This model product provides the Fortran 77 source code for the Lund-Potsdam-Jena (LPJ) Wetland Hydrology and Methane Dynamic Global Vegetation Model (LPJ-WHyMe v1.3.1), auxiliary C++ routines, ASCII and NetCDF input data, and NetCDF example output data. LPJ-WHyMe v1.3.1 simulates peatland hydrology, permafrost dynamics, peatland vegetation, and methane emissions.The model processes can be simulated on an area-averaged 0.5 or 1.0 degree grid cell basis at global, regional, or site scales and on a daily, monthly, or annual time step as appropriate. Input driver data are monthly mean air temperature, total precipitation, percentage of full sunshine, annual atmospheric CO2 concentration, and soil texture class. The simulation for each grid cell begins from "bare ground", requiring a "spin up" (under non-transient climate) of ca. 1,000 years to develop equilibrium vegetation, carbon, and soil structure. Model simulations compare favorably, with some exceptions, to field observations collected from peatland sites (e.g., Degero, Sweden; Lakkasuo, Finland; BOREAS Northern Study Area, Canada; and others) and non-peatland sites (e.g., Point Barrow, Alaska, and Spasskaya, Siberia). LPJ-WHyMe is a further development of LPJ-WHy, which dealt with the introduction of permafrost and peatlands into LPJ. Implementing peatlands in LPJ required the addition of two new plant functional types (PFTs) (flood tolerant C3 graminoids and Sphagnum mosses) to the already existing ten PFTs, the introduction of inundation stress for non-peatland PFTs, a slow-down in decomposition under inundation, and the addition of a root exudates pool. LPJ-WHyMe v1.3.1 adds a methane model subroutine. This model product has one compressed data file (*.zip) and seven companion files.

This model product contains the source code for the Ecosystem Demography Model (ED version 1.0) as well as model input and output data files for the conterminous United States. The ED is a mechanistic ecosystem model built around established sub-models of leaf level physiology, organic matter decomposition, hydrology, and functional biodiversity. It was used herein to estimate ecosystem carbon stocks and fluxes in the conterminous U.S. at 1.0 degree resolution from 1700 to 1990. Output data of carbon stocks and fluxes are stored in NetCDF format. To produce the U.S. scenario, ED was run from an estimated state of ecosystems in the year 1700 to an estimated state of ecosystems in the year 1990 for each 1 degree by 1 degree grid cell through time using ISLSCP Initiative I climate and soil data and a gridded land-use history reconstruction as inputs (Hurtt et al., 2002). The land-use history was based on several sources including: spatial distribution of potential vegetation in 1700, spatial patterns of cropland from 1700 to 1990, regional estimates of land use and logging from 1700 to 1990, and U.S. Forest Inventory and Analysis (FIA) data on the current age distribution of forest stands. The Miami Land Use History Model (Miami-LU), a far simpler empirically-based ecosystem model, was used to track the history of disturbance, land use, fire, and ecosystem recovery. The effects of fire suppression were also included. Atmospheric CO2 concentrations and climatic conditions were held constant throughout the runs to focus on the consequences of land-use and fire-management changes on carbon stocks and fluxes.

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52° N and 52° S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website(https://ecostress.jpl.nasa.gov/science).

The ECOSTRESS Tiled Ancillary NDVI and Albedo Level 2 Global 70 m (ECO_L2T_STARS) Version 2 data product provides Normalized Difference Vegetation Index (NDVI) and albedo aligned with each daytime ECOSTRESS overpass. ECO_L2T_STARS is an ancillary data product derived from Visible Infrared Imaging Radiometer Suite (VIIRS) and Harmonized Landsat Sentinel (HLS) Version 2 data sources with application to the ECOSTRESS mission. This data product fuses fine resolution inputs from HLS surface reflectance products and moderate resolution inputs from the daily NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS surface reflectance (VNP09GA) product. The data fusion is performed using a variant of the Spatial Timeseries for Automated high-Resolution multi-Sensor data fusion (STARS) algorithm to create tiles matching the ECOSTRESS standard resolution of 70 meters (m). STARS is a Bayesian timeseries methodology that provides streaming data fusion and uncertainty quantification through efficient Kalman filtering. Refer to Section 3.1 of the ECOSTRESS Jet Propulsion Laboratory (JPL) EvapoTranspiration (JET) Level-3 Algorithm Theoretical Basis Document (ATBD) for further details of the Bidirectional Reflectance Distribution Function (BRDF) implementation and albedo calculations.

