BOREAS RSS-08 BIOME-BGC Model Simulations at Tower Flux Sites in 1994

Summary

BIOME-BGC is a general ecosystem process model designed to simulate
biogeochemical and hydrologic processes across multiple scales (Running
and Hunt, 1993). In this investigation, BIOME-BGC was used to estimate
daily water and carbon budgets for the BOREAS tower flux sites for 1994.
Carbon variables estimated by the model include gross primary production
(i.e., net photosynthesis), maintenance and heterotrophic respiration,
net primary production, and net ecosystem carbon exchange. Hydrologic
variables estimated by the model include snowcover, evaporation,
transpiration, evapotranspiration, soil moisture, and outflow. The
information provided by the investigation includes input initialization
and model output files for various sites in tabular ASCII format.

Table of Contents

\* 1. Model Overview

\* 2. Investigator(s)

\* 3. Model Theory

\* 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

BOREAS RSS-08 BIOME-BGC Model Simulations at Tower Flux Sites in 1994

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. The carbon balance portion of BIOME-BGC utilizes daily
meteorological data in conjunction with general stand and soil
information to predict net photosynthesis, growth, maintenance, 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). A
description of the components of the model relating to the prediction of
hydrologic and carbon balance characteristics within different boreal
forest stands is given by Kimball et al. (1997a,b). A summary of the
important components of BIOME-BGC relating to the prediction of daily
carbon allocation and exchange is given below.

1.3 Objective/Purpose

In this investigation, BIOME-BGC was used to estimate daily and annual
hydrologic and carbon budgets for different boreal forest stands
associated with the BOReal Ecosystem-Atmosphere Study (BOREAS) tower
flux sites, and net carbon flux estimates were compared with results
derived from tower flux and biomass measurement data. These results were
used to assess the important climate and stand characteristics that
control stand hydrologic characteristics, estimated productivity
respiration, and surface-atmosphere carbon exchange.

These results constitute the initial effort in 1996 to simulate
hydrologic and carbon exchange processes for different boreal forest
stands. The results are expected to change as the models are further
modified and developed to reflect insight gained from new research
regarding boreal forest processes. These results are intended to provide
a framework for evaluating the sensitivity of the boreal forest regional
carbon balance to global warming.

1.4 Summary of Parameters and Variables

Model daily Greenwich Mean Time (GMT) input requirements:

Maximum and minimum daily air temperature (°C), precipitation (cm),
total daily solar radiation (kJ), and daylength (s).

There is also a site initialization file that describes stand morphology
and soil characteristics. Parameters included in this file are discussed
in Section 1.5. Site initialization files that were used to generate
model results for the sites in this investigation are provided.

Model daily (GMT) carbon outputs:

Net photosynthesis gross primary production (GPP); maintenance (Rm),
growth (Rg), heterotrophic (Rh), and total respiration (Rtot); net
ecosystem carbon exchange (NEE). Rm represents the daily sum of
estimated Rm rates from coarse and fine root, sapwood, and foliar carbon
pools. Foliar respiration is computed as the sum of estimated day and
night foliar respiration rates. GPP is computed as the difference
between gross photosynthesis and day leaf respiration. Net primary
production (NPP) is determined as the difference between GPP and Rm. Rg
was estimated as 32% of the daily difference between GPP and Rm. Rh is
estimated as a proportion of prescribed soil and litter carbon pools;
estimated soil water potential and soil temperature conditions regulate
this proportion. Rtot is estimated as the sum of Rm, Rg, and Rh. NEE is
estimated as the difference between GPP and Rtot.

Model daily (GMT) hydrologic outputs:

Evaporation, transpiration, evapotranspiration, soil moisture, snow
water equivalent.

1.5 Discussion

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. The carbon balance portion of BIOME-BGC utilizes daily
meteorological data in conjunction with general stand and soil
information to predict net photosynthesis, growth, maintenance 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).
Kimball et al. (1997a,b) gives a description of the components of the
model relating to the prediction of hydrologic and carbon
characteristics within different boreal forest stands. A summary of the
important components of BIOME-BGC relating to the prediction of daily
carbon allocation and exchange is given in Section 3.

1.6 Related Models

These results represent site-specific model runs using BIOME-BGC.
BIOME-BGC will also be used within the context of a Regional
Hydro-Ecological Simulation System (RHESSys) to generate landscape-level
estimates of 1994 daily hydrologic and carbon fluxes within the BOREAS
1000-km x 1000-km study region. A detailed description of the RHESSys
model is given by Band et al. (1991, 1993).

2\. Investigator(s)

2.1 Investigator(s) Name and Title

Steven W. Running and John S. Kimball (TE-21 and RSS-8)

NTSG School of Forestry, University of Montana

Missoula, MT 59812

2.2 Title of Investigation

BIOME-BGC simulations of stand hydrology, productivity,
surface-atmosphere carbon and water exchange at selected BOREAS tower
flux sites for 1994

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

Contact 2

\-\-\-\-\-\-\-\--

Jaime Nickeson

NASA/GSFC

Greenbelt, MD

\(301\) 286-3373

\(301\) 286-0239 (fax)

Jaime.Nickeson@gsfc.nasa.gov

3\. Model Theory

The sole input to the carbon budget in BIOME-BGC is the photosynthetic
fixation of CO2 by the vegetation canopy. All outputs are in the form of
respired CO2, coming either from plant tissues because of growth or
maintenance respiration, or from the litter and soil carbon pools as the
result of heterotrophic respiration.

