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: VISIT monthly
Reference Paper: Main Reference: Ito, A., Inatomi, M. et al. Use of a process-based model for assessing the methane budgets of global terrestrial ecosystems and evaluation of uncertainty. Biogeosciences,9,759–773,
Person Responsible For Model Simulations In This Simulation Round: Akihiko Ito
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
Climate Variables: tas, huss, rsds, pr
Spin-up
Was A Spin-Up Performed?: Yes
Spin-Up Design: In ISI-MIP simulations, the climate of first 30-year was repeatedly used for 100 times at each grid to drive 3000-year spin-up. It was expected that this is sufficient to attain a stable state of ecosystem matter budget.
Natural Vegetation
Natural Vegetation Partition: Only single dominant natural vegetation type was considered.
Natural Vegetation Dynamics: Distribution of natural vegetation was fixed.
Natural Vegetation Cover Dataset: Ramankutty, N., Foley, J.A., 1999. Estimating historical changes in global land cover: croplands from 1700 to 1992. Global Biogeochem. Cycles 13, 997-1027; Olson, J.S., Watts, J.A., Allison, L.J., 1983. Carbon in live vegetation of major world ecosystems. Oak Ridge National LaboratoryOlson, J.S., Watts, J.A., Allison, L.J., 1983. Carbon in live vegetation of major world ecosystems. Oak Ridge National Laboratory.
Management & Adaptation Measures
Management: No management option was applied.
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 set-up specifications
How Do You Simulate Bioenergy? I.E. What Pft Do You Simulate On Bioenergy Land?: Not applicable
How Do You Simulate The Transition From Cropland To Bioenergy?: Not applicable
How Do You Simulate Pasture (Which Pft)?: Same as grasslands
Key model processes
Dynamic Vegetation: No
Nitrogen Limitation: No
Co2 Effects: Empirical (Michaelis-Menten type) CO2 response of photosynthesis and Leuning type stomatal response
Light Interception: Lambert-Beer type light interception
Light Utilization: Michaelis-Menten type light response curve was assumed.
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
Heat Stress: Photosynthesis rate drops down under too-warm condition.
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.
Closed Energy Balance: No (sensible heat was not simulated).
Coupling/Feedback Between Soil Moisture And Surface Temperature: No (surface temperature was prescribed)
Latent Heat: Converted from evapotranspiration
Sensible Heat: Not explicitly calculated (i.e. net radiation minus latent heat)
How Do You Compute Soil Organic Carbon During Land Use (Do You Mix The Previous Pft Soc Into Agricultural Soc)?: At present, previous biome SOC is not mixed with agricultural SOC. There can be some leakage.
Do You Separate Soil Organic Carbon In Pasture From Natural Grass?: Not separated
Do You Harvest Npp Of Crops? Do You Including Grazing? How Does Harvested Npp Decay?: NPP harvest is included, but NPP decay is not considered. Grazing is not considered.
How Do You To Treat Biofuel Npp And Biofuel Harvest?: Not applicable
Does Non-Harvested Crop Npp Go To Litter In Your Output?: Simply, non-harvested crop (amount of about a half of harvested crop) goes to litter.
Causes of mortality in vegetation models
Age/Senescence: Not age-dependent (metabolic constant mortality)
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.
Drought: No
Insects: No
Storm: No
Stochastic Random Disturbance: No
Other: No
NBP components
Fire: Prognostic fire scheme (Thonicke et al., 2001) is implemented.
Land-Use Change: Land-use change and associated carbon emission were simulated in the model. Biomass carbon is partitioned into detritus, wood product, and atmospheric emissions.
Harvest: Wood harvest can be simulated, but not included in the present simulations.
Other Processes: Soil lateral displacement by water erosion was simulated.
Species / Plant Functional Types (PFTs)
List Of Species / Pfts: No (i.e., biome type)
Model output specifications
Output Format: Grid-cell
Output Per Pft?: No
Considerations: Grid values are weighted mean of natural vegetation and cropland
Land-use change implementation
Is crop harvest included? If so, how?: Crop harvest by constant harvest index was included.
Is cropland soil management included? If so, how?: Not explicitly included.
