Impact model: VISIT

VISIT is a process-based terrestrial ecosystem model, focusing on atmosphere–ecosystem trace gas exchange. VISIT is one of the 8 global models following the ISIMIP2a protocol which form the base of simulations for the ISIMIP2a biome sector outputs; for a full technical description of the ISIMIP2a Simulation Data from Biomes Sector, see this DOI link: http://doi.org/10.5880/PIK.2017.002

Sector
Biomes
Region
global
Contact Person
  • Akihito Ito (itoh@nies.go.jp), National Institute for Environmental Studies (NIES), Japan (Japan)
  • Kazuya Nishina (nishina.kazuya@nies.go.jp), National Institute for Environmental Studies (NIES) & Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Japan (Japan)

Information for the model VISIT is provided for the simulation rounds shown in the tabs below. Click on the appropriate tab to get the information for the simulation round you are interested in.

Basic information
Model Version: VISITa
Output Data
Experiments: I, Ia, II, IIa, IIb, III, IIIa, IIIb, IV, V, VI, VII
Climate Drivers: IPSL-CM5A-LR, HadGEM2-ES, GFDL-ESM2M, MIROC5
Date: 2018-11-06
Resolution
Spatial Aggregation: regular grid
Spatial Resolution: 0.5°x0.5°
Temporal Resolution Of Input Data: Climate Variables: monthly
Temporal Resolution Of Input Data: Co2: annual
Temporal Resolution Of Input Data: Land Use/Land Cover: annual
Temporal Resolution Of Input Data: Soil: constant
Input data sets used
Simulated Atmospheric Climate Data Sets Used: IPSL-CM5A-LR, HadGEM2-ES, GFDL-ESM2M, MIROC5
Observed Atmospheric Climate Data Sets Used: EWEMBI
Land Use Data Sets Used: Future land-use patterns, Historical, gridded land use (HYDE 3.2)
Climate Variables: tas, hurs, rsds, pr
Spin-up
Was A Spin-Up Performed?: Yes
Spin-Up Design: Using climate forcing data (e.g., GSWP3) for the period 1901–1930, the spin-up simulation was conducted for 3000 years, so that carbon budget at each grid comes sufficiently close to an equilibrium. The same protocol was applied for all ISI-MIP simulations.
Natural Vegetation
Natural Vegetation Partition: The present version of VISITa considers only the dominant natural vegetation type for each grid, using a prescribed natural vegetation data.
Natural Vegetation Cover Dataset: Olson's vegetation map (Olson et al., 1983) with modification by the potential vegetation data by Ramankutty and Foley (1999)
Extreme Events & Disturbances
Key Challenges: In VISIT, drought-induced mortality and windthrow are not included, although they have potentially great impacts on long-term ecosystem dynamics. Only fire effect was included in a simple and area-avaraged manner.
Additional Comments: This model accounts for lateral displacement of soil organic carbon by erosion and leaching of dissolved organic carbon. So, these small terms should be included to close the carbon balance in the model.
Model output specifications
Output Format: Grid-cell
Output Per Pft?: No
Key model processes
Co2 Effects: Empirical (Miechaelis-Menten type) CO2 response
Light Interception: Lambert-Beer type light interception
Phenology: A simple phenology scheme. Growing-degree-day is used to determine leaf onset in deciduous biomes.
Water Stress: Empirical (Miechaelis-Menten type) water-limitation curve
Evapo-Transpiration Approach: Penman-Monteith equation, with a limitation by soil water availability
Differences In Rooting Depth: Considered: Zeng, X. (2001), Global vegetation root distribution for land modeling, Journal of Hydrometeorology, 2, 525-530.
Root Distribution Over Depth: Considered: Zeng, X. (2001), Global vegetation root distribution for land modeling, Journal of Hydrometeorology, 2, 525-530.
Permafrost: None
Latent Heat: Converted from evapotranspiration
Causes of mortality in vegetation models
Fire: Burnt area is estimated for each grid at monthly time-step, using the parameterization of Thonicke et al. (2001). Biomass burning emission is estimated using carbon stock simulated by the VISIT model and emission factors by Hoelzemann et al. (2004). We assume that fire occurs only in natural biomes.
NBP components
Fire: Prognostic fire scheme (Thonicke et al., 2001) is implemented.
Species / Plant Functional Types (PFTs)
List Of Species / Pfts: VISIT uses biome types (after Olson et al. 1983) instead of PFTs as listed below: 0:water, 1:tropical & subtropical evergreen forest, 2:tropical montane forest, 3:tropical & subtropical dry forest, 4:mid-latitude mixed forest, 5:mid-latitude broad-leaved forest, 6:semiarid wood or low forest, 7:coniferous evergreen forest, 8:southern taiga, 9:main evergreen taiga, 10:main deciduous taiga, 11:northern evergreen taiga, 12:northern deciduous taiga, 13:second growth woods, 14:second growth field, 15:succulent & thorn wood, 16:tropical savanna, woodland, 17:mediterranean-type dry wood, 18:heath & moorland, 19:warm or hot shrub & grassland, 20:tibetan meadow & siberian highland, 21:tundra, 22:wooded tundra, 23:warm or hot wetlands, 24:cool bog & mire, 25:shore & hinterland, 26:cool semi-desert scrub, 27:non-polar desert, 28:non-polar sand desert, 29:paddyland, 30:cool cropland, 31:warm cropland, 32:irrigated, 33:antarctica
Fire modules
Aggregation of reported burnt area: Simulated annual burned area is disaggregated into monthly values.
Land-use classes allowed to burn: Natural vegetation, ISIMIP-Pasture (managed pastures, rangeland) and urban areas are allowed to burn but not cropland. Urban Land is treated as natural vegetation.
Included fire-ignition factors: Natural ignition based on availability of fuel, combustibility of fuel (soil moisture)