GPP represents the system\'s total gain of carbon by net photosynthesis
and is defined as the daily sum of gross photosynthesis and daily foliar
respiration. The current representation of photosynthesis differs
significantly from previously published descriptions of the BGC family
of models (Running and Hunt, 1993; Hunt and Running, 1992; Running and
Coughlan, 1988). The original FOREST-BGC representation of
photosynthesis relies primarily on the parameterization of a mesophyll
conductance to CO2, estimating the rate of fixation as a diffusion
process, driven by a prescribed internal CO2 concentration. FOREST-BGC
also does not implement an explicit treatment of the photosynthetic
biochemical pathways. The original version of BIOME-BGC presents a more
detailed representation of photosynthesis, relying on explicit models of
photosynthetic biochemistry (Leuning, 1990; Farquhar et al., 1980). The
original BIOME-BGC also includes an iterative calculation of
intracellular CO2 concentration (Ci), as well as an explicit calculation
of the CO2 compensation point. The current implementation of
photosynthetic biochemistry is closely related to the original BIOME-BGC
logic in that it is based on the Farquhar biochemical model, but the
resulting set of equations is somewhat different because of differences
in the logical constraints applied: a quadratic system of equations is
solved by eliminating Ci, instead of by specifying a value as an initial
condition. Other differences include a more detailed dependence of the
kinetic parameters on temperature (Woodrow and Berry, 1988) and a
simplifying assumption that empirically relates the maximum rate of
electron transport to the maximum carboxylation velocity (Wullschleger,
1993).

Photosynthesis is regulated by the canopy conductance to CO2 (gc), leaf
maintenance respiration, and daily meteorological conditions, including
air pressure, air temperature, and photosynthetically active photon flux
density (PPFD). The maximum canopy conductance to CO2 (gc max) defines
the upper boundary of the photosynthetic rate and is determined by leaf
area index (LAI) and prescribed leaf-scale boundary layer, cuticular,
and maximum stomatal conductances; gc is reduced when air temperature,
vapor pressure deficit (VPD), PPFD, or soil water potential deviate from
prescribed optimal conditions (Leuning, 1990; Running and Coughlan,
1988; Jarvis and Morison, 1981). BIOME-BGC represents the canopy as a
\"big leaf\" in that all units of leaf area in the canopy are
represented using a single, canopy-averaged conductance. This assumption
is generally not valid at subdaily (e.g., hourly) time-steps because the
reduction of irradiance at lower vertical layers of the canopy reduces
conductances at the bottom of the canopy. The big leaf assumption is
strengthened, however, by the integrative effects of a daily time-step
and by the implicit assumption that the allocation of leaf nitrogen
between light harvesting and carbon fixing enzymes over depth in the
canopy varies in response to the canopy light environment, allowing an
optimized use of intercepted radiation (Evans, 1989).

Total respiration from the system (Rtot) is estimated on a daily basis
as the sum of the maintenance (Rm), heterotrophic (Rh) and growth (Rg)
respiration components. Rm represents the total loss of carbon due to
day and night leaf respiration (Rdl + Rnl), sapwood (Rsw), coarse root
(Rcr) and fine root (Rfr) respiration. Respiration is estimated as a
daily proportion of carbon in living tissue that is released as the
result of cellular metabolic processes, excluding any growth processes.
Rm is calculated from mean daily air temperatures and prescribed leaf,
root, and sapwood carbon pools using an exponentially increasing
function of respiration with temperature following Amthor (1986). The
magnitude of the respiration response to temperature is governed by a
prescribed rate defined at a reference temperature (i.e. 15°C) and a
proportional change in rate for a 10°C change in temperature (Q10). In
all cases except leaf maintenance respiration, the daily average
temperature is used, and a single value is calculated for the mass lost
to maintenance respiration for the day. In the case of leaves, however,
Rdl and Rnl rates are calculated from estimated day and night air
temperatures, respectively, because Rdl is required to determine GPP.
Daily growth respiration was not determined explicitly by the model in
this investigation; instead, Rg was computed as a proportion (32%) of
the daily difference between GPP and Rm (Penning de Vries et al., 1974).

The heterotrophic respiration term in BIOME-BGC represents the system\'s
loss of carbon caused by soil microbial respiration. Daily Rh is
estimated as a proportion of prescribed soil and litter carbon pools.
The proportion of litter carbon being respired on a daily basis is
regulated by soil water potential and soil temperature conditions
following Orchard and Cook (1983), Andren and Paustian (1987), and
Running and Coughlan (1988). The proportion of soil carbon respired on a
daily basis was estimated as 1% of the proportion of litter carbon
respired based on data for boreal coniferous and deciduous stands (Fox
and Van Cleve, 1983; Cole and Rapp, 1981).

NPP represents the net accumulation of carbon by the stand and is
determined as the difference between GPP and the sum of Rm and Rg. NEE
represents the net accumulation or loss of carbon by the entire
soil-stand system and is determined as the difference between GPP and
Rtot. Positive fluxes in this investigation denote a net uptake of
carbon by the system while negative fluxes denote a net loss. Standards
for denoting positive and negative fluxes generally vary between
different disciplines, however, and net carbon uptake is often denoted
as a negative flux in the literature.

