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Prospects for an Integrated Landscape CO2 Model


... sequence diagrams for MLRA 46. Management system abbreviations are as follows: ... United States Department of Agriculture Handbook 296, 2006. ... – PowerPoint PPT presentation

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Title: Prospects for an Integrated Landscape CO2 Model

  • Prospects for an Integrated Landscape CO2 Model
  • Gordon Green 12/19/07
  • The City University of New York

Simplified Global Carbon Cycle Model
As fossil fuel stock decreases, atmospheric CO2
stock increases.
Stella model derived from NASA
  • Four areas that can help bring the system closer
    to equilibrium
  • Sequestration of CO2 in terrestrial sinks
  • Conservation and fuel substitutions
  • Geological capture and sequestration of CO2 as
    part of the energy generation process
  • Emission reductions.
  • The first two are solutions that can be
    implemented immediately geological capture and
    fuel substitutions will likely be implemented
    over the course of decades due to slow
    infrastructure turnover time MCCARL1.

Terrestrial Sinks
  • Possible role of terrestrial offsets

Chart from MCCARL1
Terrestrial Sinks
  • Relative timeframes terrestrial offsets

Chart from MCCARL1
Terrestrial Sinks
  • Net global carbon storage of plus or minus about
    2 Pg per year is expected RANDERSON.
  • Because anthropogenic factors are dominant, both
    the sign and magnitude of this net carbon storage
    will likely be determined by policy decisions.
  • Models have become an integral part of choosing
    between different policy options.

  • …descriptions of how the world might work are
    hypotheses, and often they can be translated into
    quantitative predictions via models. (Hilborn
    and Mangel, 1997, quoted in RIZZOLI)
  • …quantitative models predicting the outcome of
    natural processes on the surface of the earth
    dont work. PILKEY
  • When used improperly, mathematics becomes a
    reason to accept absurdity. J. OMally, quoted
    in PILKEY
  • The term Model is used here to refer to
    computer-based simulation models, not purely
    conceptual or mathematical models.

Modeling Pitfalls
  • Results can be difficult to validate against the
    real world.
  • Interpreting result data can become an exercise
    in circular logic, wherein all that is really
    described is the model and its output.
  • The complexity of the model can become an
    impediment to a clear understanding of the
    underlying phenomenon COUCELIS.
  • Software models are often ostensibly created for
    decision support users, but in fact are not used
    by anyone other than the model builder who has
    little to do with any real decision-making
    process MCINTOSH.
  • The underlying assumptions may be unrealistic
  • Natural human and sociological tendencies can
    also interfere for example, we tend to be blind
    to feedback mechanisms and non-linear responses
  • Consensus as to what to model, and how value
    resources, is elusive DALE.

Modeling Pitfalls
  • PILKEY recommends qualitative approaches that
    only attempt to estimate order-of-magnitude and
  • Other techniques for handling uncertainty include
  • Expert elicitation (substantial consultation with
    those with intimate heuristic knowledge of the
  • Extended peer/stakeholder review.
  • The use of multiple models for cross-calibration.
  • Monte Carlo analysis especially in the context of
    a global (all parameters) sensitivity analysis.
  • Use of multiple scenarios.
  • Others…

Steps in the Modeling Process
  • Model study plan
  • Data and conceptualization
  • Model set-up
  • Calibration and validation
  • Simulation and evaluation REFSGAARD

Variations on a theme…
Sidebar Calibration
  • UML model of calibration process

Diagram from REFSGAARD
Model Scales
Local/landscape models appears under-represented
in CO2 models
Preliminary Requirements
  • A usable integrated landscape model should
  • Accommodate frequent changes in model design.
  • Require a minimum of manually-gathered data.
  • Be temporal / dynamic.
  • Accommodate a high degree of uncertainty. Results
    expressed in orders of magnitude are likely to be
    more realistic than precise numbers with margins
    of error.
  • Be easy to use and understandable by non-experts.

C Dynamics in Forests
Stella diagram derived from CHIBA
C Dynamics in Forests
  • Forests alternate between being net sources and
    sinks of carbon, depending on the details of
    their development and management MASERA.
  • The worlds forests and other vegetation now act
    as a net carbon sink due to the recovery
    agricultural areas and possibly CO2 fertilization
    effect due to increased atmospheric CO2 levels
  • Most predictions indicate that forests will
    continue to be a net carbon sink until about
    2050, at which point their capacity will begin to
    decrease until they become a net carbon source
    around 2080 WICKS.
  • It is expected that an additional 1 to 2 Pg per
    year can be sequestered from 1995 to 2050 given
    the right management methods. Modeling efforts
    are expected to be an important part of making
    the right management decisions MASERA.

