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NASA NTRS1986

Observations of the seasonal variability of soil moisture and vegetation cover over Africa using satellite microwave radiometry

Multispectral passive microwave data from the scanning multichannel microwave radiometer (SMMR) on the Nimbus-7 satellite were processed selectively for a 1 yr period over Africa. The data show a wide dynamic range of brightness temperature (180 to 290 K), corresponding to variations in surface features such as moisture, temperature, vegetation, roughness, and large-scale topography. It appears that soil moisture variability is detectable with the SMMR over large regions of Africa. To what extent roughness and vegetation affect this capability is not clear. The lowest SMMR frequency is C-band (6.6 GHz), thus any soil moisture sensitivity at this frequency would be much improved by a sensor at L-band (1 to 2 GHz) less affected by roughness and vegetation.

Njoku, Eni G.↗

DOE OSTI2023

Observed impacts of large wind farms on grassland carbon cycling

Deployment of wind energy is an essential renewable energy source that mitigates climate change and reduces air pollution. Over the last several decades, wind energy development has increased worldwide, expanding from ~20 to ~900 GW (gigawatt) during 2001-2022. Nonetheless, researchers have identified unintended consequences of wind energy on microclimate via turbine-altered surface-atmosphere exchanges of energy, momentum, mass, and trace gases. Based on multi-source observations and models, researchers also have drawn some conclusions that wind farms could warm the land surface, especially at night, at regional and continental scales. Consequently, altered microclimates at wind farms may affect vegetation productivity and carbon sequestration, two critically important ecosystem services related to carbon dynamics; however, such potential impacts and driving mechanisms remain poorly understood. Wind energy deployment is increasing globally to meet carbon neutrality goals, with upscaling of onshore wind power capacity projected to grow from 542 GW in 2018 to 1787 and 5044 GW by 2030 and 2050, respectively. Furthermore, increased demand for wind energy deployment may lead to much larger wind farms in open, expansive landscapes. In turn, a large array of geographically clustered wind turbines could collectively modify local microclimate and amplify turbine-atmosphere interactions, which, if large enough, may produce detectable impacts on ecosystem dynamics. Thus, identifying and quantifying the potential impacts of wind farms on carbon-related ecosystem services may facilitate sustainable wind energy development globally.

17 WIND ENERGY↗

NASA NTRS1988

Mesoscale monitoring of the soil freeze/thaw boundary from orbital microwave radiometry

The fundamental objectives are to test the feasibility of delineating the lateral boundary between frozen and thawed condition in the surface layer of soil from orbital microwave radiometry and secondly to examine the sensitivity of general circulation models to an explicit parameterization of the boundary condition. Physical models were developed to relate emissivity to scene properties and a simulation package was developed to predict brightness temperature as a function of emissivity and physical temperature in order to address issues of heterogeneity, scaling, and scene dynamics. Radiative transfer models were develped for both bare soil surfaces and those obscured by an intervening layer of vegetation or snow. These models relate the emissivity to the physical properties of the soil and to those of the snow or vegetation cover. A SMMR simulation package was developed to evaluate the adequacy of the emission models and the limiting effects of scaling for realistic scenarios incorporating spatially heterogeneous scenes with dynamic moisture and temperature gradients at the pixel scale.

Ulaby, Fawwaz T.↗

DOE OSTI2021

Integrating New Arctic Plant Functional Types in a Land Surface Model Using Above- and Belowground Field Observations: Modeling Archive