BIOME-BGC uses daily maximum and minimum air temperatures, humidity,
incident solar radiation, and precipitation to determine daily carbon
and water fluxes. Average daily incident shortwave radiation (Qi) was
simulated using MT-CLIM logic described by Running et al. (1987).
Average daily net solar radiation (Qn) was estimated using a prescribed,
constant albedo for vegetation. Qn was attenuated through the vegetation
canopy using Beer\'s formulation and a prescribed extinction coefficient
modulated by LAI to derive the amount of solar radiation transmitted
through the canopy (Qt). The amount of solar radiation absorbed by the
canopy (Qa) was estimated as the difference between Qi and Qt. PPFD was
estimated based on the assumption that photosynthetically active
radiation represents approximately 50% of Qa (Running and Coughlan,
1988).

Mean daily air temperature (Ta) was estimated as the average of the
measured daily maximum and minimum air temperatures. Minimum daily air
temperature was assumed equal to the mean daily dew point and was used
to estimate the mean daily VPD. Daily soil temperatures at a 30-cm soil
depth (Tsoil) were estimated using an 11-day running average of Ta
(Zheng et al., 1993). Soil water potential (PSI) was estimated from soil
water content, soil depth, and texture information following Cosby et
al. (1984). Ta, VPD, PPFD, and PSI were used to estimate gc and GPP
following Jarvis and Morison (1981) and Farquhar and von Caemmerer
(1982), respectively. Ta and Tsoil were used to estimate Rm, while Tsoil
and PSI were used to estimate Rh (Running and Coughlan, 1988).

4\. Equipment:

BIOME-BGC is written in C with no specific hardware requirements.

5\. Data Acquisition Methods

The model requires a daily meteorological data file. This file consists
of six columns that are space delimited with each row of the file
representing a specific day of the year. Column 1 represents the day of
year (Julian day format, 1-365), column 2 represents precipitation (cm),
column 3 represents maximum 24-hr daily air temperature (°C), column 4
represents minimum 24 hr daily air temperature (°C), column 5 represents
total daily solar radiation (direct+diffuse, kJ), and column 6
represents the daylength (s). A second file is also required that
defines site initialization parameters such as soil, litter, leaf and
sapwood carbon pools, and soil type and condition. A detailed discussion
of the development of the tower site initialization parameter files is
presented below.

BIOME-BGC requires general information about stand morphology and soil
characteristics in order to simulate the water and carbon balance at a
site. Information required by the model to define initial hydrologic
characteristics of the study sites is given by Kimball et al. (1997a,b).
A list of critical parameters used to define soil and stand carbon
characteristics at the eight study sites can be found there in Table 1.
These parameters were held constant throughout the model runs. Soil
parameters were derived from measurements collected at the sites during
1994 by Cuenca et al. (1997) and values reported in the literature for
representative soil types (Hillel, 1980). The soil depth was set at 0.5
m and assumed homogeneous in regard to soil mineralized carbon,
structure, and soil moisture characteristics. Mean daily stand solar
albedos for snow-free conditions were estimated from site observations
(Sellers et al., 1995).

Estimates of average annual LAI were derived from effective LAI
measurements conducted over approximately three periods during the 1994
growing season at each study site by Chen (1996). Effective LAI was
measured using a LI-COR LAI-2000 plant canopy analyzer and adjusted for
foliage clumping. Specific leaf area (SLA) and leaf nitrogen levels were
determined from plucked needle and leaf measurements at the spruce, jack
pine, and aspen sites by Margolis et al. (1996 unpublished data). The
amounts of leaf nitrogen in ribulose bisphosphate carboxylase-oxygenase
(RuBisCO) were estimated from the literature for representative cover
types (Field and Mooney, 1986). Leaf carbon was derived from LAI and SLA
information. Sapwood carbon was estimated from sapwood biomass
measurements collected by Gower et al. (1996 unpublished data) at the
black spruce, aspen and jack pine sites and estimates of the relative
proportions of sapwood live cells (Waring and Schlesinger, 1985). Coarse
root carbon was estimated to be approximately 25% of sapwood carbon
(e.g., Grier et al., 1981; Grier and Logan, 1977).

The amount of carbon attributed to fine root biomass is highly variable
depending on species type, stand age, and nutrient availability.
Processes governing the partitioning of carbon between root and foliar
biomass are generally poorly understood and not well quantified in the
literature. Observations have shown, however, that fine root biomass is
generally greater than foliar biomass in nutrient-limited systems, which
often occur in boreal and cold temperate forests and may represent an
adaptation to maximize nutrient uptake (Nadelhoffer et al., 1985; Keyes
and Grier, 1981; Tetrealt et al., 1978). Soil carbon attributed to fine
roots was estimated from 1.5 Southern Study Area Old Aspen (SSA-OA) to
3.5 SSA-Old Jack Pine (OJP) times the estimated leaf carbon based on
observations from boreal and cold temperate coniferous and deciduous
stands on nutrient-poor sites (Gower et al., 1992; Comeau and Kimmins,
1989; Nadelhoffer et al.; 1985; Linder and Axelson, 1982; Perala and
Alban, 1982; Keyes and Grier, 1981). Soil litter and mineralized organic
carbon pools within the prescribed 0.5-m soil depths were estimated from
soil layer depth, bulk density and percent organic carbon measurements
conducted at each of the study sites by Anderson et al. (1995
unpublished data).

Leaf, stem, coarse, and fine root maintenance respiration coefficients
were estimated from measured rates for coniferous and deciduous cover
types (Sprugel et al., 1995). All other ecophysiological parameters were
obtained from the literature for general cover types (e.g., Sprugel et
al., 1995; Nobel, 1991; Waring and Schlesinger, 1985).

6\. Observations

6.1 Data Notes

None.

6.2 Field Notes

None.