Sample Forestry Models
  • Forest CO2 models usually include soil submodels
    as part of their estimates.
  • They are often well-suited to driving by remotely
    sensed data (e.g., FOREST-BGC and use AVHRR
    satellite data) CHIESI.
  • Non-spatial models can be spatialized by, for
    example, running the model for each pixel for 106
    years of climate data WICKS.
  • EFIMOD-PRO model works somewhat differently in
    that it actually models the behavior of each
    individual tree (a SPE, single-plant ecosystem)
  • CO2FIX very user-friendly, designed for
    widespread use, and models C stocks and flows in
    forest stands MASERA.

CO2FIX Model
  • Yield tables are included for multiple species
    and include stems, foliage, branches and roots.
  • Since it is targeted at the timber industry, the
    model includes parameters for thinning and
  • It simulates biomass growth with mortality
    figures due to harvesting, harvesting damage,
    senescence, and biomass turnover in to foliage,
    branches and roots.
  • Uses the YASSO submodel, which is simpler to
    parameterize than some other soil models LISKI.

CO2FIX Model
YASSO diagram from MASERA
Screenshots from SCHELHAAS
CO2FIX Model
  • CO2Fix parameterization is complex.
  • Non-spatial ability to spatialize TBD.
  • Sample simulation results

C Dynamics in Lakes and Rivers
Stella diagram derived from HANSON
C Dynamics in Lakes and Rivers
  • Carbon cycles in place in rivers and lakes are
    less well-understood.
  • Because they are not included in Kyoto
    calculations, oceanic carbon sinks are similarly
    neglected REDHANZ.
  • The behavior of carbon in rivers is closely tied
    to soil erosion, which is simply omitted from
    many soil models (models to be considered below).
  • Rivers, as carriers of eroded soil, are likely to
    be sources of CO2 RICHEY.

C Dynamics in Lakes and Rivers
Lakes that are low in dissolved organic carbon
and high in total phosphorus can act as carbon
sinks due to the general uptake of carbon in lake
life, but lakes usually tend to be sources of CO2
depending on the conditions.
Chart from HANSON
C Dynamics in Soils
Stella diagrams derived from POST
C Dynamics in Soils
  • Soils can act as both a source and a sink of
    carbon depending on the level of disturbance and
    erosion, and the kind of vegetation that is
    growing on them FALLOON.
  • Their behavior regarding CO2 is complex, and
    highly dependent on local conditions and land-use
  • MCCARL2 estimates that most of the carbon in
    terrestrial systems resides in soils about 1500
    Pg, versus about 770 in the atmosphere. GEF1
    estimates that the global C sequestration
    potential in cultivated soils could be 20-30 Pg
    over the next 50-100 years, and FALLOON that
    10 of anthropogenic CO2 could be sequestered in
  • In most soils, the majority of carbon is held in
    soil organic carbon (SOC).

C Dynamics in Soils
  • Soils that are disturbed through cultivation
    practices and erosion tend to be carbon sources,
    and soils that are undisturbed with natural
    vegetation tend to sequester carbon, at rates
    depending on the plant life they support GEF1.
  • Recovering land from agriculture, or managing it
    in such a way that CO2 sequestration is
    maximized, could result in substantial carbon
    sequestration over time MCCARL2
  • By lessening the intensity of soil tillage,
    producing crops that create more organic matter
    that can stay in the soil, and improving the
    soils with alternative management strategies, we
    can maximize the amount of carbon that remains
    sequestered MCCARL2.

Soil Models
  • The forest models above include the soil models,
    but these are designed to work with forest
  • The Global Environment Facility Soil Organic
    Carbon (GEFSOC) Modelling System is a good
    example of an integrated soil model, which seeks
    to operate as A generically applicable system
    for estimating SOC stocks and likely changes at
    regional and national scales… GEF1,
  • GEFSOC integrates three models RothC, Century
    and IPCC, in such a way that results can be
  • The Century plant growth submodel is used as
    input to RothC, which doesnt include plant
    growth information. The IPCC model is a much
    simpler carbon modeling system that takes into
    account historical climatic information EASTER.
  • Because CO2 sequestration depends heavily on
    prior land use, historical data is an important
    part of the GEFSOC model initialization EASTER.

