BOREAS RSS-08 BIOME-BGC SSA Simulations of Annual Water and Carbon Fluxes Summary The BOREAS RSS-08 team performed research to evaluate the effect of seasonal weather and landcover heterogeneity on boreal forest regional water and carbon fluxes using a process level ecosystem model, BIOME-BGC, coupled with remote sensing-derived parameter maps of key state variables. This data set contains derived maps of landcover type and crown and stem biomass as model inputs to determine annual evapotranspiration, gross primary production, autotrophic respiration and net primary productivity within the BOREAS SSA-MSA, at a 30 m spatial resolution. Model runs were conducted over a 3 year period from 1994- 1996, images are provided for each of those years. The data are stored in binary image format. Note that some of the data files on the BOREAS CD-ROMs have been compressed using the Gzip program. See section 8.2 for details. Table of Contents 1.Model Overview 2.Investigator(s) 3.Theory of Measurements 4.Equipment 5.Data Acquisition Methods 6.Observations 7.Data Description 8.Data Organization 9.Data Manipulations 10.Errors 11.Notes 12.Application of the Model 13.Future Modifications and Plans 14.Software 15.Data Access 16.Output Products and Availability 17.References 18.Glossary of Terms 19.List of Acronyms 20.Document Information 1. Model Overview 1.1 Model Identification BIOME-BGC (BioGeoChemical cycles) 1.2 Model Introduction BIOME-BGC simulates biogeochemical and hydrologic processes across multiple biomes based on the logic that differences in process rates between biomes are primarily a function of climate and general life-form characteristics. BIOME- BGC utilizes daily meteorological data in conjunction with general stand and soil information to predict components of the hydrologic budget (e.g. transpiration, evaporation, snowcover, soil water, runoff) and carbon budget (e.g. net photosynthesis, autotrophic and heterotrophic respiration) at a daily time-step. BIOME-BGC is general in the sense that the surface is represented by singular, homogeneous canopy and soil layers. Detailed descriptions of BIOME- BGC logic are given by Running and Coughlan [1988] and Running and Hunt [1993]. Model descriptions relating to the prediction of carbon and hydrologic processes within the BOReal Ecosystem-Atmosphere Study (BOREAS) region are given by Kimball et al. [1997a,b, 1998]. 1.3 Objective/Purpose The purpose of this investigation is to assess the effects of boreal forest inter-annual weather variability and landcover heterogeneity on regional water and carbon flux simulations using BIOME-BGC. Remote sensing-derived maps of landcover type and crown and stem biomass were used as model inputs to determine regional evapotranspiration (ET), net primary production (NPP), gross primary production (GPP), and autotrophic respiration (R) within the BOREAS Southern Study Area (SSA) Modeling Sub-Area (MSA). Model simulations were conducted using input data at a 30 m spatial scale. These results are used to investigate the importance of sub-grid scale landcover variability on regional biogeochemical processes over a 3 year period, from 1994 to 1996. 1.4 Summary of Parameters Model Daily Input Requirements: BIOME-BGC uses a spatial database composed of soil, vegetation, and daily meteorological characteristics registered to a common projection format, as well as an array of critical physiological constants that define the environmental response curves of individual biome types within the spatial domain (see Kimball et al. [1997a,b, 1998]). Remote sensing-derived crown and stem biomass and landcover classification maps were used to drive model simulations within the SSA-MSA. More detailed information regarding remote sensing and stand biophysical inputs is provided in Section 5. BIOME-BGC uses daily maximum and minimum air temperatures, solar irradiance (direct + diffuse) and precipitation to determine daily carbon and water fluxes. Daily meteorological data were interpolated over a 1 km resolution digital elevation map (DEM) of the SSA-MSA using a daily meteorological interpolator [Running et al., 1987; Thornton et al., 1997], digital elevation information (i.e. elevation, slope, aspect) and daily meteorological data from approximately 60 weather stations within the BOREAS region. Gridded daily meteorological data were produced for the 3 year (1994 - 1996) study period. Model Outputs: BIOME-BGC determines water and carbon components on a daily basis. The maps provided in this dataset represent annual accumulations of daily results. Net primary production (NPP) represents the net accumulation of carbon by the stand and is determined as the daily difference between gross photosynthesis and respiration from autotrophic (R) and growth respiration processes. Gross primary production (GPP) represents the total gain of carbon to the system by net photosynthesis and is defined as the daily sum of gross photosynthesis and daily foliar respiration. R represents the total loss of carbon from the system due to day and night foliar respiration, sapwood, coarse root and fine root respiration components. Evapotranspiration (ET) is computed as the daily sum of transpiration and evaporation from surface, snow and canopy components. 