TRAGNET Workshop Report
Building a U.S. Trace Gas Network (TRAGNET)
Pingree Park, Colorado, USA
26-30 September 1992
Compiled by: D. S. Ojima
E. A. Holland
J. M. Melillo
A. R. Mosier
G. P. Robertson
Sponsors:
U.S. Man and the Biosphere (US - MAB)
International Global Atmospheric Chemistry Project (IGAC/IGBP -
IGAC 5.2 - Trace Gas Exchange between Mid-Latitude
Terrestrial Ecosystems and Atmosphere - TRAGEX)
TABLE OF CONTENTS
Page
PREFACE. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
EXECUTIVE SUMMARY. . . . . . . . . . . . . . . . . . . . . . . . . . . ii
Objectives of the Network . . . . . . . . . . . . . . . . . . . . iii
Network Management. . . . . . . . . . . . . . . . . . . . . . . . iii
1. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1. Objectives of the Network . . . . . . . . . . . . . . . . . . 2
2. DEVELOPMENT OF A COORDINATED NETWORK . . . . . . . . . . . . . . . . 4
2.1. Site Membership Criteria. . . . . . . . . . . . . . . . . . . 4
2.2. Model Membership Criteria . . . . . . . . . . . . . . . . . . 4
2.3. Network Activities. . . . . . . . . . . . . . . . . . . . . . 4
2.4. Network Management. . . . . . . . . . . . . . . . . . . . . . 6
2.5. Implementation. . . . . . . . . . . . . . . . . . . . . . . . 6
2.6. Charter Sites . . . . . . . . . . . . . . . . . . . . . . . 10
2.7. Coordination with National and International Programs . . . 10
2.8. Network Components: Measurement, Model and Regional
Analysis, and Data Management. . . . . . . . . . . . . . . . 10
3. DATA MANAGEMENT. . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.1. Data Needs. . . . . . . . . . . . . . . . . . . . . . . . . 14
3.2. Data Storage and Distribution . . . . . . . . . . . . . . . 15
3.3. Data Access and Use Protocols . . . . . . . . . . . . . . . 15
APPENDIX I: WORKSHOP PARTICIPANTS AND ADDRESSES . . . . . . . . . . . 20
APPENDIX II: ANALYTICAL AND FIELD METHODOLOGY . . . . . . . . . . . . 23
II.1. Field Techniques. . . . . . . . . . . . . . . . . . . 23
II.1.1. Chambers . . . . . . . . . . . . . . . . . . . 23
II.1.2. 222Rn-calibrated fluxes. . . . . . . . . . . . 23
II.1.3. Micrometeorological measurements . . . . . . . 24
II.2. Regional Flux Measurement . . . . . . . . . . . . . . 25
II.2.1. Aircraft flux measurements . . . . . . . . . . 25
II.2.2. Planetary Boundary Layer (PBL) Budgets . . . . 25
II.3. Intercomparison of Methods. . . . . . . . . . . . . . 25
II.4. Chamber Design and Operation. . . . . . . . . . . . . 26
II.5. Gas Analyses. . . . . . . . . . . . . . . . . . . . . 26
II.6. Standards . . . . . . . . . . . . . . . . . . . . . . 27
II.7. Sampling Design . . . . . . . . . . . . . . . . . . . 27
II.8. Additional Measurements . . . . . . . . . . . . . . . 28
II.8.1. Controlling factors--nitrogen. . . . . . . . . 28
II.8.2. Net N mineralization and nitrification
measurements . . . . . . . . . . . . . . . . . . . 28
II.8.3. NO measurements for trace gas network
activity . . . . . . . . . . . . . . . . . . . . . 29
II.8.4. Measuring ammonia flux . . . . . . . . . . . . 29
APPENDIX III: MODEL AND REGIONAL ANALYSES . . . . . . . . . . . . . . 31
III.1. Modeling Architecture. . . . . . . . . . . . . . . . 33
III.2. The Role of Process Modeling . . . . . . . . . . . . 33
III.3. Regional Analyses - Scaling, Extrapolation and
Validation. . . . . . . . . . . . . . . . . . . . . . . 34
III.3.1. Data sources. . . . . . . . . . . . . . . . . 34
III.3.2. Experimental design to allow spatial
model development. . . . . . . . . . . . . . . . . 34
PREFACE
A workshop to plan the development of a trace gas network in the
US was held September 27 to 30, 1992, at Pingree Park, Colorado. The
impetus for the workshop came from two diverse directions - from
scientists seeking to better integrate on-going work on trace gas
fluxes in diverse terrestrial ecosystems, and from representatives of
U.S. federal agencies seeking to explore the feasibility of
formalizing a "network of networks." The goal was to explore
potentials for a network of native and managed ecosystem research
sites which would serve as a network for coordinated measurement of
trace gas fluxes. The workshop explored a variety of linkages between
the scientific needs and agency programs and resources. In the
following document we describe the purpose and objectives of the
workshop (both scientific and organizational background), and a
preliminary lists of sites. Many of the ideas herein have been
articulated at 3 previous workshops:
1. February 1990 Sigtuna, Sweden
"Terrestrial Biosphere Exchange with Global Atmospheric
Chemistry". Terrestrial Biosphere Perspective of the IGAC
Project: Companion to the Dookie Report. Global Change Report
No. 13, Report of the recommendations from the SCOPE/IGBP
Workshop on Trace-Gas Exchange in a Global Perspective (edited by
P. A. Matson and D. S. Ojima).
2. September 1991 Boulder, CO
"Exchanges of Methane, Nitrous Oxide and Carbon Dioxide between
Terrestrial Ecosystems and the Atmosphere in the Mid-Latitudes".
Sponsor: IGBP/IGAC (International Global Atmospheric Chemistry)
Program, IGAC Focus on "Trace-Gas Fluxes in Mid-Latitude
Ecosystems" ( K. Smith and J. M. Melillo, Conveners).
3. October 1991 Washington, DC
"Ecological Network of Networks: Selection of a Pilot Project on
Ecological Effects of Global Change, Utilizing a Network of
Ecological Research Sites", sponsored by the U.S. CEES (Committee
on Earth and Environmental Sciences) Task Group on Ecological
Systems and Dynamics (ESD, A. Janetos, Committee Chair).
The network described in this document will be the US component
of an international network, coordinated by the IGBP/IGAC Focus 5
group. This group, recently reorganized as IGAC Focus 5.2 "Trace Gas
Exchange between Mid-Latitude Terrestrial Ecosystems and Atmosphere
(TRAGEX - K.A. Smith and G.P. Robertson, co-convenors). This U.S.
network will also be linked with existing U.S. networks of ecological
sites, such as the NSF's LTER (Long-Term Ecological Research) network
of 18 North American sites, the Department of Energy's ParkNet
network, and selected other sites at which long-term gas measurement
programs are underway or proposed.
The Pingree Park workshop was funded and organized with support
from the US Man and the Biosphere Program and the IGBP/IGAC - TRAGEX.
We are grateful for the assistance of able staff at Colorado State
University's Pingree Park Conference Center, Natural Resource Ecology
Laboratory, and the ARS Plant Soil Nutrient Research Unit, Fort
Collins, Colorado for helping to make the workshop a success. We are
also indebted to the discussion leaders and raporteurs, C. Bledsoe, J.
Bogner, E.A. Holland, P.A. Matson, J.M. Melillo, A.R. Mosier, D.S.
Ojima, C. Potter, W.S. Reeburgh, C.W. Rice, G.P. Robertson, T.R.
Seastedt, K.A. Smith, and P. Steudler.
Organizing Committee:
Carolyn Bledsoe
Arvin Mosier
Dennis Ojima
G. Philip Robertson
EXECUTIVE SUMMARY
The northern temperate region has a significant influence on
global fluxes of important atmospheric trace gases, particularly CO2,
CH4, and N2O. Fluxes from mid-latitude regions are especially
important because the region is densely populated and most of its
ecosystems have been altered directly by humans. Large areas of
forests and grasslands have been converted to crop and rangeland and
most of the remaining forests are second growth. Such disturbances
directly affect the soil physical and biological properties that
control trace gas fluxes. In addition, altered chemical climates as a
result of industrial activity may further affect fluxes across broad
portions of the region.
Natural systems are both sources and sinks of trace gases, but
the magnitudes of the fluxes are very poorly known for most
ecosystems. Despite a long history of trace gas flux measurements in
temperate ecosystems, measurements are typically infrequent, short-
term (rarely extending over one to several years), small-scale, and
often not comparable to flux data collected at other sites. An
integrated, coordinated network for trace gas flux measurements is
essential for providing a framework for comparable, sufficiently
rigorous measurements at a variety of sites and thus for providing the
necessary understanding of regional sources and sinks for these
important gases.
These three research needs require different research plans.
Flux estimates require a long-term monitoring effort at a coordinated
network of sites. Long-term monitoring can also provide short-term
data on daily and interannual temporal variation. Manipulative
experiments provide information on factors that control fluxes. The
modeling efforts will use both monitoring and manipulative data to
develop and validate models, and to predict future scenarios of trace
gas fluxes under altered climates.
Participants at a September 1992 planning workshop developed a
concrete, integrated program to measure fluxes of CO2, CH4, and N2O at a
network of sites in the U.S. Other gases (carbon monoxide, non-
methane hydrocarbons, etc.) may be measured by individual sites, but
will not constitute the core program. This U.S. network will be
integrated with international efforts being implemented by IGAC, a
core project of the IGBP.
The U.S. network is meant to accomplish the following two goals:
to document contemporary fluxes of CO2, CH4 and N2O between
regionally important ecosystems and the atmosphere;
to determine the factors controlling these fluxes and improve our
ability to predict future fluxes in response to ecosystem and
climate change.
Achievement of these objectives will involve three types of
network activities:
Measurement of trace gas fluxes at an international network of
representative sites;
Identification of the factors that control fluxes at each site;
and
The development and implementation of quantitative flux models at
several geographic scales.
The trace gas measurement and modeling network outlined in the
following pages is designed to address these goals and activities
explicitly. Proposed activities, both scientific and organizational,
were developed at a planning workshop held at Pingree Park, Colorado,
27-30 September, 1992.
Objectives of the Network
The proposed network is designed to contribute to our
understanding of trace gas fluxes to the atmosphere through six
principle objectives. These are:
1. to promote the comparability of trace gas flux measurements among
sites;
2. to promote measurements in a more extensive coverage of ecosystem
3. to facilitate data synthesis;
4. to coordinate and foster interactions between measurement groups
and modeling groups.
5. to provide ready means for testing and comparing gas flux models;
and
6. to establish a long-term data archive for trace gas flux and
associated data.
The need to develop a network of trace gas measurements and
analysis follows from the fact that the northern temperate region is a
major net source of these gases. Human activities modify gas fluxes
both directly and indirectly. Directly, agricultural and forest
management practices affect soil physical and biological properties,
including those that control gas fluxes. Indirectly, air pollution
and acid deposition affect fluxes by modifying biogeochemical cycles
associated with trace gas fluxes. Natural systems are both sources
and sinks of trace gases, but the magnitudes of these fluxes are not
known. Although trace gas flux measurements in temperate ecosystems
have been made for decades, measurements are infrequent (rarely
extending beyond a growing season), usually small-scale, and rarely
comparable to other flux data collected at other sites. An
integrated, coordinated network for trace gas flux measurements is
essential to understanding these sources and sinks in a regional to
global context.
There are three critical research needs at present: (1) improved
estimates of net methane, nitrous oxide and carbon dioxide fluxes from
regionally important ecosystems, (2) a quantitative understanding of
the factors that control these fluxes, and (3) manipulative
experiments and development of models to quantify how trace gas fluxes
will respond to changes in both physical and chemical climate.