This Modeling Archive is in support of an NGEE Arctic publication "Integrating New Arctic Plant Functional Types in a Land Surface Model Using Above- and Belowground Field Observations". We used field observations of biomass and leaf traits (Salmon et al., 2019b) across a gradient of plant communities on the Seward Peninsula in northwest Alaska (Breen et al., 2020; Iversen et al., 2017) to replace the original ELM configuration with nine, arctic-specific PFTs. Original arctic PFTs in the model were (1) broadleaf deciduous boreal shrub and (2) C3 arctic grass. New PFTs were (1) lichen, (2) bryophyte, (3) evergreen dwarf shrub, (4) deciduous dwarf shrub, (5) deciduous low shrub, (6) deciduous low to tall shrub, (7) alder shrub, (8) forb, and (9) graminoid. New PFTs were parameterized and evaluated using site-level trait and biomass measurements (Salmon et al., 2019b), root C:N ratios and rooting depth patterns (Salmon et al., 2019c), and root biomass (Salmon et al., 2020). This archive contains forcing data and model output from ELM simulations conducted at the Kougarok Hillslope site (Kougarok Road Mile Marker 64). Simulations were conducted for the Kougarok Hillslope site using meteorological driving data from the Scenarios Network for Alaska and Arctic Planning (SNAP) downscaled climate projection dataset using NCAR-CCSM forcing (Bieniek et al., 2020 dataset; Walsh et al., 2018). The archive includes three versions of model simulations representing a progression from the original model configuration to an Arctic-specific configuration (see Output section below). Simulation 1 used grid cell data from a global E3SM configuration, including the fractional area of E3SM PFTs assigned to the grid cell containing the Kougarok Hillslope site in global simulations. This simulation did not distinguish between different plant communities on the Kougarok Hillslope, but instead used a single point simulation to represent the entire area. Simulation 2 used default E3SM PFT definitions combined with adjusted depth to bedrock for plant communities with shallow rocky layers to represent the role of abiotic soil factors in driving site differences (based on depth measurements of Iversen et al., 2019a), and adjusted the relative areas of the broadleaf deciduous boreal shrub and C3 arctic grass PFTs to reflect the observed spatial coverage of shrub and non-shrub PFTs across Kougarok Hillslope plant communities. Simulation 3 used the new arctic PFT definitions and parameterizations based on the vegetation types present at the site, along with the adjusted soil depths used in Simulation 2. Included are *.pdf, *.nc (NetCDF), *tar.gz, *.py, and *.bash files. We would like to thank Mary's Igloo Native Corporation for allowing us to perform this research on their land and giving us the opportunity to share this research with their community. Thanks to Shawn Serbin and Alistair Rogers for helpful discussions. Holly Vander Stel, Joanne Childs, and Stan Wullschleger helped with collection of field data. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2019

Sensitvity analysis of SMAP-Reflectometry (SMAP-R) signals to vegetation water content

Global Navigation Satellite System – Reflectometry (GNSSR) techniques have proven successful to retrieve several geophysical parameters such as, ocean wind speed, soil moisture, altimetry, wetland dynamics, snow depth estimations. In this paper, the L2C GPS signals measured from the Soil Moisture Active Passive (SMAP) radar after its malfunction is used to investigate the effect of the vegetation water content on the electromagnetic signal. The SMAP Reflectometry (SMAP-R) measurements are obtained at V and H polarizations allowing for not only signal-to-noise ratio (SNR) analysis but also for polarimetric ratio (PR) studies.

Morris, Mary↗

DOE OSTI2023

Uncertainty quantification of bank vegetation impacts on the flood flow field in the American River, California, using large‐eddy simulations

Bank vegetation plays a key role in both hydrodynamics and morphodynamics of natural rivers; however, these effects are often unaccounted for in the computational flow dynamics of natural waterways. Recent studies using the large‐eddy simulation (LES), however, have attempted to gain insights into the impacts of bank vegetation on the mean flow field of the natural rivers using a vegetation model, which applies a sink term to the momentum equations of motion. This approach accounts for the effects of the vegetation and provides a practical approach to account for the complex patches of bank vegetation in large‐scale rivers. To implement the vegetation model, a drag coefficient reflecting the overall resistance of vegetal structures to the flow is needed, but due to the lack of calibrated data and range of size, density and type of vegetation, this parameter can be a significant source of uncertainty in the model results. Here, in this study, we use uncertainty quantification (UQ) to investigate the hydrodynamics and bed shear results when a bank vegetation is incorporated in an LES model. To this end, we used the polynomial chaos expansion and Monte Carlo sampling techniques to determine the uncertainties associated with the drag coefficient in the vegetation model and from uncertainties in the bed roughness and inflow discharge. The UQ analysis provided spatially varying confidence levels for the spanwise and vertical distribution of velocity magnitude and for the bed shear stress distributions. In addition, Sobol indices were computed to indicate the relative influence that each parameter had on the overall uncertainty. In general, it was found that uncertainty in flow discharge was the dominant source of uncertainty; however, the drag coefficient in the vegetation model and the bed roughness parameter also made significant contribution to the uncertainty near the banks and bed, respectively.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

