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2019 Meander C and Meander Z floodplain groundwater chemistry from the East River Watershed, CO, USA

This dataset includes groundwater geochemistry data from floodplain piezometers collected as a part of the Watershed Function Scientific Focus Area (SFA) located in the Upper Colorado River Basin. The data were collected in order to investigate the role of hyporheic exchange and other river corridor processes on riverine export of solutes. Data includes samples from two intra-meander zones: Meander C, in the Pumphouse vicinity, and Meander Z, just upstream of the confluence with Brush Creek. Floodplain piezometers installed along two transects across Meander C (MCP and MCB wells) and Meander Z (MZA and MZB wells) were sampled on daily to weekly time scales during summer-fall 2019. Some river water grab samples are also included. Data includes in-field measurements (pH, electrical conductivity [EC], oxidation reduction potential [ORP], dissolved oxygen [DO], and groundwater level) along with laboratory measurements (dissolved inorganic carbon [DIC], dissolved organic carbon [DOC], metals and major cations, anions [chloride, sulfate, nitrate], and dissolved ammonium). Files are included in this dataset include: sample locations and depths in both a kmz file which can be opened in Google Earth and a csv file, aqueous geochemistry data in a csv files for Meander C and Meander Z, and analytical detection limits in a csv file. This dataset also includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.

54 ENVIRONMENTAL SCIENCES↗

Aggregated carbon dioxide flux and hydrometeorology data from an Amazonian palm swamp peatland in Peru: 2018, 2019, and 2022

This dataset contains eddy covariance carbon dioxide flux and hydrometeorological measurements made in an Amazonian palm swamp peatland near Iquitos, Peru. These data have been aggregated from half-hourly observations that are available from AmeriFlux (https://ameriflux.lbl.gov/; site PE-QFR). These data files are CSV (comma separated values) format and can be imported using Matlab, R, or Excel. Three full years of data are reported (2018, 2019 and 2022) during which time there were large differences in annual net ecosystem carbon dioxide exchange. The gap was caused by instrument malfunction and extended delays in repairs because of the Covid-19 pandemic. This research was conducted to better understand the carbon cycle of tropical peatlands, and was supported by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, Terrestrial Ecosystem Science Program, under Award Number DE-SC0020167.

54 ENVIRONMENTAL SCIENCES↗

Soil moisture and temperature from 2019 to 2024 along northeast- and southwest-facing hillslopes at the Lower Montane site in the East River Watershed, Colorado

Soil moisture, temperature, and electrical conductivity have been monitored at multiple depths (between 10 and 50 cm) at 4 locations along a northeast-facing slope and 3 locations on the opposite southwest-facing slope at the Lower Montane site in the East River Watershed, Colorado, from Oct 2019 to Oct 2024. The purpose of this data is to inform hydro-biogeochemical analyses for the Watershed Function Scientific Focus Area (SFA). Two locations on the northeast-facing slope were reinstalled in 2020 due to damage from wildlife, and thus data for these sites are provided in two distinct files. Overall, the data are reported in 9 CSV files containing the measurements, and the locations are provided in the Sensor_Location.csv file. There is a total of 10 *.csv data files and 3 *.csv metadata files. Older datasets associated with the northeast-facing slope are provided in another archive (see reference). These data products are part of the Watershed Function Scientific Focus Area collection effort to further scientific understanding of biogeochemical dynamics from genome to watershed scales. Feel free to contact the author with any questions or collaboration interests.

54 ENVIRONMENTAL SCIENCES↗

Metagenome-assembled genomes from Slate River floodplain sediments near Crested Butte, CO, USA (September 2019)

Microorganisms play a key role in cycling nutrients and contaminants in the terrestrial environment depending on their genetic potential. Here we present metagenome-assembled genomes (MAGs) for the bacterial and archaeal community in floodplain sediment samples taken September 2019 at one locations (OBJ1) near the confluence of the Oh-Be-Joyful Creek and Slate River. The site is one of the field sites in focus for the SLAC National Accelerator Laboratory Groundwater Quality Science Focus Area (SFA) program. Sediment samples from a deep soil pit were collected from 50 to 150 cm depth below surface at discrete depths every 20 cm for microbial analyses. A total of 6 metagenomes were sequenced through the Joint Genome Institute (JGI) and can be found under Genomes Online Database (GOLD) sequencing project: Gs0142591. Metagenomes were assembled, binned, and refined using metawrap to generate MAGs (>50% complete and < 10% contamination based on checkM scores). This dataset includes a zip file of 2562 MAG fasta files and a csv file with quality, taxonomic classification (Genome Taxonomy Database Release RS220), and metagenome accessions for MAGs. This dataset also includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.Part of this work was performed at SLAC Accelerator Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-76SF00515.