The network will meet these objectives principally via four
activities: (1) by conducting an annual workshop at which both
measurement and modeling groups will present and discuss current data
and models; (2) by providing a central clearinghouse for network site
data; (3) by facilitating communication both among network scientists,
and between network members and scientists initiating trace gas
measurement programs at non-network sites, and (4) fostering the
development of comparable methodologies.
Network Management
The network will be managed by a steering committee whose
chairperson will act as network coordinator. The steering committee
is now comprised of Drs. Elizabeth A. Holland, Jerry M. Melillo, Arvin
R. Mosier, Dennis S. Ojima, and G. Philip Robertson. The coordinator,
who acts on behalf of the steering committee, will oversee the day-to-
day activities of the network and organize an annual all-site
workshop. The Steering Committee also recommends to the general
membership the acceptance of new sites to the network and the deletion
of existing sites. Members of the steering committee will be elected
on a rotating basis by network members at the annual workshop; members
will rotate on and off of the committee on a staggered basis to
maintain network continuity.
Data management is an essential element of network management.
Without efficient means to contribute to and access a network-wide gas
flux database, individual sites will be comparatively of little
scientific impact. While we do not propose to establish an exhaustive
centralized facility to manage all network data, we do envision a
formalized process that meets the needs of both measurers and
modelers, that multiple investigators will use, and that is
affordable. We propose a plan for establishing a database that will
be both a requirement for and benefit of membership in the network.
1. INTRODUCTION
The atmospheric concentrations of greenhouse gases such as carbon
dioxide (CO2), methane (CH4), and nitrous oxide (N2O) are increasing
substantially. These increases are expected to result in global
warming and changes in precipitation patterns, and may directly affect
terrestrial ecosystems. Our understanding of the contemporary fluxes
of these gases between the land and atmosphere is incomplete. There
are large regions of the earth, including most of the former Soviet
Union and China, for which we have very little information on trace
gas fluxes. Furthermore, for no region do we fully understand how
global change, including land-use change, will affect gas fluxes.
The northern temperate region has a significant influence on
atmospheric trace gases. Fluxes from mid-latitude regions are
especially important because the region is densely populated and most
of its ecosystems are subject to human influence. For example, large
areas of forests and grasslands have been converted to row-crop
agriculture and rangeland. Agricultural practices affect soil
physical and biological properties, and hence can alter trace gas
fluxes. In addition, many systems experience altered chemical
climates as a result of upwind industrial activity.
Natural systems are both sources and sinks of trace gases, but
the magnitudes of these fluxes are not known. Although there have
been numerous trace gas flux measurements in temperate ecosystems,
these measurements are often infrequent (rarely extending over one to
several years), small-scale, and not always comparable to other flux
data collected at other sites. An integrated, coordinated network for
trace gas flux measurements is essential to understanding these
sources and sinks.
Three critical research needs are: (1) improved estimates of net
fluxes of methane, nitrous oxide and carbon dioxide and of
environmental factors which control these fluxes; (2) identification
and quantification of other potentially important regions of
significant fluxes, particularly those from geographic areas that have
been neglected in past research; and (3) manipulative experiments and
development of models to quantify how trace gas fluxes will respond to
climatic and land management changes.
These three research needs require different research plans.
Estimates of fluxes require a long-term monitoring effort at a
coordinated network of sites. Long-term monitoring can also provide
short-term data on daily and interannual temporal variation.
Manipulative experiments can complement the monitoring program and
provide information on factors that control fluxes. The modeling
efforts will use both monitoring and manipulative data to develop and
validate models, and to predict future scenarios of trace gas fluxes
under altered climates.
An integrated program to measure trace gas fluxes of CO2, CH4, and
N2O at a network of sites in the U.S. will be established. Other gases
(carbon monoxide, non-methane hydrocarbons, etc.) may be measured by
individual sites, but will not be part of the core program. The U.S.
network will be integrated with the larger network activity being
implemented by IGAC; a program designed to study trace-gas fluxes in
mid-latitude ecosystems has been identified as an activity of the IGAC
Project of IGBP.
Although there have been many studies of trace-gas fluxes in many
regions of the United States, few have developed flux estimates based
on frequent measurements made over an entire year. Most studies have
focused on small areas (e.g., individual fields or forest plots) and
these study sites have generally been selected to address specific
local research questions; thus they are not, necessarily, comparative
in nature and are not always readily incorporated into regional
estimates of trace gas fluxes. Moreover, the consequences for trace-
gas fluxes of converting "natural" lands to agricultural lands and the
effects of loading ecosystems with nutrients (N and S) and toxins have
received relatively little attention. We propose a trace gas network
that addresses this major gap in our knowledge about trace-gas fluxes
in mid-latitude ecosystems.
We propose to establish a U.S. trace gas network to accomplish
the following two goals:
to document contemporary fluxes of CO2, CH4 and N2O between
ecosystems and the atmosphere;
to determine the factors controlling these fluxes and improve our
ability to predict future fluxes.
Achievement of these goals will involve three types of network
activities:
Measurement of trace gas fluxes at a national network of
representative sites;
Manipulation of perceived controlling variables to determine
their impact on fluxes;
Modeling of the processes responsible for trace gas fluxes.
The trace gas measurement and modeling network outlined in this
document is intended to help achieve the objectives of IGAC Activity
5.2. This report outlines the results of a planning workshop held at
Pingree Park, Colorado, 27-30 September, 1992. The workshop
participants: (1) outlined the objectives of a trace gas network in
the context of the IGAC science objectives; (2) established criteria
for site selection and participation; and (3) carried out in-depth
discussions on issues relating to methods comparability, sampling
design, data management, rules governing access to network data, and
modeling and synthesis activities. The following sections of this
document describe the outcome of those discussions.
1.1. Objectives of the Network
The trace gas network is designed to contribute to our
understanding of the trace gas fluxes to the atmosphere through six
principle objectives. The objectives of the network are:
1. The network will promote the comparability of trace gas flux
measurements. As discussed later in this report, the network
will provide guidance on sampling designs that are site
appropriate; provide suggestions and updated information on
appropriate analytical and sample collection methods; and
facilitate intercomparisons and intercalibrations.
2. The network will provide for more extensive coverage of ecosystem
variability. Identification of regions where no long-term
measurements are available, or where replication is needed, will
be assessed; the network participants can then encourage the
development of research sites in such areas.
3. Data synthesis will be facilitated by the network. Data
collected from sites that span a range of controlling factors or
that represent a range of ecoregions will be available to the
group for synthesis and hypothesis setting.
4. Interactions between measurement and modeling groups will be
facilitated by the network. Data collection at appropriate
frequency and spatial scales will allow model development and
model testing. Hypotheses generated from the model synthesis
activities will foster new field research.
5. Intercomparisons of model results will be facilitated. It will
be possible to make comparisons that utilize identical estimates
of input and output. In addition, model modularity will be
developed where possible, facilitating the identification of
reasons models produce different results.
6. A long-term data archive for trace gas flux and other ecosystem
processes will be established as a component of the network. The
purpose of the network is to foster a better understanding of
mid-latitude trace gas contributions to the global budget by
facilitating data access and interactions among those measuring
and modeling gas fluxes.
The network will meet these objectives principally via four
activities: (1) by conducting an annual workshop at which both
measurement and modeling groups will present and discuss current data
and models; (2) by providing a central clearinghouse for network site
data; (3) by facilitating communication both among network scientists,
and between network members and scientists initiating trace gas
measurement programs at non-network sites, and (4) by fostering the
development of comparable methodologies.
2. DEVELOPMENT OF A COORDINATED NETWORK
The proposed network will be inclusive and probably consist of
30-40 sites. Site participation in the network will be based on
specific criteria with participation open to all sites meeting these
criteria. The geographic distribution of potential charter sites is
depicted in Figure 1. We fully expect the network to be dynamic:
some initial sites will persist longer than others, new sites will be
added as they are established or brought up to network standards, and
some established sites may become inactive for periods of time.
We propose two membership categories: site members and model
members. Membership is limited to those research groups with active
gas measurement or modeling programs that meet the criteria outlined
below.
2.1. Site Membership Criteria
1. a continuous, on-going measurement program for N2O, CH4, or CO2
made within protocol guidelines established by the network;
2. an initial and (where appropriate) continuous measurement program
for ancillary variables such as climate, soil properties, land
use practices, and C and N inputs;
3. active participation in the annual network workshop; and
4. a willingness to share data via established protocols.
Measurement protocols will be in most cases tailored to site-
specific needs, however, in order that the data collected be
comparable and appropriate to the overall goals of the network,
established measurement protocols must be followed. For some sites
sampling frequencies of only a few times per year will be adequate;
for others, more event-driven strategies of several times per week
during certain parts of the year will be needed. It will be up to
site members to define the sampling frequency for their sites.
Regardless of sampling frequency, the sampling protocols will be
established by network members as a group both for flux measurements
and for site-related support measurements such as climate (see
Appendix II, Analytical Techniques - for a detailed discussion of
protocol/methodological issues).
The network will hold annual workshops to which each site will be
required to send at least one participant to satisfy membership
criteria. We expect this participant to be one of the site's
principal investigators. The purpose and format of these workshops
are outlined below under Network Activities. Also, each site must
agree to share gas flux and support data in a timely manner via
protocols developed by the network. (see Section 3 for a description
of proposed data protocols).
2.2. Model Membership Criteria
Modelers are not expected to provide primary data to the network
but are nevertheless expected to make concrete contributions related
to extrapolation and hypothesis testing. Criteria for model
membership include:
1. a continuous, on-going modeling program that includes the
development of simulation models for N2O, CH4, or CO2 fluxes;
2. active participation in the annual network workshop; and
3. a willingness to share model algorithms with both the site
members and other model members via established protocols.
2.3. Network Activities
As noted earlier, the purpose of the network is to foster a
better understanding of mid-latitude trace gas contributions to the
Fig. 1
global budget by facilitating data access and interactions among those
measuring and modeling gas fluxes. The network will meet this
objective principally via four activities: (1) by conducting an annual
workshop to present and discuss current results; (2) by providing a
central clearinghouse for network site data; (3) by facilitating
communication both nationally and internationally among network
scientists and between network members and those initiating trace gas
measurement programs at non-network sites, and (4) by fostering the
development of comparable methodologies.
The annual workshop will be held at a different network site each
year. At this workshop, each member site will send at least one
parincipal investigator (funding for one participant to attend the
workshop will be provided by the network office) who will describe the
site's previous year's results. Each site will bring to the meeting
(or send beforehand) an abstract of this description and the
supporting data files in the prescribed format. Each modeling group
will also send a participant who will describe current progress with
specific models; modelers will also be required to provide an abstract
and an updated version of models on disk.
A substantial portion of the workshop will be devoted to
synthesis issues: detailed discussions of additional measurements,
additional sites, and additional experiments that are needed to
further current understanding of gas fluxes. We view workshops as
prime opportunities for sites to compare results and share
methodological advances without the delays and inefficiencies
associated with communication via standard scientific channels.
Equally important, workshops will offer the opportunity for modelers
to interact directly with those providing flux measurements to the
network database; these interactions should greatly facilitate model
development and validation, as well as to promote integration of site
data into process-level understanding of trace gas fluxes.
A second major network activity is to provide a clearinghouse
for flux and ancillary data and for model distribution. This function
must be accomplished through a centralized activity to distribute the
data in order to simplify and to ensure timely responses to requests
for data and models.