Parameterization of historical data can be
complex. This is part of what makes soil models
difficult to drive using remotely-sensed data
  • Figure 13. Management sequence diagrams for MLRA
    46. Management system abbreviations are as
  • NF native forestCC clearcut tree removalPC
    partial cut tree removal FIRE stand-replacing
    fire RF regenerating forestCSG continuous
    small grainsDASG dryland alfalfa-small grainFSG
    fallow-small grain (conventional tillage) FSGO
    fallow-small grain-oilseedFSGM fallow-small
    grain (minimum tillage)FSGN fallow-small grain
    (no tillage)From EASTER et al, GEFSOC users

Diagram from SCHELHAAS
IPCC Model
IPCC model is much simpler, simple enough to
potentially be driven by pre-existing datasets
Screenshot from IPCC2
Soil Model Problems
  • Parameterization complexity is just one of the
    difficulties facing soil models.
  • Another is that soil maps tend to be quite
    heterogeneous, covering different depths and
    using different soil categorizations, so GIS data
    sources can be difficult to integrate.
  • Soil dynamics tend to be complex and non-linear,
    and simple estimation strategies may be
    inaccurate GEF1.
  • Also, input data tends to be a constraint, and
    soil dynamics may not be readily apparent from
    remotely sensed data EASTER.
  • For example, moving tilled land to no-till
    agriculture should sequester carbon, while
    implementing no-till agriculture on native
    grassland would probably result in CO2 emission.

  • According to LAL2, the role of erosion in the
    global carbon calculations is not well
    understood. He estimates that The amount of
    total C displaced by erosion …may be 4-6 Pg/yr.
    Erosion-induced emission may be .8 to 1.2 Pg per
  • Most models consider respiration and decay
    without considering erosion.
  • SOC levels depend on the difference between
    inputs (esp. plant litter) and output
    (respiration, leaching and erosion).
  • IZAURRALDE reports, based on the EPIC soil
    erosion model, that no-till watersheds are carbon
    sinks, and conventional-till watersheds act as
    carbon sources.
  • The effects of erosion beyond the boundaries of
    the study watersheds are poorly understood and
    are likely to be the subject of further studies.

Other Submodels
  • Other submodels could potentially be included in
    an integrated landscape C model, e. g.,
  • Each of these is a large field in itself.
    Transportation especially has a rich simulation
    modeling tradition.
  • Much of the work done on fuel efficiency, for
    example, translates directly to CO2 emission via
    arithmetic conversions.
  • However, these aspects of the integration problem
    are beyond the scope of this current paper.

Data Sources
  • The studies described above tended to rely on
    observational data, which can be expensive to
  • Despite the huge volume of available
    remotely-sensed data, the availability of
    accurate data remains a roadblock to accurate
    modeling, particularly with regard to soils.
  • Remotely-sensed data is extremely useful for
    forest modeling, especially on a global or
    regional scale
  • For a landscape or town-level purposes,
    higher-resolution imagery is needed, e.g.,
    through IKONOS 4m2 multiband imagery, or
    high-resolution orthophotos that include an
    infrared band.
  • For estimating vegetation biomass, radar and
    lidar are excellent options PATENAUDE2.
    Unfortunately, there is as yet no global lidar
    data set, and coverage is sparse.
  • Obtaining data that can calibrate model results
    is difficult. Directly measuring CO2 fluxes is
    an interesting possibility, and the AmeriFlux
    network makes direct CO2 measurements freely
    available. However, these measurements are
    localized, and it is difficult to infer any
    trends from the widely dispersed collection
    towers AMERIFLUX.

Case Study Area
  • Northampton, Massachusetts, was chosen because it
    includes a variety of land uses, including
    agricultural and public and private forests it
    contains substantial urban and residential areas
    and there is a wealth of GIS data available
    through state and local governments.
  • Land cover can be discerned from combining land
    use/land cover polygons with remotely sensed
    vegetation indexes.
  • Soil polygons are available, but to what extent
    they can be used directly in soil models remains
    to be determined.
  • Topographical and hydrological data may be usable
    in an erosion model.
  • Transportation flows and other ancillary data are
  • A key question will be the possibility of
    obtaining datasets for calibration and validation
    of the submodels, and to what extent these steps
    in prior model studies can be carried over into
    the integrated context.

Map from Springfield Public Library,
Model Types
In an integrated model, submodels may potentially
require different model types, most likely
integrated in a GIS
Case Study Data Sets
Key question to what extent can pre-existing
datasets be used to drive an integrated model?
This depends on many factors...
Data layers from MassGIS, http//http//www.mass.g
Additional Case Study Datasets
Datasets for other aspects of C dynamics that
could be included in an integrated model are
also available
Average Daily Traffic Volumes, Town of
Northampton, MA, Pioneer Valley Planning
Commission, 2001. Light Pollution from
International Dark Sky Association, 1996-7.
Hypothetical Process
Given the available data, this is an
approximation of how the integrated model might
  • Based on this preliminary review of the models
    and data available, integrating multiple models
    into a unified landscape model is probably
    technically possible, but the usefulness of the
    results will depend on details that have not yet
    been settled. There are many key questions that
    will require further research
  • Is there a modeling integration strategy that is
    flexible enough to accommodate changing models,
    parameters, and data?
  • Should the integrated model drive existing
    submodels, or implement them in a new
  • What exactly will be needed for ground truth,
    calibration, and validation of the integrated
    model? How much of prior validation and
    calibration effort can be applied to the
    submodels in an integrated context?
  • How would accommodating multiple modeling
    architectures, such as agent-based and systems
    dynamics models, affect the integration?
  • What is the best scale to use town (e.g.,
    Northampton), landscape (e.g., Pioneer Valley),
    watershed (e.g., Connecticut River Watershed)?
    Can the scale be flexible?

