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Modelling vegetation as a dynamic component in
soil-vegetation-atmosphere-transfer schemes and
hydrological models
Vivek K. Arora
Canadian Centre for Climate Modeling and Analysis, Meteorological Service of Canada, University of
Victoria, Victoria, British Columbia, Canada
Abstract. Vegetation affects the climate by modifying the energy, momentum,
and hydrologic balance of the land surface. Soil-vegetation-atmosphere-transfer
(SVAT) schemes explicitly consider the role of vegetation in affecting water and
energy balance by taking into account its physiological properties, in particular leaf
area index (LAI) and stomatal conductance. These two physiological properties are
also the basis of evapotranspiration parameterizations in physically-based
hydrological models. However, most current SVAT schemes and hydrological
models do not parameterize vegetation as a dynamic component. The seasonal
evolution of LAI is prescribed and monthly LAI values are kept constant year after
year. The effect of CO2 on the structure and physiological properties of vegetation
is also neglected, which is likely to be important in transient climate simulations
with increasing CO2 concentration, and for hydrological models which are used to
study climate change impact.
The net carbon uptake by vegetation, which is the difference between
photosynthesis and respiration, is allocated to leaves, stems, and roots. Carbon
allocation to leaves determines their biomass and LAI. The timing of budburst, leaf
senescence, and leaf abscission (i.e., the phenology) determine the length of the
growing season. Together photosynthesis, respiration, allocation, and phenology,
which are all strongly dependent on environmental conditions, make vegetation a
dynamic component. This paper: 1) familiarizes the reader with the basic physical
processes associated with the functioning of the biosphere using simple nonbiogeochemical terminology, 2) summarizes the range of parameterizations used to
model these processes in the current generation of process-based vegetation and
plant growth models, and discusses their suitability for inclusion in SVAT schemes
and hydrological models, and 3) illustrates the manner in which the coupling of
vegetation models and SVAT schemes/hydrological models may be accomplished.
1. Introduction..................................................................... 1
2. The basic functioning of the biosphere ............................. 4
3. Parameterizing the dynamics of vegetation....................... 5
3.1. Photosynthesis ......................................................... 6
3.1.1. Biochemical approach ...................................... 6
3.1.2. Light-use efficiency approach......................... 10
3.1.3. Carbon assimilation approach......................... 11
3.2. Respiration ............................................................ 12
3.2.1 Leaf maintenance respiration........................... 12
3.2.2 Stem and root maintenance respiration ............ 13
3.2.3 Whole plant maintenance respiration ............... 13
3.3. Allocation .............................................................. 13
3.4. Phenology.............................................................. 15
4.0. Coupling of vegetation models with SVAT schemes and
hydrological models ........................................................... 17
5.0. Discussion and summary ............................................ 19
1. Introduction
The effects of vegetation in influencing climate by
modifying the radiative, momentum, and hydrologic
balance of the land surface are well known. For example,
the climate simulated by a general circulation model is
sensitive to variations in evapotranspiration (via the
canopy), and these may affect the model’s cloud cover
and precipitation. Vegetation exerts control over climate
via its physiological properties (in particular leaf area
index (LAI, the ratio of leaf to ground area), stomatal
resistance, and rooting depth), albedo, surface roughness,
and its effect on soil moisture. The effects of
physiological characteristics of vegetation on climate are
discussed in a number of studies [Douville et al., 2000;
Zeng and Neelin, 2000; Hoffman and Jackson, 2000;
Rodriguez-Iturbe et al., 1999; Pollard and Thompson,
1995]. Charney [1975] studied the feedback on climate
due to changes in land surface properties, and proposed a
biogeophysical feedback hypothesis. He proposed that the
cooling effect due to albedo increase caused by removal of
vegetation enhances local subsidence leading to a
reduction in precipitation. This further limits the
vegetation growth and makes the drought self-sustaining.
The possible effects of deforestation, and anomalies in soil
moisture and temperature, on atmospheric circulation
patterns have been investigated by a number of other
researchers including Walker and Rowntree [1977],
Rowntree and Bolton [1983], Dickinson and HendersonSellers [1988], Pielke and Avissar [1990], Fennessey and
Shukla [1999], and Douville and Chauvin [2000]. A range
of climate-vegetation interactions and the physical and
biochemical feedbacks between vegetation and climate are
discussed by Foley et al. [2000]. Baldocchi et al. [2000]
and Eugster et al. [2000] discuss the ways in which the
vegetation affects the surface energy and water balance in
the Arctic-tundra and boreal region. Land surface
processes, in particular those associated with the
biosphere, may amplify climate variability and lengthen
time-scales through positive feedback [Dickinson, 2000;
Wang and Eltahir, 2000].
scales the climate influences the vegetation in that only
vegetation types that can be supported in a certain climate
are sustained [Holridge, 1947]. The interaction between
the biosphere and the climate in atmospheric models is
represented
by
soil-vegetation-atmosphere-transfer
(SVAT) schemes. Although SVAT schemes which
include partly dynamic vegetation modules [e.g.,
Dickinson et al., 1998; Sellers et al., 1996b] and DGVMs
[e.g., Foley et al., 1996; Kucharik et al., 2000] are
emerging, the representation of vegetation dynamics
incorporated so far is extremely simplified and most
existing SVAT schemes still do not consider vegetation as
a dynamic component. In most current SVAT schemes the
seasonal evolution of LAI is prescribed and the effect of
atmospheric CO2 concentration on stomatal conductance
is not taken into account (see Table 1).
For hydrological applications, the space-time scale under
consideration is generally smaller than used in climate
modelling studies and so the bi-directional climatevegetation interactions are usually ignored. The
interactions between vegetation and soil moisture, via the
control of transpiration by vegetation and the effect of soil
moisture on vegetation, however, cannot be neglected.
The effects of antecedent soil moisture conditions on
runoff generation, and the control of topography on soil
moisture [Beven and Kirkby, 1979] are well known. The
spatial structure of soil moisture and its evolution in time,
however, are also affected by the regional vegetation. The
preferential use of moisture from different soil depths by
trees and herbaceous plants highlights the control of
vegetation on soil moisture. With deeper roots, trees are
able to extract water from deep soil layers while grasses
and herbaceous plants are effective in using moisture at
shallow depths [Walter, 1971; Walker and Noy-Meir,
1982; Scott et al., 2000]. If grass biomass is reduced, e.g.,
through heavy grazing, more water becomes available for
trees and woody plants leading to their relative abundance
and change in soil moisture patterns [Knoop and Walker,
1985]. There is also evidence that soil texture and mean
soil moisture state affect the canopy structure. Finetextured and relatively moist soils have closed upper
canopies and little understory, while as soils become
progressively drier and coarse-textured canopy structure
grades to one with significant understory shrub layer, due
to the preferential use of soil moisture of shrubs and trees
[Aber et al., 1982]. The interplay between vegetation,
climate, and soil moisture is also illustrated by RodriguezIturbe et al. [1999], D’Odorico et al. [2000], and Ridolfi
et al. [2000] using a simple model in which the effect of
vegetation is expressed in terms of prescribed
evapotranspiration rates. The manner in which riparian
vegetation affects various hydrological processes are
discussed by Tabacchi et al. [2000].
Associated with the effects of vegetation on climate are
the important questions of: 1) how the vegetation may
respond to changes in climate (e.g., due to increase in
greenhouse gas concentrations) and 2) how the resulting
changes in plant physiological properties may affect
evapotranspiration and soil moisture, and consequently the
climate in a warmer CO2 enriched environment (i.e., the
feedback process). Vegetation dynamics including growth,
reproduction, and competition for nutrients, water, and
light, are strongly determined by climate. A change in
climate will result in a change of these dynamics.
Growing interest in exploring the bi-directional
interactions between the climate and vegetation has led
researchers to couple dynamic global vegetation models
(DGVMs) with general circulation models (GCMs) [e.g.,
Levis et al., 2000; Foley et al., 1998].
The most common thread that ties climate and vegetation
is soil moisture: 1) the climate influences the soil moisture
via evaporation and transpiration (which depend on the
vegetation), 2) soil moisture and climate determine the
type of vegetation that may grow in a region, and 3) the
vegetation determines how it may control soil moisture
(depending on the climate). For a given region, over long
time-scales, the entire soil-vegetation-climate system is
generally in equilibrium. Climate and vegetation interact
with each other over a range of temporal scales. At time
scales of a few hours to a few months the vegetation
influences the atmospheric processes via its effect on
latent and sensible heat fluxes, while over longer time
2
Table 1: Soil-vegetation-atmosphere-transfer schemes which at present do not model vegetation as a
dynamic component. Most of these schemes have participated in at least one phase of the Project for
Intercomparison of Land Surface Schemes (PILPS) [Henderson-Sellers et al., 1996]. Land surface schemes
which do not model vegetation explicitly, such as the bucket type schemes, are not listed.
Soil-vegetation-atmosphere-transfer
scheme
AMBETI
BASE
CAPS
CLASS
CSIRO9
ECMWF
GISS
ISBA
JMA
LSX
MOSAIC
PLACE
SECHIBA
SEWAB
SPONSOR
SSiB
SWAP
UKMO
VIC-3L
WAVES
Reference
Braden [1995]
Desborough and Pitman [1998]
Pan and Mahrt [1987]
Verseghy [1991], Verseghy et al. [1993]
Kowalczyk et al. [1991, 1994]
Viterbo and Beljaars [1995]
Abramopoulos et al. [1988]
Noilhan and Planton [1989]
Sato et al. [1989]
Pollard and Thompson [1995]
Koster and Suarez [1992]
Wetzel and Boone [1995]
Ducoudré et al. [1993]
Mengelkamp et al. [1999]
Shmakin [1998]
Xue et al. [1991]
Gusev and Nasanova [1998]
Gregory and Smith [1994]
Liang et al. [1996]
Hatton et al. [1995]
The absence of dynamic vegetation in SVAT schemes and
hydrological models implies that the effect of climate
variability in modifying physiological characteristics of
vegetation is not taken into account. Too little or too much
precipitation, for example, is assumed to make no
difference in plant productivity and resulting LAI.