Thirdly, the network will facilitate communication among members
and provide non-members access to the network by publishing and
distributing an annual network report (to contain, among other things,
the annual site abstracts and addresses of site PI's); by distributing
a within-network newsletter; and by maintaining an e-mail network for
site members.
Finally, the network will foster the development of comparable
methodologies by including methodological issues in its annual network
meeting and by sponsoring -- where appropriate -- workshops devoted to
the inclusion of new methodologies and intercalibration activities at
network sites.
2.4. Network Management
The network will be managed by a steering committee whose
chairperson will act as network coordinator. The steering committee
is now comprised of Drs. Elizabeth A. Holland, Jerry M. Melillo, Arvin
R. Mosier, Dennis S. Ojima, and G. Philip Robertson. It is the
responsibility of the coordinator to oversee the day-to-day activities
of the network as outlined above. The coordinator acts on behalf of
the steering committee, which guides the overall direction of the
Network. The Steering Committee also recommends to the general
membership the acceptance of new sites to the network and deletion of
existing sites from the network. Members of the steering committee
will be elected on a rotating basis by network members at the annual
workshop; members will rotate on and off of the committee on a
staggered basis to maintain network continuity.
2.5. Implementation
The network will be funded with external support that will be
used to carry out the activities outlined above; network support will
not be used for measurement or modeling activities per se. Initial
support will cover the annual workshops (travel and accommodations for
participants), salary and office support for an administrative
assistant to manage network
Table 1. Potential List of Charter Sites
SITE
ECOSYSTEM
CO2*
N2O
CH4
WEATHER
MANIPULATIONS
FUNDING
CONTACT
PERSON
Sierra Range
Field Station,
California
Oak woodland
grassland
?
+
+
+
Grazing
Tree Removal
University
California
Mary
Firestone
University
of
California-
Berkeley
Fairbanks
Alaska
Grasslands
Croplands
+
+
+
+
+
USDA/ARS
Verlan
Cochran
USDA-ARS
Fairbanks,
AK
Mayaguez
Puerto Rico
Forage
grasslands
+
+
+
+
+
USDA/ARS
Arvin Mosier
USDA-ARS
Fort
Collins, CO
Central Plains
Experimental
Range, LTER
Colorado
Shortgrass
steppe
+
+
+
+
N-inputs,
cropping
management
USDA/ARS
NASA
Colorado
State
University
Arvin Mosier
USDA-ARS
Fort
Collins, CO
Glacier Lakes
Experiment
Ecosystem
Wyoming
Alpine Meadow
+
+
+
+
USDA/USFS
ARS
Arvin Mosier
USDA-ARS
Fort
Collins, CO
Konza
Tallgrass
Prairie, LTER
Kansas
Tallgrass
prairie
+
+
+
+
Irrigation, N
inputs
NSF
Kansas
State
University
NASA
Chuck Rice
Kansas State
University
Jornada LTER
Black grama
grassland,
shrubland, dry
lake bed
+
+
+
+
+
NSF
William
Schlesinger
Duke
University
North Inlet
South Carolina
Salt marsh,
upland forests
+
+
+
+
N inputs
NSF
USGS
NOAA
Jim Morris
University of
Alaska
Arboretum
Fairbanks,
Alaska
Tundra
+
+
+
+
N
fertilization
, soil
warming,
water table
NASA
NSF
EPA
DOE
W. S.
Reeburgh
Toolik Lake
LTER
Alaska
Tundra
+
+
+
+
N
fertilization
, soil
warming,
water table
NASA
NSF
EPA
DOE
W. S.
Reeburgh
Bonanza Creek
LTER
Alaska
Taiga
+
+
+
+
N
fertilization
, soil
warming,
water table
NASA
NSF
EPA
DOE
W. S.
Reeburgh
Twin Creek
Natural Area,
Olympic NP
Washington
Old growth,
Temperate rain
forests
+
-
-
+
-
NPS
Robert
Edmonds
University
of
Washington
Luquillo
Experiment
Forest
LTER Puerto
Rico
Wet sub-
tropical forest
?
+
+
+
-
USFS/EPA
Michael
Keller
USFS
Guanica State
Forest
Puerto Rico
Dry sub-
tropical forest
?
+
+
+
-
USFS/EPA
Michael
Keller USFS
H.J. Andrews
LTER
Oregon
Pacific NW
forest
+
-
-
+
Harvest
intensity
USFS/NFS
David Myrold
Oregon State
U,
Corvallis,
OR
Long-term
forest sites
USFS
Forests
-
-
-
+
Harvest
intensity/
OM Management
USFS
Walker Branch
Oak Ridge, TN
Eastern
Deciduous
forest
+
?
+
+
Irrigation
DOE/NOAA
Dennis
Baldocchi
ORNL
Hubbard Brook
New Hampshire
Temperate
Forest
+
+
+
+
Litter and
root
manipulation
NSF
Joseph
Yavitt
Cornell
Univ.
Niwot Ridge
LTER
Colorado
Alpine/Montane-
subalpine
+
+
+
+
+
NSF
EPA
NOAA
Steve
Schmidt
CU, Boulder,
CO
Congaree Swamp
National
Monument
South Carolina
Hardwood swamp,
loblolly pine
+
+
+
+
NSF
USGS
NOAA
Jim Morris
Pendleton,
Oregon (ARS)
Dry-land wheat
-
-
-
+
Long-term
plots
ARS
Paul
Rasmussen
USDA-ARS
Pendleton,
OR
OSU
Oregon
Cropland/
forests
-
-
-
+
Cropping
system
Oregon
State
University
Bob
Griffiths
Mallard Lake
N. DuPaga
County, IL
Landfills
-
-
+
+
Gas recovery
wells
NREL/DOE
Jean Bogner
Arizona Nat.
Lab
Jasper Ridge,
California
Annual
grassland
chaparral oak-
woodland
+
+
-
_
CO2
experimental
grasslands
NSF
Peter
Vitousek
Stanford U.
Central Plains
Experimental
Range, LTER
Colorado
Wheat/fallow
+
+
+
+
N-inputs,
cropping
management
USDA/ARS
NASA
Colorado
State
University
Arvin Mosier
USDA-ARS
Fort
Collins, CO
Kellogg
Biological
Station, LTER
Michigan
Cropland
+
+
+
+
+
NSF
DOE
Phil
Robertson
KBS,
Michigan
Walnut Creek
Watershed
Ames, Iowa
Agriculture
Corn soybean
+
+
+
+
Tillage
management
N-addition
USDA-ARS
Tim Parkin
USDA-ARS-
NSTL
Ames, IA
Long-term
Research on
Agricultural
Systems
Davis,
California
Agricultural
systems
?
?
?
+
Irrigation,
fertilizer,
cropping
rotation
UCD
Dennis
Rouston
U. Calif.-
Davis, CA
* + YES - NO ? Contemplated
requests for information and data/models, and (as needed) partial
salary support for the network coordinator.
The network will be formally established in the spring of 1993.
A letter of opportunity will be distributed to all known trace gas
measurement and modeling programs in the USA. This letter will
announce the formation of the network and invite recipients to apply
for membership. In addition, network activities will be presented at
appropriate scientific meetings, such as annual meetings of the
American Geophysical Union (AGU), Ecological Society of America (ESA),
American Society of Agronomy (ASA), Soil Science Society of America
(SSSA), etc. Membership will be based on the criteria described
above. Later in 1993 we will hold the first network workshop. At
this meeting network participants will present descriptions of their
sites and provide overviews of current and past flux measurement
activities. Modelers will likewise provide overviews of their current
trace gas models. As for all subsequent workshops, substantial time
will be devoted to discussions of synthesis issues.
2.6. Charter Sites
Based on criteria noted above we have identified 28 sites as
potential charter members of the network. All of these sites meet the
criteria of the network and have agreed to join. The geographic array
of these sites appears in
Figure 1; a brief overview of their trace gas activities appears in Table 1.
In order to accomplish the objectives listed above, the Network
will be comprised of three components: (1) a trace gas measurement
component, (2) a modeling and regional analyses component, and (3) a
data management component. These three components are closely coupled
to each other and described briefly in a following section.
2.7. Coordination with National and International
Programs
The Trace Gas Network proposed in this document is designed to
fulfill the U.S. contribution to the world-wide program being
established under the auspices of IGBP/IGAC. The network relates
particularly to the IGBP/IGAC Activity 5.2, which is specifically
aimed at characterizing trace gas fluxes in the mid-latitudes, a
region encompassing all of the continental U.S.
The U.S. Trace Gas Network will join comparable networks in other
continents also under the loose coordination of IGAC Activity 5.2. It
is expected that a European network will be established soon, building
on an existing national network research programs such as TERN
(Germany), the Nordic Research Program (Scandanavia), and the TIGER
program (United Kingdom), as well as international projects supported
by the European Community STEP and Environment Programs. Also, links
are being made with a proposed Australian network program; part of the
CERN network in China has been recommended as part of the Activity and
attempts are being made to initiate work in the Commonwealth of
Independent States (the former Soviet Union) and temperate South
America. Cooperation between and among these regional and national
networks provide the opportunity for comparison of trace gas fluxes
across similar and different climates, across broad geographical
distances.
Within the U.S., we envision the Trace Gas Network to be part of
the U.S. Global Change Program. We are striving to foster
interactions with the National Academy of Science Global Change
Committee, National Science Foundation, NASA, EPA, DOE, and USDA.
2.8. Network Components: Measurement, Model and Regional
Analysis, and Data Management
We propose a sampling network that includes the main temperate
ecosystems in North America and specifies data that should be
collected within this network. One of the key components of this
program is to understand how trace gas fluxes in natural ecosystems
are likely to change in response to changes in climate and
anthropogenic inputs. This need to understand how fluxes will change
in response to physical and chemical changes in climate requires
manipulative experiments at both the laboratory and field
Fig. 2
scales and the development of mechanistic models to describe the
production or consumption of trace gases in the biosphere.
Long-term monitoring programs that encompass a wide range of
temporal variation will help in understanding how fluxes change as
conditions change. However, these data will not include combinations
or extremes of conditions beyond those that have occurred in recent
times. Manipulation experiments are needed to address these new
ecosystem states. The long-term monitoring data and manipulation
experiments will allow us to develop trace gas flux "response
functions" that will account for the diverse factors that control
these gas exchanges. These response functions will form the nucleus
of models capable of depicting trace gas exchanges at the ecosystem
scale. Manipulation studies should be based at sites where gas fluxes
are already well characterized.
Many of the data collection activities within the trace gas
network will need to be long-term, encompassing a wide range of
temporal variation that will be helpful for understanding how fluxes
will respond to global change. Data acquired from manipulation
experiments will complement those obtained from the network. Both
will be used for the development of trace gas flux models that will be
used to quantify the diverse factors that control these exchanges.
These models will form the nucleus of a larger modeling activity
capable of predicting trace gas exchanges at ecosystem scales.
Linking ecosystem models to atmospheric chemical transport models and,
ultimately, to general circulation models, will permit elucidation of
trace gas fluxes at regional and global scales.
One of the primary objectives of this activity is to improve our
ability to predict future mid-latitude fluxes of CH4, N2O, and CO2 in
response to global climate change. A set of models arranged in a
hierarchy will serve as an important tool for achieving this
objective. This hierarchy includes:
(1) process-based models of specific gas fluxes,
(2) process-based models of soil biogeochemistry,
(3) higher-level models, including; process-based models of ecosystem
biogeochemistry,
(4) plant population/community models.