7\. Data Description

7.1 Spatial Characteristics

7.1.1 Spatial Coverage

These results constitute point simulations of the BOREAS NSA-Old Black
Spruce (NOBS) and Young Jack Pine (NYJP) and SSA-Old Aspen (SOA), Old
Black Spruce (SOBS) and Old Jack Pine (SOJP) tower flux sites.

7.1.2 Spatial Coverage Map

Not applicable.

7.1.3 Spatial Resolution

Tower site.

7.1.4 Projection

Not applicable.

7.1.5 Grid Description

Not applicable.

7.2 Temporal Characteristics

7.2.1 Temporal Coverage

BIOME-BGC was run over a 2-year period at each study site. The model was
initialized using 1989 AMS mesonet station meteorological data from the
Thompson airport (55.8° N, 97.9° W) for study sites in the Northern
Study Area (NSA), and from Prince Albert airport (53.2° N, 105.7° W) and
Waskesiu Lake (53.9° N, 106.1° W) for study sites in the SSA (Shewchuk,
1997). All analyses of model results were done for the second year using
the 1994 meteorological data base described in Section 7.3.

7.2.2 Temporal Coverage Map

Not applicable.

7.2.3 Temporal Resolution

Daily.

7.3 Data Characteristics

BIOME-BGC requires two input files (daily meteorological data and
initialization data) to generate two output files of daily estimates of
site hydrologic and carbon balance characteristics. The initialization
file provides site-specific information about stand morphology, soil
type, and soil condition. The meteorological data file and output files
are described further in subsequent sections.

Organized by site, the names of the initialization, meteorology, and
output files provided are:

NSA-OBS-FLXTR.IN_INI

NSA-OBS-FLXTR.IN_MET

NSA-OBS-FLXTR.OUT_CARB

NSA-OBS-FLXTR.OUT_HYD

NSA-YJP-FLXTR.IN_INI

NSA-YJP-FLXTR.IN_MET

NSA-YJP-FLXTR.OUT_CARB

NSA-YJP-FLXTR.OUT_HYD

SSA-9OA-FLXTR.IN_INI

SSA-9OA-FLXTR.IN_MET

SSA-9OA-FLXTR.OUT_CARB

SSA-9OA-FLXTR.OUT_HYD

SSA-OBS-FLXTR.IN_INI

SSA-OBS-FLXTR.IN_MET

SSA-OBS-FLXTR.OUT_CARB

SSA-OBS-FLXTR.OUT_HYD

SSA-OJP-FLXTR.IN_INI

SSA-OJP-FLXTR.IN_MET

SSA-OJP-FLXTR.OUT_CARB

SSA-OJP-FLXTR.OUT_HYD

Air temperature, solar radiation, and precipitation were measured at
approximate 15-minute intervals at each of the study sites during 1994.
These data were obtained from BOREAS principal investigators at each
study site and the Saskatchewan Research Council\'s mesonet data base
(BOREAS Science Team 1995). The 1994 meteorological records for each
study site were incomplete because of periods of instrument malfunction,
calibration, and measurement inactivity. Continuous meteorological
records for 1994 were obtained for each study site by temporally
interpolating missing data or substituting data from adjacent sites.
Daily maximum and minimum air temperatures, precipitation, and solar
radiation were then derived from the continuous data records for each
site and used to generate model results.

7.3.1 Parameter/Variables

Input Meteorological data:

DOY PCP TMAX TMIN SOLIN DAYLEN

Output Hydrologic Data:

DOY SNOWW SOILW T ET E Q

Output Carbon data:

DOY GPP Rdl Rnl Rsw Rcr Rfr Rh Rm Rg NPP NEE Rtot

\*\*NOTE: NEE denoted with a (-) sign indicates net carbon release from
the stand to the atmosphere, while a positive sign indicates net carbon
uptake by the stand.

7.3.2 Variable Description/Definition

Input Meteorological data:

DOY Day of year (1-365)

PCP daily precipitation (cm)

TMAX maximum 24-hour air temperature (°C)

TMIN minimum 24-hour air temperature (°C)

SOLIN total daily solar radiation (kJ)

DAYLEN daylength (s)

Output Hydrologic Data:

DOY julian day

SNOWW Snow water equivalent of the snowcover (mm)

SOILW Water held in the soil layer (mm)

T Transpiration from the canopy (kg/m2 day)

ET Evapotranspiration (kg/m2 day)

E Evaporation from the canopy and surface (kg/m2 day)

Q outflow (mm/day)

Output Carbon data:

DOY Julian day

GPP Net daily photosynthesis or gross primary production (mg C/m2 day)

Rdl Daytime leaf respiration (mg C/m2 day)

Rnl Night leaf respiration (mg C/m2 day)

Rsw Sapwood respiration (mg C/m2 day)

Rcr Coarse root respiration (mg C/m2 day)

Rfr Fine root respiration (mg C/m2 day)

Rh Heterotrophic respiration (mg C/m2 day)

Rm Maintenance respiration (mg C/m2 day)

Rg Growth respiration (mg C/m2 day)

NPP Net primary production (mg C/m2 day)

NEE Net ecosystem carbon exchange (mg C/m2 day); (-) sign indicates net
release to the atmosphere, while a positive sign indicates net

carbon uptake by the stand.