54 ENVIRONMENTAL SCIENCES↗

Metagenome-assembled genomes from East River floodplain sediments near Crested Butte, CO, USA (June to September 2019)

Microorganisms play a key role in cycling nutrients and contaminants in the terrestrial environment depending on their genetic potential. Here, we present metagenome-assembled genomes (MAGs) for the bacterial and archaeal community in floodplain sediment samples taken in 2019 in June (flooded conditions) and September (drained conditions) at two locations (MCB1 and MCB3) near the Meander C/Pumphouse floodplain sites of the East River. Sediment cores were collected from 2 depths, a near-surface, generally unsaturated depth (30-40 centimeter (cm) depth below surface) and a deeper depth influenced by flooding with redoximorphic features (70-80 cm depth below surface). Sediments were homogenized from the 10 cm core for microbial analyses. A total of 24 metagenomes were sequenced through the Joint genome institute (JGI) corresponding to 8 samples sequenced in triplicate. These metagenomes can be found under Genomes Online Database (GOLD) sequencing project: Gs0141020. Metagenomes were assembled, binned, and refined using metawrap to generate MAGs (>50% complete and < 10% contamination based on checkM scores). This dataset includes a zip file of 436 MAG fasta files and a csv file with quality, taxonomic classification (Genome Taxonomy Database Release RS220), and metagenome accessions for MAGs. This dataset also includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. Part of this work was performed at SLAC Accelerator Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-76SF00515.

54 ENVIRONMENTAL SCIENCES↗

Meteorological and Soil Data from Ecohydrology Sensor Towers at Pump House and Snodgrass Mountain in East River Watershed, Colorado, 2019-2025

This data package includes hourly meteorological and soil sensor data at eight ecohydrology monitoring sites in East River Watershed, Colorado as part of the Watershed Function Scientific Focus Area (WFSFA) research led by Lawrence Berkeley National Lab (LBNL). Four field sites were located on the hillslope of East River (ER) near Pump House (PH) at Mount Crested Butte (ER-PHS1 to 4), and the other four are in the Snodgrass Mountain (SG) area (SG-EHS5 to 8). In terms of vegetation cover, three sites are in montane grasslands (ER-PHS1, ER-PHS2, and SG-EHS5), three are below evergreen conifer canopy (ER-PHS3, SG-EHS6, and SG-EHS7), and two are below deciduous aspen canopy (ER-PHS4 and SG-EHS8). The monitoring period began in October 2019 at the East River sites, in October 2020 at SG-EHS5 and SG-EHS6, and in October 2021 at SG-EHS7 and SG-EHS8. In September 2024, all four East River sites were fully retired. The four Snodgrass Mountain sites remain active. Each site is equipped with a comprehensive suite of meteorological sensors on a tripod and soil sensors that measure weather, energy fluxes, and soil variables. This data package includes measurements from ten different types of sensors and up to thirteen individual sensors per site, including (1) a weather station (measurement height ranges from 2.8~3.8 meters (m) above ground), (2) a quantum sensor for photosynthetic active radiation (PAR) (2.4~3.3m), (3) a net radiometer (1.7~2.1m), (4) an infrared radiometer (1.6~2.2m), (5) a sonic distance sensor (1.5~1.9m), (6) a soil carbon dioxide (CO2) flux chamber (0m), (7) a soil heat flux plate (-0.05m below ground), (8) a soil oxygen sensor (-0.3m), (9) a soil water potential sensor (-0.3m), and (10) soil water content sensors at 3~4 depths (-1.15 ~ -0.1m). A total of twenty-three variables is reported in this data package, including (1) atmospheric variables: air temperature (TA), atmospheric pressure (PA), vapor pressure (VP), and vapor pressure deficit (VPD), (2) precipitation variables: rain precipitation (P) and snow depth (D_SNOW), (3) energy fluxes variables: four-component net radiation (NETRAD) (shortwave/longwave incoming/outgoing radiation, SW_IN, SW_OUT, LW_IN, LW_OUT), photosynthetic photon flux density (PPFD), and soil heat flux (G), (4) soil variables: soil water content (SWC), soil water potential (SWP), soil temperature (TS), soil bulk electrical conductivity (COND_SOIL), and soil gaseous oxygen concentration (O2_SOIL), (5) wind variables: two-dimensional wind speed (WS), gust speed (WS_MAX), and wind direction (WD), and (6) surface variables: surface infrared temperature (T_CANOPY) and soil CO2 flux (CO2_SOIL). Please see the Methods section for data processing and QA/QC steps taken to generate the hourly datasets. The following files are included in this data package (notes on version: v{x}-{y}, where x is the metadata version, and y is the data version, when applicable): (1) “metadata_site_v{x}-{y}.csv” - a site metadata file that summarizes location information of all sites, including site ID, description, coordinates, timeframe, elevation, and vegetation cover, (2) “metadata_instrument_v{x}-{y}.csv” - an instrument metadata file that summarizes sensor information of all sites, including sensor manufacturer and model, measurement height, and sampling and averaging interval of all variables, (3) "data_{SITE_ID}_v{x}-{y}.csv" - eight data files that contain hourly data of each site indicated by {SITE_ID} in the filename, (4) “/figure/data_{SITE_ID}_v{x}-{y}.png" - eight figures that help visualize data of each site indicated by {SITE_ID} in the filename, (5) “/photo/*” - photos of each site indicated by {SITE_ID} in the filename, and (6) four file level metadata (flmd.csv) and data dictionary (*_dd.csv) files that summarize file, header, column, and variable information of all files. Notes: (1) Measurement height: Each variable name is followed by conventional positional qualifiers “H_V_R”, where H indicates the relative horizontal positions of that specific variable, V the vertical positions, and R the replicates. In this data package, only the vertical qualifier V varies, and V increases from the highest vertical position (V=1) to the lowest. Variables with the same qualifier are not necessarily measured by the same sensor, and the same variable with the same qualifier across different sites are not necessarily measured at the same height. Please refer to “metadata_instrument.csv” for the sensor information and measurement heights, and whether a variable is measured below the canopy. (2) Variable availability: Snow depth is not available at ER-PHS3 and SG-EHS7. SWC, soil temperature, and soil bulk EC at the deepest depth (<-1m) are not available at SG-EHS6 and SG-EHS7. The missing value code for numeric variables is -9999, except for SWP. For SWP, the missing value code is +9999, because SWP values are negative. (3) Sampling frequency: Please refer to “metadata_instrument.csv” for the increase of sampling frequency of some variables from 30-min to 1-min at ER-PHS1 to 4 in July 2020. (4) Sensors: While the methods of each sensor are not detailed, all sensors are commercially available, and their methods can be found in their manuals. Please refer to “metadata_instrument.csv” for the sensor manufacturer and model information. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