These models will have a modular structure so that different
versions of a module can be used as part of the overall modeling
approach. One critical aspect of the modular approach is that the
inputs to and the outputs from a particular module will be clearly
defined. This will permit all versions of a module to be used
interchangeably, facilitating model comparisons and future revisions
(Figure 2).
We refer to this entire hierarchy of models as the Terrestrial
Community Model--a model developed by and available to the terrestrial
ecosystem research community (Figure 3). The process-based models for
specific gases represent the first level of the hierarchy; these
define exchanges between soils and the atmosphere and the factors that
control them (temperature, moisture, carbon and nitrogen
availability). Inputs that define these controlling factors come from
the next level in the hierarchy, the process-based soil biogeochemical
models.
Fig. 3
3. DATA MANAGEMENT
Data management is an essential element of a national network
whose goals include the synthesis of research from individual sites
and investigators. Practical solutions to data management are needed
that work, that multiple investigators will use, and that are
affordable. Data management should therefore be formalized early in
the organizational process of the network. Participation in
developing and maintaining databases must be considered a necessary
focus of all participants in the network.
There exist two levels of benefits to this activity. At the
general scientific level, well-documented multi-site data will provide
a baseline data set for future scientific investigations. The
availability of this data offers the potential to advance the science
more rapidly and in directions previously unavailable to individuals.
More generally, these data provide the basis for cross-site
comparisons and synthesis, permitting regional model building and
validation. Finally, well-managed data will facilitate international
investigations beyond the scope of the national network.
At the individual investigator level, benefits must be provided
to compensate for an activity not directly related to site-specific
scientific questions. The major benefit is the more comprehensive
perspective provided by cross-site comparisons that are facilitated by
clear data management at individual sites. In particular, the nesting
of "add-on" individual investigator projects to ongoing trace gas
studies should provide a mechanism of cost-effective science.
Leverage of funding from granting agencies is a clear benefit.
Individuals need to be "rewarded" for their data management efforts
with opportunities to participate in network workshops, symposia and
synthesis publications.
Data management of the network must address the following issues.
A consensus of data needs must be established at various levels. Data
storage and distribution must be centralized and well supported both
financially and philosophically. Simplicity of access and use must be
balanced with appropriate quality control criteria. These criteria
include adequate documentation and use of accepted methodologies.
Finally, the network must establish data access and use protocols.
3.1. Data Needs
Data acquisition needs are determined by network goals. Figure 2
suggests a conceptual model for the scientific processes and data
exchanges involved in regional and global trace gas research. The
figure identifies categories and specific kinds of data needed within
the framework of this network. The foci of this diagram are trace gas
models which operate at local to global scales. Models strive to
embody our best understanding of trace gas fluxes and allow projection
to larger space/time scales.
Expanding upon Figure 2, we recognize that five general types of
data are essential within this network: (1) intensive site data; (2)
extensive site data; (3) remotely-sensed and other data appropriate
for G.I.S.; (4) tower/aircraft data; and (5) model output and
documentation of models. Research at intensive sites will conduct
process level and manipulative studies to better understand what
controls trace gas flux at their specific site. Extensive site data
with limited measurements expand spatial resolution with respect to
controls identified by the intensive site studies. Geographically-
based information, typically organized with G.I.S., provides the
opportunity for model validation, regional flux estimation, and
identification of regional driving variables. Tower/aircraft data
will be used to validate projected flux values at the meso-scale.
It is clear that data collection will vary significantly from
site to site depending on the specific scientific questions relevant
to the site, and on personnel and technological resources available.
There is a basic set of "site characterization" data that is required
for all sites (Table 2). Information is required on fluxes of
methane, nitrous oxide, nitric oxide and carbon dioxide at each site,
although the methods of flux measurement will vary from site to site.
Carbon monoxide and non-methane hydrocarbon fluxes will be measured at
some sites. At some sites, manipulations of soil water content, soil
temperature and/or nitrogen availability will be carried out, to
investigate the processes controlling fluxes, and to provide
information for modeling. At these sites, additional data will be
collected from manipulative and process studies (Table 3). It would
be preferable if these data were available in a spatially explicit
format, e.g., in a geographic information system (GIS). In addition
to the flux data, basic site characterization data are required from
all sites (Table 2). It will be desirable to establish protocols for
intercalibration, data handling and storage, to be used generally
throughout the network.
3.2. Data Storage and Distribution
The complexity inherent in the proposed network requires a data
management system that provides: (1) maximum ease of data
documentation and archiving at the local level; and (2) flexibility
for data retrieval and manipulation to address a diversity of
scientific issues germane to the network (e.g., flux estimation,
elucidation of mechanisms controlling flux, identification and
quantification of driving variables, and development of predictive
models for trace gas flux). Two primary aspects include the
individual site archives and the central database structure,
management, and distribution protocols. With respect to the central
database, management supported by the network is needed.
For individual sites, data manipulation and storage will use a
wide variety of existing software and hardware. Accordingly, we
propose a generic file structure similar to that advocated by Conley
and Brunt (1991) be adopted. The generic ASCII file structure will
contain the following features:
(1) log: history of file, date created, site, author name and
address.
(2) doc: text description of dataset, including specific methodology
and organization.
(3) type: description of the basic nature of the data, such as
- Statistical files (rows and columns of data)
- Text files (bibliographical data)
- Models
- Metadata files(model-generated data)
- Graphics files (information that can write directly to printers
or plotter)
(4) Header: for statistics files, labels for data columns
(5) Data: with provisions for imbedded comment lines.
It is proposed that a generic ASCII file structure be used in
both archiving site files to the central data base as well as
dissemination of information from the data base to users at individual
sites.
3.3. Data Access and Use Protocols
Centralized data management requires accessibility both by
contributors to the database and by users not directly involved in
data collection. The primary users will be the contributors; the
ultimate users will be the modeling community. Contributors will want
to know how their flux rates compare to fluxes from other sites with
different climatic and process-level controls. Such comparisons
should lead to the development of new hypotheses about the
distribution and control of fluxes, and should also allow contributors
to identify potential collaborators elsewhere for testing these
hypotheses. Modelers will need access to data in order to develop,
test, and extrapolate models at different scales.
Protocols to govern access to network data and model algorithms
must balance the individual contributor's right of first publication
against the larger community's need for access to this data in a
timely manner. Additionally, individual investigators will likely
want to maintain some oversight with respect to how their data is
being used and interpreted by others. There are numerous examples of
past and on-going projects in which these potentially-conflicting
needs have been successfully balanced. Current examples
Table 2. Variables to be measured, methods used, and frequency of
measurements for the extensive sites measuring trace gas flux.
Variable
Measurement
technique
Frequency
Site Characterization -
Supporting data
Soil and vegetation
classification
SCS & NASA Pathfinder
One time
Regional topography (slope and
aspect)
Digital elevation maps
One time
Land use and disturbance
history
Archive data
One time
Rooting depth
SCS
One time
Bulk density (surface)
Standard methods
One time*
Texture (surface)
Bouyoucos hydrometer
One time
Total organic C and N
(surface)
Direct combustion
One time*
Soil pH, CEC, %BS
Standard methods
One time*
Litter chemistry (C, N,
lignin)
Standard methods
One time*
Aboveground net primary
production
Direct or simulated
Annually
Depth to water table
Standard methods
Time of
flux
Depth to permafrost
Standard methods
Time of
flux
NH4+, NO3-
M KCl extracts
Time of
flux
N mineralization
In situ undisturbed
Time of
flux
Site Characterization -
Driving Variables
Climate (Temperature (Average,
max/min), Precipitation
Micromet
Daily
Soil temperature (surface)
Thermometer
Time of
flux
Soil moisture (surface)
Time-domain
reflectrometry (TDR)
or gravimetric
Time of
flux
* More frequent measurements may be required based on the amount of
disturbance or temporal variability.
Table 3. Additional variables to be measured, methods used, and
frequency of measurements for the intensive sites measuring trace
gas flux.
Variable
Measurement
technique
Frequency
Site Characterization -
Supporting data
Moisture release profiles
Tension plate
One time
Soil diffusivity
Tracer method
One time
Leaf Area Index
LiCor or Direct
One time*
Available P
Standard methods
Annually
Litterfall
Standard methods
Annually
Above and below ground
biomass
Standing crop
Annually
Nitrification rates
Potential
Monthly
N mineralization
In situ
undisturbed
Monthly
Site Characterization -
Driving Variables
Soil moisture profiles
Time-domain
reflectrometry
(TDR)
Daily
Soil temperature profiles
Thermistors
Daily
* More frequent measurements may be required based on the amount of
disturbance or temporal variability.
include the LTER network, the NADP network, and the EPA's Soil Organic
Matter Network.
For the Trace Gas Network we expect to institute an access
protocol that allows all network participants full access to the
entire database, but with publication restrictions for at least a one-
year period. This means that information will be readily accessible
to contributors and modelers as the data are compiled (following an
annual meeting), but that inclusion of data in a submitted manuscript
requires the explicit permission of the data contributor for one year
thereafter. We expect that in most cases, especially for model
development and regional syntheses, permission will be readily granted
by contributors within this period, especially if contributors are
brought in as co-authors. After one year in the database all data
enters the public domain, open to both network and non-network
scientists to use. The network will strongly encourage (but not
require) users to contact contributors when the data are requested;
this will allow users to issue additional caveats as appropriate and
perhaps add more recent information. The network will, however,
require proper attribution of data in any publications that ensue.
Sugggested Readings
Andreae, M.O. and D.S. Schimel (editors). 1989. Exchange of Trace
Gases between Terrestrial Ecosystems and the Atmosphere. Report
of the Dahlem Workshop on Exchange of Trace Gases between
Terrestrial Ecosystems and the Atmosphere, Berlin, February 19-
24, 1989. A Wiley-Interscience Publication, John Wiley & Sons,
New York.
Bouwman, A.F. (editor). 1990. Soils and the Greenhouse Effect. The
Present Status and Future Trends Concerning the Effect of Soils
and their Cover on the Fluxes of Greenhouse Gases, the Surface
Energy Balance and the Water Balance. Proceedings of the
International Conference Soils and the Greenhouse Effect. John
Wiley and Sons, New York.
Conley, W. and J.W. Brunt. 1991. An institute for theoretical
ecology? Part V: Practical data management for cross-site
analysis and synthesis of ecological information. Coenoses
6:173-180.
Galbally, I.E. (editor). 1992. The International Global Atmospheric
Chemistry (IGAC) Programme. A Core Project of the International
Geosphere-Biosphere Programme. IAMAP Commission on Atmospheric
Chemistry and Global Pollution, with cooperation of Cloud Physics
Commission, Ozone Commission, Radiation Commission, and IUPAC
Commission on Atmospheric Chemistry.
Houghton, J.T., G.J. Jenkins and J. J. Ephraums (editors). 1990.
Climate Change. The IPCC Scientific Assessment. Cambridge
University Press, Cambridge.
Houghton, J.T., B.A. Callander and S.K. Varney (editors). 1992.
Climate Change 1992. The Supplementary Report to the IPCC
Scientific Assessment. Cambridge University Press, Cambridge.
Moore, III, B. and D. Schimel (editors). 1992. Trace Gases and the
Biosphere. Proceedings of 1988 OIES Global Change Institute at
Snowmass, CO, August 8-19, 1988. UCAR/Office for
Interdisciplinary Earth Studies, Boulder, CO.
Sharkey, T.D., E.A. Holland, and H.A. Mooney (editors). 1992. Trace
Gas Emissions by Plants. Academic Press, Inc., Harcourt Brace
Javanovich, Publishers, New York.