Rtot Total respiration (mg C/m2 day)

7.3.3 Unit of Measurement

Input Meteorological data:

DOY days

PCP centimeters

TMAX degrees Celcius

TMIN degrees Celcius

SOLIN kilo-Joules

DAYLEN seconds

Output Hydrologic Data:

DOY day

SNOWW mm

SOILW mm

T kg/m2 day

ET kg/m2 day

E kg/m2 day

Q mm/day

Output Carbon data:

GPP mg C/m2 day

Rdl mg C/m2 day

Rnl mg C/m2 day

Rsw mg C/m2 day

Rcr mg C/m2 day

Rfr mg C/m2 day

Rh mg C/m2 day

Rm mg C/m2 day

Rg mg C/m2 day

NPP mg C/m2 day

NEE mg C/m2 day

Rtot mg C/m2 day

7.3.4 Data Source

Daily meteorological data were derived from approximate 15 minute
measurements obtained from SRC mesonet and flux tower sites for 1994
(BOREAS Science Team 1995; Shewchuk, 1997). The initialization data
files were created using information obtained from measurements by other
BOREAS investigators and the literature for similar stand types (see
Section 5).

Hydrologic and carbon data were outputs from the BIOME-BGC model.

7.3.5 Data Range

None given.

7.4 Sample Data Record

Sample records from selected input and output files are:

Input Meteorological data file sample:

1 0.00 -28.50 -42.00 16.40 24467

2 0.00 -25.50 -42.40 28.70 24554

3 0.04 -15.70 -30.70 23.40 24649

Output Hydrologic data file sample:

DOY SNOWW SOILW T ET E Q

1 55.39 190.00 0.00 0.01 0.01 0.00

2 55.78 190.00 0.00 0.01 0.01 0.00

3 55.77 190.00 0.00 0.01 0.00 0.00

Output Carbon data file sample:

DOY GPP Rdl Rnl Rsw Rcr Rfr Rh Rm Rg NPP NEE Rtot

1 11 43 92 39 10 278 0 462 0.0 -451 -451 462

2 14 46 91 41 10 289 0 477 0.0 -463 -463 477

3 22 53 113 48 12 338 0 564 0.0 -542 -542 564

8\. Data Organization

8.1 Data Granularity

The smallest unit of obtainable data is the entire modeling data set,
which contains a total of 20 input and output American Standard Code for
Information Interchange (ASCII) files, and this document.

8.2 Data Format(s)

The model input and output files are in ASCII format with
space-delimited

columns.

9\. Data Manipulations

See Kimball et al. (1997a,b) and Running and Hunt (1993) for detailed
descriptions of model, methods, and processing steps.

9.1 Formulae

See Section 9.

9.1.1 Derivation Techniques and Algorithms

See Section 9.

9.2 Data Processing Sequence

See Section 9.

9.2.1 Processing Steps

See Section 9.

9.2.2 Processing Changes

See Section 9.

9.3 Calculations

See Section 9.

9.3.1 Special Corrections/Adjustments

Not applicable.

9.3.2 Calculated Variables

See Sections 7.3.1 and 7.3.272.

9.4 Graphs and Plots

Not applicable.

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, LAI, and carbon storages were
spatially and temporally uniform on an annual basis. Soil conditions
such as 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
(Sellers et al., 1995). Some sites also had significant vegetation
understories that were not explicitly modeled in this investigation.
Evidence suggests that these vegetation types contributed significantly
to the daily carbon budget (e.g., Black et al., 1996). Further
discussion of potential error sources for this investigation is given by
Kimball et al. (1997a,b).

10.2 Quality Assessment

See Section 10.1.

10.2.1 Model Validation by Source

Model results were compared with daily carbon and water fluxes derived
from site tower flux measurements for 1994. Model estimates of annual
NPP were also compared with NPP estimates derived from site biomass
measurements and allometric equations for 1994 (Gower et al.,
unpublished data). Model estimates of SNOWW and SOILW were compared with
measured data for 1994 (Shewchuck, 1997). Detailed discussions of these
comparisons are given by Kimball et al. (1997a,b).

10.2.2 Confidence Level/Accuracy Judgment

Currently, there is not enough information regarding measurement error
associated with model inputs or model sensitivity to these inputs to
establish documented confidence levels in model results. This problem is
currently being addressed using sensitivity analyses with multiple-year
data and spatial aggregations of remote sensing inputs for the BOREAS
region. This work is being funded under a different, but related,
project with the jet propulsion Laboratory (JPL)

.

Also see Section 10.2.1.

10.2.3 Measurement Error for Parameters

See Section 10.2.2.

10.2.4 Additional Quality Assessments

See Sections 10.1 and 10.2.1.

10.2.5 Data Verification by Data Center

BOREAS Information System (BORIS) staff have looked at the input and
output files and reviewed the model documentation.

11\. Notes

None.

11.1 Limitations of the Model

See Sections 10.1 and 10.2.1.

11.2 Known Problems with the Model

See Sections 10.1 and 10.2.1.

11.3 Usage Guidance

None.

11.4 Other Relevant Information

None.

12\. Application of the Model

These results constitute the initial effort in 1996 to simulate
hydrologic and carbon exchange processes for different boreal forest
stands. These results are expected to change as the models are further
modified and developed to reflect insight gained from new research
regarding boreal forest processes. These results are intended for
comparison with other models.

13\. Future Modifications and Plans

This model will be used in the context of RHESSys to generate
landscape-level estimates of daily and annual water and carbon exchange
processes over the 1,000-km x 1,000-km BOREAS grid at a 1-km spatial
resolution.