Is grass harvest included? If so, how?: No
Is shifting cultivation included?: No
Is wood harvest included? If so, how?: Wood harvest can be simulated, but not included in the present simulations.
Carbon-cycle benchmarking
Does your model reach a (near) steady state after spin up (characterized by NBP of < 0.2 PgC y-1)? (Yes/no, provide number): Yes
What is your modeled NBP for the 1990-2000 decade? Is it within 1.2 +/- 0.8 GtC/yr (1-sigma) of observed data from O2/N2 trends (Keeling and Manning 2014) for 1990-1999 (Yes/no, provide number): NBP by VISIT in 1990-2000 is 1.0 Pg C/yr
Fire modules
Aggregation of reported burnt area: Burnt area is estimated monthly and then reported.
Land-use classes allowed to burn: All natural biomes
Included fire-ignition factors: Soil moisture and litter (fuel) load
Is fire ignition implemented as a random process?: Implicitly, yes
Is human influence on fire ignition and/or suppression included? How?: No
How is fire spread/extent modelled?: Empirical function of soil moisture and fuel load
Are deforestation or land clearing fires included?: Implicitly yes
What is the minimum burned area fraction at grid level?: No
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 set-up specifications
How Do You Simulate Bioenergy? I.E. What Pft Do You Simulate On Bioenergy Land?: Not applicable
How Do You Simulate The Transition From Cropland To Bioenergy?: Not applicable
How Do You Simulate Pasture (Which Pft)?: No
Key model processes
Dynamic Vegetation: No
Nitrogen Limitation: No
Co2 Effects: Empirical (Michaelis-Menten type) CO2 response
Light Interception: Lambert-Beer type light interception
Light Utilization: Non linear, Michaelis-Menten type, light response curve
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
Heat Stress: Photosynthesis rate drops down under heat stree
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
Closed Energy Balance: Not exactly
Coupling/Feedback Between Soil Moisture And Surface Temperature: No
Latent Heat: Converted from evapotranspiration
Sensible Heat: Not explicitly (i.e. net radiation minus latent heat)
Do You Separate Soil Organic Carbon In Pasture From Natural Grass?: No
How Do You To Treat Biofuel Npp And Biofuel Harvest?: Not applicable
Does Non-Harvested Crop Npp Go To Litter In Your Output?: Yes. A certain amount of non-harvested NPP goes to litter.
Causes of mortality in vegetation models
Age/Senescence: Yes (base senescence rate).
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.
Drought: No
Insects: No
Storm: No
Stochastic Random Disturbance: No
Other: No
NBP components
Fire: Prognostic fire scheme (Thonicke et al., 2001) is implemented.
Land-Use Change: Land-use change is included. Associated emissions are calculated using the Grand-Slam Protocol.
Harvest: No. (If harvest data is available, the model can consider this effect.)
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
Model output specifications
Output Format: Grid-cell
Output Per Pft?: No
Land-use change implementation
Is crop harvest included? If so, how?: Yes. A constant harvest index is used.
Is cropland soil management included? If so, how?: No.
Is grass harvest included? If so, how?: No.
Is shifting cultivation included?: No.
Is wood harvest included? If so, how?: This process is data-driven. In ISI-MIP2b experiments, this process was unactivated.
Carbon-cycle benchmarking
Does your model reach a (near) steady state after spin up (characterized by NBP of < 0.2 PgC y-1)? (Yes/no, provide number): Yes. About 0.1 Pg C y-1.
What is your modeled NBP for the 1990-2000 decade? Is it within 1.2 +/- 0.8 GtC/yr (1-sigma) of observed data from O2/N2 trends (Keeling and Manning 2014) for 1990-1999 (Yes/no, provide number): Average NBP in 1990-2000 is 1.0 Pg C y-1, when driven by CRU TS3.25.
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? How?: 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
Model output license: CC BY 4.0
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.
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
Model output specifications
Output Format: Grid-cell
Output Per Pft?: No
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? How?: 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? How?: 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
Output Data
Experiments: historical, rcp26, rcp45, rcp60, rcp85
Climate Drivers: GCM atmospheric climate data (Fast Track)
Date: 2013-12-17