance the different dataset duration among analyzed sites as well as to simplify our analysis, results shown in this study correspond to one year of data per site. We have considered the assumption that experimental sites where VE were not detected after analyzing four years of data are not VE predisposed sites (in accordance with our algorithm design). Thus, the algorithm was performed to the available datasets under the next conditions: (1) for site-specific databases lasting from one to four years, the algorithm was applied to the whole database; (2) for site-specific databases lasting from five to six years, the algorithm was applied to the first four consecutive years; (3) for site-specific databases lasting more than six years, the algorithm was applied to the first four non-consecutive years. In those sites where VE-driven CO2 emissions were detected over several years, the year finally selected corresponded to the one with more VE detected. The list of FLUXNET and regional EC networks sites used in this study with respective basic information appears in the supporting information (tables S1, S2 and S3).

The list of FLUXNET and regional EC networks sites used in the study with respective basic information appears in the supporting information (tables S1, S2 and S3) of "ECOSYSTEM CO2 RELEASE DRIVEN BY WIND OCCUR IN DRYLANDS AT GLOBAL SCALE" DOI: 10.1111/gcb.16277

The Flux data kit is an effort to expand upon the existing work by Ukkola et a. (2022) to synthesize various sources of ecosystem flux data (i.e. the PLUMBER2 data set, gathered from all major networks). We further expand upon the original data set by integrating data which was either expanded upon (temporally) or where sites were added (e.g. the integration of ICOS data).

The effort uses the FluxnetLSM package by the above mentioned authors, as well as their general workflow. In contrast to the PLUMBER2 data set we do not apply stringent quality control, and all quality control on the availability of variables and/or their duration should be done by the user. Furthermore, we include both leaf area index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) in the netcdf output, where PLUMBER2 only provided LAI. On all other parts the formatting and naming conventions as well as quality control specifications remain the same as in PLUMBER2. We therefore refer to Ukkola et al. (2022) for details.

Data included

The data included consists of the following files, containing different versions of the same data and site meta information.

• FLUXDATAKIT_LSM.tar.gz file contains compressed NetCDF files compatible with the ALMA scheme for land surface modelling.
• FLUXDATAKIT_FLUXNET.tar.gz file contains data in a CSV format according to the FLUXNET specifications.
• rsofun_driver_data_v3.3.rds file is a compressed serialized R file containing data formatted for use with the {rsofun} R package.
• fdk_site_info.csv contains site meta information in tabular form
• fdk_site_fullyearsequence.csv contains information about complete sequences of good-quality data by site (see also here).

Data generation

Data is generated using the FluxDataKit project. Although this project is not meant for continuous releases, and no support is provided in using this code with data provided AS IS, it might still be useful to some:

https://github.com/geco-bern/FluxDataKit

The data can be further complimented using the FluxnetEO dataset, which is accessible through the package with the same name as found here:

https://github.com/geco-bern/FluxnetEO

Acknowledgements

The flux data kit is part of the LEMONTREE project and funded by Schmidt Futures and under the umbrella of the Virtual Earth System Research Institute (VESRI).

References:

• Ukkola, Anna M., Gab Abramowitz, and Martin G. De Kauwe. "A flux tower dataset tailored for land model evaluation." Earth System Science Data 14.2 (2022): 449-461.

Surface flux measurements were made at selected sites within the FIFE area. Each surface flux station was capable of measuring the fluxes of net radiation, sensible heat, and latent heat. The Bowen ratio stations measured the soil heat flux as well. The surface flux and micrometeorological measurements available in this data set were collected from 15 locations within the FIFE study area between 1987 and 1989. Six automatic surface energy and radiation balance systems were operated continuously for 144 days from May 16 to October 16, 1987. Variables including net radiation, air temperature, vapor pressure and wind speed, were quite similar for the sites even though the sites were as much as 10 km apart and represented the four cardinal slopes and a top. The Bowen ratio was low during most of the season, increasing sharply toward the end of the season after a long dry spell. The average Bowen ratio was 0.35. About 72% of the available energy was converted into latent heat flux density. Since the data systems and instrumentation used were of similar design, the variability in results can be ascribed to treatment and locations. These results can be used to estimate the number of stations needed to represent a rolling prairie topography.