Is fire ignition implemented as a random process?: No.
Is human influence on fire ignition and/or suppression included?: No.
How is fire spread/extent modelled?: Fire extent is an empirical function of soil moisture and fuel load.
Are deforestation or land clearing fires included?: No.
What is the minimum burned area fraction at grid level?: 0
Basic information
Model Version: VISITa
Output Data
Experiments: historical
Climate Drivers: GSWP3, PGMFD v.2 (Princeton), WATCH (WFD), WATCH+WFDEI
Date: 2016-05-17
Resolution
Spatial Aggregation: regular grid
Spatial Resolution: 0.5°x0.5°
Temporal Resolution Of Input Data: Climate Variables: monthly
Temporal Resolution Of Input Data: Co2: annual
Temporal Resolution Of Input Data: Land Use/Land Cover: annual
Temporal Resolution Of Input Data: Soil: constant
Input data sets used
Observed Atmospheric Climate Data Sets Used: GSWP3, PGMFD v.2 (Princeton), WATCH (WFD), WATCH+WFDEI
Climate Variables: co2, tas, rhs, rsds, pr
Spin-up
Was A Spin-Up Performed?: Yes
Spin-Up Design: Using climate forcing data (e.g., GSWP3) for the period 1901–1930, the spin-up simulation was conducted for 3000 years, so that carbon budget at each grid comes sufficiently close to an equilibrium. The same protocol was applied for all ISI-MIP simulations.
Natural Vegetation
Natural Vegetation Partition: The present version of VISITa considers only the dominant natural vegetation type for each grid, using a prescribed natural vegetation data.
Natural Vegetation Cover Dataset: Olson's vegetation map (Olson et al., 1983) with modification by the potential vegetation data by Ramankutty and Foley (1999)
Extreme Events & Disturbances
Key Challenges: In VISIT, drought-induced mortality and windthrow are not included, although they have potentially great impacts on long-term ecosystem dynamics. Only fire effect was included in a simple and area-avaraged manner.
Additional Comments: This model accounts for lateral displacement of soil organic carbon by erosion and leaching of dissolved organic carbon. So, these small terms should be included to close the carbon balance in the model.
Model output specifications
Output Format: Grid-cell
Output Per Pft?: No
Key model processes
Co2 Effects: Empirical (Miechaelis-Menten type) CO2 response
Light Interception: Lambert-Beer type light interception
Phenology: A simple phenology scheme. Growing-degree-day is used to determine leaf onset in deciduous biomes.
Water Stress: Empirical (Miechaelis-Menten type) water-limitation curve
Evapo-Transpiration Approach: Penman-Monteith equation, with a limitation by soil water availability
Differences In Rooting Depth: Considered: Zeng, X. (2001), Global vegetation root distribution for land modeling, Journal of Hydrometeorology, 2, 525-530.
Root Distribution Over Depth: Considered: Zeng, X. (2001), Global vegetation root distribution for land modeling, Journal of Hydrometeorology, 2, 525-530.
Permafrost: None
Latent Heat: Converted from evapotranspiration
NBP components
Fire: Prognostic fire scheme (Thonicke et al., 2001) is implemented.
Species / Plant Functional Types (PFTs)
List Of Species / Pfts: VISIT uses biome types (after Olson et al. 1983) instead of PFTs as listed below: 0:water, 1:tropical & subtropical evergreen forest, 2:tropical montane forest, 3:tropical & subtropical dry forest, 4:mid-latitude mixed forest, 5:mid-latitude broad-leaved forest, 6:semiarid wood or low forest, 7:coniferous evergreen forest, 8:southern taiga, 9:main evergreen taiga, 10:main deciduous taiga, 11:northern evergreen taiga, 12:northern deciduous taiga, 13:second growth woods, 14:second growth field, 15:succulent & thorn wood, 16:tropical savanna, woodland, 17:mediterranean-type dry wood, 18:heath & moorland, 19:warm or hot shrub & grassland, 20:tibetan meadow & siberian highland, 21:tundra, 22:wooded tundra, 23:warm or hot wetlands, 24:cool bog & mire, 25:shore & hinterland, 26:cool semi-desert scrub, 27:non-polar desert, 28:non-polar sand desert, 29:paddyland, 30:cool cropland, 31:warm cropland, 32:irrigated, 33:antarctica
Fire modules
Aggregation of reported burnt area: Simulated annual burned area is disaggregated into monthly values.
Land-use classes allowed to burn: Natural vegetation, ISIMIP-Pasture (managed pastures, rangeland) and urban areas are allowed to burn but not cropland. Urban Land is treated as natural vegetation.
Included fire-ignition factors: Natural ignition based on availability of fuel, combustibility of fuel (soil moisture)
Is fire ignition implemented as a random process?: No.
Is human influence on fire ignition and/or suppression included?: No.
How is fire spread/extent modelled?: Fire extent is an empirical function of soil moisture and fuel load.
Are deforestation or land clearing fires included?: No.
What is the minimum burned area fraction at grid level?: 0
Fire modules
Aggregation of reported burnt area: Simulated annual burned area is disaggregated into monthly values.
Land-use classes allowed to burn: Natural vegetation, ISIMIP-Pasture (managed pastures, rangeland) and urban areas are allowed to burn but not cropland. Urban Land is treated as natural vegetation.
Included fire-ignition factors: Natural ignition based on availability of fuel, combustibility of fuel (soil moisture)
Is fire ignition implemented as a random process?: No.
Is human influence on fire ignition and/or suppression included?: No.
How is fire spread/extent modelled?: Fire extent is an empirical function of soil moisture and fuel load.
Are deforestation or land clearing fires included?: No.
What is the minimum burned area fraction at grid level?: 0