However, it is well known that precipitation, along with
temperature, and soil moisture determine photosynthesis
(uptake of CO2) and plant productivity. In SVAT
schemes, used in atmospheric models to perform climate
change simulations with increasing CO2 concentrations,
absence of dynamic vegetation implies that the role of
CO2 in modifying the structure and physiological
properties of vegetation is neglected. Yet, plants exposed
to elevated CO2 show increased growth and increased
rates of photosynthesis. Pritchard et al. [1999] summarize
results from more than 80 published studies and find an
average increase in leaf area per plant of 24% when grown
in CO2 enriched environment. Curtis and Wang [1998]
who statistically analyze results from 79 published studies
and 59 plant species report a 31% increase in above
ground biomass under unstressed conditions, a 16%
increase in above ground biomass for nutrient-stressed
conditions, and a 11% decrease in stomatal conductance
as a mean response to elevated CO2 conditions. A
decrease in transpiration and an increase in carbon uptake
(in elevated CO2 conditions) implies increased water use
efficiency and increased soil moisture. A number of
studies have reported higher soil moisture under
conditions of elevated CO2 [e.g., Knapp et al., 1996;
Fredeen et al., 1997; Lutze and Gifford, 1998; Morgan et
al., 1998; and Volk et al., 2000]. However, hydrological
models, which are used to study the impact of climate
change on hydrology and water resources, rarely consider
the effects of changes in LAI and stomatal conductance
(associated with a change in climate and CO2
concentration), the resulting changes in soil moisture, and
the effect on evapotranspiration and runoff. There is also a
growing body of evidence which suggests that
concomitant changes in forest LAI with age are
responsible for water yield and forest age relationship.
Watson et al. [1999b], for example using the Macaque
hydrological model, suggest that changes in LAI and
decreasing stomatal conductance with forest age can
explain the yield–age relationship quantified by Kuczera
[1987] for mountain ash forests in Victoria, Australia.
From a modelling perspective, the dependence of
vegetation on soil moisture requires that ecological
models which simulate canopy photosynthesis and plant
growth must incorporate some form of soil moisture
accounting. Indeed, most ecological models have simple
hydrological components. For example, the Sim-CYCLE
terrestrial ecosystem model [Ito and Oikawa, 2000] uses
the ratio of actual to potential evapotranspiration to
account for the effect of soil moisture on photosynthesis.
The Frankfurt Biosphere Model [Lüdeke et al., 1994], and
the CEVSA model [Cao and Woodward, 1998] which
uses a similar approach, parameterize evapotranspiration
as a product of potential evapotranspiration and an
empirical function of soil moisture, and runoff is
calculated as surplus water when the soil moisture reaches
3
Table 2: Quasi-physical and physically-based hydrological models (listed below) which consider
vegetation explicitly are the best candidates for representing vegetation as a dynamic component.
Conceptual models that do not consider vegetation explicitly (and do not use LAI or stomatal conductance
to determine evapotranspiration) are not listed since these models mostly determine evapotranspiration by
scaling potential evaporation using empirical soil moisture and/or vegetation functions.
Hydrological model
DHVM
IHDM
Macaque
SHE, MIKE/SHE
THALES
TOPOG
VIC
Reference
Wigmosta and Lettenmaier [1994]
Beven et al. [1987]
[Watson et al., 1999a]
Abott et al. [1986 a,b]
Grayson et al. [1992]
Vertessy et al. [1993]
Liang et al. [1994]
field capacity (similar to earlier bucket-type land surface
schemes). However, there are ecosystem models such as
BIOME-BGC [Running and Hunt, 1993] and RESSys
[Running et al., 1989] which include a more complete and
relatively complex description of hydrological processes
including canopy interception and transpiration. Recent
versions of the Topog [Vertessy et al., 1996; Silberstein et
al., 1999] and Macaque [Watson et al., 1999a]
hydrological models also simulate detailed carbon
dynamics and growth which are linked to their
hydrological components. Except for these few models
which include a detailed description of both vegetation
and hydrologic processes, most hydrological models do
not even explicitly represent vegetation let alone
parameterize vegetation as a dynamic component. Unlike
SVAT schemes, vegetation is not explicitly represented in
most hydrological models. These models still use
conceptual parameterizations to find evapotranspiration.
Estimates of actual evapotranspiration in such models are
obtained by scaling potential evaporation using empirical
functions of soil moisture and/or vegetation. To model
vegetation as a dynamic component in hydrological
models it is essential to first represent vegetation as a
separate component (i.e., at least use LAI and stomatal
conductance to find evapotranspiration), a condition at
present met by only a limited number of physically-based
hydrological models (see Table 2).
experiments is essential for understanding the response of
vegetation to changes in climate and atmospheric CO2
concentration, and to assess the effect of changes in
vegetation on the climate via feedback processes.
This paper attempts to: 1) familiarize the reader with the
basic physical processes associated with the functioning of
the biosphere using simple non-biogeochemical
terminology, 2) summarize the range of parameterizations
used to model these processes in the current generation of
process-based vegetation and plant growth models, and 3)
illustrate the manner in which the coupling of vegetation
models and SVAT schemes/hydrological models may be
accomplished.
2. The basic functioning of the biosphere
The energy source for photosynthesis, sunlight, drives the
fixation of CO2 in plants. Stomata, the microscopic
openings which are generally more numerous on the
underside of leaves, provide for the exchange of CO2 and
water between the plants and the atmosphere. The fixation
of CO2 at the cell surface inside the leaf reduces the
internal CO2 concentration, causing more CO2 to diffuse
into the leaf along this concentration gradient. The amount
of carbon sequestered by photosynthesis is called gross
primary productivity (GPP). Since the cell surfaces are
constantly moist they cause the evaporation of water (i.e.,
transpiration) via the stomatal openings. The stomatal
resistance, which limits the flow out of leaves, is a
function of environmental conditions and atmospheric
CO2 concentration. Under any conditions of less than
100% humidity and full cloud cover, a gradient of water
concentration (relative humidity or vapour pressure
deficit) will exist in the direction opposite to the CO2
gradient. Water loss is, thus, an inevitable result of CO2
uptake. The water loss – carbon gain process, along with
the fact that lack of water limits the plant growth, has led
to the concept of water use efficiency, defined as the
amount of carbon gained per unit amount of water lost.
The coupling of ecological models, which explicitly
model plant growth, with physically-based hydrological
models and SVAT schemes offers a range of opportunities
to explore the bi-directional interactions between
vegetation and hydrology, and vegetation and climate. The
effects of logging and recovery of forests on the
availability of water, for example, can be better predicted
by coupling of hydrological and vegetation-growth
models. This coupling also gives the ability to model
carbon fluxes, together with water and energy fluxes, in a
hydrological framework at the catchment and river basin
scales. Incorporation of dynamic vegetation in the next
generation of SVAT schemes used in climate change
4
Like all other living organisms plants also respire (release
of CO2). For the biosphere, respiration can be separated
into growth and maintenance respiration. Growth
respiration is used to synthesize new plant material and is
highly correlated with the total growth of plants.
Maintenance respiration is used to keep existing tissue
alive and functioning, and is a function of environmental
stress on vegetation. If the stress levels are high, e.g. due
to high temperature, maintenance respiration levels will
increase. The proportion of respiration for growth and
maintenance is not constant and primarily depends on the
age of plants. Generally when plants are young and
growing rapidly seasonal growth respiration is higher than
maintenance respiration. As plants age, maintenance
respiration increases due to the increasing mass of the
living tissue, and can create large carbon sinks. Combined
growth and maintenance respiration are called autotrophic
respiration (RA). The difference between GPP and
autotrophic respiration is the amount of carbon
sequestered after respiratory losses have been taken into
account and is called net primary productivity (NPP), i.e.,
NPP = GPP – RA.
leaves to seasonal and climatic changes to the
environment. The timing of budburst, senescence (leaf
maturity or browning), and leaf abscission (leaf fall),
which are functions of the environmental conditions (in
particular temperature) and vegetation types are described
by leaf phenology. For vegetation types which are
characterised by leaf onset and offset, no allocation is
made to leaves after leaf abscission until the
environmental conditions are favourable again for
budburst.
In summary, the net uptake of CO2 by vegetation,
obtained as the difference between photosynthesis and
autotrophic respiration is allocated to leaves and other
tissues. The strong response of biospheric processes
(photosynthesis, respiration, allocation, and phenology) to
environmental
conditions
including
temperature,
precipitation, humidity, light availability, and soil
moisture, thus makes vegetation a dynamic component.
3. Parameterizing the dynamics of vegetation
To parameterize vegetation as a dynamic component in
SVAT schemes and hydrological models it is essential to
incorporate photosynthesis, autotrophic respiration,
allocation, and phenology, albeit in a simple manner. The
availability of nutrients in soil also affects plant
productivity. In particular, the cycling of nitrogen from
soil to plants and back to soil via leaf and tree litter and its
decomposition is important since nitrogen is the most
limiting nutrient to plant growth [Field and Mooney,
1986]. However, a discussion about nitrogen-cycling is
beyond the scope of this paper and the reader is referred to
work done by other researchers [McGuire et al., 1992;
McGuire et al., 1995]. As a first step towards modelling
vegetation as a dynamic component in SVAT schemes
and hydrological models the effect of nutrients on plant
productivity may be ignored or included in a simple
manner.
If the amount of carbon sequestered (NPP) is positive then
a tree grows by allocating carbon for construction of new
leaves and roots, and increasing the biomass of stem and
branches which supports roots and leaves and allows
movement of nutrients, water, and carbon between them.
If NPP is negative, the biomass reduces. In general, it is
thought that respirational demands are met before
allocation is made for growth of new tissues [Aber and
Melillo, 1991, pp 159]. The direction and rate of flow of
carbon and nutrients through plants are controlled by the
relative demands for resources exerted by different tissues
at different times. Areas where demand for carbon is
greater than photosynthesis (e.g., growing leaves and
roots) are carbon sinks, and areas where demand is less
than the production, such as mature leaves which are
actively photosynthesizing, are carbon sources. The
stronger the sink, the greater the allocation to that tissue.
Patterns of allocation vary with species and growth stages.
For example, in trees different parts act as sinks at
different stages of their growth. Top growth is usually
accomplished first, followed by radial expansion of the
stem. The availability of nutrients, in particular nitrogen,
also affects the distribution of allocated carbon. For
example, in nutrient-poor environments plants allocate
more resources to roots in order to reduce nutrient
deficiency [Hattenschwiler and Kroner, 1997], while in
nutrient-rich environments more resources are allocated to
leaves. The allocation patterns of plants, which are the
result of natural selection, are best suited for the
environment in which they grow. The amount of carbon
allocated to the leaves determines the biomass of the
leaves and their LAI. Allocation to leaves is also linked to
their phenology. Phenology describes the response of the
In this section the range of parameterizations used for
modelling photosynthesis, respiration, allocation, and
phenology in the current generation of vegetation and
plant growth models are illustrated and their suitability for
inclusion in SVAT schemes and hydrological models is
discussed. These parameterizations, especially the ones
used for modelling photosynthesis and allocation, vary a
great deal in their complexity. The suitability of these
parameterizations for SVAT schemes is discussed in the
context of their use in general circulation models (GCMs)
for
climate
simulations.