1.5 Discussion Since its inception as a point-scale model, BIOME-BGC has evolved to simulate regional scale processes by incorporating spatially distributed daily meteorological fields derived from a microclimate simulator, and remote sensing- derived surface parameter maps to define important landscape characteristics. The model employs a biome-level stratification of landcover conditions to minimize spatial variability in conversion efficiencies and potential environmental controls. For landscape simulations, BIOME-BGC uses a spatial database composed of soil, vegetation and daily meteorological characteristics registered to a common projection format, as well an array of critical physiological constants that define the environmental response curves of individual biome types within the spatial domain (see Kimball et al. [1997a,b, 1998]). These physiological constants were obtained from BOREAS field measurements when possible. When these data were unavailable, values were selected from the literature for representative cover types under similar environmental conditions. For more detailed information regarding the extraction of model inputs see Section 5. 1.6 Related Models BIOME-BGC represents the evolution of a forest ecosystem process model (FOREST- BGC) to include biophysical representations of additional growth forms and biome characteristics. Descriptions of FOREST-BGC are provided by Running and Coughlan [1988], while descriptions of BIOME-BGC development and logic are provided by Running and Hunt [1993]. BIOME-BGC and FOREST-BGC models have also been coupled with remote sensing and gridded daily microclimate information to assess ecological processes at landscape and global scales [Kimball et al., 1998; Pierce et al., 1994; Running et al. 1987, 1989]. 2.Investigator(s) 2.1 Investigators name and title Dr. Steven W. Running Dr. John S. Kimball 2.2 Title of Investigation BIOME-BGC Regional Simulations of Annual Water and Carbon Fluxes within the BOREAS SSA-MSA 2.3 Contact Information Contact 1 --------- John S. Kimball University of Montana Missoula, MT (406) 243-5616 (406) 243-4510 (fax) email: johnk@ntsg.umt.edu web site: http://www.forestry.umt.edu/ntsg/ Contact 2 --------- Jaime Nickeson Raytheon ITSS NASA/GSFC Greenbelt, MD (301) 286-3373 (301) 286-0239 (fax) Jaime.Nickeson@gsfc.nasa.gov 3.Theory of Measurements Not Applicable 4.Equipment Not Applicable 5.Data Acquisition Methods Crown and stem biomass and landcover classification maps derived from remote sensing were used to drive model simulations within the SSA-MSA. Crown and stem biomass maps were derived from 1994 airborne synthetic aperture radar (AIRSAR) remote sensing measurements of the SSA-MSA collected from a DC-8 aircraft [Boreal Ecosystem-Atmosphere Study: Experiment Plan. Version 1994-3.0; Saatchi and Rignot, 1997]. A Landcover classification map of the SMSA was also obtained from AIRSAR data collected during the 1994 growing season [Saatchi and Rignot, 1997]. These maps are currently available from the BOREAS RSS-16 Team. A second landcover classification map, derived from 1994 Landsat TM information, was obtained from the BOREAS TE-18 Team and was used to distinguish broadleaf deciduous stands within areas defined as mixed forest within the AIRSAR landcover classification. The RSS-16 landcover classification maps were used to define the number of individual biome types represented in the ecosystem model, while RSS-16 biomass maps were used to define leaf area index (LAI) and foliar and stem carbon pools within each grid cell. The classification map was used to distinguish 6 landcover classes within the SSA-MSA, representing dry conifer, wet conifer, open water, disturbed, mixed deciduous/conifer forest, and wetland areas. Dry conifer areas were mainly composed of jack pine stands while wet conifer areas consisted mainly of black spruce stands. Wetland areas were composed of a mixture of black spruce, bog and fen