Dynamic General Vegetation Models and Climate Change: Current State and Future Potential

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Dynamic General Vegetation Models and Climate
Change Current State and Future Potential

• Ron Neilson
• Leader, MAPSS Team
• Pacific Northwest Research Station
• USDA Forest Service
• Pacific Northwest Research Station
• Corvallis, Oregon 97333
• rneilson_at_fs.fed.us
• [phone]

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Vegetation and Fire Dynamics (MC1 Model)
Savanna Structure Required
Growth and Nutrient Cycling (CENTURY)
Vegetation Distribution (MAPSS)
FIRE (Surface, Crown, Rate of Spread)
H2O, N
Drought Responses Fire Risks Carbon Sequestration
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Plant Functional Type Rules
Leaf Form Evergreen Deciduous Broadleaf
Needleleaf Grass, C3 C4
Thermal Zone Physiologically Based
Neilson,R.P. 1995. A model for predicting
continental-scale vegetation distribution and
water balance. Ecological Applications
5362-385.
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Plant Functional Type Rules
Stomatal Conductance (MAPSS, BIOMAP)

• Wilting Point
• Maximum Conductance

Neilson,R.P. 1995. A model for predicting
continental-scale vegetation distribution and
water balance. Ecological Applications
5362-385.
5
Plant Functional Type Rules
Wilting Point, ?
White,M.A., P.E.Thornton, S.W.Running, and
R.R.Nemani. 2000. Parameterization and
sensitivity analysis of the BIOME-BGC terrestrial
ecosystem model net primary production controls.
Earth Interactions 41-85.
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Canopy Biophysics (SDGVM)
Woodward,F.I., T.M.Smith, and W.R.Emanuel. 1995.
A global land primary productivity and
phytogeography model. Global Biogeochemical
Cycles 9471-490.
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Biogeochemistry MC1 (from CENTURY)
Daly,C., D.Bachelet, J.M.Lenihan, W.Parton,
R.P.Neilson, and D.Ojima. 2000. Dynamic
simulations of tree-grass interactions for global
change studies. Ecological Applications
10449-469.
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Fire Effects MC1 (MAPSS CENTURY, v. 1)
Daly,C., D.Bachelet, J.M.Lenihan, W.Parton,
R.P.Neilson, and D.Ojima. 2000. Dynamic
simulations of tree-grass interactions for global
change studies. Ecological Applications
10449-469.
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Post hoc Physiognomic Classification MAPSS has
ca. 45 Vegetation Types
Neilson,R.P. 1995. A model for predicting
continental-scale vegetation distribution and
water balance. Ecological Applications
5362-385.
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MAPSS Simulated Vegetation Distribution
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Net Ecosystem Exchange, LPJ (NPP Rh)
Sitch, S. Smith, B. Prentice, I. C. Arneth,
A. Bondeau, A. Cramer, W. Kaplan, J. O.
Levis, S. Lucht, W. Sykes, M. T. Thonicke, K.
Venevsky, S. 2003. Evaluation of ecosystem
dynamics, plant geography and terrestrial carbon
cycling in the LPJ dynamic global vegetation
model. Global Change Biology. 9 161-185.
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Monthly Runoff (SDGVM)
Beerling,D.J., F.I.Woodward, M.Lomas, and
A.J.Jenkins. 1997. Testing the responses of a
dynamic global vegetation model to environmental
change a comparison of observations and predict
ions. Global Ecology and Biogeography Letters
6439-450.
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Biomass Consumed, 1988
Disturbance Mediates Ecosystem Change MC-FIRE
Simulation
Yellowstone Fire
Cornbelt Fires
MC1 DGVM
Bachelet, D., R.P. Neilson, J.M. Lenihan, and
R.J. Drapek. 2001. Climate change effects on
vegetation distribution and carbon budget in the
United States. Ecosystems 4164-185.
Biomass Consumed 1895-2100
2035
1988 Regime Shift
Large, Catastrophic Fires
1910
1930s
Future Trend
Historic Trend
Increase in Background Fire Severity
High Warming
Small Warming
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Observed and Simulated Fire Area for the
Conterminous U.S. (Millions of Acres) (MC1
Dynamic General Vegetation Model)
Trend 432K Acres/Yr Increase
Simulated Fire Suppression Suppression Factor
.125
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NBP, Ecosystem Carbon Gain or Loss By Climate
Scenario
High CO2 Response Beta .65
Fire
Low CO2 Response Beta .25
MC1
Fire Supression
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Simulated Historical Vegetation MC1
(MAPSS-CENTURY, v.1)