APPENDICES
APPENDIX I: WORKSHOP PARTICIPANTS AND ADDRESSES
APPENDIX II: ANALYTICAL AND FIELD METHODOLOGY
APPENDIX III: MODEL AND REGIONAL ANALYSES
APPENDIX I: WORKSHOP PARTICIPANTS AND ADDRESSES
( * Participa nt not in attendance)
Baldocchi, Dennis
NOAA/ERL/Atmospheric Turbulence
and Diffusion Division
P.O. Box 2456
Oak Ridge, TN 37831
Tel: 615 576 1243
Telefax: 615 576 1327
Telemail: d.baldocchi/omnet
Bledsoe, Caroline
Dept of Land, Air & Water
Research
Hoagland Hall
University of California-Davis
Davis, CA 95616
Tel: 916-752-0388
Telefax: 916-752-1552
Bledsoe, Sam
Dept of Land, Air & Water
Research
Hoagland Hall
University of California-Davis
Davis, CA 95616
Tel: 916-752-0388
Telefax: 916-752-1552
Bogner, Jean
Argonne National Laboratory,
ES/362,
9700 S. Cass Ave
Argonne, IL 60439
Telefax: 708 972 7288
Burke, Ingrid C. (Indy)
Dept. of Forest Sciences
Colorado State University
Fort Collins, CO 80523
Tel: 303 491 1620
Telefax: 303 491 1965
Telemail:
indy@artemisia.cfnr.colostate.ed
u or
iburke@lternet.washington.edu
*Cicerone, Ralph
Dept of Geoscience
School of Physical Sciences
University of California
Irvine, California 92717
Desjardins, R. L.
Agrometerology Section
Land Resource Research Institute
Central Experimental Farm, Bldg.
74
Ottawa, Ontario
KIA OC6 Canada
Tel: 613 995 5011
Telex: OTTA:TELEX
Telefax: 613 966 9564
Robert L. Edmonds
College of Forest Research, AR10
University of Washington
Seattle, WA 98195
Tel: 206 543 2757 (direct 685-
0953)
Elliott, Edward T.
Natural Resource Ecology
Laboratory
Colorado State University
Fort Collins, CO 80523
Tel: 303 491 5645
Telefax: 303 491 1965
Telemail:
tede@poa.nrel.colostate.edu
Firestone, Mary K.
108 Hilgard Hall
Dept. of Soil Science
University of California
Berkeley, CA 94720
Tel: 510 642 3677
Freney, John
CSIRO, Division of Plant
Industry)
P.O. Box 1600
Canberra, ACT 2601
Australia
Tel: 61 62 465 422
Telex: AA 62351
Telefax: 61 62 465 5000
*Fung, Inez
NASA Goddard Space Flight Center
Goddard Institute for Space
Studies
2880 Broadway
New York, NY 10023
Tel: 212 678 5590
Telefax: 212 678 5552
Telemail:
[fung/nasa]nasamail/usa
Groffman, Peter
New York Botanical Garden,
Institute of Ecosystem Studies
Millbrook, NY 12545
Tel: 914 677 5343
Telefax: 914 667 5976
Hartley, Anne
Dept. Botany
Duke University
Durham, NC 27706
Tel: 919 684-3715
Telefax: 919 684 5412
Telemail: Bitnet-Anne @ Duke Ml
Holland, Beth
NCAR.ACD
P.O. Box 3000
Boulder, CO 80307-3000
Tel: 303 497 1433
Telefax: 303 496 1400
Telemail: eholland@acd.ucar.edu
Keller, Michael
USDA/Forest Service
Institute of Tropical Forestry
Agricultural Experiment Station
Grounds
Call Box 25000
Rio Piedras, Puerto Rico 00928-
2500
Tel: 809 766 5335
Telex: 237408083
Telefax: 809 250 6924
Lowrance, R. Richard
Southeast Watershed Res Lab
P.O. Box 946
Tifton, GA 31793
Tel: 912 386 3514
Telefax: 912 386 7215
Matson, Pamela
NASA
Ames Research Center
Mail Stop 239-12
Moffett Field, CA 94035
Tel: 415 604 6884
Telefax: 415 604 4680
Melillo, Jerry M.
Marine Biological Laboratory
Woods Hole, MA 02543
Tel: 508 548 6704
Telex: 951679
Telefax: 508 540 6902
Telemail: j.melillo/omnet
Morris, James T.
Dept. of Biology
University of So. Carolina
Columbia, SC 29208
Tel: 803 777 3948
Mosier, Arvin
USDA/ARS
P.O. Box E
Fort Collins, CO 80522
Tel: 303 490 8250
Telefax: 303 490 8213
Telemail:
amosier@lamar.colostate.edu
Myrold, David D.
Dept of Soil Science
Oregon State University
Agriculutre & Life Sci. Bldg.
3017
Corvallis, OR 97331-7306
Tel: 503 737 5737
Telefax: 503 737 5725
Telemail:
Myrold@CCmail.orst.edu
Ojima, Dennis
Natural Resource Ecology
Laboratory
Colorado State University
Fort Collins, CO 80523
Tel: 303 491 1976
Telefax: 303 491 1965
Telemail:
dennis@nrel.colostate.edu
Parkin, Tim
USDA/ARS/MWA
National Soil Tilth Lab
Iowa State University
Ames, IA 50011
Tel: 515 294 6888
Potter, Chris
NASA Ames Res. Ctr.
MS 239-20
Moffett Field, CA 94035
Tel: 415 604 6164
Reeburgh, William S.
Inst. of Marine Science
University of Alaska
Fairbanks, AK 99775-1080
Tel: 907 474 7830
Telex: 2331740 2055
Telefax: 907 474 7209
Telemail: omnet W.Reeburgh
GTETLM
Rice, Charles W.
Dept of Agronomy
Throckmorton Hall
Kansas State University
Manhattan, KS 66505
Tel: 913 532 7217
Telefax: 913 532 6094
Telemail: cwrice@ksuvm
Robertson, G. Philip
W.K. Kellogg Biological Station
Michigan State University
Hickory Corners, MI 49060
Tel: 616 671 2267
Telefax: 616 671 2351
Telemail: robertson@msukbs
bitnet
Rolston, Dennis E.
Soils & Biogeochemistry
Hoagland Hall
University of California
Davis, CA 95616-8627
Tel: 916 752 2113
Telefax: 916 752 1552
Schimel, David S.
Climate System Modeling Program
NCAR
P.O. Box 3000
Boulder, CO80307-3000
Tel: 303 497 1610
Telefax: 303 497 1137
Telemail:
schimel@niwot.scd.ucar.edu
Seastedt, T. R.
EPO Biology INSTAAR
University of Colorado
Campus Box 450
Boulder, CO 80309
Tel: 303 492-3302
Telemail:
tims@culter.colorado.edu
Smith, Keith A.
Scottish Agricultural College
Edinburgh School of Agriculture
West Mains Road
Edinburgh EH9 3JG
United Kingdom
Tel: 44 31 667 1041
Telex: 727617
Telefax: 44 31 667 2601
Steudler, Paul
Ecosystems Center
Marine Biological Laboratory
Woods Hole, MA 02543
Tel: 508 548 3705 Ext 491
Telefax: 508 457 1548
Telemail: Steudler @ lupine
mbl.edu
Su, Weihan
Telefax:
Yavitt, Joseph
Dept of Natural Resources
Fernow Hall
Cornell University
Ithaca, NY 14853
Tel: 607 255 6601
Telemail: D7PJ@CORNELLA
APPENDIX II: ANALYTICAL AND FIELD METHODOLOGY
A number of analytical techniques will be employed in order to
achieve a better understanding of controls on trace gas exchange and
to estimate regional fluxes of trace gases. In the field, chamber and
micrometeorological techniques available for trace gas flux
measurements cover a wide range of time and space scales, so meeting
goals 1 and 2 will require several techniques applied over time to
resolve the documentation and control factor questions. The
techniques, their operating assumptions and practices, their
strengths, weaknesses and intercomparability are discussed below.
Techniques to analyze the observations of trace gas fluxes range from
statistical correlative studies to more detailed process-level
modeling to regional extrapolation of known fluxes. The linkages
across these various techniques are given below.
II.1. Field Techniques
II.1.1. Chambers. Chamber measurements involve enclosing a
volume of atmosphere adjacent to the soil surface and measuring
increases or decreases in chamber trace gas concentration over time.
The trace gas flux is computed using the rate of concentration change
over a given period of time and the area of ground the chamber is
sampling. Chambers represent a compromise as they influence flow
field, temperature and concentrations at the soil:air interface, but
if they are operated over short time periods and minimize disturbance,
they yield reliable flux measurements. They also have other
significant advantages. Their sensitivity can be adjusted to site
conditions by varying volumes. Of the methods available, chamber
techniques are the only method that can unequivocally measure soil
uptake of trace gases. They can operate within the variability scale
of a given system and are a logical choice for trace gas measurements
in 1-10 m2 scale manipulation experiments. A great deal of
understanding has resulted from chamber-based trace gas flux
measurements in a wide variety of systems. Rapid data turn-around is
another advantage. Chamber measurements have been used to measure and
estimate annual fluxes with some confidence. Further, these
techniques have been used in inter-annual comparisons. Chamber
techniques are a logical choice for determining the effects of land
use change and disturbance on trace gas fluxes. Chamber techniques
are used extensively because of their low cost and easy deployment.
Chambers can be operated dynamically or statically. Dynamic
measurements involve air flow through the chamber and are usually
associated with measurements of highly reactive and/or gas species
that can only be detected by continuous flow-through detectors.
Static chambers involve no air flow, but must be provided with a means
of pressure equalization to avoid bias following sampling.
II.1.2. 222Rn-calibrated fluxes. The radon 222 technique (Dorr
and Munnich, 1987) provides an independent check on chamber flux
determinations, and involves determining chamber flux measurements of
222Rn and soil concentration gradients of 222Rn and another gas. The
technique involves use of naturally-occurring 222Rn, which is inert and
affected only by radiodecay and diffusion, to estimate fluxes of other
gases. The technique s major advantage is that it operates on a time
and space scale similar to chambers and offers a more direct
comparison as well as the ability to calibrate chambers independently.
The technique has been used extensively in Germany by the originators,
but has seen limited application elsewhere. An intercomparison
experiment involving CH4 and CO2 is underway at Bonanza Creek (Whalen
et al. 1992). Trace gas fluxes can be calculated as follows:
JCH4 = JRn (DCH4/DRn)(DCH4/DRn) ,
where the J s are fluxes (mg m-2d-1 or dpm m-2d-1), the D s are binary
diffusion coefficients in air, and the DC s are soil concentration
gradients. The technique assumes homogeneous soils and uniform 222Rn
production rates, so waterlogged soils and low-radium soils represent
a limit. In cases where the zone of trace gas production or
consumption are not well distributed throughout the soil profile, the
222Rn method may give spurious results. An advantage of the technique
is concurrent determination of soil diffusivity.
II.1.3. Micrometeorological measurements. Micrometeorological
methods offer the researcher an additional tool with different time
and space scale characteristics than chambers for measuring
ecosystem:atmosphere trace gas exchange rates. They are made in the
atmosphere's surface layer. Trace gas flux densities measured at a
given height are assumed to represent the flux density to or from the
underlying surface. This assumption is valid when the concentration
of the scalar being studied is steady with time and the underlying
surface is uniform and extended for an appreciable distance upwind; as
a rule of thumb, the upwind surface should extend 75 to 100 times the
flux measurement height. To insure that flux measurements are valid
at a given site, practitioners seek closure of the surface energy
balance, which involves measurements of sensible and latent heat
exchange, soil heat conduction and the surface radiation balance. The
strength of micrometeorological methods is a capability to measure
fluxes from a wide area, with minimal disturbance to the underlying
surface. These methods can be automated and are useful in studies of
diurnal and seasonal variations in trace gas fluxes. There are,
however, only a few instances where these measurements have been
conducted continuously over an entire year (above canopy CO2, Harvard
Forest); they are most generally associated with short-term campaigns
several times during a season. Weaknesses associated with the methods
include the need for expensive and sophisticated instrumentation and
surface and meteorological constraints that limit use at all times and
places.