Oregon Wildfires: Integrating ECOSTRESS to Map & Analyze Vegetation Moisture for Wildfire Modeling

Wildfire season in the western USA is starting earlier and gaining in intensity. The Bootleg Fire in Southern Oregon began on July 6th, 2021, and burned over 1675 km2 before it was fully contained on August 15th, 2021. Evapotranspiration (ET) is one indicator of vegetation moisture and there is interest in using high-resolution ET products from ECOsystem and Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) in future wildfire modeling. In partnership with the Pacific Northwest National Laboratory and US Forest Service, the team examined ECOSTRESS ET for the two years before the Bootleg Fire and assessed the relationship between ET, topography, and vegetation. Remotely sensed data from Shuttle Radar Topography Mission (SRTM) and Global Ecosystem Dynamics Investigation (GEDI) along with ancillary data from the National Land Cover Database (NLCD) and Landscape Fire Resource Management Planning Tools (LANDFIRE) were incorporated. The team examined data in relation to soil burn severity from the Burned Area Emergency Response (BAER) program. From ET median composites for April 1st – July 5th, 2021 and 2019, the Bootleg Fire area showed a 7 mm/day decrease in ET and a relative 75% decrease in ET between 2019 and 2021. Approximately 6% of the Bootleg Fire area was identified as having a high soil burn severity and these areas were found predominantly in the evergreen forest land cover class and northward facing slopes with a mean ET decrease of 3 mm/day between 2019 and 2021. The team also analyzed ECOSTRESS Water Use Efficiency products as an additional vegetation moisture indicator of pre-fire conditions in the study area. The end products will allow the partners to assess if higher resolution vegetation moisture datasets from ECOSTRESS will improve wildfire modeling for other susceptible areas.

Brenna Hatch↗

DOE OSTI2023

Land surface dynamics and meteorological forcings modulate land surface temperature characteristics

This study examines the effect of land cover, vegetation health, climatic forcings, elevation heat loads, and terrain characteristics (LVCET) on land surface temperature (LST) distribution over West Africa (WA). We employ fourteen machine-learning models, which preserve nonlinear relationships, to downscale LST and other predictands while preserving the geographical variability of WA. Our results showed that the random forest model performs best in downscaling predictands. This is important for the sub-region since it has limited access to mainframes to power multiplex machine-learning algorithms. In contrast to the northern regions, the southern regions consistently exhibit healthy vegetation. Also, areas with unhealthy vegetation coincide with hot LST clusters. The positive Normalized Difference Vegetation Index (NDVI) trends in the Sahel underscore rainfall recovery and subsequent Sahelian greening. The southwesterly winds cause the upwelling of cold waters, lowering LST in southern WA and highlighting the cooling influence of water bodies on LST. Identifying regions with elevated LST is paramount for prioritizing greening initiatives, and our study underscores the importance of considering LVCET factors in urban planning. Topographic slope-facing angles, heat loads, and diurnal anisotropic heat all contribute to variations in LST, emphasizing the need for a holistic approach when designing resilient and sustainable landscapes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Validation of the Community Land Model Version 5 over the Contiguous United States (CONUS) using in-situ and remote sensing datasets