Carbon allocation, growth respiration, and nitrogen cycle routines will
be

activated (see Running and Hunt, 1993), and model runs will be conducted
over

longer time periods (50 to several hundred years) to investigate the
effects

of interannual climate variations on site to regional water and carbon

budgets.

A sensitivity analysis with multiple-year data and spatial aggregations
of remote sensing inputs for the BOREAS region is currently underway.
This work is being funded under a different, but related, project with
JPL.

14\. Software

14.1 Software Description

BIOME-BGC was written in C on a UNIX platform.

14.2 Software Access

To request a copy of the model, please send email to one of the
individuals from the University of Montana listed in Section 2.3.

14.3 Software/Platform Limitations

None known.

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 Identification

See Section 15.1.

15.3 Procedures for Obtaining Data

Users may place requests by telephone, electronic mail, or fax.

15.4 Data Center Status/Plans

The RSS-08 BIOME=BGC model files 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

None.

16.2 Film Products

None.

16.3 Other Products

Model results are stored as ASCII files and are available either online
or by contacting BORIS staff directly. See Section 15.1.

17\. References

17.1 Model Documentation

Band, L.E., D.L. Peterson, S.W. Running, J.C. Coughlan, R.B. Lammers, J.
Dungan, and R. Nemani. 1991. Forest ecosystem processes at the watershed
scale: basis for distributed simulation. Ecological Modelling, 56:
151-176.

Band, L.E., P. Patterson, R. Nemani, and S.W. Running. 1993. Forest
ecosystem processes at the watershed scale: incorporating hillslope
hydrology. Agric. For. Meteorol., 63: 93-126.

Hunt, R.E. and S.W. Running. 1992. Simulated dry matter yields for aspen
and spruce stands in the North American boreal forest. Canadian Journal
of Remote Sensing, 18(3):126-133.

Kimball, J.S., M.A. White, and S.W. Running. 1997a. BIOME-BGC
simulations of BOREAS stand hydrologic processes. Journal of Geophysical
Research (in press).

Kimball, J.S., P.E. Thornton, M.A. White, and S.W. Running. 1997b.
Simulating forest productivity and surface-atmosphere carbon exchange in
the BOREAS study region. Tree Physiology, 17, 589-599.

Running, S.W. and J.C. Coughlan. 1988. A general model of forest
ecosystem processes for regional applications, I. hydrologic balance,
canopy gas exchange and primary production processes. Ecological
Modelling, 42:125-154.

Running, S.W. and R.E. Hunt. 1993. Generalization of a forest ecosystem
process model for other biomes, BIOME-BGC, and an application for
global-scale models. In Scaling Physiologic Processes: Leaf to Globe.
Eds. J.R. Ehleringer and C.B. Field. Academic Press, San Diego, CA, pp.
141-158.

17.2 Journal Articles and Study Reports

Amthor, J.S. 1986. Evolution and applicability of a whole plant
respiration model. Journal of Theoretical Biology, 122: 473-490.

Andren, O. and K. Paustian. 1987. Barley straw decomposition in the
field: a comparison of models. Ecology, 68(5):1190-1200.

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. 1996. Annual
cycles of water vapour and carbon dioxide fluxes in and above a boreal
aspen forest. Global Change Biology, 2:219-229.

Bonan, G.B. and H.H. Shugart. 1989. Environmental factors and ecological
processes in boreal forests. Annual Review of Ecology and Systematics,
20:1-28.

Chen, J.M. 1996. Optically-based methods for measuring seasonal
variation of leaf area index in boreal conifer stands. Agricultural and
Forest Meteorology, 80:135-163.

Cole, D.W. and M. Rapp. 1981. Elemental cycling in forest ecosystems. In
Dynamic Principles of Forest Ecosystems. Ed. D.E. Reichle. Cambridge
University Press, London and New York, pp. 341-409.

Comeau, P.G. and J.P. Kimmins. 1989. Above- and below-ground biomass and
production of lodgepole pine on sites with differing soil moisture.
Canadian Journal of Forest Res., 19:447-454.

Cosby, B.J., G.M. Hornberger, R.B. Clapp, and T.R. Ginn. 1984. A
statistical exploration of the relationships of soil moisture
characteristics to the physical properties of soils. Water Resources
Research, 20:682-690.

Edwards, N.T., H.H. Shugart, S.B. McLaughlin, W.F. Harris, and D.E.
Reichle. 1981. Carbon metabolism in terrestrial ecosystems. In
InterBiol. Programme No. 23, \"Dynamic Properties of Forest
Ecosystems.\" Ed. D.E. Reichle. Cambridge University Press, London and
New York, pp 499-536.

Evans, J.R. 1989. Photosynthesis and nitrogen relationships in leaves of
C3 plants. Oecologia, 78:9-19.

Farquhar, G.D. 1989. Models of integrated photosynthesis of cells and
leaves. Phil. Trans. Roy. Soc. Lond., 323B:357-367.

Farquhar, G.D. and S. von Caemmerer. 1982. Modelling of photosynthetic
response to environmental conditions. In Encyclopedia of Plant
Physiology, New Series, Vol. 12B, \"Physiological Plant Ecology II.\"
Eds. O.L. Lange, P.S. Nobel, C.B. Osmond, and H. Ziegler. Springer
Verlag, Berlin, Germany, pp. 549-587.

Farquhar, G.D., S. von Caemmerer, and J.A. Berry. 1980. A biochemical
model of photosynthetic CO2 assimilation in leaves of C3 species.
Planta, 149:78-90.