sites, while disturbed sites represented a mixture of recently logged and burned areas, roads and other sparsely vegetated and non-water surfaces. Mixed deciduous/conifer forest areas represented a mixture of aspen forest and mixed jack pine/aspen forest with no clear dominance of either species type. Our analysis focused on vegetated areas within the SSA- MSA only. Open water areas were not represented in the model and were masked from further analysis. The mass of living stem carbon was derived from the RSS-16 AIRSAR stem biomass map and estimates of the relative proportions of living and total stem biomass, and the proportions of living cells in sapwood tissue. This information was obtained from BOREAS TE-6 Team's biomass harvest plots within the SSA-MSA and information reported in the literature for representative vegetation types [Gower et al., 1997; Waring and Running, 1998]. The mass of living coarse root carbon was estimated as a proportion of live stem carbon using allometric relationships for representative cover types [Grier et al., 1981; Vogt, 1991; Steele et al., 1997]. The mass of living fine root carbon was estimated from 1.5 to 3.0 times foliar carbon estimates based on SSA-MSA biomass measurements and information reported in the literature for nutrient limited arctic, boreal and cold temperate environments [Bigger and Oechel, 1982; Mitsch and Gosselink, 1993; Schimel et al., 1996; Gower et al., 1997; Steele et al., 1997]. The mass of foliar carbon was derived from RSS-16 AIRSAR crown biomass (i.e. leaves and branches) map and estimated proportions of foliar to crown biomass obtained from the TE-6 biomass harvest plots within the SSA-MSA [Gower et al., 1997]. LAI was derived from foliar carbon maps and specific leaf area (SLA) values obtained from canopy biophysical measurements within the SSA-MSA collected by BOREAS Team TE-9 [Dang et al., 1997b]. The LAI for coniferous vegetation was held constant over each year. For deciduous vegetation, LAI was regulated between a prescribed seasonal minimum (i.e. 0.0) and the remote sensing defined seasonal maximum using a phenology model based on daily meteorological predictions of satellite-observed dates of greenness onset and offset [White et al., 1997]. The model predicts the onset of greenness using a combined thermal and radiation summation, while offset is determined using a thermally adjusted photoperiod trigger. The foliar carbon pool was increased on a daily basis using a stepped 45-day linear ramping function between the onset date and the RSS-16 AIRSAR-derived LAI, while foliage drop occurred at the offset date. Foliar leaf nitrogen concentrations strongly influence the photosynthetic capacity of the system and are directly related to the amount of radiation absorbed by the canopy [Pierce et al., 1994]. Because canopy absorption is also related to LAI, foliar nitrogen was estimated as a proportion (0.7-4.5%) of leaf carbon. These fractions were derived from BOREAS Team TE-9 site measurements within BOREAS aspen, jack pine and black spruce stands [Dang et al., 1997b; Sullivan et al., 1997] and values reported in the literature for representative cover types [Aerts et al., 1992; Mitsch and Gosselink, 1993; Schimel et al., 1996]. Soil rooting depth and water holding capacity information were derived from a 1:1,000,000 scale digital soils inventory database of Canada obtained through BOREAS Staff from the Land Resource Research Centre of Agriculture Canada [Acton et al., 1991] and volumetric soil moisture and soil survey measurements conducted at several sites within the SSA by the BOREAS HYD-1 team [Boreal Ecosystem-Atmosphere Study: Experiment Plan. Version 1994-3.0; Cuenca et al., 1997]. Soil b-parameter values define the slope of the functional soil water potential (?? response to changes in soil water and were derived from values reported in the literature for representative soil types [Cosby et al., 1984]. Soil structural characteristics were assumed constant within the area represented by each landcover class. Daily meteorological data were interpolated over a 1 km resolution, digital elevation map (DEM) of the SSA-MSA using a daily meteorological interpolator [Running et al., 1987; Thornton et al., 1997], digital elevation information (i.e. elevation, slope, aspect) and daily weather data from approximately 60 stations within the BOREAS region. Gridded daily meteorological data were produced for the 3-year (1994 - 1996) study period. The meteorological data were obtained from the National Climatic Data Center's Global Surface Summary of the Day database, the