The Community Land Model (CLM) is an effective tool to simulate the biophysical and biogeochemical processes and their interactions with the atmosphere. Although CLM Version 5 (CLM5) constitutes various updates in these processes, its performance in simulating energy, water and carbon cycles over the Contiguous United States (CONUS) at scales which land surface changes and hydrometeorological and hydroclimatological applications are more locally relevant is yet to be assessed. In this study, we conducted three simulations at 0.125? during 1979-2018 over the CONUS using different configurations of CLM, namely CLM5-biogeochemistry (CLM5BGC), CLM4.5BGC, and CLM5-satellite phenology (CLM5SP). We validated and compared their simulations against multiple remote-sensed and in-situ datasets. Overall, the parametric and structural updates (e.g., carbon cost for nitrogen uptake, variable soil thickness, dry surface layer) in CLM5 improve its ability in capturing terrestrial biogeochemical dynamics. The low evapotranspiration in CLM5BGC is associated with biases in simulating vegetation phenological characteristics rather than soil water limitations. The mismatch between CLM5BGC-simulated peak leaf area index and reference data can be attributed to CLM5BGC's inability in simulating phenology of trees and grasses. The differences between CLM-simulated irrigation and reference estimates can be attributed to differences between processes represented in models and in reality, and uncertainties in input and validation datasets. Evaluation against observations at small catchments suggest that hydrologic parameters needed to be calibrated to improve simulations of runoff, especially subsurface runoff. Additional efforts are needed to incorporate spatially-distributed plant phenology and physiology parameters and regional-specific agricultural management practices (e.g., planting, harvest).

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2016

Very High Spectral Resolution Imaging Spectroscopy: the Fluorescence Explorer (FLEX) Mission

The Fluorescence Explorer (FLEX) mission has been recently selected as the 8th Earth Explorer by the European Space Agency (ESA). It will be the first mission specifically designed to measure from space vegetation fluorescence emission, by making use of very high spectral resolution imaging spectroscopy techniques. Vegetation fluorescence is the best proxy to actual vegetation photosynthesis which can be measurable from space, allowing an improved quantification of vegetation carbon assimilation and vegetation stress conditions, thus having key relevance for global mapping of ecosystems dynamics and aspects related with agricultural production and food security. The FLEX mission carries the FLORIS spectrometer, with a spectral resolution in the range of 0.3 nm, and is designed to fly in tandem with Copernicus Sentinel-3, in order to provide all the necessary spectral / angular information to disentangle emitted fluorescence from reflected radiance, and to allow proper interpretation of the observed fluorescence spatial and temporal dynamics.

Moreno, Jose F.↗

NASA NTRSDate not supplied

Lower Illinois River Valley Ecological Forecasting: Inundation Mapping of the Lower Illinois River Valley Using Synthetic Aperture Radar and Optical Satellite Imagery for Wetland Conservation and Restoration Prioritization Efforts

The Lower Illinois River Valley (LIRV) is home to some of the richest agricultural lands in the United States and its wetlands provide key ecosystem services like clean water and flood reduction. It has also experienced extensive degradation due to development and urban pollution. The Great Rivers Land Trust (GRLT), the National Great Rivers Research & Education Center, Principia College, and the American Geophysical Union’s (AGU) Thriving Earth Exchange sought to incorporate inundation and surface water extent layers into their geodatabases to more accurately identify priority areas for wetland restoration. This project aimed to determine the feasibility of detecting inundation extent and duration along the valley using remotely sensed data. The team used Sentinel-1 C-band Synthetic Aperture Radar (SAR) data to classify open water and inundated vegetation within the study site. The open water classification was compared to Dynamic Surface Water Extent (DSWE) derived from Landsat 8 Operational Land Imager. The team successfully created layers of inundation minimum and maximum extent, as well as inundation duration across the study area for 2019 and 2020. The open water classification resulted in an overall accuracy of 86% when validated against DSWE classifications. These analyses will help end users to identify high priority areas along the LIRV best suited for land conversion projects in the future.

Vanessa Machuca↗

NASA NTRS2022

Lower Illinois River Valley Ecological Forecasting: Inundation Mapping of the Lower Illinois River Valley Using Synthetic Aperture Radar and Optical Satellite Imagery for Wetland Conservation and Restoration Prioritization Efforts

The Lower Illinois River Valley (LIRV) is home to some of the richest agricultural lands in the United States and its wetlands provide key ecosystem services like clean water and flood reduction. It has also experienced extensive degradation due to development and urban pollution. The Great Rivers Land Trust (GRLT), the National Great Rivers Research & Education Center, Principia College, and the American Geophysical Union’s (AGU) Thriving Earth Exchange sought to incorporate inundation and surface water extent layers into their geodatabases to more accurately identify priority areas for wetland restoration. This project aimed to determine the feasibility of detecting inundation extent and duration along the valley using remotely sensed data. We used Sentinel-1 C-band Synthetic Aperture Radar (SAR) data to classify open water and inundated vegetation within the study site. The open water classification was compared to Dynamic Surface Water Extent(DSWE) derived from Landsat 8 Operational Land Imager. We successfully created layers of inundation minimum and maximum extent, as well as inundation duration across the study area for 2019 and 2020. The open water classification resulted in an overall accuracy of 86% when validated against DSWE classifications. These analyses will help end users to identify high priority areas along the LIRV best suited for land conversion projects in the future.

