AVIM
(9) Calibration variable(s) and method:
variables: latent and sensible heat flux, CO2 flux net radiation, soil temperature and water content, biomass, LAI, NPP. method: observed vegetation and climate data at sites or remote sensing
(10) Scaling of the processes to the grid cell using grid cell mean characteristic parameters or to integrated the fluxes in a grid cell.
(11) Disturbance: grazing, harvest
(12) Vegetation I/O: actual
(13) Input drivers (climatic, site, veg, soils) and resolution (e.g.
daily, monthly) required for model initialization:
climatic : CO2 concentration, hourly air temperature, humidity,radiation, wind, cloudiness.
vegetation : global PAT 0.5*0.5 latitude*longitude.
soil : global soil texture types in 0.5*0.5 latitude*longitude.
BIOME - BGC
(9) Calibration variable(s) and method:
All the vegetation parameters are commonly measured ecophysiological variables, and we use a standard set of parameters for global runs that are averages from a large number of observations. This includes the allocation parameters, the photosynthesis parameters, the stomatal conductance and stomatal control parameters, the light extinction and rainfall interception parameters, the vegetation C:N values, and the proportions of each vegetation state variable that is labile, cellulose, and lignin.
Phenology model parameters were determined by comparison of satellite-derived phenological information with long-term meteorological data.
All the soil rate constants for turnover of organic matter are objectively derived from 14C labeling experiments, using data from the literature.
(10) Scaling of the processes to the grid cell
Each fractional vegetation type is simulated entirely independently, with no implicit or explicit spatial scaling. Fractional components can then be weighted by proportion to get flux densities for a grid cell, and further weighted by grid cell area to get total fluxes.
(11) Disturbance: (fire, grazing, harvest, tree removal, etc.)
Fire, whole plant mortality, harvest. Whole plant mortality is intended to represent the combined effects of windthrow, death due to insect or disease, grazing, or any process that leaves most of the dead plant material on site. Harvest can be defined in different ways, to simulate taking or leaving different fractions.
(12) Vegetation I/O: (e.g. potential, actual)
Takes a description of the fractional cover by fundamental vegetation type as an input.
(13) Input drivers (climatic, site, veg, soils) and resolution (e.g. daily, monthly) required for model initialization:
Daily max and min temperature, daily total precipitation, elevation, slope, aspect, latitude, soil texture (%sand,%silt,%clay, %rocky), rooting zone depth, atmospheric deposition of mineral N, biological fixation of atmospheric N. Optional: fire frequency and intensity information, time since last stand-replacing disturbance, management practices (including fertilization, irrigation, thinning).
CARAIB*
(9) Calibration variable(s) and method:
The ratio between root and microbial contributions to soil respiration has been determined for grasslands and forests in such a way to optimize the agreement between observation and simulation of seasonal changes in atmospheric CO2. Atmospheric transport of CO2 has been simulated with model TM2 driven by carbon fluxes (NEP) from CARAIB.
(12) Vegetation I/O: (e.g. potential, actual)
standard simulations: actual vegetation is considered, and crops are C3 or C4 plants.
Optionally, the model predicts potential vegetation if biogeography module is used.
Input drivers (climatic, site, veg, soils) and resolution (e.g. daily, monthly) required for model initialization:
In standard version, CARAIB needs monthly climatic fields with the resolution 1° x 1° in longitude and latitude. These fields are (monthly values)
surface air temperature
range of daily temperature variation (Tmax – Tmin)
precipitation
fractional cloud cover or sunshine hours
magnitude of surface wind
relative humidity
Please, see also comment 2.
The model also needs soil texture (fractions of sand, silt and clay) and rooting depth.
Additional comments:
comment 1: the standard resolution of the model is 1° x 1° but it can run at 0.5° x 0.5°, provided that all inputs are supplied at the same resolution.
comment 2: it should be interesting to dispose on daily fields of temperature and precipitation, at least in some regions during some parts of the year, for example during growing period.
CASA
(9) Calibration variable(s) and method: Maximum light use efficiency from observed conversion efficiencies adjusted to reproduce observed NPP in various grid cells.
(10) Scaling of the processes to the grid cell
Multiplication with grid cell area
(11) Disturbance: (fire, grazing, harvest, tree removal, etc.)
Large scale natural disturbance parametrized, turnover time of living woody biomass adjusted.
(12) Vegetation I/O: (e.g. potential, actual)
Actual, 12 types derived from NVDI
(13) Input drivers (climatic, site, veg, soils) and resolution (e.g. daily, monthly) required for model initialization:
Monthly data of temperature, precipitation, PAR, NDVI
Soil type/soil water capacity
Vegetation type
CEVSA
(9) Calibration variable(s) and method:
Comaprison of the estimated with data we can find, site measurement, empirical estimate,
land survey
(10) Scaling of the processes to the grid cell
We assume a homogeneous grid cell.
(11) Disturbance: (fire, grazing, harvest, tree removal, etc.)