Dynamic
vegetation
parameterizations which make suitable candidates for
GCMs are expected to be 1) mechanistic or process-based,
2) generalized, 3) applicable globally, and 4)
computationally inexpensive. Parameterizations for
hydrological models, however, need not be generalized
5
and can use site specific parameters since most
hydrological models are applied at local and regional
scales. The suitability of photosynthesis, respiration, and
allocation parameterizations for hydrological models is
discussed in the context of the purpose of the modelling
exercise and the complexity of the vegetation
parameterizations themselves.
3.1.1. Biochemical approach
Plants convert CO2 to organic matter by reducing CO2 to
carbohydrates in a complex set of reactions. This is
achieved by two key processes: 1) the removal of
hydrogen atoms from water molecules, and 2) the
reduction of CO2 by these hydrogen atoms to form organic
matter. The energy for this processes is supplied by the
light which is absorbed by the chlorophyll pigment. The
electrons (e-) and protons (H+) that make up the hydrogen
atom are stripped away from water molecules. These
electrons and protons are used for reduction of NADP
(nicotine adenine dinucleotide phosphate), an important
co-enzyme, and synthesis of ATP (adenosine
triphosphate), a high-energy molecule. ATP and reduced
NADP that result from the light reactions are used for CO2
fixation. The initial CO2 fixation reaction involves the
enzyme Rubisco, which can react with either oxygen (a
process called photorespiration and not resulting in carbon
fixation) or CO2. Farquhar et al. [1980] presented a
biochemical model of leaf photosynthesis which describes
the rate of CO2 assimilation A, limited by: 1) enzyme
kinematics, in particular the amount of Rubisco, Jc, 2)
electron transport, which is a function of available light,
Je, and 3) the capacity to transport or utilize
photosynthetic products, Js. The Rubisco limited rate, Jc,
is a function of the maximum catalytic capacity of
Rubisco, Vm. The Farquhar et al.’s [1980] model has been
subsequently extended by von Caemmerer and Farquhar
[1985], and Collatz et al. [1991] present the details of
mature version of this model for C3 plants. A modification
of the model for C4 (mainly tropical grasses) plants is
presented by Collatz et al. [1992]. C3 plants account for
about 80% of the world’s vegetation cover while the rest
are mainly C4 plants. The difference between C3 and C4
plants is in the manner in which they fix CO2. Carbon
fixation in C3 plants occurs within chlorophyll-bearing
palisade cells, which are more or less lined up across the
top of the leaves. Palisade cells are absent in C4 plants.
Instead, the chloroplasts are located within bundle sheaths
concentrated in the center of the leaves. In C4 plants, any
CO2 generated by respiration can be refixed before its lost
to the atmosphere. As a result, carbon fixation can occur
at a faster rate for a given rate of transpiration, so the
water use efficiency is increased but at an increased
energy-cost. C4 plants are more common in the tropics and
sub-tropics and also in semi-arid areas where their greater
water-use efficiency offers them a competitive advantage.
Since C4 plants fix CO2 in a relatively efficient manner
they are expected to show little or no response to CO2
enrichment [Wand et al., 1999], and it is generally
accepted that increased CO2 concentrations will tend to
favour C3 plants [Prentice et al., 2000].
3.1. Photosynthesis
Plant metabolism is based on the photosynthetic reaction
in which the shortwave radiation is used to combine water
and CO2 into sugars, starch, and other organic compounds.
The portion of shortwave radiation between wavelengths
of 0.4–0.7 µm (i.e., the visible spectrum) is called
photosynthetically active radiation (PAR) and is used by
the plants. To photosynthesize, plants must regulate the
flow of CO2 via the stomata, opening of which results in
water loss. Photosynthesis and transpirational losses are
thus strongly linked and they both depend on the amount
of available energy. The fluxes of carbon, water, and
energy are thus coupled to each other. If photosynthesis is
modelled at short time steps (~30 60 minutes),
comparable to that of SVAT schemes, the coupling
between the carbon, water, and energy fluxes can be
relatively well represented. If, however, photosynthesis is
modelled at large time steps, such as daily or monthly as
is the case for most ecological models, it is not possible to
represent this coupling explicitly. This difference is used
as the basis of classification of models that parameterize
photosynthesis into two broad categories: 1) models that
explicitly represent the coupling between carbon, water,
and energy fluxes, and model photosynthesis within a
SVAT scheme framework, and 2) models that do not
represent this coupling and whose primary objective is to
model NPP. Models in the second category are thus
suitable candidates for coupling with hydrological models,
which usually do not model energy balance explicitly. On
the basis of the actual method used, photosynthesis
parameterizations may be broadly classified as ones which
use: 1) a biochemical approach, 2) a light-use efficiency
approach, or 3) simpler carbon assimilation approaches.
Table 3 shows 21 different models that parameterize
photosynthesis using one of these approaches. The models
are also classified on the basis of the coupling between
carbon, water, and energy fluxes. Models listed in the first
row simulate photosynthesis in a SVAT scheme
framework, use smaller model time steps, and explicitly
represent the coupling between the various fluxes. Models
listed in the second row are mostly ecological models
which use daily and/or monthly time steps to model NPP
and do not explicitly represent the coupling between the
various fluxes.
Models listed in panel (a) of Table 3 use parameterizations
which are based on the biochemical approach [Farquhar
6
7
Models which simulate
photosynthesis within the
framework of a ecological
model
The BETHY model is designed to use both the biochemical and the LUE approach
b)
BIOME3 [Haxeltine and Prentice, 1996b]
CARAIB [Warnant et al., 1994]
DOLY [Woodward et al., 1995]
HYBRID [Friend et al., 1997]
BIOME-BGC [Running and Hunt, 1993]
Models which simulate
photosynthesis within the
framework of a SVAT scheme
a)
MOSES + TRIFFID [Cox et al. 1998, 1999;
Cox 2001]
SiB2 [Sellers et al., 1996b]
IBIS [Foley et al., 1996]
BATS [Dickinson et al., 1998]
Two-leaf model [Wang and Leuning, 1998]
BETHY [Knorr, 2000]
Biochemical approach
c)
d)
CASA [Potter et al., 1993]
GLO-PEM [Goetz et al., 2000]
BIOME2 [Haxeltine et al., 1996a]
ALEX [Anderson et al., 2000]
BETHY [Knorr, 2000]
Models using …
Light use efficiency (LUE)
approach
Table 3: Classification of 21 models on the basis of the method they use to estimate photosynthesis and if
they model photosynthesis in the framework of a SVAT scheme or an ecological model.
Frankfurt Biosphere Model, FBM
[Ludeke et al., 1994]
PnET-DAY [Aber et al, 1996]
Sim-CYCLE [Ito and Oikawa, 2000]
FOREST-BGC [Running and Coughlan, 1988]
CENTURY [Parton et al., 1993]
e)
TEM [McGuire et al., 1992]
Carbon assimilation approach
et al., 1980; Collatz et al., 1991, 1992] in a SVAT
scheme framework. The typical equations used by these
models are shown in the appendix and show the functional
dependence of the Rubisco (Jc), light (Je), and transport
capacity (Js) limited leaf assimilation rates on various
biophysical parameters and environmental conditions,
including the partial pressure of CO2 in the leaf interior
(ci). The simplest approach is to assume that the
assimilation rate A, is the minimum of Jc, Je, and Js, i.e., A
= min (Jc, Je, Js) [Farquhar et al., 1980]. However, it has
been observed that the transition between these three
limiting rates is not abrupt but rather gradual. Collatz et
al. [1991] describe this co-limitation effect by combining
the rate limiting terms into two quadratic equations, the
smallest root of which is selected as the physically
realistic one.
β1 J p2 − J p ( J c + J e ) + J e J c = 0,
(1)
β2 A 2 − A( J p + J s ) + J p J s = 0,
also computationally expensive compared to the single big
leaf models.
The scaling up from leaf to canopy in most of these
models is based on the assumption that the profile of leaf
nitrogen content along the depth of the canopy follows the
time-mean profile of radiation [Sellers et al., 1992]. Since
maximum photosynthetic rates of leaves, Rubisco and
electron transport rates, and respiration rate have been
shown to co-vary and increase more or less linearly with
leaf nitrogen content [Ingestad and Lund, 1986; Field and
Mooney, 1986], knowledge of the leaf nitrogen content
profile can be used to scale up leaf photosynthesis to the
canopy level. The central assumption of this hypothesis is
that the photosynthetic properties of leaves, including leaf
nitrogen content, acclimate fully to the prevailing light
conditions within a canopy so that the photosynthetic
capacity is proportional to the time-integrated absorbed
radiation, normalized with respect to photosynthetic
capacity and absorbed radiation, respectively, at some
reference point, typically at the top of the canopy [Kull
and Jarvis, 1995]. The vertical profile of radiation itself
along the depth of the canopy is described by the Beer’s
law,
(2)
PAR L = PAR0 e − kL
where Jp is the smoothed minimum of Jc and Je, and β1
and β2 are empirical constants, governing the sharpness of
transition between the three limiting rates and can vary
between 0 and 1. Observations suggest that β1 and β2
generally vary between 0.8 to 0.99 [Collatz et al., 1991;
Sellers et al., 1992]. SiB2 [Sellers et al., 1996b], IBIS
[Foley et al., 1996], and MOSES [Cox et al., 1998 and
1999] use quadratic equations like these to parameterize
the transition between the three limiting rates, while
BATS [Dickinson et al., 1998] uses the minimum of the
three limiting rates, and Wang and Leuning [1998] use the
minimum of Jc and Je in their two-leaf model. The
BETHY model [Knorr, 2000] also estimates the
assimilation rate as the minimum of the light and Rubisco
limited rates. SiB2, IBIS, MOSES, and BATS treat the
canopy as a single big leaf. In these models
photosynthesis is modelled for a leaf at the top of canopy
and then scaled up for the whole canopy (as discussed
later). Wang and Leuning [1998] model the canopy using
a two-leaf model which considers the sunlit and the
shaded portions of the canopy separately. They argue that
modelling the canopy as a single big leaf is theoretically
incorrect since the response of leaf photosynthesis to light
is non-linear and the use of mean absorbed radiation will
significantly over-estimate the canopy photosynthesis.
