Dynamic global vegetation model explained

A Dynamic Global Vegetation Model (DGVM) is a computer program that simulates shifts in potential vegetation and its associated biogeochemical and hydrological cycles as a response to shifts in climate. DGVMs use time series of climate data and, given constraints of latitude, topography, and soil characteristics, simulate monthly or daily dynamics of ecosystem processes. DGVMs are used most often to simulate the effects of future climate change on natural vegetation and its carbon and water cycles.

Model development

DGVMs generally combine biogeochemistry, biogeography, and disturbance submodels. Disturbance is often limited to wildfires, but in principle could include any of: forest/land management decisions, windthrow, insect damage, ozone damage etc. DGVMs usually "spin up" their simulations from bare ground to equilibrium vegetation (e.g. climax community) to establish realistic initial values for their various "pools": carbon and nitrogen in live and dead vegetation, soil organic matter, etc. corresponding to a documented historical vegetation cover.

DGVMs are usually run in a spatially distributed mode, with simulations carried out for thousands of "cells", geographic points which are assumed to have homogeneous conditions within each cell. Simulations are carried out across a range of spatial scales, from global to landscape. Cells are usually arranged as lattice points; the distance between adjacent lattice points may be as coarse as a few degrees of latitude or longitude, or as fine as 30 arc-seconds. Simulations of the conterminous United States in the first DGVM comparison exercise (LPJ and MC1) called the VEMAP project,[1] in the 1990s used a lattice grain of one-half degree. Global simulations by the PIK group and collaborators,[2] using 6 different DGVMs (HYBRID, IBIS, LPJ, SDGVM, TRIFFID, and VECODE) used the same resolution as the general circulation model (GCM) that provided the climate data, 3.75 deg longitude x 2.5 deg latitude, a total of 1631 land grid cells. Sometimes lattice distances are specified in kilometers rather than angular measure, especially for finer grains, so a project like VEMAP [3] is often referred to as 50 km grain.

Several DGVMs appeared in the middle 1990s. The first was apparently IBIS (Foley et al., 1996), VECODE (Brovkin et al., 1997), followed by several others described below:

Groups

Several DGVMs have been developed by various research groups around the world:

The next generation of models – Earth system models (ex. CCSM,[22] ORCHIDEE,[23] JULES,[24] CTEM[25]) – now includes the important feedbacks from the biosphere to the atmosphere so that vegetation shifts and changes in the carbon and hydrological cycles affect the climate.

DGVMs commonly simulate a variety of plant and soil physiological processes. The processes simulated by various DGVMs are summarized in the table below. Abbreviations are: NPP, net primary production; PFT, plant functional type; SAW, soil available water; LAI, leaf area index; I, solar radiation; T, air temperature; Wr, root zone water supply; PET, potential evapotranspiration; vegc, total live vegetation carbon.

process/attributeformulation/valueDGVMs
shortest time step1 hourIBIS, ED2
2 hoursTRIFFID
12 hoursHYBRID
1 dayLPJ, SDGVM, SEIB-DGVM, MC1 fire submodel
1 monthMC1 except fire submodel
1 yearVECODE
photosynthesisFarquhar et al. (1980)[26] HYBRID
Farquhar et al. (1980)
Collatz et al. (1992)[27] 		IBIS, LPJ, SDGVM
Collatz et al. (1991)[28]
Collatz et al. (1992)		TRIFFID
stomatal conductanceJarvis (1976)[29]
Stewart (1988)[30] 		HYBRID
Leuning (1995)[31] IBIS, SDGVM, SEIB-DGVM
Haxeltine & Prentice (1996)[32] LPJ
Cox et al. (1998)[33] TRIFFID
productionforest NPP = f(PFT, vegc, T, SAW, P, ...)
grass NPP = f(PFT, vegc, T, SAW, P, light competition, ...)		MC1
GPP = f(I, LAI, T, Wr, PET, CO2)LPJ
competitionfor light, water, and NMC1, HYBRID
for light and waterLPJ, IBIS, SDGVM, SEIB-DGVM
Lotka-Volterra in fractional coverTRIFFID
Climate-dependentVECODE
establishmentAll PFTs establish uniformly as small individualsHYBRID
Climatically favored PFTs establish uniformly, as small individualsSEIB-DGVM
Climatically favored PFTs establish uniformly, as small LAI incrementIBIS
Climatically favored PFTs establish in proportion to area available, as small individualsLPJ, SDGVM
Minimum 'seed' fraction for all PFTsTRIFFID
mortalityDependent on carbon poolsHYBRID
Deterministic baseline, wind throw, fire, extreme temperaturesIBIS
Deterministic baseline, self-thinning, carbon balance, fire, extreme temperaturesLPJ, SEIB-DGVM, ED2
Carbon balance, wind throw, fire, extreme temperaturesSDGVM
Prescribed disturbance rate for each PFTTRIFFID
Climate-dependent, based on carbon balanceVECODE
Self-thinning, fire, extreme temperatures, droughtMC1