54 ENVIRONMENTAL SCIENCES↗

Evapotranspiration partitioning estimates from 8 methods from 47 NEON sites, 2019-2021

This dataset provides daily estimates of evapotranspiration (ET) and the transpiration-to-evapotranspiration ratio (T/ET) across 47 terrestrial National Ecological Observatory Network (NEON) sites spanning diverse environmental and biome conditions in the United States across three years of data (2019-2021). Daily ET is reported in both energy units (MJ m⁻² day⁻¹) and equivalent water depth (mm day⁻¹), assuming a constant latent heat of vaporization of 2.45 MJ/kg. The primary method uses a hybrid recurrent neural network–Penman–Monteith framework (RNN-PM), which integrates physically based surface energy balance constraints with data-driven learning to partition ET into transpiration and evaporation components. Model inputs include in situ meteorological observations (air temperature, vapor pressure deficit, wind speed, and radiation) combined with satellite-derived land surface temperature, leaf area index, and soil moisture. For benchmarking and uncertainty assessment, T/ET estimates from seven additional models are included: Priestley-Taylor Jet Propulsion Laboratory (PT-JPL), Penman-Monteith (P-M), Two-Source Energy Balance (TSEB), Support Vector Regression (SVR), and Categorical Boosting (CatBoost), among others—spanning empirical, machine-learning, and process-based approaches (see methods section or linked publication for detailed descriptions). Data Package Contents: The dataset a csv files containing daily ET and T/ET estimates for each site and model, along with associated metadata files these variables. Data can be accessed using common spreadsheet software (e.g., Microsoft Excel, LibreOffice) or programming environments such as R or Python. Together, these data support cross-site comparisons of ecosystem water use, evaluation of ET partitioning methods, and development of improved land–atmosphere exchange models.