Vanessa Machuca↗

DOE OSTI2024

Importance of viewing angle: Hotspot effect improves the ability of satellites to track terrestrial photosynthesis

The product of near-infrared reflectance of vegetation and photosynthetic active radiation (NIRvP) is a new tool for monitoring gross primary productivity (GPP) dynamics in terrestrial ecosystems, due to the discovered linear correlation between NIRvP and GPP. While remote sensing-based NIRvP is considerably influenced by sensor geometry, such geometry impacts on the NIRvP-GPP relationship remain underexplored. In this study, we calculate NIRvP using observations from the Deep Space Climate Observatory (DSCOVR) that provide unique hotspot observation geometry in which the sensor viewing angle coincides with the sun direction. We evaluated the linear correlation between NIRvP and GPP in both the common nadir direction and the special hotspot direction. The results indicate that NIRvP in the hotspot direction significantly outperforms that in the nadir direction for tracking GPP variations across different ecosystems from diurnal to daily scales. This conclusion is further supported by data from the MODerate resolution Imaging Spectroradiometer (MODIS) and simulations using the Soil Canopy Observation Photosynthesis Energy (SCOPE) model. Finally, our research highlights the value of using the unconventional hotspot-based sun-tracking satellite observations for a more accurate characterization of GPP dynamics in terrestrial ecosystems.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2006

Effects of Vegetation and of Heat and Vapor Fluxes from Soil on Snowpack Evolution and Radiobrightness

The radiobrightness of a snowpack is strongly linked to the snow moisture content profile, to the point that the only operational inversion algorithms require dry snow. Forward dynamic models do not include the effects of freezing and thawing of the soil beneath the snowpack and the effect of vegetation within the snow or above the snow. To get a more realistic description of the evolution of the snowpack, we reported an addition to the Snow-Soil-Vegetation-Atmosphere- Transfer (SSVAT) model, wherein we coupled soil processes of the Land Surface Process (LSP) model with the snow model SNTHERM. In the near future we will be adding a radiobrightness prediction based on the modeled moisture, temperature and snow grain size profiles. The initial investigations with this SSVAT for a late winter and early spring snow pack indicate that soil processes warm the snowpack and the soil. Vapor diffusion needs to be considered whenever the ground is thawed. In the early spring, heat flow from the ground into a snow and a strong temperature gradient across the snow lead to thermal convection. The buried vegetation can be ignored for a late winter snow pack. The warmer surface snow temperature will affect radiobrightness since it is most sensitive to snow surface characteristics. Comparison to data shows that SSVAT provides a more realistic representation of the temperature and moisture profiles in the snowpack and its underlying soil than SNTHERM. The radiobrightness module will be optimized for the prediction of brightness when the snow is moist. The liquid water content of snow causes considerable absorption compared to dry snow, and so longer wavelengths are likely to be most revealing as to the state of a moist snowpack. For volumetric moisture contents below about 7% (the pendular regime), the water forms rings around the contact points between snow grains. Electrostatic modeling of these pendular rings shows that the absorption of these rings is significantly higher than a sphere of the same volume. The first implementation of the radiobrightness module will therefore be a simple radiative transfer model without scattering.