Notes and References

  1. December 1995 . Vegetation/ecosystem modeling and analysis project- Comparing biogeography and biogeochemistry models in a continental-scale study of terrestrial ecosystem responses to climate change and CO2 doubling . Global Biogeochamical Cucles . 9 . 4 . 407–437.
  2. Cramer . Wolfgang . Bondeau . Alberte . Woodward . F. Ian . Prentice . I. Colin . Betts . Richard A. . Brovkin . Victor . Cox . Peter M. . Fisher . Veronica . Foley . Jonathan A. . Friend . Andrew D. . Kucharik . Chris . Lomas . Mark R. . Ramankutty . Navin . Sitch . Stephen . Smith . Benjamin . April 2001 . Global response of terrestrial ecosystem structure and function to CO 2 and climate change: results from six dynamic global vegetation models: ECOSYSTEM DYNAMICS, CO 2 and CLIMATE CHANGE . Global Change Biology . en . 7 . 4 . 357–373 . 10.1046/j.1365-2486.2001.00383.x.
  3. Web site: Vegetation-Ecosystem Modeling and Analysis Project . cgd.ucar.edu.
  4. Sitch S, Smith B, Prentice IC, Arneth A, Bondeau A, Cramer W, Kaplan JO, Levis S, Lucht W, Sykes MT, 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.
  5. Web site: LPJ & LPJML Web Distribution Portal — PIK Research Portal . 2011-01-08 . https://web.archive.org/web/20101213071217/http://www.pik-potsdam.de/research/cooperations/lpjweb . 2010-12-13 . dead .
  6. Foley . Jonathan A. . Prentice . I. Colin . Ramankutty . Navin . Levis . Samuel . Pollard . David . Sitch . Steven . Haxeltine . Alex . December 1996 . An integrated biosphere model of land surface processes, terrestrial carbon balance, and vegetation dynamics . Global Biogeochemical Cycles . en . 10 . 4 . 603–628 . 10.1029/96GB02692.
  7. Kucharik . Christopher J. . Foley . Jonathan A. . Delire . Christine . Fisher . Veronica A. . Coe . Michael T. . Lenters . John D. . Young-Molling . Christine . Ramankutty . Navin . Norman . John M. . Gower . Stith T. . September 2000 . Testing the performance of a dynamic global ecosystem model: Water balance, carbon balance, and vegetation structure . Global Biogeochemical Cycles . en . 14 . 3 . 795–825 . 10.1029/1999GB001138.
  8. Web site: ORNL DAAC for Biogeochemical Dynamics . 2023-09-07 . daac.ornl.gov.
  9. Bachelet . Dominique . Lenihan . James M. . Daly . Christopher . Neilson . Ronald P. . Ojima . Dennis S. . Parton . William J. . 2001 . MC1: a dynamic vegetation model for estimating the distribution of vegetation and associated carbon, nutrients, and water—technical documentation. Version 1.0. . Gen. Tech. Rep. PNW-GTR-508. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 95 p . en . 508 . 10.2737/PNW-GTR-508.
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  11. Web site: 2018-06-20 . MC1 Dynamic Vegetation Model . 2023-09-07 . fsl.orst.edu/dgvm . 2018-06-20 . https://web.archive.org/web/20180620101353/https://www.fsl.orst.edu/dgvm/ . bot: unknown .
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  13. Woodward . F. I. . Lomas . M. R. . Betts . R. A. . 1998-01-29 . Beerling . D. J. . Chaloner . W. G. . Woodward . F. I. . Vegetation-climate feedbacks in a greenhouse world . Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences . en . 353 . 1365 . 29–39 . 10.1098/rstb.1998.0188 . 0962-8436 . 1692170.
  14. Web site: Sato . Hisashi . Spatially Explicit Individual Based - Dynamic Global Vegetation Model . 2023-09-07 . Yokohama Institute for Earth Sciences seib-dgvm.com.
  15. Web site: Hadley Centre: Carbon cycle models . www.metoffice.gov.uk . dead . https://web.archive.org/web/20010822010330/http://www.metoffice.gov.uk/research/hadleycentre/models/carbon_cycle/models_terrest.html . 2001-08-22.
  16. Brovkin . Victor . Ganopolski . Andrei . Svirezhev . Yuri . 1997-08-15 . A continuous climate-vegetation classification for use in climate-biosphere studies . Ecological Modelling . 101 . 2 . 251–261 . 10.1016/S0304-3800(97)00049-5 . 0304-3800. 11858/00-001M-0000-0023-E605-4 . free .
  17. The Community Land Model's Dynamic Global Vegetation Model (CLM-DGVM): Technical description and user's guide . Levis . Samuel . Bonan . Gordon . 2004 . UCAR/NCAR . 10.5065/d6p26w36 . 1505 KB . en . Vertenstein . Mariana . Oleson . Keith.
  18. Moorcroft . P. R. . Hurtt . G. C. . Pacala . S. W. . November 2001 . http://doi.wiley.com/10.1890/0012-9615(2001)071[0557:AMFSVD2.0.CO;2 A METHOD FOR SCALING VEGETATION DYNAMICS: THE ECOSYSTEM DEMOGRAPHY MODEL (ED) ]. Ecological Monographs . en . 71 . 4 . 557–586 . 10.1890/0012-9615(2001)071[0557:AMFSVD]2.0.CO;2 . 0012-9615.
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  20. Zeng . Ning . Glacial-interglacial atmospheric CO2 change—The glacial burial hypothesis . Advances in Atmospheric Sciences . September 2003 . 20 . 5 . 677–693 . 10.1007/BF02915395. 2003AdAtS..20..677N . 15094502 .
  21. Web site: UMD Earth system model . atmos.umd.edu.
  22. Web site: Community Climate System Model (CCSM) Community Earth System Model . 2023-09-07 . www.cesm.ucar.edu.
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  25. Web site: 2019-06-03 . The Canadian Terrestrial Ecosystem Model (CTEM) . 2023-09-07 . CLASSIC . en-us.
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