EARTH SCIENCE > ATMOSPHERE↗

Daily evapotranspiration changes during heatwaves at 32 NEON sites, 2019-2021

This dataset provides partitioned evapotranspiration (ET, the combined loss of water from soil and plant surfaces) anomalies during heatwave events—soil evaporation (E) and transpiration (T)—for 268 heatwave events across 32 National Ecological Observatory Network (NEON) flux sites in the contiguous United States from 2019–2021. Using an ensemble of four high-frequency turbulence methods (Flux-variance Similarity, Conditional Eddy Covariance [CEC], CEC with Water-Use Efficiency, and Conditional Eddy Accumulation; see Zahn and Bou-Zeid 2024), half-hourly transpiration-to-evapotranspiration (T/ET) ratios were derived from 20 hertz (Hz, cycles per second) eddy covariance measurements of carbon dioxide (CO₂) and water vapor (H₂O) concentrations. The dataset spans six vegetation types including evergreen and deciduous forests, grasslands, cultivated crops, shrublands, and emergent herbaceous wetlands. Data Package Contents: The dataset includes a single CSV (comma-separated values) file containing daily anomalies (deviations from baseline conditions) for transpiration (Delta_T), evaporation (Delta_E), total evapotranspiration (Delta_ET), and T/ET ratio (Delta_T_ET) during each day of identified heatwave events. The file also includes site codes, dates, heatwave event identifiers, and day-of-heatwave indicators. The CSV file can be opened with spreadsheet software (Microsoft Excel, Google Sheets) or programming environments (Python, R, MATLAB). This resource enables researchers to investigate ecosystem-specific responses to thermal extremes, validate land surface model partitioning of ET fluxes, and examine feedbacks between water cycling and surface energy balance during heatwaves. The dataset is particularly valuable for studies linking vegetation hydraulic strategies to climate resilience, as it captures the divergent responses of shallow-rooted versus deep-rooted ecosystems. Potential applications include improving drought early warning systems, informing irrigation management strategies, and advancing our mechanistic understanding of land-atmosphere interactions under extreme heat conditions.

Day of Heatwave↗

Processed sap flow and fine-root trait data associated with summer drought responses in temperate trees in Lisle, Illinois, USA (2019–2021)

These data support the manuscript “Acquisitive root exploration strategies help maintain higher peak sap flux rates during summer drought, but more root biomass does not”. The dataset includes processed sap flow measurements and fine-root trait data collected between 2019 and 2021 from temperate monodominant tree plots established in the 1920s to 1930s ranging in size from 0.05 to 0.8 ha at The Morton Arboretum in Lisle, IL. Sap flow was measured with ICT sap flow sensors using the heat ratio method. Fine-root traits were measured from soil cores which includes specific root length (SRL), specific root area (SRA), diameter, biomass, and length for diameter classes ≤1 mm and ≤2 mm. The package contains comma separated value (CSV) data files and associated metadata that can be viewed and analyzed using common software such as spreadsheet programs, R, and Python. These data are used to investigate how variation in fine-root traits relate to tree water use and drought response during summer drought linking belowground root traits and aboveground physiological responses.

drought↗

Soil Water Retention and Hydraulic Conductivity Data and Model at Pump House in East River Watershed, Colorado 2019-2024

This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors near Pump House at Mount Crested Butte in the East River Watershed. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format ER-X-Y, where ER refers to East River, X is the location identifier, and Y is the depth identifier at the same X (shallow Y=1). Specifically, ER-PHS, ER-LMC, ER-LMF, and ER-SMN are associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and ER-RBTn (upslope n=1) are sampling transects during the 2019 Rootball Campaign. The sample and location information can be found in metadata.csv. Sampling and Measurements Each sample falls into one of the three sampling methods – (1) intact cores, (2) repacked samples, or (3) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. Both intact cores and repacked samples were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores and in-situ soil sensors could be prioritized because these sampling methods are less destructive. While the repacked samples were packed to the target bulk density (estimated post-sampling, when sample volume was known), these samples had altered pore structures. Nevertheless, intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method also has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all files. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