Gross Primary Productivity (GPP) represents the cumulative amount of carbon dioxide (CO2) assimilated by green plants through photosynthesis at specific time intervals and spatial scales. It is the main component of the carbon exchange between the terrestrial biosphere and the atmosphere, and has a major influence on global climate and terrestrial ecosystem functioning. Over the last two decades, the continuous and reliable collection of global land surface variables by EOS-MODIS, and the parallel development of the eddy-covariance flux tower network (FLUXNET) have enabled the integration of MODIS observations with tower measurements for the calibration and validation of remote sensing models to obtain global GPP estimates. Despite the significant progress and success to date, current remote sensing GPP models based on the light use efficiency (LUE) concept share several limitations, including the difficulty in accurately predicting LUE variability and the associated use of land cover m..., The eLUE Gross Primary Productivity (eLUE-GPP) is based on a simple yet ecologically sound ecosystem light use efficiency (eLUE) GPP model, using the more than two decades of global MODIS Enhanced Vegetation Index (EVI) product and the publicly available FLUXDATA2015 dataset, to generate a global GPP product (eLUE-GPP) from February 2000 to March 2024. The eLUE-GPP is available in both global and site scales and at various temporal and spatial resolutions. , , # eLUE-GPP (MODIS): A Global Gross Primary Productivity Product based on ecosystem light-use-efficiency model and MODIS EVI

https://doi.org/10.5061/dryad.v9s4mw74h

The eLUE Gross Primary Productivity (eLUE-GPP) is based on a simple yet ecologically sound ecosystem light use efficiency (eLUE) GPP model, using the more than two decades of global MODIS Enhanced Vegetation Index (EVI) product and the publicly available FLUXDATA2015 dataset, to generate a global GPP product (eLUE-GPP) from February 2000 to March 2024. The eLUE-GPP is available in both global and site scales and at various temporal and spatial resolutions.

Description of the data and file structure

The eLUE-GPP product has two Science Datasets, comprising GPP and GPP uncertainty. To reduce data storage size, the original values have been adjusted to integers. Users are advised to refer to the scaling factors for value restoration when using the data.

The eLUE-GPP dataset prov...

This dataset contains 5 minute surface flux data, collected by three NCAR/EOL flux-PAM stations during the Flatland Atmospheric Boundary Layer Experiment of 1996 (FLATLAND96). Each flux-PAM station measured standard surface meteorological variables, momentum flux, sensible and latent heat flux, net radiation, and soil heat flux, while Station 1 also included surface ozone flux measurements. These data are in NetCDF format and have been quality controlled. The sonic anemometer winds have been tilt corrected, but remain in instrument coordinates.

This dataset is used to present the impact of hydrological fluctuations on methane flux dynamics in wetlands. The data, from 31 wetland sites in FLUXNET, include measurements of water table levels, methane fluxes, and relevant environmental variables, which are essential for understanding the methane-related biogeochemical processes in wetlands. The observational data have been made publicly available for transparency and to support further research on wetland methane emissions and their implications for climate change.

The maximum entropy production (MEP) approach has been little used to simulate evaporation in forests and its sensitivity to input variables has yet to be systematically evaluated. This study addresses these shortcomings. First, we show that the MEP model performed well in simulating evaporation during the snow-free period at six sites in temperate and boreal forests (0.68 ≤ NSE ≤ 0.82). Second, we computed a sensitivity coefficient S representing the proportion of change in the input variable transferred to the latent heat flux (λE). Net radiation (Rn) was the most influential variable (S » 1) at all sites, indicating that an increase in Rn translates into an equivalent increase in λE. The MEP model avoided the issue of oversensitivity to air temperature (S < 0.5 at peak evaporation) and captured limitations to transpiration associated with the atmospheric evaporative demand. Overall, the MEP model offers a promising tool for climate change studies.