No disturbance
(12) Vegetation I/O: (e.g. potential, actual)
Vegetation types estimated from land survey, modeling, or satellite remote sensing
(13) Input drivers (climatic, site, veg, soils) and resolution (e.g.
daily, monthly) required for model initialization:
Daily mean temperature, precipitation, relative humidity, wind
Soil texture (%sand,%clay%silt), soil depth, bulk density, water holding capacity, and water
saturation point.
GLO-PEM 4.0
9) Calibration variable(s) and method:
Stress = f(VPD, soil moisture, Ta)
Comparison of NPP with field measurements
10) Scaling of the processes to the grid cell
Remotely sensed inputs, hence, it is intrinsically gridded
11) Disturbance: (fire, grazing, harvest, tree removal, etc.)
As detected by satellite radiances
12) Vegetation I/O: (e.g. potential, actual)
None
13) Input drivers (climatic, site, veg, soils) and resolution (e.g. daily, monthly) required for model initialization:
Solar radiation (PAR, 1º x 1º), water vapor (2.5º x 2º), AVHRR channels 1,2,4, and 5 (8 km), climatological mean air temperature (1º x 1º)
GTEC 2.0
(9) Calibration variables and method: model is calibrated against observations of leaf and ecosystem (stand) scale carbon and water flux through a non-automated iterative process of model verification, validation, and revision.
(10) Scaling of processes to the grid cell: assumes homogeneous vegetation and soils for each grid cell, and multiplies results from a point simulation at the centroid of the cell by the area of the cell
(11) Disturbance: disturbance is not simulated
(12) Vegetation I/O: uses actual (but static) vegetation
(13) input drivers for model initialization: daily or monthly climate (shortwave irradiance [or clouds], air temperature, relative humidity, precipitation), daily or monthly leaf area index, vegetation type, soil type.
HRBM 3.0*
(9) Calibration variable(s) and method:
empirical functions
(10) Scaling of the processes to the grid cell:
not necessary
(11) Disturbance: (fire, grazing, harvest, tree removal, etc.):
fire, agriculture, changing CO2, changing NOx
(12) Vegetation I/O: (e.g. potential, actual)
potential or actual
(13) Input drivers (climatic, site, veg, soils) and resolution
(e.g. daily, monthly) required for model initialization:
temp. , precip., cloudiness
soil texture, soil type,
vegetation map,
agricultural pattern,
atmospheric CO2, NOx
LPJ
(9) Calibration variable(s) and method: site data, NPP, biomass, nep, seasonal carbon cycle compared against station measurement, aet data.
(10) Scaling of the processes to the grid cell optimization of leaf nitrogen in the canopy.
(11) Disturbance: (fire, grazing, harvest, tree removal, etc.)
Fire and vegetation competition and mortality
(12) Vegetation I/O: (e.g. potential, actual) not prescribed, fractional pft coverage is an output of the model
(13) Input drivers (climatic, site, veg, soils) and resolution (e.g. daily, monthly) required for model initialization:
Monthly(or daily): temperature, precipitation, % sunshine hours (or incoming solar radiation), soil texture class.
MC1
(9) Calibration variable(s) and method:
biogeography map against Kuckler for historical period in the US,
or potential veg map of the world.
NPP or biomass with available data we can find, site data or large databases
any variable we can get field data or estimate for.
(10) Scaling of the processes to the grid cell
We assume a homogeneous grid cell. When we go above 0.5 deg lat-long we assume
fire only affects a fraction of the grid cell that we calculate. We assume two lifeforms (grass-tree) are always competing for resources.
(11) Disturbance: (fire, grazing, harvest, tree removal, etc.)
natural fire always; we can also simulate prescribed fire and prescribed grazing, harvest of trees. Irrigation and fertilization are also possible.
(12) Vegetation I/O: (e.g. potential, actual)
potential vegetation
(13) Input drivers (climatic, site, veg, soils) and resolution (e.g. daily, monthly) required for model initialization:
monthly T, Tmin, Tmax, PPT, VPR, (wind)
soil texture (%sand,%clay), soil depth, bulk density, rock fragment
NASA-CASA
(10) Scaling of the processes to the grid cell: Modified MODIS algorithm products (LAI and FPAR) ) derived from radiative transport modeling for global land surface properties
(11) Disturbance: (fire, grazing, harvest, tree removal, etc.): None explicit
(12) Vegetation I/O: (e.g. potential, actual): Actual
(13) Input drivers (climatic, site, veg, soils) and resolution (e.g. daily, monthly) required for model initialization:
-- ALL MONTHLY --
Air surface temperature (min,max)
Surface solar irradiance
Total precipitation
LAI and FPAR (AVHRR or MODIS products)
Actual vegetation class
Soil texture fertility class
PnET
(10) Scaling of the processes to the grid cell
Grid cell aggregated parameters are used. If there is are mixed vegetation types within the grid cell a weighted average of the parameters are used.
(11) Disturbance: (fire, grazing, harvest, tree removal, etc.)