EARTH SCIENCE > LAND SURFACE > SOILS↗

Leaf gas exchange data, Nov 2018 and May 2019, Daintree Rainforest, Australia

This data package contains leaf gas exchange data for 11 canopy tree species in the control and drought treatments at the Daintree Rainforest Observatory in Queensland, Australia (AU-DRO) with the purpose of testing for differences in gas exchange between treatments. Data were collected at the end of the dry season (November 2018) and end of the wet season (May 2019). Using a canopy crane, large branches (>1 m) were collected from the top of the canopy and immediately placed in a bucket of water, upon which the LI-6800 leaf chamber was clamped onto a recently mature leaf with minimal herbivory. Measurements were recorded every 3 seconds for 3 minutes total. The species included in the dataset are: Castanospermum australe, Cleistanthus myrianthus, Cryptocarya mackinnoniana, Dysoxylum papuanum, Elaeocarpus angustifolius (Elaeocarpus grandis), Endiandra microneura, Mallotus paniculatus, Myristica globosa, Rockinghamia angustifolia, Synima cordierorum, Syzygium spp. The two data files included are: 1. LICOR_Data_AU_DRO_2019_GasEx.csv (a compilation of individual LICOR output files, with correction of user input errors such as incorrect species spelling) 2. ESS_DIVE_Data_AU_DRO_2019_GasEx.csv (selected variables and converted units from LICOR_Data_AU_DRO_2019_GasEx.csv to follow the standard gas exchange reporting format from Ely et al. 2021 Ecological Informatics). The metadata files included are: (3) ESS_DIVE_Metadata_AU_DRO_2019_GasEx.xlsx (4) File_Submission_Metadata_AU_DRP_2019_GasEx.xlsx (5) E-Field_Log_AU_DRO_2019_GasEx.xlsx (6) Data_Column_Description_AU_DRO_2019_GasEx.xlsx.

54 ENVIRONMENTAL SCIENCES↗

Raw leaf gas exchange data, Leaf traits and 13CO2 experiment in the Amazon basin, Manaus, Brazil, 2019

This data package contains raw light response curves of mature leaves from 26 individuals, distributed among 18 species with different canopy positions, and leaf traits (LMA, N, P, and NSC) from Manaus-Brazil (June-November 2019). Branches were cut from the tree and re-cut under water. All measurements were made using (Li-6400XT, Li-Cor®, Lincoln, USA) under standard environmental conditions. Also, have data from an experiment with 13CO2 using a combined method with Licor 6400XT and Picarro. The experiment was conducted with one tropical species and measured the concentration of ¹³CO2 and ¹²CO2 to estimate rates of leaf respiration. All the files included in this data package are in .csv format with additional metadata spreadsheets in Excel file formats.

54 ENVIRONMENTAL SCIENCES↗

AmeriFlux US-PFb NW1 Pine-1 CHEESEHEAD 2019

This is the AmeriFlux version of the carbon flux data for the site US-PFb NW1 Pine-1 CHEESEHEAD 2019. Site Description - This tower (32m, trailer-based, telescoping) is the northwestern most tower in the 10 x 10km study domain. It is located in a red pine forest (canopy height ~25m). There is a regrowing/trap layer consisting of broadleaf aspen and other deciduous vegetation.

Desai, Ankur↗

AmeriFlux US-PFc NW2 Aspen-1 CHEESEHEAD 2019

This is the AmeriFlux version of the carbon flux data for the site US-PFc NW2 Aspen-1 CHEESEHEAD 2019. Site Description - This tower (12m Rohn) is located in the northwestern quadrant of the 10 x 10km study domain. It is located in a forest of short aspen (3m tall). Several older aspen and conifers are interspersed within the regrowing aspen forest (15-20m tall).

Desai, Ankur↗

AmeriFlux US-PFd NW3 Tussock-1 CHEESEHEAD 2019

This is the AmeriFlux version of the carbon flux data for the site US-PFd NW3 Tussock-1 CHEESEHEAD 2019. Site Description - This tower (3m tripod) is located in the northwestern quadrant of the 10 x 10km study domain. It is located in a tussock (1 - 1.5m tall grasses). There is a southwest-to-north-oriented stream that passes by the east side of the tower.

Desai, Ankur↗

AmeriFlux US-PFe NW4 Lake-1 CHEESEHEAD 2019

This is the AmeriFlux version of the carbon flux data for the site US-PFe NW4 Lake-1 CHEESEHEAD 2019. Site Description - This tower (32m, trailer-based, telescoping) is located in the northwestern quadrant of the 10 x 10km study domain. It is located in a red pine and aspen forest (20.1 m height) on the bank of a lake (500m across).

Desai, Ankur↗

AmeriFlux US-PFg NE1 Pine-2 CHEESEHEAD 2019

This is the AmeriFlux version of the carbon flux data for the site US-PFg NE1 Pine-2 CHEESEHEAD 2019. Site Description - This tower (32m, trailer-based, telescoping) is located in the northeastern quadrant of the 10 x 10km study domain. It is located in a forest with red pine (~32m) overstory and maple (10-15m) understory.

Desai, Ankur↗

AmeriFlux US-PFh NE2 Pine-3 CHEESEHEAD 2019

This is the AmeriFlux version of the carbon flux data for the site US-PFh NE2 Pine-3 CHEESEHEAD 2019. Site Description - This tower (32m, trailer-based, telescoping) is located in the northeastern quadrant of the 10 x 10km study domain. It is located in a forest jack pine forest (18 - 20m), with some aspens.

Desai, Ankur↗