The ECO_L2T_STARS Version 2 data product is provided in Cloud Optimized GeoTIFF (COG) format using a modified version of the Military Grid Reference System (MGRS), which divides Universal Transverse Mercator (UTM) zones into square tiles that are 109.8 km by 109.8 km with a 70 m spatial resolution. Each band is distributed as a separate COG. This product contains four layers including NDVI, NDVI uncertainty, albedo, and albedo uncertainty. The ECO_L2T_STARS ancillary NDVI and albedo product is only generated for corresponding daytime ECOSTRESS Tiled Land Surface Temperature and Emissivity Instantaneous Level 2 Global 70 m (ECO_L2T_LSTE) Version 2 tiles.

The ECOSTRESS Tiled Ancillary NDVI and Albedo Level 2 Global 70 m (ECO_L2T_STARS) Version 2 data product provides Normalized Difference Vegetation Index (NDVI) and albedo aligned with each daytime ECOSTRESS overpass. ECO_L2T_STARS is an ancillary data product derived from Visible Infrared Imaging Radiometer Suite (VIIRS) ( https://lpdaac.usgs.gov/product_search/?query=viirs&status=Operational&view=list&sort=title) and Harmonized Landsat Sentinel (HLS) ( https://lpdaac.usgs.gov/product_search/?collections=HLS&status=Operational&view=list) Version 2 data sources with application to the ECOSTRESS mission. This data product fuses fine resolution inputs from HLS surface reflectance products and moderate resolution inputs from the daily NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS surface reflectance (VNP09GA) ( https://doi.org/10.5067/VIIRS/VNP09GA.001) product. The data fusion is performed using a variant of the Spatial Timeseries for Automated high-Resolution multi-Sensor data fusion (STARS) algorithm to create tiles matching the ECOSTRESS standard resolution of 70 meters (m). STARS is a Bayesian timeseries methodology that provides streaming data fusion and uncertainty quantification through efficient Kalman filtering. Refer to Section 3.1 of the ECOSTRESS Jet Propulsion Laboratory (JPL) EvapoTranspiration (JET) Level-3 Algorithm Theoretical Basis Document (ATBD) for further details of the Bidirectional Reflectance Distribution Function (BRDF) implementation and albedo calculations.

The ECO_L2T_STARS Version 2 data pr

Existing 30-m land cover Thematic Mapper classification by CCRS was aggregated and reprocessed and are now available at multiple resolutions (10x5 minutes and 30 minutes).

These images were produced by aggregating a reclassified version of the 30-m land cover Thematic Mapper classification by CCRS and are now available at multiple resolutions (10x5 minutes, and 30 minutes).

These images were produced by aggregating the 1-km land cover classification by Lou Steyaert at multiple resolutions (2 km, 10x5 minutes, and 0.5 degree). These data were regridded for use by the BOREAS Follow-on Carbon and Hydro-Meteorological modeling groups to have a number of data sets available in common grid projections and scales for intercomparison studies.

Existing 1-km moss cover classifications were reprocessed and are now available at multiple resolutions (2 km, 10x5 min, and 0.5 degree). These data were regridded for use by the BOREAS Follow-on Carbon and Hydro-Meteorological modeling groups to have a number of data sets available in common grid projections and scales for intercomparison studies.

BOREAS follow-on group DSP-9 mapped surface moss type at three scales (1 km, 30 m, and 10 m) based on observed associations between moss cover and land cover type.

This land cover product was produced by NBIOME to generate an up-to-date, spatially and temporally consistent land cover map of the landmass of Canada for use by scientists and other users interested in environmental information at national and regional scales. This data set is gridded and was produced from 10-day composite data of surface parameters.

The objective of this land cover mosaic is to provide a data product that characterizes the detailed land cover of a significant portion of the BOREAS Region. Seven Landsat-5 TM images have been assembled to completely cover the BOREAS Transect.