Sunlit leaves can be several degrees warmer than the
shaded leaves under sunny and dry conditions. Since the
two-leaf approach requires the energy balance,
photosynthesis, and stomatal conductance equations to be
solved separately for the sunlit and the shaded portions of
the canopy it is computationally more expensive and may
not be considered a suitable approach for SVAT schemes
used in long climate simulations. The BETHY model
estimates the canopy photosynthesis rate by summation of
rates calculated individually over the three canopy layers
each with its own partial leaf area index. This approach is
where PAR0 and PARL are the values of photosynthetically
active radiation at the top of the canopy and under a leaf
area index L, respectively, and k is the radiation extinction
(or attenuation) coefficient, which amongst other factors
depends on the vegetation type, sun’s zenith angle, and
leaf angle distribution, and has a typical value of 0.5.
Total photosynthetically active radiation PART, reaching
the entire canopy can be obtained by integrating equation
(2) over the depth of the canopy,
LT
PART = PAR0
∫e
0
− kL
dL = PAR0
(
)
1
1 − e − kLT = PAR0 f par
k
(3)
where LT is the total LAI. Equation (3) implies that PAR
at the top of the canopy can be scaled by fPAR to obtain the
canopy averaged value. Photosynthesis and respiration
estimated at the top of the canopy can thus similarly be
scaled to obtain the total canopy values.
The coupling between photosynthesis and transpiration is
expressed via stomatal conductance. Since the opening of
stomata is related to how much CO2 is fixed by the plants,
the photosynthetic rate can be used to determine stomatal
conductance which in turn can be used to estimate
transpiration. In most SVAT schemes which do not model
photosynthesis stomatal conductance is parameterized
using Jarvis [1976] type formulations (see equation (4)
below) that express stomatal conductance (gs) as a
function of maximum stomatal conductance (gmax) and a
8
series of environmental dependences (with values between
0 and 1) whose effects are assumed to be multiplicative.
g s = g max f 1 (S ) f 2 (∆e) f 3 (w) f 4 (Ta )
gs =
16
. An RTa
ca − ci p
(7)
where Ta is the air temperature in Kelvin, ca is the
atmospheric CO2 concentration in µmol/mol (or ppm), and
R is the gas constant. Leuning [1995] suggested a revised
equation based on Ball et al.’s [1987] formulation, but
with dependence on relative humidity replaced by one on
the humidity deficit at the leaf surface. SiB2 uses stomatal
conductance formulation of Ball et al. [1987]; IBIS and
the two-leaf model of Wang and Leuning [1998] use the
stomatal conductance formulation of Leuning [1995]; and
MOSES and BETHY use stomatal conductance
formulation similar to that of Schulze et al. [1994].
(4)
where f1, f2, f3, and f4 are dependences on incoming solar
radiation S, vapour pressure deficit e, soil or leaf water
potential w, and air temperature Ta, respectively. Ball et
al. [1987] proposed a semi-empirical relationship to
describe the response of stomatal conductance (gs) to the
rate of net CO2 uptake (An, mol m-2 s-1), the relative
humidity (hs), and CO2 partial pressure at the leaf surface
(cs).
An hs
(5)
gs = m
p+b
cs
where gs is expressed in mol m-2 s-1, m is a coefficient
obtained from observations (m 9, 4, and 6 for C3, C4, and
conifers respectively), hs is the relative humidity, p is
atmospheric pressure, and b is also a coefficient obtained
from observations (b 0.01 and 0.04, for C3 and C4 plants,
respectively). The net rate of CO2 uptake by leaves An is
obtained by subtracting the leaf respiration rate Rleaf
(discussed in section 3.2) from the assimilation rate A, i.e.,
An = A Rleaf. Stomatal conductance (or rate of CO2
uptake) expressed in units of mol m-2 s-1 is the amount of
carbon that passes through the stomata (or is fixed by the
plants) per unit area per unit time (1 mole of carbon = 12 g
of carbon). The units of gs can be converted from molar
units (mol m-2 s-1) to the traditional units used in SVAT
schemes and hydrological models (ms-1) using a simple
formula shown in the appendix. Collatz et al. [1991]
demonstrate the superiority of Ball et al.’s [1987]
photosynthesis-stomatal conductance formulation over
Jarvis [1976] type formulations and show good agreement
between predicted and measured gs values over a wide
range of leaf temperatures.
The biochemical approach has also been successfully used
in ecological models. Models listed in panel (b) of Table 3
are ecological models which use the biochemical approach
to parameterize photosynthesis at a daily time step. Since
ecological models do not necessarily simulate the energy
balance and the diurnal cycle (although most models do
estimate evapotranspiration) it is possible to use a time
step longer than that used for the SVAT schemes. The
HYBRID model of Friend et al. [1997] assumes that net
photosynthesis, transpiration, and respiration can be
scaled up linearly with their mean rates and that there is
no need to calculate these quantities at a sub-daily time
step. HYBRID uses a Jarvis [1976] type stomatal
conductance formulation and an empirical linear response
of stomata closure to increasing CO2 (40% for a doubling
of CO2 with no further closure above ~800 ppm). The
BIOME3 model [Haxeltine and Prentice, 1996b]
calculates the daily photosynthesis using a standard nonrectangular hyperbola formulation, which gives a gradual
transition between the light and the Rubisco limited
assimilation rates, and uses the stomatal conductance
formulation similar to that of Schulze et al. [1994]. The
CARAIB model [Warnant et al., 1994] estimates the
assimilation rate described by the two quadratic equations
(equation (1)) and the DOLY model of Woodward et al.
[1995] uses the minimum of three limiting rates. Both,
CARAIB and the DOLY models use Ball et al.’s [1987]
stomatal conductance formulation. The new version of the
BIOME-BGC model of Running and Hunt [1993] also
models photosynthesis using the biochemical approach at
a daily time step.
The system is closed by calculating partial pressure of
CO2 in the leaf interior ci
16
. An
(6)
ci = cs −
p
gs
where the factor of 1.6 accounts for the different
diffusivities of water and CO2 across the leaf stomata.
Since net photosynthesis and stomatal conductance both
depend on ci the resulting set of equations must be solved
iteratively [see Sellers et al., 1996b and Wang and
Leuning, 1998]. Photosynthesis-stomatal conductance
formulations other than that of Ball et al. [1987] (equation
5) have also been used to relate stomatal conductance to
amount of carbon uptake. Schulze et al. [1994], for
example, relate non-stressed canopy conductance (in units
of ms-1) to the diffusion gradient in CO2 concentration
implied by the difference in CO2 concentration between
the leaf intercellular air spaces (ci) and the atmosphere,
The photosynthesis-stomatal conductance formulations
mentioned here do not take into account the effect of soil
water stress on stomata closure directly, and this is
parameterized separately. For example, the BETHY
model accounts for soil water stress by reducing stomatal
conductance in response to air vapor pressure deficit;
DOLY accounts for the influence of soil water stress on
stomatal conductance using a hyperbolic response
function f ( w) = s1 ( w − s0 ) w − 2 s0 + s2 , where w is the
9
soil moisture content and the s coefficients define the
shape of the response curve; CARAIB uses a linear
function to decrease stomatal conductance when relative
humidity is less than 0.46; IBIS uses a fractional
exponential
dependence
−2.5( w − wwilt ) 1− wwilt
−2.5 , where w
] [1 − e ]
f ( w) = [1 − e
wilt is the
soil moisture content at the wilting point; and the two-leaf
model of Wang and Leuning [1998] uses the empirical
function , f ( w) = min1, 10 ( w − wwilt )  , of Gollan et al.
The LUE approach has been used both within a SVAT
scheme framework and for modelling NPP at large
temporal and spatial scales within ecological models. The
ALEX model of Anderson et al. [2000] and the BETHY
model use the LUE approach in a SVAT scheme
framework (models listed in panel (c) of Table 3). The
CASA [Potter et al., 1993] and the BIOME2 [Haxeltine et
al., 1996a] models which use a model time step of one
month, and the GLO-PEM model [Goetz et al., 2000]
which estimates all model variables on a 10-day basis, use
the LUE approach within the framework of an ecological
model (models listed in panel (d) of Table 3). Since these
models simulate carbon uptake at such large time steps it
is not possible to represent the coupling between various
fluxes explicitly. The net carbon assimilation rate NPP is
given by
 3 ( wmax − wwilt ) 
[1986].
3.1.2. Light-use efficiency approach
Analogous to water-use efficiency, light-use efficiency
(LUE) is defined as the ratio between the net unstressed
canopy carbon assimilation rate (NPP) and the
photosynthetically active radiation (PAR) absorbed by the
canopy (APAR).
NPP
APAR
APAR = PAR × FPAR
LUE = β =
NPP = f ( S ) β APAR
(9)
where β is the LUE, and f(S) is a function that allows for
effects of water, temperature, and nutrient stress on β. The
BETHY model parameterizes β as a prescribed value
which is a function of the vegetation type, while the
ALEX model parameterizes β as a function of the ratio of
intercellular to atmospheric CO2 concentration (µ). The
CASA model assumes a constant value of β (1.4 g C/MJ-1
PAR) for all vegetation types. Both BIOME2 and GLOPEM parameterize β as a function of ambient air
temperature, while GLO-PEM also takes into account the
dependence of β on the CO2 compensation point (i.e., the
CO2 concentration at which net photosynthesis is equal to
zero – the point where gross photosynthetic rate equals
respiration).
(8)
where FPAR = fraction of PAR absorbed by the canopy.
The typical units of β are mol (CO2)/mol (photons) or g
(C)/Joule (energy). LUE has been measured for many
different plant species, and has been found to be fairly
constant within vegetation classes when plants are
unstressed. Numerous studies have demonstrated linearity
in the relationship between the increase in canopy biomass
during vegetative growth and the amount of visible light
absorbed by the leaves in the canopy [Monteith, 1966,
1972; Puckridge and Donald, 1967; Duncan, 1971].
Haxeltine and Prentice [1996a] demonstrate that the
biochemical photosynthesis model of Collatz et al. [1991,
1992] yields a linear relationship between carbon
assimilation and light interception when integrated over
the depth of the canopy, assuming that nitrogen is
optimally distributed (as discussed in the previous section)
and the ratio of intercellular to atmospheric CO2
concentration ( µ = ci ca ) is constant. There is also some
evidence that the slope of the NPP APAR relationship
may be fairly constant amongst vegetation classes [Field,
1991; Goetz and Prince, 1998]. The linear relationship
between carbon uptake and intercepted light implies that
LUE can be used to construct photosynthesis models
which require fewer equations, fewer parameters, and
fewer ground-based measurements than scaled-leaf
parameterizations. LUE models are also well-suited for
application over large geographical regions since APAR
can be estimated by remote sensing reasonably accurately
(as discussed later).
The basis of using LUE approach at long time steps (10day to months) in ecological models is the reduced
variability in the value of β over increasing time scales. β
is generally measured on time scales longer than a day.