All of the above and additionally N deposition, agriculture and fertilization.
(12) Vegetation I/O: (e.g. potential, actual)
Both potential and actual vegetation but as input only.
(13)Input drivers (climatic, site, veg, soils) and resolution (e.g. daily, monthly) required for model initialization:
Climate (daily or monthly) - Maximum temperature, minimum temperature, precipitation, and radiation.
Site - Water holding capacity and latitude.
Vegetation - Physiologic group (deciduous, coniferous, grassland, etc.)
Soils - Water holding capacity.
(14)Additional comments:
Nitrogen cycling module (PnET-CN) requires disturbance history.
SiB2
(9) Calibration variable(s) and method:
Biome mean annual NPP calibrated to observational data and a prescribed hierachy of biome productivity. Adjustment of maximal net photosynthetic rate, minimal, maximal and optimal temperatures for photosynthesis under consideration of a prescribed hierachy of rates and temperatures and rates for the biomes.
(10) Scaling of the processes to the grid cell
Multiplication with grid cell area
(11) Disturbance: (fire, grazing, harvest, tree removal, etc.)
Large scale natural disturbance parametrized, turnover time of living woody biomass adjusted.
(12) Vegetation I/O: (e.g. potential, actual)
actual, 12 types derived from NVDI
(13) Input drivers (climatic, site, veg, soils) and resolution (e.g. daily, monthly) required for model initialization:
Diurnal data of temperature, precipitation, wind speed, short wave, long wave radiation, water vapor pressure deficit
NDVI, canopy height, LAI, fraction of green leaves, canopy roughness
Soil texture
Vegetation type
SILVAN 2.4*
(9) Calibration variable(s) and method:
Biome mean annual NPP calibrated to observational data and a prescribed hierachy of biome productivity. Adjustment of maximal net photosynthetic rate, minimal, maximal and optimal temperatures for photosynthesis under consideration of a prescribed hierachy of rates and temperatures and rates for the biomes.
(10) Scaling of the processes to the grid cell
Multiplication with grid cell area
(11) Disturbance: (fire, grazing, harvest, tree removal, etc.)
Large scale natural disturbance parametrized, turnover time of living woody biomass adjusted.
(12) Vegetation I/O: (e.g. potential, actual)
Potential, 16 types according to BIOME 1 model of C. Prentice at al.
(13) Input drivers (climatic, site, veg, soils) and resolution (e.g. daily, monthly) required for model initialization:
Monthly air temperature, precipitation, cloudiness and diurnal temperature range
Soil water capacity
Vegetation type
STOMATE
(9) Calibration variable(s) and method:Phenology calibrated on satellite data
(10) Scaling of the processes to the grid cell
Canopy integration of GPP, LAI,...Subgrid vegetation distribution (mosaic)
(11) Disturbance: (fire, grazing, harvest, tree removal, etc.)Fire
(12) Vegetation I/O: (e.g. potential, actual)
Potential (calculated by the model) or actual (from satellite)
(13) Input drivers (climatic, site, veg, soils) and resolution (e.g. daily, monthly) required for model initialization:
30 minutes resolution of air surface temperature, precipitation, incoming radiation, specific humidity, wind speed, soil types and vegetation cover (if vegetation dynamic is not calculated).
(14) Additional comments:
STOMATE is coupled to SECHIBA for hydrology and energy balance and to LPJ, for vegetation dynamics. It can run without LPJ (prescribed vegetation).
TEM 4.1
(9) Calibration variable(s) and method:
Veg C, Soil C, Veg N, Soil Organic N, Soil Inorganic N, GPP, NPP, NPP with no N limitations, net N mineralization.
(10) Scaling of the processes to the grid cell: Prescribed by spatially explicit data sets
(11) Disturbance: (fire, grazing, harvest, tree removal, etc.): None explicitly simulated
(12) Vegetation I/O: (e.g. potential, actual): Potential
(13) Input drivers (climatic, site, veg, soils) and resolution (e.g. daily, monthly) required for model initialization: Vegetation type, elevation, percent silt + percent clay in soils, monthly cloudiness or solar radiation at the top of the canopy, monthly precipitation, mean monthly air temperature, monthly atmospheric CO2 concentrations
VECODE
(9) Calibration variable(s) and method: The model is validated globally on a base of observed climate, vegetation, and NPP/biomass distribution. It has not been validated for local sites.
(10) Scaling of the processes to the grid cell: no downscaling procedure
(11) Disturbance: (fire, grazing, harvest, tree removal, etc.) - not applied. Disturbances are implicitly taken into account because parameterizations of equilibrium vegetation distribution and time scale of vegetation dynamics are climate-dependent
(12) Vegetation I/O: (e.g. potential, actual) Potential vegetation cover
(13) Input drivers (climatic, site, veg, soils) and resolution (e.g. daily, monthly) required for model initialization:
Climatic information (temperature, precipitation) only; monthly data
(14) Additional comments:
Version with seasonal dynamics is under development.