Several popular methods exploit micrometeorological theory to
measure fluxes directly or to infer them by means of
micrometeorological measurements and models. Direct methods include
the eddy correlation and eddy accumulation methods. Indirect methods
include gradient, mass balance, and variance techniques. These
methods are briefly described in the following sections:
(a) Eddy correlation.
Fluctuations in wind transfer trace gases between the surface and free
atmosphere. The eddy correlation methods determines flux densities
from the covariance between vertical wind velocity (w) and scalar
concentration fluctuations. To determine this covariance, the
frequency and duration of wind and chemical measurements must include
all turbulent events that contribute to the flux. In general,
sampling rates of 5 to 10 time per second and sampling durations of 30
to 60 minutes are desirable to measure a given flux. At present, the
eddy correlation method is limited to chemical species that can be
measured with rapid response sensors; the method can be applied to
measuring fluxes of CO2, water vapor, CH4, O3, and NO; fast response
instrumentation for N2O is underdevelopment. Fluctuations in humidity
and temperature cause density fluctuations and a mean vertical flow.
This results in a bias flux that must be applied to the direct eddy
correlation measurements; evaluation of the bias flux requires
concurrent measurements of latent and sensible heat during any trace
gas flux measurement experiment. The relative magnitude of this bias
flux depends on the ratio between the flux and its ambient
concentration. It is greatest for N2O (50-100%), substantial for CO2
(±10-40%) and small for ozone.
(b) Eddy Accumulation and Conditional Sampling
The eddy accumulation method partitions gas samples into two bins
according to whether an updraft or down draft occurs. Early attempts
to implement the method sampled air proportionally to the vertical
wind velocity. Fluxes were then proportional to the mass difference
between the updraft and down draft sample. This method is complex in
an engineering sense, requiring precise flow control that responded to
atmospheric turbulence and precise analytical measurement of small
concentration differences between the up and down draft bins. Simpler
methods have recently been proposed that have promise. The
conditional sampling methods samples updrafts and down drafts at a
constant rate. Fluxes are proportional to the standard deviation in
vertical velocity, a coefficient equal to 0.6 and the concentration
difference between up and down bins. This method is most attractive
for measuring fluxes of non-methane hydrocarbons and reduced sulfur
species, which at present are dependent on slower analytical methods
such as gas chromatography. Initial demonstration studies with the
conditional sampling method, on CO2 and water vapor are promising.
(c) Gradients
Gradient methods assume turbulent transfer is analogous to
molecular diffusion. This assumption is valid in the atmospheric
surface layer when the scales of turbulence are fine compared to the
scale that defines the curvature of the concentration profile.
Gradient fluxes equal the product of the vertical concentration
gradient and a turbulent diffusivity, K. Concentration gradients of a
particular species are measured with standard analytical procedures.
The difficulty in this procedure is evaluating K, the turbulent
diffusivity. Methods exploit the surface energy balance, momentum,
heat, vapor, CO2 and radon as tracers. Because diffusion varies
relative to the proximity to a source or a sink, K for one species may
differ from another. For example, K for water and momentum differ.
There are trade-offs on where one measures the concentration
gradients. When close to the surface, gradients are easier to
measure, but counter-gradient transfer may occur, making gradient
methods invalid. Further from the surface, turbulent mixing is
greater, so gradients are smaller, but conditions where K s for
different species do not match are rare.
(d) Mass Balance Methods.
Mass balance methods are the only micrometeorological technique
that can be applied to small area manipulated treatments. The most
successful method is the integrated profile method. In this case
profile measurements of wind speed and concentration are made at the
center of a circular treatment and integrated with height. The method
has worked best to study fluxes associated with fertilizer,
insecticide, and herbicide treatments.
II.2. Regional Flux Measurement
Scientists desire to scale trace gas fluxes from leaves and plots
to canopies and landscapes. Leaf and soil cuvettes address leaf and
plot scales, tower based micrometeorological measurements address
canopy scales and airplane and planetary boundary layer measurements
address landscape fluxes.
II.2.1. Aircraft flux measurements. Aircraft mounted turbulence
and chemical instruments can measure spatially integrated fluxes over
a landscape. Fluxes require aircraft transects ~10 km long, so the
method is not as practical as towers for measuring temporal trends.
Airplane systems apply the eddy correlation method on a movable
platform. The movement of the aircraft relative to the wind and
ground motion increases the complexity of the system and determination
of fluxes. To account for the movement of the aircraft relative to
the wind and land surface vectors, exact measurements of aircraft
position in the vertical and coordinate space must be made at
relatively rapid rates, along with other measurements. Rapid data
acquisition by many instruments also creates a data storage and
processing problem, yet these problems have been overcome (Ritter, in
press). Another problem with the method derives from a safety need to
fly 10s of meters above the surface. As one rises above the ground,
the assumption of a constant flux layer is less valid. Hence,
corrections are needed to extrapolate aircraft fluxes to the surface.
II.2.2. Planetary Boundary Layer (PBL) Budgets. The inversion
of the temperature profile in the atmosphere's planetary boundary
layer (PBL) acts as a lid and defines the boundary of a 'box' on which
one can perform a mass balance study. In this case the surface flux
can be defined from knowledge of changes in PBL concentration, the
height of the PBL and the rate of material flowing in and out
laterally and at the top. While these variables are difficult to
attain, the method has the power of being a means to assess the
integrated surface fluxes. Aircraft can be used to assess components
of the regional mass balance box budgets, such as entrainment fluxes,
PBL height and horizontal advection. Tethersondes and remote sensing
techniques can be used to measure time changes in concentration and
PBL growth rates.
II.3. Intercomparison of Methods
Chamber and micrometeorological flux determinations have
developed on parallel paths, and because of the vast differences in
time and space scales sensed, there have been few opportunities to
conduct valid intercomparisons, in part because of the difficulty in
determining the footprint of micrometeorological techniques, the
actual land surface are accounting for the trace gas measurements
being observed. However, recently several large projects have
specifically addressed intercomparison of CO2 and methane flux
measurements, namely, the recent ABLE-2 experiments in Alaska and the
Hudson s Bay Lowlands (ABLE-3/NOWES), International Satellite Land
Surface Climatology Project in Kansas, FIFE, and also Verma s
Minnesota wetland study. The results have been presented at AGU
meetings and are in press in dedicated issues of JGR-Atmospheres.
They show agreement between chambers and several micrometeorological
methods at the 10% or better level, which promotes confidence in a
multi-method approach. Parrish and colleagues have shown that tower
and chamber measurements of NO from soils give comparable rates.
Aircraft-tower intercomparisons have been conducted by the Desjardins'
group and NOAA's Atmospheric Turbulence and Diffusion Division. The
results from these comparisons are encouraging, but instances occur
where differences that may be physically real are observed. Rigorous
intercomparison between tower and aircraft flux measurements may need
further interpretation by boundary layer models.
II.4. Chamber Design and Operation
Workshop participants were polled for information about chambers
used in their research programs. Three general design types were
represented at the workshop: (1) Aluminum construction, with deep
skirts and water seals between chamber components, PVC bases with (2)
rigid, and (3) plastic sheet, variable volume chambers. All chambers
had footprint areas and chamber volumes that ranged between 0.06 - 0.1
m2 and 2 to 12 L, respectively. Dimensions and materials for various
chamber designs can be characterized as follows:
Permanently-deployed square Al collars (27.4 x 27.4 cm) (0.075
m2) with water seal and skirt (10-20 cm deep).
Stackable chamber units (24 x 24 cm x 7.3) (4.2 L) or (24 x 24 x
14.5) (8.5 L), lid (24 x 24 x 7) (3.7 L) equipped with septum
for syringe sampling and capillary bleed to equalize pressure.
Chambers and lids fabricated from lucite sheet or Al sheet.
Circular PVC collars (20 cm to 28 cm diam); Steudler - 28.7 cm
diam x 9.3 cm high (0.065 m2, 5.4 L); Keller 25 cm diam x 20 cm
high; Mosier 20 cm diam x 10 cm high.
NCAR flexible chamber model, base: 20-50 cm diam; cover made of
sheet teflon tube supported by internal wire frame. Can be
adjusted to cover wetland plants; must determine volume by
standard addition of a tracer, usually SF6.
Perhaps the largest point of divergence among chamber operators is the
depth to which permanent bases are installed; the differences reflect
root structure (horizontal vs. vertical) in the range of systems
studied.
The methods used for chamber gas sample collection (syringes) and
storage (vials and syringes) were similar and represent a great deal
of practical experience. Mixed results have been obtained for storage
of samples in vacutainers. All-glass syringes lubricated with
distilled water and nylon syringes are good for storing CH4 and CO2 and
N2O, however storage times are variable and should be checked. Glass
syringes with butyl rubber pistons are suitable for CH4 and CO2.
Carbon monoxide concentrations typically increase on storage due to
photochemical reactions involving precursor compounds. Serum vials
with clear silicone rubber stoppers, glass syringes with teflon
pistons, and painted polypropylene syringes seem suitable for
short-term storage of CO samples. We emphasize that storage and
compatibility tests should be a continuing part of any trace gas flux
measurement program, as there are substantial batch to batch
variations in the gas storage properties of plastic and rubber items.
II.5. Gas Analyses
Methane concentration determinations are most commonly made by
gas chromatography with flame ionization detection, although gas
filter correlation infra-red detection has been used for real-time
detection in field studies. Nitrous oxide concentrations are
universally determined by gas chromatography and electron capture
detection. Carbon dioxide can be detected by infra-red absorption, or
by gas chromatography with thermal conductivity or electron capture
detection. Carbon monoxide is determined by gas chromatography
followed by either reaction with a HgO column followed by mercury
vapor detection, or by methanization followed by flame ionization
detection.
II.6. Standards
Chamber measurements do not necessarily require high accuracy
absolute concentration measurements, as the slope of concentration
change vs. time, a relative measure, is used in flux determinations.
However, because of the precision requirements, it is advisable to
make concentration measurements using absolute standards as a means of
determining instrument performance. Periodic intercalibrations are
recommended. Field intercomparisons involving chambers as well as
meteorological methods should continue in well-understood systems.
II.7. Sampling Design
Experience in a wide range of systems (agricultural systems, such
as paddy rice and other grains, forests, tundra, wetlands, and
grasslands) has demonstrated that trace gas fluxes are extremely
variable in space and time. For any site there will be a minimum
level of exploratory measurements to scale and evaluate spatial and
temporal variability. Sites should incorporate obvious topographic
and floral heterogeneity, and their selection should be tempered with
an ecological understanding of the system driving parameters.
Spatial variability - One approach to estimating spatial
variability is to perform a large number of flux measurements at
different sites to determine the sample number required to reach a
desired coefficient of variation. A study of spatial variability of
field-measured denitrification gas fluxes by Folurunso and Rolston
(1984) provides a concrete example. They found coefficients of
variation of 282 to 379% in chamber measurements of N2O flux. Using
geostatistics, they calculated that it would require as many as 350
measurements to estimate the true mean flux within 10% on a 3 m x 36 m
plot. Because n varies as a quadratic function, obtaining a value
within 20% of the mean flux requires less than 20 samples or ~350½
samples. Realistically, there are equipment, time, and human
limitations on implementation of this strategy and most published
studies fall well short of the desired precision levels. In practice,
15 to 30 chamber flux measurements can be routinely performed per day
at closely located sites, which is close to a 20% estimate.