Medlyn [1998] shows how variability in β decreases from
daily, to monthly, to annual time scales. She suggests
since PAR is the major source of variability in β,
increasing the time scale over which β is obtained should
reduce variability in β. The high variability in β over daily
and shorter time steps suggests that use of constant β
value in SVAT schemes is not suitable.
FPAR (and hence APAR) can be estimated reasonably
accurately via remote sensing which makes the top-down
approach of using LUE well-suited for large scale
applications [Steinmetz et al., 1990; Myneni et al., 1995;
Landsberg et al., 1997] . Sellers [1987] and Sellers et al.
[1992] show that for ideal conditions of uniform green
canopy and dark underlying soil surface the spectral
vegetation indices (SVI), measured via remote sensing,
should be proportional to FPAR. The most commonly
10
used SVI are the simple ratio ( SR = a N / a V ) and the
normalized
difference
vegetation
index
( NDVI = (a N − aV ) / (a N + aV ) ), where aN and aV are the
canopy reflectances for near-infrared and visible
wavelength intervals. Using the photosynthesis model of
Collatz et al. [1991] for C3 plants, Sellers et al. [1992]
demonstrate a simple and robust relationship between
photosynthesis and spectral vegetation indices. The CASA
model uses SR to determine FPAR following Sellers et al.
[1996a], and the GLO-PEM model parameterizes FPAR
as a linear function of NDVI.
framework. The maximum carbon assimilation rate may
be either prescribed as a constant, a function of the
vegetation type, or determined in some other manner e.g.,
as a function of the leaf nitrogen content. The terrestrial
ecosystem model (TEM) [McGuire et al., 1992, 1997]
expresses monthly gross photosynthetic rate GPP, as
follows.
GPP = Cmax f 1 ( PAR) f 2 ( LAI ) f 3 (ca , g s ) f 4 ( N )
where Cmax is the maximum rate of carbon assimilation,
and f1, f2, f3, and f4, are environmental dependences on
PAR, LAI, ca and gs, and nitrogen availability N. The
Frankfurt biosphere model (FBM) [Lüdeke et al. 1994],
which uses a daily model time step, defines Cmax as the
production rate at light saturation and soil water and
temperature optimum, and then reduces Cmax using
environmental dependences on PAR, LAI, temperature,
and soil moisture content. The Century model of Parton et
al. [1993] assumes the maximum monthly potential above
ground production to be a constant (250 gC/m2/month)
and reduces this maximum rate based on soil moisture and
temperature, and the effect of shading on plant growth.
The Sim-CYCLE model [Ito and Oikawa, 2000] expresses
daily GPP as a function of maximum photosynthetic rate
(a function of air temperature) and includes the effects of
PAR, LAI, and mid-day radiation. The PnET-DAY model
[Aber et al., 1996] expresses maximum photosynthetic
rate as a function of leaf nitrogen content based on field
data [Reich et al., 1995] and then reduces this rate to take
into account the effect of stomata closure due to increased
vapour pressure deficit. The FOREST-BGC model
[Running and Coughlan, 1988] adopts a slightly different
approach and parameterizes photosynthesis as a function
of CO2 diffusion gradient, a radiation and temperature
controlled stomatal conductance, and LAI.
Jarvis [1993] noted that the bottom-up models constructed
from detailed mechanistic representations of leaf-level
processes (e.g., the biochemical approach) are often more
susceptible to errors in input and scaling assumptions than
the top-down models (e.g., the ones using LUE approach)
which are constrained by some relationship developed at
the canopy level. However, this also implies that topdown models, such as the ones using LUE approach, have
usefulness only within the range of conditions they were
developed for [Jarvis, 1993]. Anderson et al. [2000], for
example, reported that they have no assurance that the
ALEX model (based on the LUE approach) will perform
well in predicting regional carbon fluxes under conditions
of elevated CO2. The use of prescribed LUE for different
vegetation types is not suitable in transient climate
simulations since LUE for C3 plants is a function of both
temperature and atmospheric CO2 concentration
[Ehleringer et al., 1997]. The use of the LUE approach in
SVAT schemes used in climate simulations with
increasing CO2 concentrations may therefore not be
suitable. Since the use of remotely sensed data for
hydrological applications has been increasing [Wood,
1999; O’Donnell et al., 2000], ecological models which
use the LUE approach to model photosynthesis using
remotely sensed data may make suitable candidates for
coupling with hydrological models. The use of fewer
equations and parameters in such ecological models also
makes them attractive for coupling with hydrological
models.
3.1.3.
(10)
The carbon assimilation approach is largely empirical and
requires calibration of model parameters used to define
the dependences on various environmental factors. The
empirical nature of this approach and the fact that model
parameters are generally difficult to obtain at a global
scale implies that the carbon assimilation approach is not a
suitable candidate for use in SVAT schemes for GCM
climate simulations, which require model parameters at a
global scale. The empirical nature of this approach also
implies that the relationships developed for the current
climate and CO2 concentrations may not be applicable if
the climate and/or CO2 concentration change. For
example, the near-linear relationship between maximum
photosynthetic rate and leaf nitrogen content (used in the
PnET-DAY model) has although been shown to hold true
for a variety of plant species, the parameters of the
relationship may change with a change in climate and CO2
concentration [Field and Mooney, 1986].
Carbon assimilation approach
Models using the carbon assimilation approach express
canopy level photosynthesis as a function of some
maximum assimilation rate and a series of environmental
dependences with values generally varying between 0 and
1, which imply the effect of the environmental factor on
photosynthesis as being severely limiting and not limiting
at all, respectively. This approach has been used in a
variety of ecological models which simulate
photosynthesis at a daily or monthly time step (see panel
(e) Table 3). I am not aware of any application of the
carbon assimilation approach in a SVAT scheme
11
In summary, photosynthesis in the current generation of
vegetation and plant growth models is parameterized
using 1) the biochemical approach, 2) the LUE approach,
and 3) the carbon assimilation approach. The processbased biochemical approach which considers leaf-level
processes is more suitable for inclusion in a SVAT
scheme framework than the largely empirical LUE and the
carbon assimilation approaches, which owing to their
simplicity, are better suited for hydrological models.
Moreover, since NPP is estimated directly in LUE
approach (i.e. growth and maintenance respiration are not
estimated separately) the use of this approach in SVAT
schemes for climate simulations implicitly implies that
respiration losses are assumed to be a constant fraction of
GPP even when the climate and CO2 concentration
change. This may, however, not be true since maintenance
respiration depends strongly on temperature (as discussed
in the next section). Physically-based distributed
hydrological models which include a relatively complex
description of hydrologic processes can be coupled with
biochemical photosynthesis models of comparable
complexity. The computationally less expensive singleleaf approach is likely to be chosen over two-leaf (sunlit
and shaded) and multi-layered canopy approaches in
climate simulations by most modellers, although in
principle all three approaches can be used. Since the
primary purpose of hydrologic models is to estimate water
fluxes reliably and not to model photosynthesis,
distributed hydrologic models that may use the
biochemical approach may also choose to use the singleleaf approach. Photosynthesis parameterizations based on
the carbon assimilation approach are the simplest and can
be easily incorporated into hydrological models, although
this would mean that additional model parameters will
have to be calibrated.
respiration from leaves, stems (including branches) and
roots (Rm=Rleaf+Rstem+Rroot). Some models also make the
distinction between woody and non-woody portions of the
root. The non-woody (fine roots) portion is treated
separately, while the woody portion may be treated as part
of the stem in which case combined stem and woody
portion of the root are referred to as transport tissues.
Maintenance respiration rates from leaves, stem, and roots
have been observed to correlate better with nitrogen than
with their carbon content [Ryan 1991; Reich et al., 1998].
Simulating plant nitrogen explicitly, however, requires
modelling of the nitrogen-cycle which also involves the
soils. Instead, the fact that maximum catalytic capacity of
Rubisco Vm is closely related to leaf nitrogen content is
used to estimate respiration. Not all models, however, take
into account the dependence of maintenance respiration
rates on nitrogen content. Models which do not estimate
NPP and whose objective is to estimate net leaf carbon
uptake only to determine stomatal conductance (SiB2 and
the two-leaf model) do not need to account for stem and
root respiration. These models require the estimates of net
CO2 uptake by leaves, An = A Rleaf, and thus only need to
estimate leaf maintenance respiration. Amongst the
models which simulate NPP, maintenance respiration Rm
is estimated in four different ways depending on the level
of complexity models may choose to incorporate, 1) Rm is
estimated separately for the leaves, stem, and the roots
(e.g., IBIS, and BIOME-BGC), 2) Rm is estimated
separately for the leafy and the woody (stem and roots)
portions of the plant (e.g., CARAIB, DOLY and FBM), 3)
Rm is estimated for the leaves, fine roots, and transport
tissues (stems and roots) (e.g., HYBRID and BIOME3),
and 4) Rm is estimated for the entire plant and distinction
between leaves, stem, and root, or leafy and woody
portions is not made (e.g., BETHY, PnET-DAY, SimCYCLE, TEM).
3.2. Respiration
3.2.1 Leaf maintenance respiration
Autotrophic plant respiration is made up of growth and
maintenance components (RA=Rgrowth + Rm), as mentioned
earlier. Growth and maintenance respiration rates are
required to estimate NPP. Models based on the LUE
approach which estimate NPP directly need not estimate
respiration separately. Most models that do not estimate
NPP directly contain separate parameterizations of the two
respiration components. Since growth respiration is
related to the total growth of plants, it is usually expressed
as a fraction of net primary productivity (NPP), Rgrowth = fg
NPP, where fg typically varies between 0.2 and 0.3 [Ryan,
1991]. The IBIS model estimates growth respiration as
33% of the difference between GPP and maintenance
respiration (Rgrowth = 0.33[GPP – Rm]). The BETHY
model estimates Rgrowth as 25% of NPP. BIOME3
estimates Rgrowth as 20% of [GPP – Rm] and DOLY and
BIOME-BGC estimate Rgrowth as 33% and 32% of NPP,
respectively. Total maintenance respiration is the sum of
Expressing leaf respiration as a function of the maximum
catalytic capacity of Rubisco Vm Collatz et al. [1991,
1992] proposed the following relationships for C3 and C4
plants.