Saskatchewan Research Council's Automatic Meteorological Stations database and BOREAS tower flux site measurements within the SSA-MSA [National Weather Service, 1988; Boreal Ecosystem-Atmosphere Study: Experiment Plan. Version 1994-3.0; Shewchuk, 1997]. DEM information were provided by the BOREAS HYD-08 Team and BOREAS staff [Boreal Ecosystem-Atmosphere Study: Experiment Plan. Version 1994-3.0]. Daily model simulations were conducted for the BOREAS SSA-MSA study region from January 1, 1994 to December 31, 1996. Meteorological conditions were defined using the 3-year gridded daily meteorological fields, while 1994 landcover type and biomass maps defined surface physical conditions for the three 3-year period. Spatially distributed estimates of initial soil water and snow water equivalent depth were required to initialize the 1994 water balance. The model was initialized with a uniform, snow water equivalent depth of 3.3 cm and a soil water depth set at 95% of field capacity. These values were determined from BIOME-BGC point simulations at BOREAS SSA-MSA tower sites using 1993 daily meteorological data from Nipawin Airport (53.20N 104.00W) near the SE corner of the SSA-MSA. Deciduous and coniferous canopies within mixed cells were simulated separately because of marked differences in biophysical characteristics and physiological responses to environmental controls. Dry and Wet Conifer classes were assumed to be composed entirely of coniferous vegetation. Disturbed, mixed conifer/deciduous, and wetland classes were represented as a mixture of 50% deciduous and 50% coniferous lifeforms. Landcover classification information derived from Landsat TM data were used to distinguish broadleaf deciduous stands (See above) within AIRSAR-defined mixed conifer/deciduous areas. These areas were assumed to be 100% deciduous while other mixed conifer/deciduous areas were represented as a mixture of 50% coniferous and 50% deciduous. Ideally, information on deciduous and coniferous proportional cover characteristics could be used to simulate the relative contributions of different lifeforms to the total water and carbon flux within each grid cell. Unfortunately, that information was not available for this investigation and thus simplifying assumptions were necessary. 6.Observations 6.1 Data Notes None 7.Data Description 7.1 Spatial Characteristics 7.1.1 Spatial Coverage The image files cover the entire area within the BOREAS SSA-MSA (approximately 40 km x 50 km). However, model runs were limited by the extent of available AIRSAR data; unknown and open water landcover classes were also masked from model analyses so that the actual size of the simulated surfaces represent a smaller area of approximately 1200 km2. Model results were generated with a minimum pixel size of 30 m, which represents the spatial resolution of the AIRSAR data. The upper-left corner of the upper-left pixel are (NAD83): UTM UTM Longitude Latitude Easting Northing ------------ ----------- --------- --------- 105.46023° W 54.28602° N 470039.5 6015442.5 7.1.2 Spatial Coverage Map None. 7.1.3 Spatial Resolution The spatial resolution of the images is 30 m x 30 m. 7.1.4 Projection The area mapped is projected in the ellipsoidal version of the Albers Equal Area Conic projection. The projection has the following parameters: Datum: NAD83 Ellipsoid: GRS80 or WGS84 Origin: 111.000 degrees West Longitude 51.000 degrees North Latitude Standard Parallels: N 52 deg 30' 00" N 58 deg 30' 00" Units of Measure: kilometers 7.1.5 Grid Description The data is gridded in 30 m intervals based on projection described in Section 7.1.4. 7.2 Temporal Characteristics 7.2.1 Temporal Coverage The images represent annual accumulations of daily model results for 1994, 1995 and 1996. 7.2.2 Temporal Coverage Map None 7.2.3 Temporal Resolution The images represent annual accumulations of daily model output. 7.3 Input Data Characteristics 7.3.1 Input Parameter/Variable See Section 1.4 7.3.2 Variable Description/Definition See Section 1.4 7.3.3 Unit of Measurement See Section 1.4 7.3.4 Data Source See Section 1.4 7.3.5 Data Range See Section 1.4 7.4 Output Data Characteristics 7.4.1 Output Parameter/Variable GPP, NPP, R, ET 7.4.2 Variable Description/Definition See Section 1.4 7.4.3 Unit of Measurement GPP, NPP and R (kg C m-2 yr-1) ET (kg m-2 yr-1) 7.4.4 Data Source GPP, NPP, R and ET images represent annual accumulations of BIOME-BGC daily output. 7.4.5 Data Range Not Applicable 7.5 Sample Data Records Not Applicable 8.Data Organization 8.1 Data Granularity The smallest unit of data is the complete set of simulated images described in this document. 