Methods We selected six sites hosting eddy-covariance flux towers from the AmeriFlux and Fluxnet networks. At each site, we modeled evaporation using the maximum entropy production method (Wang and Bras, 2011) for the snow-free period. We performed a sensitivity analysis using the method of Beven (1979) to assess the sensitivity of the MEP model to variations to input variables.

Wind speed, wind direction (3D) and gas concentrations are measured at 10 Hz and statistically processed using the eddy covariance technique to half-hour estimates of flux rates of gas exchange between the ecosystem and atmosphere. Three gases were measured: carbon dioxide, methane and water vapor. Simultaneous measurements of environmental drivers are also measured and recorded as half-hourly values: photosynthetic photon flux density, downwelling and upwelling short and long wave radiation, air temperature, relative humidity and precipitation. Variable names and units follow AmeriFlux and FLUXNET standards. This site is a flotant herbaceous freshwater marsh in Salvador Wildlife Management Area near Luling, LA.

Wind speed, wind direction (3D) and gas concentrations are measured at 10 Hz and statistically processed using the eddy covariance technique to half-hour estimates of flux rates of gas exchange between the ecosystem and atmosphere. Three gases were measured: carbon dioxide, methane and water vapor. Simultaneous measurements of environmental drivers are also measured and recorded as half-hourly values: photosynthetic photon flux density, downwelling and upwelling short and long wave radiation, air temperature, relative humidity and precipitation. Variable names and units follow AmeriFlux and FLUXNET standards. This site is a flotant herbaceous freshwater marsh in Salvador Wildlife Management Area near Luling, LA.

Data from article Stocker et al. (in review) Nature Geosci. The datasets provided here include: Site-level GPP model results from the P-model (Wang et al., 2017) Model outputs from global simulations with the P-model (Wang et al., 2017) as implemented for the study by Stocker et al. (2019) This data may be used to partly reproduce results presented in Stocker et al. (2019) Nature Geosci. "Partly" because we used data for our analysis that was not open access but was confidentially shared with us. This includes remote sensing-based GPP estimates from the BESS and VPM models. Other open access data that was used for the analysis may not be distributed under this DOI. This includes FLUXNET 2015 data and MODIS data. For reproducing results of Stocker et al. (2019) regarding site-scale evaluations, run for example the scripts plot_bias_all.R and plot_bias_problem.R, available from Github or Zenodo, using CSV files provided here (see comments in scripts). For more insight, including analysis of global simulation outputs, see RMarkdown file si_soilm_global.Rmd. This renders the supplementary information PDF document provided along with Stocker et al. (2019), which is available also on RPubs. The present datasets are prepared by script prepare_data_openaccess.R on Github or Zenodo. Data description Site-level data Data is provided as CSV files: gpp_daily_fluxnet_stocker18natgeo.csv: Daily data for full time series (not including MODIS GPP) gpp_8daily_fluxnet_stocker18natgeo.csv: Data aggregated to 8-day periods corresponding to MODIS dates (including MODIS GPP) gpp_alg_daily_fluxnet_stocker18natgeo.csv: Data filtered to periods with substantial soil moisture effects ("fLUE droughts" following Stocker et al. (2018a)) gpp_alg_8daily_fluxnet_stocker18natgeo.csv: Data aggregated to 8-day periods and filtered to periods with substantial soil moisture effects. Each column is a variable with the following name and units (not all variables are available in all files): site_id: FLUXNET site ID date: Date of measurement, units: YYYY-MM-DD gpp_pmodel and gpp_modis: Simulated GPP from the P-model and MODIS (see Stocker et al. (2018b), Methods, RS models), units: g C m-2 d-1 (mean across 8 day periods in respective files) aet_splash: Simulated actual evapotranspiration from the SPLASH model (Davis et al., 2017), units: mm d-1 pet_splash: Simulated potential evapotranspiration from the SPLASH model (Davis et al., 2017), units: mm d-1 soilm_splash: Soil moisture simulated by the SPLASH model (Davis et al., 2017), normalised to vary between zero and one at the maximum water holding capacity, unitless. flue: fLUE estimate from Stocker et al. (2018). Estimates soil moisture stress on light use efficiency from flux data, unitless. beta_a, beta_b, and beta_c: Empirical soil moisture stress, used as multiplier to simulated GPP as described in Stocker et al. (2018b), unitless. Global P-model simulation outputs GPP and soil moisture output is provided as NetCDF files for simulations s0, and s1b (see Stocker et al. (2018b)). All meta information is provided therein. Files for simulation s1b are names as follows (for outputs from other simulations replace s1b with other simulation name). The fraction of each gridcell covered by land (not open water or ice) is given by separate file s1b_fapar3g_v2_global.fland.nc. s1b_fapar3g_v2_global.d.gpp.nc: Daily GPP from simulation s1b. s1b_fapar3g_v2_global.d.wcont.nc: Daily soil moisture from simulation s1b (is identical in other simulations, therefore not provided.) Due to limited total file size allowed for uploads to Zenodo, only outputs from s1b are provided here. Other outputs may be obtained upon request addressed to benjamin.stocker@gmail.com. References Davis, T. W. et al. Simple process-led algorithms for simulating habitats (SPLASH v.1.0): robust indices of radiation, evapotranspiration and plant-available moisture. Geoscientific Model Development 10, 689–708 (2017). Hufkens, K. khufkens/gee_subset: Google Earth Engine subset script & library. (2017). doi:10.5281/zenodo.833789Running, S. W. et al. A Continuous Satellite-Derived Measure of Global Terrestrial Primary Production. Bioscience 54, 547–560 (2004). Stocker, B. et al., Quantifying soil moisture impacts on light use efficiency across biomes, New Phytologist, doi: 10.1111/nph.15123 (2018a). Stocker, B. et al., Satellite monitoring underestimates the impact of drought on terrestrial primary productivity, Nature Geoscience (2019). Wang, H. et al. Towards a universal model for carbon dioxide uptake by plants. Nat Plants 3, 734–741 (2017).