R leaf =
0.015 Vm ,
0.025 Vm ,
for C3
for C4
(11)
SiB2, MOSES, the two-leaf model, IBIS, BATS, and
BETHY model leaf respiration using equation (11) in a
SVAT scheme framework, while BIOME3, CARAIB, and
HYBRID use equation (11) at a daily time step in the
framework of an ecological model. Note that Vm is a
function of temperature as well (see appendix). The
DOLY model also estimates leaf respiration as a function
of leaf nitrogen content N and temperature T but uses a
different formulation [Harley et al., 1992],
12
model
R leaf =
N
e
50
r (T )
r1 (T )− 2
8.31Tk
(12)
∆T 10
respiration
The BETHY, PnET-DAY, Sim-CYCLE, and TEM
models do not estimate maintenance respiration separately
for the different plant components but assume that total
plant respiration can be approximated by an average
respiration rate. PnET-DAY, Sim-CYCLE, and TEM use
respiration formulations similar to that of equation (14).
The BETHY model estimates the total plant maintenance
respiration by multiplying the canopy leaf maintenance
respiration with a factor of 2.5, assuming that leaf
respiration represents 40% of the total maintenance
respiration.
(13)
In summary, in most models, growth respiration is
expressed as a constant fraction of NPP, while
maintenance respiration is estimated separately for
different plant components. Most models estimate leaf
maintenance respiration as a function of the maximum
catalytic capacity of Rubisco Vm, and root and stem
maintenance respiration is expressed as a function of a
respiration rate at a reference temperature, the total
amount of carbon present in these components, and a
temperature response function. Ryan [1991] summarizes
maintenance respiration formulations used in various
ecological models and argues tissue nitrogen
concentration to be a much better predictor of
maintenance respiration than the total biomass. He,
however, points out that one of the major disadvantages of
maintenance respiration formulations is their inability to
account for acclimation of respiration rates to climate
change. If plant maintenance rates acclimate to a slowly
changing average temperature, the current formulations
will overestimate respiratory costs. The manner in which
plant respiration rates may acclimate to climate change,
however, is not completely understood. Thus, although the
current maintenance respiration formulations may be used
for the present climate with some confidence, uncertainty
remains about the magnitude of the increase in
maintenance respiration costs with increasing warming.
3.2.2 Stem and root maintenance respiration
Stem and root maintenance respiration in most models is
parameterized as a function of specified rates at a
reference temperature, amount of live carbon present in
these components, and a temperature response function.
For example, the IBIS model uses specified rates at a
reference temperature of 15oC (βroot and βstem – in units of
amount of carbon released per unit carbon in the
component per unit time, gC/gC/yr), the amount of carbon
in the root and stem (Croot and Cstem), and a temperature
response function f(T) similar to Q10.
Rroot = βroot Croot e
root
3.2.3 Whole plant maintenance respiration
where Rm(T) is the maintenance respiration at temperature
T, and Rm(T0) is the maintenance respiration at some
reference temperature T0.
R stem = βstem f sapwood C stem e f ( T )
fine
rate (Lfall), which itself is parameterized as a constant
multiple (50 gC/m2) of LAI. The CARAIB, DOLY, and
the FBM models, which consider a plant in terms of its
leafy and woody portions, also estimate respiration from
the woody portion in a manner similar to equation (14).
where Tk is the absolute temperature, and ri(T) are the
temperature response functions. The original version of
the BIOME-BGC model [Running an Hunt, 1993] uses
prescribed leaf (as well as stem and root) maintenance
respiration rates for its three vegetation classes (conifers,
broadleaf, and grasses) which are adjusted for a change in
temperature using a function called Q10, while the new
version [Keyser et al., 2000] also takes into account the
leaf nitrogen content as per Ryan [1991]. A Q10 function
represents an exponential increase in some quantity with
an increase in temperature. For example, a Q10 value of
2.0 (as used in the BIOME-BGC model) implies a
doubling of respiration rate with a 10 increase in
temperature.
Rm (T ) = Rm (T0 ) Q10
∆T = T − T0
parameterizes
(R fine_root = aL fall , a = 1) as a function of annual leaf litterfall
(14)
f (T )
where fsapwood is the fraction of sapwood of the total stem
biomass. The sapwood fraction is used to estimate the
fraction of live carbon in the stem. Both HYBRID and
BIOME3 treat the stem and the roots together as transport
tissues and estimate maintenance respiration in a similar
fashion. Both these models estimate respiration from fine
roots separately. The HYBRID model estimates the fine
root respiration as a function of total fine root nitrogen
content and a temperature response function. Raich and
Nadelhoffer [1989] present data from a range of forest
ecosystems that show a strong correlation between below
ground allocation and annual leaf litterfall. Since fine root
construction and maintenance respiration account for
about half of below ground allocation [Sprugel et al.,
1995; Ryan, 1991; Runyon et al., 1994], the BIOME3
3.3. Allocation
The distribution of carbon fixed by photosynthesis into
leaves, stems, and roots pools is, at least in theory, aimed
to achieve maximum growth [Iwasa and Roughgarden,
1984]. A plant could allocate all its resources to leaves in
13
order to maximize photosynthesis, but allocation to stem
(which provides support and helps to gather light) and to
roots (which provide nutrients) is also essential. Tilman
[1988] argued that light and nutrients are the most
important factors which influence biomass allocation,
although factors such as water stress and elevated CO2
could also be important. There is a long history of
modelling the response of root-shoot ratio (where shoot
refers to both leaves and stem) to changes in nutrient,
water, and light availability [e.g., see Wilson, 1988;
Gillespie and Chaney, 1989]. The relationships which
describe the allocation pattern are commonly called
allometric relationships. Models whose primary objective
is to estimate photosynthesis and/or NPP, and which do
not simulate the dynamics of LAI, do not need to model
carbon allocation (e.g., SiB2, the two-leaf model, ALEX,
GLO-PEM, BIOME2, PnET-DAY, and TEM). Models
which do allocate carbon resources may make the
distinction between 1) leafy and the woody (or green and
remaining) parts, 2) leaves, stems, and roots, or 3) leaves,
fine and coarse roots, and stems, depending on their level
of complexity. On the basis of the carbon allocation
scheme models may broadly be classified as: 1) those that
allocate fixed amounts of carbon to the plant components
or those in which the allocation pattern is determined by
vegetation type (e.g., IBIS, CASA, Sim-CYCLE, and
FOREST-BGC), 2) those that allocate carbon on the basis
of plant allometric relationships and constraints (e.g.,
TRIFFID, BATS, CARAIB, HYBRID, BIOME-BGC,
FBM), and 3) those that explicitly calculate the allocation
pattern in order to optimize growth or LAI (e.g, BETHY,
BIOME3, DOLY). Most models do not explicitly take
into account the variation in allometric patterns according
to the growth stages in a plant’s life.
coarse root and stem root carbon, which are based on an
extensive review of published data. The HYBRID model
uses three allometric relationships, 1) relationship between
tree breast height diameter and the amount of woody
carbon, 2) proportionality between foliage and sapwood
area, and 3) the fixed ratio between foliage and fine root
carbon. The TRIFFID dynamic vegetation model [Cox,
2001], which uses the biochemical photosynthetic
subroutines of MOSES, allocates equal resources to leaves
and roots, while the allocation to stems Astem is
parameterized as a function of seasonal maximum LAI
5/ 3 , where a=0.65 for trees and 0.005 for
( Astem = a LAI max
grasses). The BATS model makes the basic distinction
between leafy and woody parts, it lumps green stems with
leaves and large roots are lumped with wood. The
allocation to leaves is parameterized as an exponential
function of LAI (Aleaves = e-0.25 LAI). The CARAIB model
specifies the fraction of carbon allocated to leaves Aleaves
(varying between 0.2-0.3) as a function of vegetation type,
and allocation to the woody part is parameterized as a
function of Aleaves and the fractional area covered by the
ground vegetation. The FBM, which makes the distinction
between the green and remaining (woody) parts, adopts a
slightly novel approach. It assumes that it is possible to
identify forbidden regions in the state-space of vegetation
characterized by the amount of green (Gcarbon) and woody
(Wcarbon) carbon. The forbidden regions have a woody
carbon amount too small to support and maintain the
green carbon. A power relationship, Wcarbon = a (Gcarbon)k,
between woody and green carbon amounts is assumed,
where a and k are vegetation type dependent constants.
Vegetation tends to maximize the amount of
photosynthetic tissue, i.e., leaves, but keeping into account
that there must be enough woody carbon to support and
maintain these leaves.
The CASA model adopts the simplest approach and
allocates equal amounts of carbon to the leaves, stem, and
roots such that each component gets one-third of the total
carbon fixed. The IBIS model prescribes the constant
allocation fractions for leaves, stem, and roots as a
function of the vegetation type, e.g., for tropical evergreen
trees allocation fractions of 0.25, 0.25, and 0.50 are used
for leaves, root, and stem, respectively. The original
version of the FOREST-BGC model used allocation
fractions of 0.25, 0.35, and 0.4 for leaves, stem, and root,
respectively.
The third category of allocation schemes used in terrestrial
carbon models explicitly calculates allocation patterns to
maximize growth or LAI. The BIOME3, DOLY, and
BETHY models use the concept of equilibrium between
vegetation and water availability to determine LAI.
Woodward [1987], Haxeltine et al. [1996b], and Kergoat
[1998] show that LAI can be predicted from a
hydrological budget. The problem of estimating leaf area
is regarded as an optimization problem in which the
benefits of increasing leaf area in terms of light
interception are traded off against the costs in terms of
transpiration. The benefits are usually expressed in terms
of NPP and the optimization is conceptually equivalent to
maximizing NPP with respect to leaf area. The DOLY
model, for example, adjusts its leaf area index to the
maximum value for the given mean monthly climate such
that both hydrological and NPP constraints are satisfied.
The implicit assumption made in this approach is that the
vegetation and the climate are both in equilibrium with
each other. This approach is therefore not a suitable
Amongst models which use plant allometric relationships
and constraints as their criteria, various forms of
relationships have been used. The BIOME-BGC model
uses four allometric relationships which are solved
algebraically to obtain the amount of carbon allocated to
fine and coarse roots, leaves, and stems. These four
relationships are the ratios between 1) new fine root and
new leaf carbon, 2) new stem and new leaf carbon, 3) new
live wood carbon and new total wood carbon, and 4)
14
candidate for inclusion in SVAT schemes for transient
climate change simulations. It is also computationally
expensive to estimate the optimum LAI since the
optimization is carried out by increasing LAI in small
steps. Hydrological models, used for studying climate
change effects, which may like to include the effects of
changes in vegetation on water balance, however, can use
this approach under the assumption of equilibrium
between climate and vegetation.
timing of budburst and leaf senescence determine the
length of growing season, and this considerably affects the
NPP, the annual cycle of LAI, and consequently the
energy and water fluxes. For example, Hogg et al. [2000]
discuss the feedbacks of leaf phenology and suggest that
leaf phenology of deciduous aspen forests, through its
effects on energy and water balance, may contribute
significantly to the distinctive seasonal patterns of mean
annual temperature and precipitation in the western
Canadian interior.