8.2 Data Format(s) 8.2.1 Uncompressed Files This dataset contains the following 13 files: file description blocksize ------ ----------------------- --------- 1 Evapotranspiration 1994 3000 2 Gross Primary Production 1994 3000 3 Net Primary Production 1994 3000 4 Autotrophic Respiration 1994 3000 5 Evapotranspiration 1995 3000 6 Gross Primary Production 1995 3000 7 Net Primary Production 1995 3000 8 Autotrophic Respiration 1995 3000 9 Evapotranspiration 1996 3000 10 Gross Primary Production 1996 3000 11 Net Primary Production 1996 3000 12 Autotrophic Respiration 1996 3000 13 AIRSAR-derived Landcover 1994 1500 The first 12 files contain three years of model output of ET, GPP, NPP, and R for 1994, 1995, and 1996. . File 13 is the AIRSAR-based landcover image of 1994. Files 1-12 contain 1120 records, each 3000 bytes long. The records for these 12 files contain 1500 2-byte values (pixels), for a total of 3000 bytes in each of 1120 records (lines). File 13 is a single-byte image and thus has just 1500 bytes (pixels) in each of the 1120 records (lines). BIOME-BGC output image data are presented as binary image maps of annual ET, GPP, NPP, and R. As stored, the values are scaled integers that can be converted to physical units by using the following equations: GPP, NPP and R (kg C m-2 yr-1) = (DN/1000)-5. ET (kg m-2) = (DN/10)-1. The 6-class landcover image (used for the model simulations) is a single-byte image. This image was extracted from the RSS-16 aircraft SAR landcover map [Saatchi and Rignot, 1997]. This image is spatially equivalent to the BIOME-BGC output images. Landcover values correspond to the following landcover types: 0 = Unknown, 1 = Dry conifer, 2 = Disturbed, 3 = Water, 4 = Wet conifer, 5 = Mixed forest, 6 = Fen/Wetland. The size of the modeling area was governed by the extent of available AIRSAR data used to provide landcover information. Unknown and open water landcover classes were masked from model analyses. Similarly, areas outside the bounds of available AIRSAR coverage were also masked. Masked pixels are designated with a missing data "-9999.0" flag. 8.2.2 Compressed CD-ROM Files On the BOREAS CD-ROMs, the image files been compressed with the Gzip (GNU zip) compression program (file_name.gz). These data have been compressed using gzip version 1.2.4 and the high compression (-9) option (Copyright (C) 1992-1993 Jean-loup Gailly). Gzip uses the Lempel-Ziv algorithm (Welch, 1994) also used in the zip and PKZIP programs. The compressed files may be uncompressed using gzip (with the -d option) or gunzip. Gzip is available from many websites (for example, the ftp site prep.ai.mit.edu/pub/gnu/gzip-*.*) for a variety of operating systems in both executable and source code form. Versions of the decompression software for various systems are included on the CD-ROMs. 9.Data Manipulations See Kimball et al. [1997a,b, 1998] 10.Errors 10.1 Sources of Error BIOME-BGC is a process level model designed to be general enough to apply at regional to global scales. The model uses several simplifying assumptions regarding stand and meteorological conditions in order to facilitate application at regional scales. A fundamental model assumption for this investigation was that stand physiological conditions such as age, stand structure and carbon storage were spatially and temporally uniform on an annual basis. Soil conditions such as rooting depth, density, and moisture content were also assumed spatially uniform with no lateral or subsurface drainage. Stand conditions at the study sites were both spatially and temporally diverse and were composed of different age types, biomass densities and species compositions (BOREAS Science Team 1995). Some sites also had significant vegetation understories that were not explicitly modeled in this investigation. Recent research indicates that understory processes can contribute significantly to annual carbon and water budgets. Other factors such as stand disease and mortality effects, and soil nutrient variability were also not addressed. Although much the boreal forest is relatively flat, sub-grid scale variability in precipitation, wind, solar irradiance, albedo and the surface energy balance may induce greater heterogeneity in surface fluxes than we were able to distinguish using a 1 km gridded daily meteorological database and a 30 m landcover database. Other major uncertainties include the relative proportions of deciduous and coniferous vegetation within each landcover class and amounts of fine and coarse root biomass for different stands. These factors strongly influence stand environmental response curves and respiration rates but are difficult to quantify from available BOREAS data. Further discussion of potential error sources for this investigation is given by Kimball et al. [1997 a,b, 1998]. 