Temporal variability - It is advisable to fix or limit spatial
variability by operating at permanent sites to determine temporal and
seasonal variability. As with the determinations of spatial
variability, flux measurements should incorporate diel, daily, and
weekly measurements at several periods during the year, especially
when the biological system is most active, to characterize the system.
Sampling frequency can also be guided by experience at other sites,
e.g., weekly measurements for CH4 appear satisfactory, but N2O sampling
strategy must be guided by rain events.
Two types of sites will be required to meet goals 1 and 2:
intensive and extensive. A characteristic intensive site will involve
trace gas flux measurements plus the parameters described in Table 2;
an extensive site might involve less frequent trace gas flux
measurements and a more limited selection of parameters as shown in
Table 2. At intensive sites, we recommend regular sampling
(determined by site temporal and spatial variability requirements), as
well as event-driven sampling to capture rainfall, thaw, and time of
fertilization events (Table 3). In order to contribute to objective
2, both field and laboratory experiments may be performed at intensive
sites to develop relationships between factors controlling trace gas
fluxes. Soil temperature, carbon, nutrient and moisture manipulations
are one means of determining the sensitivities of trace gas fluxes
across a range of factors. Additional whole-system CO2 fertilization
experiments with and without irrigation would also be informative.
II.8. Additional Measurements
The following tables list the required measurements needed to
interpret and model trace gas flux from a network of sites.
II.8.1. Controlling factors--nitrogen. In a general sense, soil
N reflects overall ecosystem productivity and acts as a driving
variable in trace gas emissions. Mineralization of N and C are often
tightly coupled as the breakdown of organic N compounds results in the
production of CO2 and NH4+. Nitrogen fertilization, by increasing plant
productivity, may increase organic inputs, especially over the long-
term, and thereby enhance soil respiration and methane production. In
some cases, there has been a short-term depression of soil
respiration following inorganic N additions, possibly by inhibiting
lignin degradation.
Biological production of N2O occurs as the result of both
nitrification of NH4+ to NO3- (an aerobic process) and in the anaerobic
reduction of NO3- by denitrification. Higher rates of nitrification
are commonly observed in soils with either high concentrations of NH4+
or high rates of N mineralization. Similarly, denitrification rates
have been positively correlated with either NO3- concentrations or
nitrification rates (Robertson and Tiedje, 1987).
Emission of CH4 from soils is the net process of methane
production by methanogenesis (an obligately anaerobic process) and CH4
oxidation by methylotrophic and/or NH4+-oxidizing bacteria. The
oxidation of CH4 can be inhibited by high concentrations of NH4+.
Because of these various positive and negative interactions
between N mineralization and nitrification and CO2, N2O, and CH4
emissions, it is important to characterize net N mineralization and
nitrification in the field at appropriate spatial and temporal scales
in relation to these trace gas emissions. These measurements will
likely be valuable in explaining fluxes of other trace gases, such as
NO, as well.
II.8.2. Net N mineralization and nitrification measurements.
Numerous methods for expressing net N mineralization and nitrification
have been developed, including: potential activity assays, laboratory
incubations, and in situ field measurements. Field measurements are
most appropriate for the purposes of evaluating trace gas models and
determining the driving factors relevant to field gas fluxes.
Three major in situ methods for estimating net N mineralization
and nitrification have been evaluated: buried bags, cores, and cores
with an ion exchange resin trap. Cores have the advantage of more
easily preserving soil structure and can allow for more natural
temperature and water fluctuations. The use of cores, either capped
or not (Raison et al., 1987), are simpler than those used with resin
bags and may more accurately measure net rates (Binkley and Hart,
1989).
We propose using open cores to estimate in situ net N
mineralization and nitrification rates for both intensive and
extensive sites. Open cores were chosen over capped cores to allow
more natural water content dynamics. The potential for leaching
losses, particularly of NO3-, will be minimized by using a sampling
schedule keyed to major precipitation periods. That is, more frequent
sampling during wetter seasons because wet conditions are periods of
higher trace gas emissions.
Cores can be conveniently made from PVC pipe. Diameters between
2.5-5.0 cm are adequate. The length of the core should approximate
the biologically active depth of the soil (e.g., rooting depth or A
horizon). Sharpening one end of the core facilitates its insertion
using a small sledge hammer.
The number of cores per sampling site is dependent upon the
spatial variation of net N mineralization and nitrification. Ideally
this will be determined at each site, but in practice it might equal
the number of gas emission chambers.
At each gas emission sampling date, two PVC mineralization cores
will be driven into the soil within close proximity to an emission
chamber. One core will be removed for water content and zero-time NH4+
and NO3- concentrations. The other will be left in place until the
next gas emission measurement when this sampling procedure will be
repeated. At times when there are frequent gas emission measurements,
mineralization cores will be left in place for at least two weeks.
Concentrations of NH4+ and NO3- will be determined by standard KCl
extraction and colorimetric determinations (ASA Methods book, 1982).
Data are to be expressed on a surface area basis.
In addition to the listed data, atmospheric deposition of N
species (i.e., NH3 and NO3-) and SO4-2 are desirable. At the present
time this can be obtained from the National Atmospheric Deposition
Program. In addition to the suggested measurements, some discussion
on other desirable measurements would include biomass allocation, soil
aggregate distribution, organic matter size fractions, microbial
biomass, depth profile of methane oxidation, profile measurements of
soil gases, (N2O, CH4, CO2) microbial activity (respiration), and
denitrification potential.
II.8.3. NO measurements for trace gas network activity. We
recommend using dynamic chambers for NO measurement. Because it is a
moderately reactive gas, storage of NO in simple cheap containers such
as the syringes used for N2O and CH4 is impractical. Measurement of NO
is normally done at the field site by drawing air through field
chambers for detection by using fast response chemiluminescence
instruments. Chambers of the same configuration and materials used
for N2O and CH4 assays may be used for NO. Comparisons of PVC and ABS
plastic chambers with teflon coated aluminum chambers showed no
consistent bias (Davidson and Keller, unpublished data). Dynamic
chambers can produce artifacts when chamber pressure falls below
ambient pressure. It is necessary to design plumbing and flow rates
to avoid mass flow from the soil. Flow rates of 150 to 250 mL/min are
suitable for many field conditions. We recommend testing NO flux
versus air flow rate for site-specific conditions.
There are two chemiluminescence techniques commonly used to
quantify NO. A highly specific technique directly measures photons
produced by the reaction of NO + O3. These instruments generally
require line power. A battery operated, portable instrument is
commercially available which measures the chemiluminescence from the
reaction of NO2 with luminol. NO must be converted to NO2 prior to
measurement usually by reaction with O3 or by reaction of NO on a CrO3
catalyst. Ozone, PAN, and SO2 interfere with the luminol based
detection although these compounds react with the soil in the chamber
and do not affect flux measurements. The CrO3 catalyst is sensitive to
moisture. Changes in relative humidity in the sample stream, affect
the efficiency of NO to NO2 conversion. The luminol instrument is not
linear below 1 ppb NO, therefore we recommend a standard addition of
NO to the sample stream to bring concentrations within the linear
range for analysis. Concentration measurements are usually
standardized by diluting a high concentration (1 ppm) standard of NO
in oxygen-free nitrogen into the sample stream.
NO emissions have different drivers than N2O and CH4 hence
different temporal and spatial sampling strategies are necessary. NO
emissions are particularly sensitive to soil temperatures. We
recommend diel sampling schemes to avoid bias in NO emission
estimates. Measured NO emissions should not be treated as system
fluxes. NO emitted from the soil may be lost to plant canopies either
directly or after conversion to NO2 by reaction with O3.
II.8.4. Measuring ammonia flux. The main factors which control
ammonia loss from the terrestrial system are wind speed, and the pH,
temperature and ammoniacal (NH3 + NH4+) nitrogen concentration of the
soil solution(at the surface) or water-body. As ammonia is a reactive
gas it readily combines with water, and accumulates where ever water
accumulates. In addition ammonia is readily absorbed and lost from
plant surfaces, and the exchange depends on the compensation point of
the plant and the ammonia concentration of the atmosphere near the
plant surface.
As a consequence of these factors it is not appropriate to use
chambers to monitor ammonia volatilization. The micrometeorological
method most useful for assessing ammonia volatilization in studies
such as these is the gradient technique described above.
REFERENCES
ASA. 1982. Methods of Soil Analysis. Agronomy 9 (2nd edition). Am.
Soc. Agron.
Binkley, D. and S.C. Hart 1989. The components of nitrogen
availability assessments in forest soils. Springer-Verlag, New
York.
Denmead, O.T. 1983. Micrometeorological methods for measuring
gaseous losses of nitrogen in the field. In: J.R. Freney and
J.R. Simpson (eds) Gaseous Loss of Nitrogen from Plant-Soil
Systems, pp. 133-157. Martinus Nijhoff. Dr. W. Junk, The Hague.
Denmead, O.T., J.R. Freney and J.R. Simpson. 1976. A closed ammonia
cycle within a plant canopy. Soil Biol. Biochem. 8:161-164.
Dorr, H and K.O. Munnich. 1987. Annual variation in soil respiration
in selected ar3eas of the temperate zone. Tellus 39:114-121.
Folorunso, O.A. and D.E. Rolston. 1984. Spatial variability of field
measured denitrification gas fluxes. Soil Sci. Soc. Am J.
48:1214-1219.
Raison R., M. Connell and P. Khanna. 1987. Methodology for studying
fluxes of soil mineral-N in situ. Soil Biol. Biochem. 19:521-
530.
Robertson, G.P. and J.M. Tiedje. 1987. Nitrous oxide sources in
aerobic soils: nitrification, denitrification and other
biological processes. Soil Biol. Biochem. 19:187-193.
Whalen, S.C., W.S. Reeburgh and V.A. Barber. 1992. Oxidation of
methane in boreal forest soils: A comparison of seven measures.
Biogeochemistry 16:181-211.
APPENDIX III: MODEL AND REGIONAL ANALYSES
Many of the data collection activities within the network will
need to be long-term, encompassing a wide range of temporal and
spatial variation that will be helpful for understanding how fluxes
will respond to climate and land management changes. This information
will be critical for the development of trace gas flux models that
will be used to quantify the diverse factors that control trace gas
exchanges. Table 2 and 4 list the data needs at various resolutions
of model application. The models developed will include detailed
process models, which will represent the proximal relationships of
controlling variables to trace gas processes. In addition, simplified
models that represent these processes in a more aggregated fashion
will be developed and tested against the more detailed models. These
more simplified models will have less detailed input requirements and
will be useful for extrapolating across larger spatial scales.
The role of models in the network is two fold:
to test causal relationships of environmental factors affecting
trace gas fluxes
to provide a basis to extrapolate site level findings across a
regional data base that is geographically delineates the spatial
pattern of the driving variables.
Essential to the development of these models and to the further
understanding of trace gas fluxes will be the close interaction of the
field measurement scientist and the modeler. There must be a constant
two-way dialogue among modelers and data gatherers. The
experimentalist will observe results that will challenge the models,
while the models will assist in determining the desired temporal
resolution of data collection. This exchange of ideas between
modelers and data gatherers should be encouraged through a series of
annual or biannual network workshops. Furthermore, as models are
developed, they should be documented and placed in the network data
archives where they would be available to member sites for testing.