  • How reliable are the vegetation estimates
    available from infrared orthophotos? Are canopy
    structure measurements required, or will LAI
  • What methodologies make sense for handling
    uncertainty? How do these affect the data,
    parameter, and computational requirements?
  • Can complexity be reduced while maintaining
    enough accuracy for results to be meaningful,
    even if they are order-of-magnitude estimates?
  • Are there any other steps that can be taken to
    reduce the consequences of other modeling
    pitfalls such as omission of feedback loops and
    invalidating assumptions?
  • How and to what extent should human behavior and
    economic factors be integrated?
  • How accurate can the results be given especially
    limited soil data availability?
  • Who would use it??

CHIBA Chiba, Y. Simulation of CO2 budget and
ecological implications of sugi (Cryptomeria
japonica) man-made forests in Japan. Ecological
Modelling. Vol. 111, 1998. CHIESI M. Chiesi,
F. Maselli, M. Bindi, L. Fibbi, L. Bonora, A.
Raschia, R. Tognetti, J. Cermak and N.
Nadezhdina. Calibration and application of
FOREST-BGC in a Mediterranean area by the use of
conventional and remote sensing data.
Ecological Modelling, Volume 154, Issue 3, 1
September 2002. EASTER Easter, M. et al. The
GEFSOC Soil Carbon Modelling System A Tool for
Conducting Regional-Scale Soil Carbon Inventories
and Assessing the Impacts of Land Use Change on
Soil Carbon. Agriculture Ecosystems and
Environment, 122, 2007. HANSON Hanson, P. C.,
Pollard, A.I., Bade, D.L., Predick, K.,
Carpenter, S. R., Foley, J. A. A Model of Carbon
Evasion and Sedimentation in Temperate Lakes.
Global Change Biology, 10, 2004. GEF1
Assessment of Soil Organic Carbon Socks and
Change at National Scale. University of Reading,
Technical Report of GEF Co-Financed Project No.
GFL-2740-02-4381, June 2002-July 2005. IPCC2
Tool for Estimation of Changes in Soil Carbon
Stocks Associated with Management Changes in
Croplands and Grazing Lands Based on IPCC Default
Data. Intergovernmental Panel on Climate Change,
IZAURRALDE Izzauralde, R. C., Williams, J. R.,
Post, W. M., Thoson, A. M., McGill, W. B., Owens,
L. B., Lal, R. Long-Term Modeling of Soil C
Erosion and Sequestration at the Small Watershed
Scale. Climatic Change, 2007. LAL2 Lal R.
Soil erosion and the global carbon budget.
Environmental International, January
2003. MASERA Omar R. Masera, J.F.
Garza-Caligaris, M. Kanninen, T. Karjalainen,
Liski, J., G.J. Nabuurs, A. Pussinen, B.H.J. de
Jong, G.M.J. Mohren. Modeling carbon
sequestration in afforestation, agroforestry and
forest management projects the CO2FIX V.2
approach. Ecological Modelling, 164 (2003)
177199. MCCARL1 McCarl, B., Metting, F.B.,
Rice, C. Soil Carbon Sequestration. Climatic
Change, 2007, 80 1-3. NASA NASA Earth Science
Enterprise, http//
ry/ CarbonCycle/carbon_cycle4.html, accessed
11/2007. PAHL-WOSTL Pahl-Wostel, C. The
implications of complexity for integrated
resources management. Environmental Modelling
and Software. Vol. 22, No. 5, 2007. POST Post,
W. M., and Kwon, K. C. Soil Carbon Sequestration
and Land-Use Change Process and Potential.
Global Change Biology, 2000, 6. SCHELHAAS
Schelhaas, M.J., P.W. van Esch, T.A. Groen, M.
Kanninen, J. Liski, O. Masera, G.M.J. Mohren,
G.J. Nabuurs, L. Pedroni, A. Pussinen, A.
Vallejo, T. Palosuo, T. Vilén. 2004. CO2FIX V
3.1 A modelling framework for quantifying
carbon sequestration in forest ecosystems.
ALTERRA Report 1068. Wageningen, The
Netherlands. USDA US Department of
Agriculture, Natural Resources Conservation Unit.
Land Resource Regions and Major Land Resource
Areas of the United States, the Caribbean, and
the Pacific Basin. United States Department of
Agriculture Handbook 296, 2006.