10.2 Quality Assessment 10.2.1 Model Validation by Source Modeled AIRSAR-derived landcover, biomass and LAI inputs have been compared with similar results derived from optical remote sensing data, surface optical LAI, and biomass measurements (e.g. Saatchi and Rignot, 1997). Model outputs have been compared with tower flux site daily water and CO2 flux estimates, as well as soil water, snowcover, and biomass allometric measurements (e.g. Kimball et al., 1997a,b). Annual 1994 NPP results averaged approximately 2.7 (+/-0.4), 3.4 (+/-0.3), 1.9 (+/-0.3), 2.1 (+/-0.3), 1.0 (+/-0.5) and 2.0 (+/-0.6) (Mg C ha-1 yr-1 for wetland, mixed conifer/deciduous, dry and wet conifer, disturbed and broadleaf deciduous stands, respectively. These values were approximately 24% (NPP) smaller for 1995 and 1996 in response to cooler spring and warmer, drier summer conditions. Simulated ranges and landcover differences in NPP were similar to observations reported by other investigators using above- and below- ground biomass measurements and allometric relationships within SSA black spruce, jack pine and aspen stands [Gower et al., 1997; Steele et al., 1997]. NPP observation data for fen and other wetland areas were not available within the SSA-MSA. However, model simulations were similar to the magnitudes and ranges of NPP (above-ground only) reported in the literature for northern bog marshes, rich fen, forested peatland and fen forest sites within Canada and the Northern U.S.; reported values range from 0.5-9.7 Mg C ha-1 yr-1 [Mitsch and Gosselink, 1993]. Annual 1994 ET results averaged approximately 23.3 (+/-1.7), 23.7 (+/-1.7), 21.4 (+/-1.6), 24.6 (+/-2.1), 19.3 (+/-0.3), and 22.3 (+/-0.7) cm yr-1 for wetland, mixed conifer/deciduous, dry and wet conifer, disturbed and broadleaf deciduous stands, respectively, and decreased from 0.04-5.6% for 1995 and 1996. The magnitudes and relative differences in ET between landcover classes were similar to 1994 cumulative ET estimates obtained from tower eddy-flux measurements at SSA black spruce [Jarvis et al., 1997], jack pine [Baldocchi et al., 1997] and aspen sites [Black et al., 1996]. Earlier comparisons between model results and field measurements at individual tower sites showed that model results explained approximately 62 and 98 percent of the respective variances in observed daily ET and soil water [Kimball et al., 1997b]. 10.2.2 Confidence Level/Accuracy Judgement See Sections 10.1 and 10.2.1 10.2.3 Measurement Error for Parameters See Sections 10.1 and 10.2.1 10.2.4 Additional Quality Assessments See Sections 10.1 and 10.2.1 10.2.5 Data Verification by Data Center BORIS staff have viewed the imagery to verify image size and type and to establish data ranges. 11.Notes 11.1 Limitations of the Model See Section 12 11.2 Known Problems with the Model See Section 10.1 11.3 Usage Guidance Before uncompressing the Gzip files on CD-ROM, be sure that you have enough disk space to hold the uncompressed data files. Then use the appropriate decompression program provided on the CD-ROM for your specific system. Also, see Section 10.1 and 12 11.4 Other Relevant Information See Section 12 12.Application of the Model These data represent research in progress. We expect our results to improve as existing datasets improve and additional remote sensing parameter maps and measurement data regarding stand and soil morphology become available. These results are intended for visual interpretation, assessment of yearly variations in annual fluxes, and comparison with other models and additional measurement data. 13.Future Modifications and Plans This model will be coupled with gridded daily meteorological data, soil and remote sensing parameter maps (e.g. landcover, LAI, radar freeze-thaw) to generate landscape level estimates of water and carbon exchange processes over the 1x106 km2 BOREAS grid at a 1 km spatial resolution. There are currently no plans to generate further high resolution (30 m) results. 14.Software 14.1 Software Description FOREST-BGC is currently available in C format and can be obtained via ftp download. BIOME-BGC is still under development and is not available for public release at this time. Gzip uses the Lempel-Ziv algorithm (Welch, 1994) used in the zip and PKZIP commands. 