More complex methods based on nutrient, water, and
resource availability have also been used. The ITEEdinburgh model of Thornley [1991] uses a mechanistic
approach for allocation that is based on the transport of
carbon and nitrogen and is driven by concentration
differences and resistances between various plant
components. The model attains a functional root-shoot
balance since the uptake of carbon depends on the
concentration of nitrogen in the leaves, and the uptake of
nitrogen from the soil depends on the amount of carbon
present in the roots. Friedlingstein et al. [1999] use an
allocation scheme based on the availability of nitrogen
(N), water (W), and light (L) expressed as scalars ranging
between 0.1 (severely limited) to 1.0 (readily available).
Their scheme is based on a generic relationship [Sharpe
and Rykiel, 1991] between carbon allocation to a
compartment and the availability of a particular resource,
and is implemented in the new version of the CASA
model [Friedlingstein et al., 1999]. In brief, the allocation
to stem Astem is controlled by the availability of light, the
allocation to roots Aroots is controlled by the availability of
water and/or nitrogen, and the remainder is allocated to
leaves (Aleaves).
L
L + 2 min(W , N )
min(W , N )
Astem = 3s0
2 L + min(W , N )
Aleaves = 1 − Aroots − Astem
In trees, the initiation of growing season, or the onset of
greenness, has been successfully modelled using a
cumulative thermal summation technique commonly
known as degree day sum or growing degree days (GDD)
[Thomson and Moncrieff, 1981; Murray et al., 1989;
Caprio, 1993]. The degree day sum is defined as the sum
of positive differences between daily mean air (or soil)
temperature and some threshold (Tthresh) temperature
(usually 0oC or 5oC) following a predetermined date
(usually January 1 in the northern hemisphere). When the
degree day sum exceeds a critical value leaf onset is
predicted to occur. Most tress must also fulfill a chilling
requirement before warmer temperatures begin to affect
spring time growth, and some models include this
parameter. Chilling day sum is the number of days on
which the mean temperature is below Tthresh following
some predetermined date, usually earlier than that for the
degree day sum. Phenology is also affected by day length
and short days can induce dormancy or offset of greenness
[Nooden and Weber, 1978]. Also, the growing season can
be prolonged by warm temperatures and curtailed by cold
temperatures [Smit-Spinks et al., 1985]. Approaches which
use carbon cost/benefit analysis and nutrient availability
have also been used to predict the patterns of leaf
phenology [Kikuzawa, 1995; Lüdeke et al., 1994]. Like all
living tissues, plant tissues eventually age and die. Older
leaves, stems, and roots no longer contributing to the
producitivity of the plant are discarded via turnover of
plant tissues to litterfall. In most models leaf fall, tree fall,
and root mortality are simulated as a constant fraction of
the amount of carbon present in these components.
Aroots = 3r0
(15)
where r0 and s0 are set to 0.3, giving an allocation of 0.3,
0.3, and 0.4 to roots, stem, and leaves, respectively, under
conditions of non-limiting resources, i.e. when W=N=L=1.
In summary, allocation in most vegetation models is
parameterized on the basis of, 1) fixed allocation
fractions, 2) allometric relationships, and 3) maximization
of LAI. More complex methods based on resource
availability are also available, however, since this requires
modelling nitrogen availability, simpler allocation
schemes appear to be attractive alternatives for SVAT
schemes and hydrological models.
At large spatial scales, it is extremely difficult to obtain
consistent field phenology observations against which the
model predictions can be validated. The phenology of
vegetation at large spatial scales is representative of
ecosystem response rather than the species response.
Normalized difference vegetation index, NDVI, which has
been related to several biophysical parameters including
percent canopy cover [Yoder and Waring, 1994], APAR
[Myneni et al., 1995], and LAI [Spanner et al., 1990], has
also been used to quantify vegetation phenology at large
spatial scales [Goward et al., 1985].
3.4. Phenology
Accurate prediction of recurring vegetation cycles as a
function of climate is important in vegetation models. The
15
Models which prescribe the seasonal evolution of LAI
explicitly, or use measured LAI values, do not need to
parameterize phenology (e.g., SiB2, the two-leaf model,
and ALEX). Models which use NDVI or SR to estimate
NPP, such as CASA and GLO-PEM, implicitly include
vegetation phenology since the seasonality in these
spectral vegetation indices is representative of seasonality
in vegetation characteristics including phenology. In a
simplest manner, phenology may be prescribed or
parameterized as a function of temperature. For example,
the original BIOME-BGC model [Running and Hunt,
1993] specifies the leaf on and off dates for its three
vegetation types. The IBIS model assumes that winter
deciduous plants (temperate deciduous trees, boreal
deciduous trees) drop their leaves when the daily average
temperature falls below a critical temperature threshold
(5oC), and in the spring leaves reappear when temperature
rises above the critical temperature threshold. The
CARAIB, BETHY, and TRIFFID models parameterize
phenology using simple functions of temperature.
Following Pitman et al. [1991], the CARAIB model
parameterizes LAI as a function of monthly temperature
Tm, LAI = LAI max − ∆LAI [1 − f (Tm )] , where f(Tm) is a
burst occurs at the same rate when the leaf mortality rate
drops back below this threshold, and full leaf is
asymptotically approached. A similar approach is adopted
by the BATS model which prescribes a fixed leaf
mortality rate but also takes into account additional leaf
mortality due to cold (function of temperature and
vegetation type) and drought (function of soil moisture)
stresses.
BIOME3 and PnET-DAY parameterize phenology on the
basis of degree day sum which is specified as a function of
the vegetation type. The HYBRID model estimates degree
day sum using an empirical formula for cold deciduous
broadleaf trees which uses the number of chilling days as
its parameter. Slightly more complex approach is adopted
by White et al. [1997] whose phenology model is now
used in BIOME-BGC. They use combined soil
temperature and radiation summation to predict onset of
greenness for deciduous broadleaf trees. Offset is
predicted to occur when one of the two conditions is met,
1) when the day length is less than 655 min and soil
temperature is less than ~11oC, or 2) when the soil
temperature drops below ~2oC.
function of temperature, LAImax and LAI are the
maximum leaf area index and the amplitude of seasonal
variation in LAI, respectively, and both are dependent on
the vegetation type. The formulation used in CARAIB
depends on monthly temperature and thus can not be used
interactively within a climate simulation, since the value
of monthly temperature won’t be available until a month
has passed. The BETHY model assumes that LAI is given
by the minimum of temperature, LAIT, and carbon- and
water-limited LAI, LAIW. The carbon- and water-limited
LAI is calculated based on the assumption that plants
maximize their LAI on the basis of available water, as
mentioned in section 3.3. The temperature-limited LAI is
obtained as per Dickinson et al. [1993] using three
parameters, the leaf onset and offset temperature Ton/off,
the maximum limiting temperature Tmax, and LAImax.
 0

  T

max − T0.5
LAI T =  LAI max 1 − 

T
− Ton / off

max


 LAI
max
î
The FBM adopts an approach based on carbon benefit and
loss. It assumes that the annual vegetation cycle, for
vegetation types characterized by leaf on and off times,
can be partitioned into five stages: 1) shooting phase, 2)
secondary growth phase, 3) standby phase, 4) leaf
shedding phase, and 5) dormancy phase. Vegetation
passes from phase 2 to phase 3 when negative growth
starts, i.e. when NPP becomes negative. If the negative
growth persists for a specified number of days vegetation
starts shedding its leaves and goes into phase 4. Once in
phase 5, after leaves have been shed, a specified number
of days with positive growth brings vegetation back to
phase 1.
Since evergreen trees are not characterised by leaf on and
offset times, their phenology is prescribed in terms of
constant LAI (BIOME3) or constant fractional plant
coverage (BIOME2) and net loss of biomass occurs only
due to temperature and/or drought stress, and normal leaf
mortality. Parameterizations for grasses are similar to that
for tress but with different parameter values. For example,
the degree day sum requirement for grasses is smaller than
that for trees.
if T0.5 < Ton/ off
2
 
 
 


if Ton / off < T0.5 < Tmax
if T0.5 > Tmax
(16)
where T0.5 is the temperature at 0.5 m soil depth. The
TRIFFID model increases the specified leaf mortality
rates (γo) by a factor of 10 when the temperature drops
more than 1oC below the threshold temperature (0oC for
the broadleaf and 30oC for the needle leaf trees). Since
realistic phenology is not obtained by this formulation
alone, TRIFFID also prescribes an additional leaf
dropping rate when the daily mean value of leaf mortality
rate exceeds twice its minimum specified value (γo). Bud-
In summary, phenology can be modelled on the basis of:
1) prescribed leaf on and off dates, 2) prescribed threshold
temperature or simple functions of temperature, 3) degree
days and chilling day sum, and 4) carbon benefit and loss
approach. Since all these phenology parameterizations are
simple they can be relatively easily incorporated into the
framework of both SVAT schemes and hydrological
models. The use of prescribed leaf on and off times in
16
Table 4: The extent to which parameterization of various physical processes make vegetation a dynamic
component.
Process modelled
Photosynthesis and leaf
respiration
Extent to which vegetation acts as a dynamic component
Stomata responds to variability in environmental conditions and CO2
concentration when net carbon uptake is related to stomatal
conductance.
Stem and root respiration
Allows to model NPP; GPP – RA = NPP
Allocation
LAI varies in response to change in climatic conditions from year to
year, as well as the seasonal cycle of LAI can be simulated rather than
being prescribed. Biomass allocated to leaves is used to determine LAI.
Phenology
The timing of leaf onset and offset are modelled prognostically rather
than being prescribed.
Competition between plant
functional types (PFTs)
Vegetation reacts to long-term change in climate and the PFTs which
are best suited for a given region and climate succeed. This is not
discussed in this paper but described briefly in Section 5.
transient climate simulations is not desirable since this
defeats the purpose of parameterizing vegetation as a
dynamic component, and the use of simple temperature
functions, degree day sum, or the carbon benefit and loss
approach is more suitable.
what extent the various processes make vegetation a
dynamic component.
Like any other model, vegetation models must be
carefully parameterized and validated against observations
to achieve realistic results. Imbalances within the models
and unrealistic dynamical behaviour can lead to extreme
cases where the entire simulated ecosystem may die
within a short duration, or flourish to unrealistic biomass
amounts.