50m Resolution
0.5o Resolution
Multi-Scale Assessment
2.5 Resolution
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Drought and Fire in the West (Simulated Fire, no
Fire Suppression)
Interdecadal Climate Regime Shifts
1976 - 77
1988 - 89
1940s
1983
1998
El Niño
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Current Vegetation (1961-1990) Suppressed Fire
MC1 DGVM
MAPSS Team, In Prep.
CGCM2-A2 Scenario
Suppressed Fire
With Fire
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Average Biomass Burned CGCM2a (2050-2099)
In the Future The West gets Woodier, and It burns
a lot more!... But, look at the East!
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LPJ DGVM 16 Climate Scenarios
Different Ecological Model Different Climate
Scenarios Same Changes in Fire
Red Fire Green - Fire
Changes relative to Base Period 1961 1990
Scholze et al. 2006. Proc. Natl. Acad. Sci.
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VINCERA HADCM3-A2 Eastern Deciduous Forest Region
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Lynx Conservation Project MC1 Simulated Modal
Vegetation Type Historical 1961-1990
USDA Forest Service Nature Serve The Nature
Conservancy Oregon State University
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A2
A1B
B1
MIROC3_MEDRES
HADCM3
CSIRO_MK3
percent
Percent Change Biomass consumed by Fire 2051-2100
vs. 1951-2000.
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A2
A1B
B1
MIROC3_MEDRES
HADCM3
CSIRO_MK3
percent
Percent Change in Vegetation Carbon 2070-2099 vs.
1961-1990.
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MC1 Simulations in Progress 800 m Resolution
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H.J. Andrews at 800 m resolution
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Scaling from Tree to Gap to Landscape to Grid
Regional Hydro-Ecological System (RHESSys) Key
Investigator Jennifer Dungan (NASA)
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Scaling from Tree to Gap to Landscape to Grid
Sub-Grid Heterogeneity
Static Topoedaphic and Climatic Constraints
South Facing
Riparian
North Facing
Water Flows in Red
Floodplain
Spatially Explicit Spatially Aggregated
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Scaling from Tree to Gap to Landscape to Grid
Sub-Grid Heterogeneity
Dynamic Age Cohorts, Post-Disturbance
10 years old
1 year old
50 years old
Old Growth
Spatially Implicit Spatially Aggregated
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Scaling from Tree to Gap to Landscape to Grid
Bugmann,H. 2001. A review of forest gap models.
Climatic Change 51259-305.
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Scaling Gap (Individual Tree) to Coarse Grid, the
ED Model
Moorcroft,P.R., G.C.Hurtt, and S.W.Pacala. 2001.
A method for scaling vegetation dynamics The
ecosystem demography model (ED). Ecological
Monographs 71557-585.
Size and Age structured Simulation (red) vs.
individual Gap (green) Simulation SAS simulated
via Statistical Mechanics
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Conclusions

• Vegetation shifts will be very complex, tearing
  species assemblages apart and creating new ones,
  even within a constant physiognomic type, e.g.
  Temperate Forest
• All regions will experience Impacts
• Vegetation either grows better, or declines
  (drought)
• Early greenup later browndown
• Greater climate variance and possible regime
  shifts
• Rapid, catastrophic ecosystem change and
  ecosystem lags, could unload carbon to the
  atmosphere
• High-carbon systems (forests, peatlands) possess
  long internal lags and are vulnerable

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Forecasting Management Implications

• Management goals must look forward to an
  uncertain context, rather than backward to a
  presumed, certain historical Potential Natural
  Vegetation
• Fire, Carbon, Water and other policies may be
  antithetical, thus demanding a Systems
  Perspective and creative management of change
• The current DGVMs do not replicate the
  capabilities of the standard quantitative forest
  management tools, but they are rapidly going
  there
• DGVMs are the only tools that can extend into
  unknown territory, forecast threshold changes and
  new vegetation types
• Extension capabilities of DGVMs are unlimited,
  for example
• Scaling from leaf to plot, landscape, continent
  and Globe
• Hierarchical from species assemblages to
  physiognomic communities
• Insects and diseases
• Wildlife and wildlife habitat