The ON-TP39 site, also known as the CA-TP4 on Global Fluxnet and ON-WPP39 in some of the Fluxnet-Canada Research Network (FCRN)/Canadian Carbon Program (CCP) publications. ON-TP39 is the mature eastern white pine (Pinus strobusL.) forest of the Turkey Point Flux Station. It was planted in 1939 (ON-TP39) on cleared oak-savannah land. Meteorological data collection was started in late autumn 2001 and flux measurements were started in June 2002. The data set documented here includes carb on, water and energy fluxes and meteorological and soil measurements. A unique aspect of Turkey Point Flux Station is its geographic location between the boreal and the broadleaf deciduous forest transition zone. It provides an excellent opportunity to investigate and quantify the strength of the carbon sink or source for planted temperate conifer forests, and its sensitivity to seasonal and annual climate variability. Also white pine is an important species in the North American landscape, because of its ability to adapt to dry environments. It grows eff iciently on nutrient poor, dry, sandy soils. Generally, it is the first woody species to flourish after a disturbance such as fire or clearing and over longer time periods helps more native forest species to establish through succession. White pine trees can live for about 350–400 years and their height may reach up to 45–60 m. These characteristics make white pine a preferred plantation (afforestation) species in eastern North America. Fluxes, meteorological and soil measurement conducted at this site help us to explore carbon sequestration potential of chronosequence of planted or afforested white pine stands in southern Ontario. The main objectives are (i) to make year-round measurements of energy, water vapour and carbon dioxide (CO2) fluxes and other meteorological variables over mature, middle-aged, young and seedling white pine plantation forests (established in 1939, 1974, 1989 and 2002) (ii) to relate gross ph otosynthesis and respiration of this stand to environmental factors (iii) determine the effects of seasonal and inter-annual climate variability on net ecosystem produc tivity, and to better understand the processes of production, storage and transport of soil CO2 and (iv) use these data to further improve process-based photosynthesis and respiration models.