14.2 Software Access The NTSG website (See Section 2.3) currently provides software updates and download information on model changes and availability. web site: http://www.forestry.umt.edu/ntsg/ Gzip is available from many Web sites across the Internet (for example, ftp site prep.ai.mit.edu/pub/gnu/gzip-*.*) for a variety of operating systems in both executable and source code form. Versions of the decompression software for various systems are included on the CD-ROMs. 14.3 Platform Limitations The FOREST-BGC family of models have been written and compiled on UNIX AIX platforms but should compile with any standard C compiler. web site: http://www.forestry.umt.edu/ntsg/ 15. Data Access 15.1 Contact Information Ms. Beth Nelson NASA GSFC Greenbelt, MD (301) 286 4005 (301) 286 0239 (fax) beth@ltpmail.gsfc.nasa.gov 15.2 Data Center Status/Plans The RSS-08 BIOME-BGC simulation images are available from the Earth Observing System Data and Information System (EOSDIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC). The BOREAS contact at ORNL is: ORNL DAAC User Services Oak Ridge National Laboratory Oak Ridge, TN (423) 241-3952 ornldaac@ornl.gov ornl@eos.nasa.gov 16. Output Products and Availability 16.1 Tape Products The data can be made available on 8-mm or Digital Archive Tape (DAT) media. 16.2 Film Products None. 16.3 Other Products None. 17.References 17.1 Model Documentation See section 17.2. Welch, T.A. 1984, A Technique for High Performance Data Compression, IEEE Computer, Vol. 17, No. 6, pp. 8 - 19. 17.2 Journal Articles and Study Reports Acton, D. F., G. A. Padbury and J. A. Shields, Soil landscapes of Canada- Saskatchewan digital map data: scale 1:1000000, CanSIS No. SK018200, version 90.11.30, CLBRR Archive, Agriculture Canada Research Branch, Ottawa, Canada, No. 91-107D, 1991. Aerts, R., H. De Caluwe, and H. Konings, Seasonal allocation of biomass and nitrogen in four Carex species from mesotrophic and eutrophic fens affected by nitrogen supply, J. Ecol., 80, 653-664, 1992. Baldocchi, D. D., C. A. Vogel and B. Hall, Seasonal variation of energy and water vapor exchange rates above and below a boreal jack pine forest canopy, J. Geophys. Res., 102(D24), 28939-28951, 1997. Bigger, C. M., and W. C. Oechel, Nutrient effect on maximum photosynthesis in arctic plants, Holarctic Ecology, 5, 158-163, 1982. Black, T. A., G. den Hartog, H. H. Neumann, P. D. Blanken, P. C. Yang, C. Russell, Z. Nesic, X. Lee, S. G. Chen and R. Staebler, Annual cycles of water vapour and carbon dioxide fluxes in and above a boreal aspen forest, Global Change Biol., 2, 219-229, 1996. BOREAS Science Team, Boreal Atmosphere-Ecosystem Study, Experimental Plan, Version 3.1, NASA/GSFC, Greenbelt MD, 1995. Cosby, B. J., G. M. Hornberger, R. B. Clapp and T. R. Ginn, A statistical exploration of the relationships of soil moisture characteristics to the physical properties of soils, Water Resour. Res., 20, 682-690, 1984. Cuenca, R. H., D. E. Stangel and S. F. Kelly, Soil water balance in a boreal forest, J. Geophys. Res., 102(D24), 29355-29366, 1997. Dang, Q. -L., H. A. Margolis, M. R. Coyea, M. Sy and G. J. Collatz, Regulation of branch-level gas exchange of boreal trees: roles of shoot water potential and vapor pressure difference, Tree Physiol., 17, 521-535, 1997a. Dang, Q. -L., H. A. Margolis, M. Sy, M. R. Coyea, G. J. Collatz and C. L. Walthall, Profiles of photosynthetically active radiation, nitrogen and photosynthetic capacity in the boreal forest: Implications for scaling from leaf to canopy, J. Geophys. Res., 102(D24), 28,845-28,859, 1997b. Farquhar, G. D. and S. von Caemmerer, Modelling of photosynthetic response to environmental conditions, in Encyclopedia of Plant Physiology, New Series, vol. 12B, Physiological Plant Ecology II, edited by O. L. Lange, P. S. Nobel, C. B. Osmond and H. Ziegler, Springer-Verlag, Berlin, Germany, 549-587, 1982. Gower, S. T., J. Vogel, J. Norman, C. J. Kucharik, S. Steele, and T. K. Stow, Carbon distribution and above ground net primary production in aspen, jack pine and black spruce stands in Saskatchewan and Manitoba, Canada, J. Geophys. Res., 102(D24), 29029-29041, 1997. Grier, C. C., K. A. Vogt, M. R. Keyes and R. L. Edmonds, Biomass distribution and above- and belowground production in young and mature Abies amabilis zone ecosystems of the Washington Cascades, Can. J. For. Res., 11, 155-157, 1981. Hunt, R. E. and S. W. Running, Simulated dry matter yields for aspen and spruce stands in the North American boreal forest, Can. J. Remote Sens., 18, 126-133, 1992. Hunt, R. E., S. C. Piper, R. Nemani, C. D. Keeling, R. D. Otto and S. W. Running, Global net carbon exchange and intra-annual atmospheric CO2 concentrations predicted by an ecosystem