Field, C. and H.A. Mooney. 1986. The photosynthesis-nitrogen
relationship in wild plants. In On the Economy of Plant Form and
Function. Ed. T.J. Givnish. Cambridge University Press, Cambridge, pp.
25-55.

Fox, J.F. and K. Van Cleve. 1983. Relationships between cellulose
decomposition, Jenny\'s k, forest-floor nitrogen, and soil temperature
in Alaskan taiga forests. Canadian Journal of Forest Research,
13:789-794.

Gates, D.M. 1993. Plant-Atmosphere Relationships. Chapman and Hall, New
York, 92 p.

Gower, S.T., K.A. Vogt, and C.C. Grier. 1992. Carbon dynamics of Rocky
Mountain Douglas-fir: Influence of water and nutrient availability.
Ecological Monographs, 62:43-65.

Grier, C.C. and R.S. Logan. 1981. Old-growth pseudotsuga menziesii
communities of a western Oregon watershed: biomass distribution and
production budgets. Ecological Monographs, 47:373-400.

Grier, C.C., K.A. Vogt, M.R. Keyes, and R.L. Edmonds. 1981. Biomass
distribution and above- and below-ground production in young and mature
Abies amabilis zone ecosystems of the Washington Cascades. Canadian
Journal of Forest Research, 11:155-157.

Hillel, D. 1980. Fundamentals of Soil Physics. Academic Press, New York,
413 p.

Jarvis, P.G. and J.I.L. Morison. 1981. Stomatal control of transpiration
and photosynthesis. In Stomatal Physiology. Eds. P.G. Jarvis and T.A.
Mansfield. Cambridge University Press, Cambridge, pp. 247-279.

Keyes, M.R. and C.C. Grier. 1981. Above- and below-ground net production
in 40-year-old Douglas-fir stands on high and low productivity sites.
Canadian Journal of Forest Res., 11:599-605.

Kinerson, R.S., C.W. Ralston, and C.G. Wells. 1977. Carbon cycling in a
loblolly pine plantation. Oecologia, 29:1-10.

Leuning, R. 1990. Modeling stomatal behavior and photosynthesis of
Eucalyptus Grandis. Australian Journal of Plant Physiology, 17:159-175.

Linder, S. and B. Axelsson. 1982. Changes in carbon uptake and
allocation patterns as a result of irrigation and fertilization in a
young Pinus sylvestris stand. In \"Carbon Uptake and Allocation: Key to
Management of Subalpine Forest Ecosystems.\" Ed. R.H. Waring.
International Union Forest Research Organization (IUFRO) Workshop,
Forest Research Laboratory, Oregon State University, Corvallis, Oregon,
pp. 38-44.

Malkonen, E. 1974. Annual primary production and nutrient cycle in some
Scots pine stands. Commun. Inst. For. Fenn. (Helsinki), No. 84.

Nadelhoffer, K.J., J.D. Aber, and J.M. Melillo. 1985. Fine root
production in relation to total net primary production along a nitrogen
mineralization gradient in temperate forests: a new hypothesis. Ecology,
66:1377-1390.

Nobel, P.S. 1991. Physicochemical and Environmental Plant Physiology.
Academic Press Inc., New York, 635 p.

Orchard, V.A. and F.J. Cook. 1983. Relationship between soil respiration
and soil moisture. Soil biology and Biochemistry, 15(4):447-453.

Paavilainen, E. 1980. Effect of fertilization on plant biomass and
nutrient cycle on a drained dwarf shrub pine swamp. Comm. Inst. For.
Fenn. (Helsinki), No. 98.

Penning de Vries, F.W.T., A. Brunsting, and H.H. Van Laar. 1974.
Products, requirements and efficiency of biosynthesis: A quantitative
approach. Journal of Theoretical Biology, 45:339-377.

Perala, D.A. and D.H. Alban. 1982. Biomass, nutrient distribution and
litterfall in Populus, Pinus and Picea stands on two different soils in
Minnesota. Plant and Soil, 64:177-192.

Rastetter, E.B., A.W. King, B.J. Cosby, G.M. Hornberger, R.V. O\'Neill,
and J.E. Hobbie. 1992. Aggregating fine-scale ecological knowledge to
model coarser-scale attributes of ecosystems. Ecological Applications,
2:55-70.

Running, S.W., R.R. Nemani, and R.D. Hungerford. 1987. Extrapolation of

synoptic meteorological data in mountainous terrain and its use for
simulating forest evapotranspiration and photosynthesis. Canadian
Journal of Forest Research, 17:472-483.

Sellers, P. and F. Hall. 1994. Boreal Ecosystem-Atmosphere Study:
Experiment Plan. Version 1994-3.0, NASA BOREAS Report (EXPLAN 94).

Sellers, P., F. Hall, H. Margolis, B. Kelly, D. Baldocchi, G. den
Hartog, J. Cihlar, M.G. Ryan, B. Goodison, P. Crill, K.J. Ranson, D.
Lettenmaier, and D.E. Wickland. 1995. The boreal ecosystem-atmosphere
study (BOREAS): an overview and early results from the 1994 field year.
Bulletin of the American Meteorological Society. 76(9):1549-1577.

Sellers, P., F. Hall, and K.F. Huemmrich. 1996. Boreal
Ecosystem-Atmosphere Study: 1994 Operations. NASA BOREAS Report (OPS DOC
94).

Sellers, P. and F. Hall. 1996. Boreal Ecosystem-Atmosphere Study:
Experiment Plan. Version 1996-2.0, NASA BOREAS Report (EXPLAN 96).