This interactive development of data collection, analysis and model
development and application is a vital component of the network that
will ensure future development of our basic understanding of the
processes controlling trace gas fluxes.
The overall objective of modeling efforts should be to derive a
series of modular algorithms of abiotic and biotic soil processes that
allow for estimates of current fluxes and predictions of future fluxes
of important trace gases including CO2, N2O, and CH4.
Modeling activities will assist in the experimental design of
manipulative experiments, collection of data, and selection of sites
among the network. For example, site selection should be made with
regard to the range of existing abiotic and biotic conditions
identified as important controlling variables. Theorists and
empiricists alike agree on a suite of variables identified as
significant biotic and abiotic controls of trace gas flux. Given our
current level of understanding of abiotic and biotic controls of CO2,
N2O, and CH4 flux, a reduced suite of control variables that can be
routinely measured at extensive monitoring sites, and an expanded
suite of variables at intensive sites can be identified.
A significant product of the modeling effort should be
development of detailed, process-oriented models that would serve to
generate testable predictions of CO2, CH4, and N2O flux from soils of
representative biomes, disturbance regimes (e.g., clear cut forest),
and land use (e.g., land fills, feed lots, etc.). These models will
place a high demand on data collection activities and will be
developed in conjunction with efforts at a number of network sites
identified for their ability to generate intensive data sets and their
representation of a range of abiotic and biotic conditions. The
process level model should include sufficient detail to allow for
integration of discontinuous flux measurements.
The long term goal of the modeling effort should be the
estimation of spatially and temporally integrated fluxes (regional and
continental scales) of trace gases from soils. The models should be
capable of generating current flux estimates as well as estimates of
the range of trace gas fluxes under a variety of climate change or
land use scenarios. We recognize that the data
Table 4. Data requirements for trace gas model development.
1. Intensive site data--(also see Table 3)
Abiotic/biotic variables:
air/soil temperature (min,max,mean);
precipitation (daily/event);
atmospheric inputs (ammonia,nitrate);
soil texture;
bulk density;
rooting depth;
soil carbon and nitrogen (and phosphorus);
pH;
moisture regime (moisture release, infiltration, water-
holding capacity);
litter inputs (timing, mass, management);
litter chemistry (carbon, nitrogen, lignin)
Site characteristics: (historic and current land use,
vegetation, terrain)
Trace Gas Fluxes (carbon dioxide, methane, nitrous oxide, others)
2. Extensive site data--Trace gas fluxes and selected ancillary data
(see Table 2)
3. Variables appropriate for input to G.I.S.-based models or
regional models
Mean annual precipitation and temperature (daily, weekly,
monthly -- depending upon objectives)
Soil texture
Elevation
Land use(AVHRR)
Vegetation indices(NDVI)
Soil moisture
4. Tower/aircraft data--trace gas concentrations and fluxes, land
use variables
intensive, process level models will require a level of spatial detail
among the suite of proximal, independent variables that is impractical
to achieve. Therefore, the process level models will be simplified by
searching for relationships between trace gas flux and the more easily
quantified distal variables such as, but not limited to, soil and
vegetation type, precipitation, and temperature. These distal
variables should be available through existing GIS data archives
and/or achieved by remote sensing techniques.
III.1. Modeling Architecture
One of the primary objectives of this activity is to improve our
ability to predict future mid-latitude fluxes of CH4, N2O, CO2, and CO
in response to global climate change. A set of models arranged in a
hierarchy will serve as an important tool for achieving this
objective. These models will form the nucleus of a larger modeling
activity capable of predicting trace gas exchanges at ecosystem
scales. The linking of ecosystem models to atmospheric chemical
transport models and, ultimately, to general circulation models will
permit elucidation of trace gas fluxes at regional and global scales.
This hierarchy includes:
(1) process-based models of specific gas fluxes,
(2) process-based models of soil biogeochemistry,
(3) process-based models of ecosystem biogeochemistry,
(4) plant population/community models.
These models will have a modular structure so that different versions
of a module can be used as part of the overall modeling approach. One
critical aspect of the modular approach is that the inputs to and the
outputs from a particular module will be clearly defined. This will
permit all versions of a module to be used interchangeably,
facilitating model comparisons and future revisions.
We refer to this entire hierarchy of models as the Terrestrial
Community Model--a model developed by and available to the terrestrial
ecosystem research community (Figure 3). The process-based models for
specific gases represent the first level of the hierarchy; these
define exchanges between soils and the atmosphere and the factors that
control them (temperature, moisture, carbon and nitrogen
availability). Inputs that define these controlling factors come from
the next level in the hierarchy, the process-based soil biogeochemical
models (IGAC TER-EX report Colorado, 1992).
The soil biogeochemical models define how soil carbon and
nitrogen dynamics are influenced by abiotic factors (temperature,
moisture, soil texture) as well as by plant processes. In addition to
conveying information "down" to the gas flux modules, the soil
biogeochemical modules permit transfer of information "up" to the
whole-ecosystem biogeochemical models.
The development of these trace gas modules will be linked to the
"soil biogeochemical" module through input and output variables that
control C substrate availability, N availability, and through physical
parameters related to driving variables (e.g., moisture, temperature).
The value of developing the modular approach is provided by allowing
various modules to be tested using an identical modeling environment.
III.2. The Role of Process Modeling
The core of the network modeling effort will be towards the
development of soil process models which can be linked to higher level
ecosystem and regional models. Process models relate environmental
variables to fluxes though mathematical representation of the biology
of the system. Many process models consist of a set of difference
equations. In contrast to strictly empirical approaches, process
modeling allows the examination and manipulation of factors
controlling carbon and nutrient fluxes, in addition to making
predictions of trace gas fluxes.
Trace gas process models generally simulate decomposition of
organic matter by microbes, which leads to production of CO2, N2O and
CH4. Modeling can occur at several levels of detail. Detailed models
include representation of soil microsite biochemistry and/or
population dynamics of microbes which mediate specific trace gas
fluxes (i.e., N2O, CH4) are modeled. In this case, information is
required on growth and death rates of one or more microbial
populations and is attainable only with an intensive research effort.
Alternatively, trace gas fluxes may be estimated more simply
through calculation of the overall turnover of carbon and nitrogen
that form the substrates for soil microbial maintenance. Although
this is an aggregate approach, it is conceptually intuitive, since
decomposition of carbon and nitrogen mineralization results in the
production of CO2, CH4 and N2O -- the gases of interest. This approach,
which relies chiefly on determination of rates of soil organic carbon
turnover, may be more appropriate approach for modeling in an
extensive network of trace gas measurement sites.
Rates of microbial activity are commonly constrained by
temperature, moisture/aeration and organic matter quality. There are
various mathematical representations of these controls. Levels of
regulation can range from proximal (i.e., soil moisture) to distal,
whereby precipitation inputs are used in a hydrological model to
derive soil moisture values. Likewise for temperature, in situ soil
measurements are the proximal level of regulation required, whereas
air temperature can be used in a soil heat flux model to derive scalar
values. Regardless of the level of detail represented in a given
model, each network site can contribute to and benefit from modeling
efforts by providing the following list of variables to serve as
inputs to the process modeling network. Many of these data correspond
to distal variables that can drive soil biophysical models.
At intensive study sites, we anticipate that manipulation
experiments will be conducted. These experimental manipulations and
their associated gas flux measurements will facilitate parallel tests
of model performance under different management practices and regional
extrapolation studies. Irrigation, fertilization, liming, and
vegetation manipulation are priority examples for manipulation
experiments.
III.3. Regional Analyses - Scaling, Extrapolation and Validation
III.3.1. Data sources. Scaling and extrapolation of a network
of tower-flux measurements to regional scales requires a hierarchy of
additional flux measurements made on appropriately broader scales. The
goal of the regional analysis activity is the projection of trace gas
fluxes at mesoscale or regional distances (100 - 1000's kms). This
might be accomplished with a hierarchical series of scaling steps,
from plot level to watershed level to sub-regional levels to regional
scales. The models used to scale across spatial and temporal factors
will include models such as process models, watershed-scale models,
and regional models. The process level models will not necessarily be
directly used for regional estimates of trace gas fluxes due to the
inadequacy of input data, large computational demands, and difficult
to scale particular processes directly to large scales. However,
these more detailed models could be used to develop simplified
algorithms to extrapolate from plot level studies to sub-regional
(e.g., watershed areas) and regional scale areas.
Other techniques that could potentially be used include
appropriate empirical modeling methods such as, non-linear parameter
estimation or neural-net AI approaches. Previous studies using FIFE
data indicate that evenly weighted driving functions across 100 m
spatial scales are adequate to scale chamber measurements to tower
level flux measurements of CO2. Tower data was heterogeneous at
broader spatial scales, however. At the regional to continental level
of synthesis, direct griding of sub-regional scale models will be
within computational capability. The sub-regional level models will
be appropriate for direct interface to circulation models.
III.3.2. Experimental design to allow spatial model development.
Scales of variability of the driving functions identified for the
canopy level process-specific models will be different. Experimental
design at both the inter- and intra-site levels must recognize the
need for both flux and controlling variable measurement across a broad
gradient. Without such cross-gradient measurements it will be
impossible to develop spatially robust broad scale flux models. For
some controlling variables (e.g., temperature and moisture), this
implies use of inter-site data, perhaps cross-continental or inter-
continental data. Effects of other controlling variables might be
addressed by cross-gradient chamber measurements within a single site.
Manipulation of controlling variables will be an important source
of information for identification of factors controlling fluxes at the
plot, landscape, and regional scale, in addition to different temporal
scales. Manipulation of similar controlling variables at different
sites will be useful for identification of variable interaction
effects which may not be apparent at a single site.
While the fundamental models at the chamber level must be
mechanistic and process specific in order to allow extrapolation,
scaling to broader spatial scales can utilize empirical modeling
methods. It is reasonable to expect that functional mechanisms at the
plot level which control a flux, will have analogous shapes in
response to appropriately aggregated controlling variables at a
broader spatial scale. For example, CO2 evolution may have a
temperature optimum for a given system state and level of other
driving functions, at a small scale level. At a watershed level, the
aggregate flux should also exhibit an optimum to a synoptically
measured temperature, as by a radiometer. The exact temperature of
the optimum may be different at these two scales, however. This is
due both to subtle differences in the method of defining the relevant
temperatures at the two spatial scales, as well as to the aggregation
at the watershed scale of heterogenous plot level functions. It is
also possible that the shape of the response function may vary in the
broad scale system and that the shape and optimum may vary among
watersheds across broader scale gradients.
The process of flux aggregation in response to a heterogenous
mixture of controlling variables, together with a synoptic or
aggregated measure of controlling variables, will yield a spatial
model varying from the small plot scale model to regional or sub-
continental scale models. Identification of the appropriate spatial
model format, for several gas species and a broad range of controlling
variables and system states is important to estimate fluxes at
different scales. It may be appropriate to use automated model and
parameter identification methods in this situation. These are not
anticipated to be fitting of data to arbitrary functions, but
operations guided by experimental data and knowledge gained from small
scale process studies and models and knowledge of the aggregating
methods used for spatial variable measurement.
A number of techniques are being used, including neural net
methods, for exploration of large parameter spaces to relate complex
signals with a large set of causal variables. Their prediction error
is always quantifiable by subdivision of data into independent
"training" and "testing" sets. In order to develop spatially robust
flux models, neural nets may be developed independently for single
sites and with various multiple site data sets. Development of a net-
based flux predictor for two distinct sites across a gradient and
testing against an intermediate site is useful for detection of
homogeneity of response mechanisms across that gradient.
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