process model and three-dimensional atmospheric transport model, Global Biogeochemical Cycles, 10(3), 431-456, 1996. Jarvis, P. G., J. M. Massheder, S. E. Hale, J. B. Moncrieff, M. Rayment, and S. L. Scott, Seasonal variation of carbon dioxide, water vapor, and energy exchanges of a boreal black spruce forest, J. Geophys. Res., 102(D24), 28953- 28966. Kimball, J. S., P. E. Thornton, M. A. White and S. W. Running, Simulating forest productivity and surface-atmosphere carbon exchange in the BOREAS study region, Tree Physiol., 17, 589-599, 1997a. Kimball, J. S., S. W. Running and S. S. Saatchi, Sensitivity of boreal forest regional water flux and net primary production simulations to sub-grid scale landcover complexity, J. Geophys. Res., (submitted, 1998). Kimball, J. S., M. A. White and S. W. Running, BIOME-BGC simulations of stand hydrologic processes for BOREAS, J. Geophys. Res., 102(D24), 29043-29051, 1997b. Lavigne, M. B. and M. G. Ryan, Growth and maintenance respiration rates of aspen, black spruce and jack pine stems at northern and southern BOREAS sites, Tree Physiol., 17, 543-551, 1997. Mitsch, W. J. and J. G. Gosselink, Wetlands, 2nd. ed., Van Nostrand Reinhold, New York, 1983. National Weather Service, Surface observations, Federal Meteorological Handbook No. 1, (FCM-H1-1988), Office of the Federal Coordinator, Department of Commerce, Washington, DC, 1988. Penning de Vries, F.W.T., A. Brunsting, and H.H. Van Laar, Products, requirements and efficiency of biosynthesis: A quantitative approach. Journal of Theoretical Biology. 45:339-377, 1974. Pierce, L. L., S. W. Running and J. Walker, Regional-scale relationships of leaf area index to specific leaf area and leaf nitrogen content, Ecol. Appl., 4(2), 313-321, 1994. Running, S. W., and J. C. Coughlan, A general model of forest ecosystem processes for regional applications, I. Hydrologic balance, canopy gas exchange and primary production processes. Ecol. Model. 42, 125-154, 1988. Running, S.W., R.R. Nemani, and R.D. Hungerford, Extrapolation of synoptic meteorological data in mountainous terrain and its use for simulating forest evapotranspiration and photosynthesis. 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Vogt, K., Carbon budgets of temperate forest ecosystems, Tree Physiol., 9, 69- 86, 1991. Waring, R. H. and S. W. Running, Forest ecosystems analysis at multiple scales, Academic Press, San Diego, CA, 1998. White, J. D. and S. W. Running, Testing scale dependent assumptions in regional ecosystem simulations, J. Veg. Sci., 5, 687-702, 1994. 17.3 Archive/DBMS Usage Documentation None. 18.Glossary of Terms None 19.List of Acronyms AIRSAR - Airborne Synthetic Aperture Radar BIOME-BGC - Biome Biogeochemistry Model BOREAS - BOReal Ecosystem-Atmosphere Study BORIS - BOREAS Information System DAAC - Distributed Active Archive Center DEC - Broadleaf deciduous landcover DEM - Digital elevation model DN - Scaled binary image value (digital number) DSTRB - Disturbed landcover EOS - Earth Observing System EOSDIS - EOS Data and Information System ET - Evapotranspiration (kg/m2) GPP - Gross primary production or net photosynthesis (kg C/m2) GSFC - Goddard Space Flight Center LAI - Leaf area index (m2/m2) NASA - National Aeronautics and Space Administration NPP - Net primary production (kg C/m2) NSA - BOREAS northern study area ORNL - Oak Ridge National Laboratory R - Autotrophic or maintenance respiration (kg C/m2) SLA - Specific leaf area (m2/kg C) SSA-MSA - SSA modeling sub-area SSA - BOREAS southern study area URL - Uniform Resource Locator 20.Document Information 20.1 Document Revision Date Written: 18-Sep-1998 Last Updated: 08-Dec-1998 20.2 Document Review Date(s) BORIS Review: 30-Nov-98 Science Review: 20.3 Document 20.4 Citation If using this data, please reference the following publications: Kimball, J. S., P. E. Thornton, M. A. White and S. W. Running, Simulating forest productivity and surface-atmosphere carbon exchange in the BOREAS study region, Tree Physiol., 17, 589-599, 1997a. Kimball, J. S., S. W. Running and S. S. Saatchi, Sensitivity of boreal forest regional water flux and net primary production simulations to sub-grid scale landcover complexity, J. Geophys. Res., (submitted, 1998). Kimball, J. S., M. A. White and S. W. Running, BIOME-BGC simulations of stand hydrologic processes for BOREAS, J. Geophys. Res., 102(D24), 29043-29051, 1997b. 20.5 Document Curator 20.6 Document URL Keywords: BIOME-BGC RHESSys NPP Scaling Primary production Productivity, Photosynthesis Evapotranspiration Carbon Water BOREAS Remote sensing Modeling RSS08_BIOME_BGC_Imgs.doc 01/13/99