Sellers, P., F. Hall, and K.F. Huemmrich. 1997. Boreal
Ecosystem-Atmosphere Study: 1996 Operations. NASA BOREAS Report (OPS DOC
96).

Sellers, P.J., F.G. Hall, R.D. Kelly, A. Black, D. Baldocchi, J. Berry,
M. Ryan, K.J. Ranson, P.M. Crill, D.P. Lettenmaier, H. Margolis, J.
Cihlar, J. Newcomer, D. Fitzjarrald, P.G. Jarvis, S.T. Gower, D.
Halliwell, D. Williams, B. Goodison, D.E. Wickland, and F.E. Guertin.
(1997). \"BOREAS in 1997: Experiment Overview, Scientific Results and
Future Directions\", Journal of Geophysical Research (JGR), BOREAS
Special Issue, 102(D24), Dec. 1997, pp. 28731-28770.

Shewchuk, S.R. 1997. The surface atmospheric sciences mesonet for
BOREAS. Journal of Geophysical Research (in press).

Sprugel, D.G., M.G. Ryan, J.R. Brooks, K.A. Vogt, and T.A. Martin. 1995.
Respiration from the organ level to the stand. In Resource Physiology of
Conifers, Acquisition, Allocation and Utilization. Eds. W.K. Smith and
T.M. Hinckley. Academic Press, San Diego, pp. 255-299.

Tetreault, J.P., B. Bernier, and J.A. Fortin. 1978. Nitrogen
fertilization and mycorrhizae of balsam fir seedlings in natural stands.
Naturaliste Canadien (Quebec) 105:461-466.

Waring, R.H. and W.H. Schlesinger. 1985. Forest Ecosystems Concepts and
Management. Academic Press Inc., San Diego, 340 p.

Woodrow, I.E. and J.A. Berry. 1988. Enzymatic regulation of
photosynthetic CO2 fixation in C3 plants. Annual Reviews of Plant
Physiology and Plant Molecular Biology, 39:533-594.

Wullschleger, S.D. 1993. Biochemical limitations to carbon assimilation
in C3 plants - a retrospective analysis of the A/Ci curves from 109
species. Journal of Experimental Botany, 44:907-920.

Zheng, D., E.R. Hunt, and S.W. Running. 1993. A daily soil temperature
model based on air temperature and precipitation for continental
applications. Climate Research, 2:183-191.

17.3 Archive/DBMS Usage Documentation

None.

18\. Glossary of Terms

None.

19\. List of Acronyms

ASCII - American Standard Code for Information Interchange

BOREAS - BOReal Ecosystem-Atmosphere Study

BORIS - BOREAS Information System

DAAC - Distributed Active Archive Center

DAYLEN - daylength (s)

DOY - day of year or Julian day

E - Evaporation from the canopy and surface (kg/m2 day)

EOS - Earth Observing System

EOSDIS - EOS Data and Information System

ET - Evapotranspiration (kg/m2 day)

gC - Canopy Conductance

GMT - Greenwich Mean Time

GPP - Gross Primary Production (mg C/m2 day)

GSFC - Goddard Space Flight Center

JPL - Jet Propulsion Laboratory

LAI - Leaf Area Index (m2/m2)

NASA - National Aeronautics and Space Administration

NEE - Net Ecosystem Carbon Exchange (mg C/m2 day)

NPP - Net Primary Production (mg C/m2 day)

NSA - Northern Study Area

OA - Old Aspen

OBS - Old Black Spruce

OJP - Old Jack Spruce

ORNL - Oak Ridge National Laboratory

PANP - Prince Albert National Park

PCP - Daily Precipitation (cm)

PPFD - Photosynthetically Active Photon Flux Density

PSI - Soil Water Potential

Q - Outflow (mm/day)

Rcr - Coarse root respiration rate (mg C/m2 day)

Rdl - Daytime leaf respiration rate (mg C/m2 day)

Rfr - Fine root respiration rate (mg C/m2 day)

Rg - Growth respiration (mg C/m2 day)

Rh - Heterotrophic respiration (mg C/m2 day)

RHESSYS - Regional Hydro-EcologicalSimulation System

Rm - Maintenance respiration (mg C/m2 day)

Rnl - Nighttime leaf respiration rate (mg C/m2 day)

RSS - Remote Sensing Science

Rsw - Sapwood respiration rate (mg C/m2 day)

Rtot - Total respiration (mg C/m2 day)

RuBisCO - Ribulose Bisphosphate Carboxylase-Oxygenase

SLA - Specific leaf area (m2/kg C)

SOILW - Water held in the Soil Layer (mm)

SOLIN - Total Daily Solar Radiation (kJ)

SNOWW - Snow Water Equivilent of the Snowcover (mm)

SSA - Southern Study Area

T - Transpiration from the canopy (kg/m2 day)

TE - Terrestrial Ecology

TMAX - maximum 24-hour air temperature (°C)

TMIN - minimum 24-hour air temperature (°C)

URL - Uniform Resource Locator

YJP - Young Jack Pine

20\. Document Information

20.1 Document Revision Date

Written: 19-Sep-1996

Last Updated: 06-Oct-1998

20.2 Document Review Date(s)

BORIS Review: 16-Sep-1997

Science Review: 01-Nov-1997

20.3 Document ID

20.4 Citation

Use references in Section 17.1 when citing BIOME-BGC.

20.5 Document Curator

20.6 Document URL

Keywords:

\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\--

BIOME-BGC

carbon

water

hydrology

NPP

tower sites

modeling

productivity