soil↗

DOE OSTI2021

Hyporheic Exchange Driven by Submerged Rigid Vegetation: A Modeling Study

Flow-vegetation interaction affects fluid flow hydraulics and associated material transport in river corridors. Concomitant changes in pressure within the flow field due to the presence of vegetation may act as a driver for the formation of hyporheic flow across the sediment-water interface. This potentially important process, however, has yet to be studied. In order to investigate vegetation-induced hyporheic exchange, a series of numerical models of interlinked surface-subsurface flow modified by plant stems was conducted. Periodically staggered plant stem arrays on a flat sediment bed were considered within a coupled multiphysics computational fluid dynamics approach. Plants were idealized as rigid cylinders and arranged in different streamwise and spanwise spacing distances. Each vegetation array was then subjected to a broad range of flow Reynolds Numbers (Re). The results showed that hyporheic flow occurs in all conditions with the presence of vegetation. The vegetation-induced hyporheic flux is found to be a function of Re via a power law. The flux increases with interstem space until the space reaches the distance that rigid stems no longer affect the flow structures in the vicinity of each other. Larger intervegetation distances lead to a larger hyporheic zone. A direct comparison with bedforminduced hyporheic flow showed that vegetation can induce higher hyporheic flux through relatively shallower exchange zones. The results of all the simulations were synthesized into predictive models for hyporheic flux, bulk residence time and exchange depth based on drag coefficient, vegetation density, and Reynolds Number.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Snow Depth Datasets for Snodgrass Catchment, Colorado, Water Year 2022-2023

This data package presents snow depths data from distributed temperature probes at 18 locations near Snodgrass catchment, Colorado. These data show that snow melt-out dates are approximately one or two weeks later under evergreen forests compared to other vegetation types even at the same elevation. These data were collected to understand how snowmelt heterogeneity impacts headwater hydrology, including streamflow and groundwater levels. They were also used to compare with process-based model simulations of snow depth to evaluate whether the model accurately represents snowmelt dynamics and their effects on headwater hydrology. Snow_DTPs_locations.csv includes all probes locations and their associated elevation and vegetation types. Snow_Depth_Snodgrass_WY2022_2023.csv includes processed snow depths datasets for Water Year (WY) 2022 and 2023. 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. Several probes have recordings for WY 2021.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2016

Exploring the Role of Humans and Climate over the Balkan Landscape: 500 Years of Vegetational History of Serbia

We present the first, well-dated, high-resolution record of vegetation and landscape change from Serbia, which spans the past 500 years. Biological proxies (pollen, spores, and charcoal), geochemical analysis through X-ray Fluorescence (XRF), and a detailed chronology based on AMS C-14 dating from a western Serbian sinkhole core suggest complex woodland-grassland dynamics and strong erosional signals throughout the Little Ice Age (LIA). An open landscape with prominent steppe vegetation (e.g. Poaceae, Chenopodiaceae) and minor woodland exists during 1540-1720 CE (early LIA), while the late LIA (1720-1850 CE) in this record shows higher tree percentages possibly due to increased moisture availability. The post LIA Era (1850-2012 CE) brings a disturbed type of vegetation with the presence of weedy genera and an increase in regional woodland. Anthropogenic indicators for agricultural, pastoral and fire practices in the region together attest to the dominant role of humans in shaping this Balkan landscape throughout the interval. The changing nature of human interference, potentially as a response to underlying climatic transitions, is evident through large-scale soil depletion resulting from grazing and land clearance during the early LIA and stabilization of arable lands during the late and post-LIA eras.

Topography↗

DOE OSTI2020

Flux Seasonality Metrics Database: A companion dataset for FLUXNET annual product

This dataset include standardized phenological transition dates, and derived season lengths of active, dormant and transition seasons of ecosystem carbon and energy fluxes, as reported in FLUXNET. Such metrics are unique in that they characterize the seasonal dynamics of these fluxes in their own terms rather than in reference to traditional phenology indicators that mostly relate to vegetation structural characteristics. The Flux Seasonality Metrics Database (FSMD) generated 17 standardized seasonality metrics for 4 different fluxes including gross primary productivity (GPP), ecosystem respiration (RE), latent heat (LE) and sensible heat (H) reported in FLUXNET2015 Dataset for about 200 sites and 1500 site-years of data globally. Each metric is reported as an estimate and a confidence interval, which is a function of data coverage and flux variability. We anticipate that the FSMD will facilitate new model validation exercises, and stimulate new research in global change and Earth science disciplines where land-atmosphere exchange dynamics play a central role.

54 ENVIRONMENTAL SCIENCES↗