From static biogeographical model to dynamic global vegetation model: a global perspective on modelling vegetation dynamics

From static biogeographical model to dynamic global vegetation model: a global perspective on... Predicting the potential effects of future climatic change and human disturbances on natural vegetation distribution requires large-scale biogeographical models. There have been two basic approaches to modelling vegetation response to changing climates: static (time-independent) or dynamic (time-dependent) biogeographical models. This paper reviews and compares two major types of static biogeographical models, climate–vegetation classification and plant functional type models, and the first generation of Dynamic Global Vegetation Models (DGVMs). These models have been widely used to simulate the potential response of vegetation to past and future climate change. Advantage and disadvantage of each type of model are discussed. Global vegetation modelling for investigations of climate change effects has progressed from empirical modelling to process-based equilibrium modelling to the first generation of DGVMs. Some DGVMs are able to capture the responses of potential natural vegetation to climate change with a strong orientation towards population processes. Nevertheless, the uncertainty around the quantitative simulated results indicates that DGVMs are still in the early stages of development. Validating and capturing disturbance-related effects are major challenges facing the developers of the next generation of DGVMs. In future, DGVMs will become an important tool for assessing the effects of climate change on potential vegetation dynamics and terrestrial carbon storage and for managing terrestrial ecosystem sustainability. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Ecological Modelling Elsevier

From static biogeographical model to dynamic global vegetation model: a global perspective on modelling vegetation dynamics

Ecological Modelling, Volume 135 (1) – Nov 25, 2000

Loading next page...
 
/lp/elsevier/from-static-biogeographical-model-to-dynamic-global-vegetation-model-a-KTm0VSFE9K
Publisher
Elsevier
Copyright
Copyright © 2000 Elsevier Science B.V.
ISSN
0304-3800
eISSN
1872-7026
DOI
10.1016/S0304-3800(00)00348-3
Publisher site
See Article on Publisher Site

Abstract

Predicting the potential effects of future climatic change and human disturbances on natural vegetation distribution requires large-scale biogeographical models. There have been two basic approaches to modelling vegetation response to changing climates: static (time-independent) or dynamic (time-dependent) biogeographical models. This paper reviews and compares two major types of static biogeographical models, climate–vegetation classification and plant functional type models, and the first generation of Dynamic Global Vegetation Models (DGVMs). These models have been widely used to simulate the potential response of vegetation to past and future climate change. Advantage and disadvantage of each type of model are discussed. Global vegetation modelling for investigations of climate change effects has progressed from empirical modelling to process-based equilibrium modelling to the first generation of DGVMs. Some DGVMs are able to capture the responses of potential natural vegetation to climate change with a strong orientation towards population processes. Nevertheless, the uncertainty around the quantitative simulated results indicates that DGVMs are still in the early stages of development. Validating and capturing disturbance-related effects are major challenges facing the developers of the next generation of DGVMs. In future, DGVMs will become an important tool for assessing the effects of climate change on potential vegetation dynamics and terrestrial carbon storage and for managing terrestrial ecosystem sustainability.

Journal

Ecological ModellingElsevier

Published: Nov 25, 2000

References

  • The relationship between land-use change and climate change
    Dale, V.H.
  • Dynamic simulation of tree-grass interactions for global change studies.
    Daly, C.; Bachelet, D.; Lenihan, J.M.; Neilson, R.P.; Parton, W.; Ojima, D.
  • A new global 1-km dataset of percentage tree cover derived from remote sensing
    Defries, R.S.; Hansen, M.C.; Townshend, J.R.G.; Janetos, A.C.; Lovelands, T.R.
  • An integrated biosphere model of land surface processes, terrestrial carbon balance, and vegetation dynamics
    Foley, J.A.; Prentice, I.C.; Ramankutty, N.; Levis, S.; Pollard, D.; Sitch, S.; Haxeltine, A.
  • Coupling dynamics models of climate and vegetation
    Foley, J.A.; Levis, S.; Prentice, I.C.; Pollard, D.; Thompsons, S.L.
  • Carbon stocks and isotopic budgets of the terrestrial biosphere at mid-Holocene and last glacial maximum times
    François, L.M.; Godderis, Y.; Warnant, P.; Ramstein, G.; de Noblet, N.; Lorenz, S.
  • Inverse vegetation modelling by Monte Carlo sampling to reconstruct palaeoclimates under changed precipitation seasonality and CO 2 conditions: application to glacial climate in Mediterranean region
    Guiot, J.; Torre, F.; Jolly, D.; Peyron, O.; Boreau, J.J.; Cheddadi, R.
  • BIOME3: An equilibrium terrestrial biosphere model based on ecophysiological constraints, resources availability, and competition among plant function types
    Haxeltine, A.; Prentice, I.C.
  • Modeling potential future individual tree-species distributions in the eastern United States under a climate change scenario: a case study with Pinus virginiana
    Iverson, L.R.; Prasad, A.; Schwartz, M.K.
  • Scaling processes and problems.
    Jarvis, P.G.
  • Simulating effects of fire on northern Rocky Mountain landscapes with the ecological process model FIRE-BGC
    Keane, R.E.; Ryan, K.C.; Running, S.W.
  • A 70-year retrospective analysis of carbon fluxes in the Canadian forest sector
    Kurz, W.A.; Apps, M.J.
  • Plant functional types and disturbance dynamics – Introduction
    McIntyre, S.; Diaz, S.; Cramer, W.
  • Modelling response of net primary productivity (NPP) of boreal forest ecosystems to changes in climate and fire disturbance regimes
    Peng, C.H.; Apps, J.M.
  • Terrestrial ecosystem production: a process model based on global satellite and surface data
    Potter, C.S.; Randerson, J.T.; Field, C.B.; Matson, P.A.; Vitousek, P.M.; Mooney, H.A.; Klooster, S.A.
  • Dynamic global vegetation modelling for predicting of plant functional types and biogenic trace gas fluxes
    Potter, C.S.; Klooster, S.A.
  • Adapting a patch model to simulate the sensitivity of central-Canadian boreal ecosystems to climate variability
    Price, D.T.; Halliwell, D.; Apps, J.M.; Peng, C.H.
  • Forest succession models
    Shugart, H.H.; West, D.C.
  • Transient response of forests to CO 2 -induced climate change: Simulation modeling experiments in eastern North America
    Solomon, A.M.
  • Global vegetation models: incorporating transient changes to structure and composition
    Steffen, W.L.; Cramer, W.; Plöchl, M.; Bugmann, H.
  • The high-latitude terrestrial carbon sink: a model analysis
    White, A.; Cannell, M.G.R.; Friend, A.D.
  • A global land primary productivity and phytogeography model
    Woodward, F.I.; Smith, T.M.; Emanuel, W.R.

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create folders to
organize your research

Export folders, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off