The inclusion of some or all of the vegetation processes
(photosynthesis, respiration, allocation and phenology)
into a SVAT scheme or a hydrological model will depend
on the level of vegetation dynamics that a modeller
intends to incorporate and the purpose of the modelling
exercise. For example, modelling photosynthesis using the
biochemical approach in order to estimate stomatal
conductance implies that transpiration and photosynthesis
are coupled, and the effect of CO2 on stomata closure, and
thus energy and water balances, can be explicitly
modelled. If the purpose of the modelling exercise is to
include this effect only, a model does not have to
incorporate stem and root respiration, allocation, and
phenology parameterizations, and the seasonal evolution
of LAI can be prescribed. In SVAT schemes used in
transient climate simulations the purpose of making
vegetation a dynamic component is not only to include the
effect of CO2 in changing the structural and physiological
properties of vegetation, but also to represent the changes
in vegetation due to interannual climate variability, and to
investigate how the growing season may be lengthened
due to climate warming. To represent this level of
vegetation dynamics it is important to model respiration,
allocation, and phenology as well. Hydrological models
used to investigate the effect of year to year variability of
LAI on water balance, or to study how the regeneration of
forest affects water availability for the current climate, for
example, may choose to prescribe the leaf on and off dates
on the basis of observations rather modelling the
phenology. This is summarized in Table 4 which shows to
4.0. Coupling of vegetation models with SVAT
schemes and hydrological models
The coupling between the dynamic vegetation module and
the host model (SVAT scheme or a hydrological model)
can be accomplished in different ways depending on 1)
the processes that may be incorporated, 2) the time scales
on which the vegetation modules are allowed to interact
with the host model, and 3) the manner in which the
modelling exercise may be set up which itself depends on
the purpose of the model. The primary variables which
couple the dynamic vegetation module to its host model
are soil moisture, temperature, and LAI. The soil moisture
and temperature simulated by the host model are used by
the vegetation module which in turns provide LAI to the
host model. Since most hydrological models do not
simulate energy balance explicitly and temperature is used
as an input variable, the coupling between the vegetation
module and the hydrological models will be primarily
expressed via soil moisture and LAI. Secondary variables
that may be exchanged between the vegetation module
and the host model will of course depend on the type of
parameterizations chosen.
Figure 1 shows how the coupling between a dynamic
vegetation module and a hydrological model may be
17
Precipitation
Temperature
Radiation
Evapotranspiration
Gross Primary
Productivity (GPP)
Control of leaf area
index on interception
and evaporation
Canopy interception
and storage
Canopy drip
Throughfall
Evaporation
Evaporation
CO2 concentration
GPP – Auto. Respiration=
Net Primary Productivity (NPP)
Surface storage
Surface runoff
Phenology
Evaporation
Transpiration
Soil moisture storage
Control of soil moisture on
photosynthesis. Total carbon uptake
depends on LAI, which in turns
determines transpiration.
Allocation of carbon to leaves, stem,
and roots - determines LAI
Leaf area index
determines
transpiration
Leaf fall, tree fall and
root mortality
Baseflow
Groundwater storage
Evaporation
Autotrophic respiration
Channel storage
VEGETATION MODULE
Channel streamflow
denotes primary inputs
HYDROLOGICAL MODEL
denotes processes modelled
denotes primary outputs
dashed line denotes linkages between the
hydrological and the ecological model
Figure 1: The manner in which the coupling between a dynamic vegetation module and a hydrologic
model may be accomplished.
accomplished. The primary processes that are modelled by
the hydrologic model and the vegetation module are also
shown. The linkage between the two models is expressed
by soil moisture, which affects photosynthesis, and LAI
which affects transpiration, interception, and evaporation
from the canopy leaves. Hydrologic models may choose to
estimate photosynthesis and respiration at their time step
(usually daily) or use a longer time step (e.g., monthly)
depending on the type of approach used to estimate
photosynthesis. The coupling between transpiration and
photosynthesis may or may not be explicitly represented
in hydrological models. Modellers may use a dynamic
vegetation module only to determine LAI and stomatal
conductance may be parameterized or prescribed in the
usual manner, e.g., as in the HYBRID model. Allocation
of carbon in most ecological models is done at an annual
time step. NPP is accumulated over a period of one year
and at the end of every year the carbon is distributed
amongst the different plant components, after leaf litter
fall, tree fall, and root mortality have been taken into
account. If leaf on and off dates are not prescribed, a
dynamic phenology module can be used to provide
information about the presence of leaves on the canopy at
a daily time step. A similar coupling structure may be
used when coupling a SVAT scheme with a dynamic
vegetation module and this is shown in Figure 2. The plant
physiology module, which estimates photosynthesis and
respiration, provides information about the stomatal
conductance to the canopy affecting its water and energy
balance calculations. Information about canopy and soil
temperature is passed on to the phenology module, and
soil moisture information is passed to the plant physiology
module for use in estimating photosynthesis. Allocation
and phenology modules are coupled, and exchange
information, in a similar fashion as for coupling with the
hydrological model. Accumulated carbon uptake and
respiration information is passed on to the allocation
module at a convenient time interval, e.g., half hourly
values may be accumulated for a day before being passed
on to the allocation module, and at year’s end allocation to
different components is made. The phenology module
which takes canopy (or soil) temperature information from
18
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the SVAT scheme and leaf biomass/LAI information from
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back to the SVAT scheme.
vegetation parameterizations for inclusion in a host model
will depend on various factors including the purpose of
the modelling exercise, availability of computing
resources, and a modeller’s subjective assessment, this
paper attempts to rationalize the use of various
parameterizations in the framework of SVAT schemes and
hydrological models.
5.0. Discussion and summary
Vegetation plays a significant role in influencing water
and energy balance at the land surface via its effect on
transpiration, interception, and the evaporation of
precipitation from the canopy leaves. For example, in an
analysis of 17 years of monthly moisture budget data from
the CCCma GCM3 Arora and Boer [2001] show that on a
global average vegetation processes more than 70% of the
total precipitation, and the combined evaporation from
canopy leaves and transpiration account for 72% of total
evaporation from the land surface. The primary vegetation
characteristics which affect water and energy balance are
LAI, stomatal conductance, the rooting depth, albedo, and
surface roughness. To this end, this paper summarizes the
range of parameterizations used in the current generation
of vegetation and plant-growth models for modelling
primary physical processes which make vegetation a
dynamic component and which affect the water and the
energy balance. The suitability of these parameterizations
for use in SVAT schemes and hydrological models is also
discussed. Although the final choice of dynamic
Hydrological models which include carbon dynamics and
growth are emerging. The recent version of the Topog
model [Vertessy et al., 1996; Silberstein et al., 1999]
includes the stomatal conductance formulation of Ball et
al. [1987] (equation 5). Watson et al. [1999a], more
recently, incorporated a complete carbon and nitrogen
cycling component in the Macaque model based on the
latest features of the BIOME-BGC model. Modelling of
vegetation as a dynamic component is the first step
towards integrated eco-hydrological modelling. Other
processes necessary for the modelling of carbon fluxes are
decomposition of plant litter, its contribution to the soil
organic carbon pool, and heterotrophic respiration from
the soil carbon pool. The primary processes that need to
be incorporated for modelling nitrogen dynamics include
1) transfer of organic nitrogen from leaf litter to the soil,
2) decomposition of organic nitrogen by microbes to yield
mineral nitrogen, 3) uptake of mineral nitrogen by plants,
and 4) leaching of mineral nitrogen by runoff.
19
At least two major factors which affect the dynamic
behaviour of vegetation are not reviewed, since their
discussion is beyond the scope of this paper, but are
mentioned here briefly for completeness. The first is the
nitrogen-cycle and the role of nutrients in affecting plant
productivity which is likely to be important both for
hydrological models and SVAT schemes. For SVAT
schemes used in transient climate simulations
consideration of anthropogenic nitrogen deposition, which
has been increasing in recent decades, may also be
important. Since nitrogen is the most limiting nutrient to
plant growth an increased supply of nitrogen is likely to
increase plant productivity [Brix, 1981; Tamm 1991].
However, increased nitrogen deposition can also lead to
nitrogen saturation and thus may contribute to a decline in
productivity [Aber, 1992]. Increased nitrogen deposition
can also cause 1) elevated leaf nitrogen content which
(along with increasing photosynthetic capacity) increases
sensitivity to drought stress, pests, and diseases, and 2)
reduced root-shoot ratio which may lead to root
weakening [Erisman and de Vries, 2000]. The second
factor, which is especially important for SVAT schemes
used in transient climate simulations and, not reviewed in
this paper is the effect of change in climate on the change
in plant functional types. The concept of plant functional
types (PFTs) has been strongly promoted in the past
decade since every plant species cannot be modelled
individually [Woodward, 1987]. The basic rationale
behind PFT approach is that classifying plants on the basis
of their functionality, rather than at the species level, is an
appropriate way to reduce complexity. Parameterizing
photosynthesis, respiration, allocation, and phenology
using a mechanistic approach can account for vegetation
dynamics resulting from year to year variability in climate
and changes in CO2 concentration, but these processes do
not allow simulation of the change in plant functional type
itself. Since climate primarily governs the type of
vegetation that may grow in a region a change in climate
can lead to a change in the type of vegetation. For
example, the vegetation/ecosystem modeling and analysis
project (VEMAP) which used six ecosystem models
(BIOME2, DOLY, MAPSS, TEM, CENTURY and
BIOME-BGC) to simulate the changes in vegetation type
over contiguous United States for doubled CO2 scenario
(using data from 3 GCMs) generally showed that warmtemperate and evergreen forests move northwards
displacing temperate deciduous forests [VEMAP members,
1995]. At least three approaches are available for
simulating the changes in plant functional types as a
function of climate. These approaches are 1) assume an
equilibrium between climate and vegetation (e.g.,
BIOME2, MAPSS, and DOLY), 2) use frame-based
models consisting of different ecosystem (frames)
between which the model can switch depending on the
climatic conditions (e.g., ALFRESCO, Starfield and
Chapin, 1996), and 3) use NPP as a criteria to determine
which functional type succeeds in a given climate (e.g.,
HYBRID and IBIS). Usually it is assumed that plant
functional type which achieves maximum net productivity
in a given region for a given climate is the one which
succeeds.
The physical processes of photosynthesis, respiration,
allocation, and phenology which are strongly dependent
on environmental conditions make vegetation a dynamic
component. The dynamic role of vegetation in affecting
energy and water balance makes land surface an interface
rather than just a boundary. Incorporation of these
processes in SVAT schemes is necessary to model
vegetation as an interactive component of the climate
system, to understand the response of vegetation to
changes in climate, and to assess the effect of changes in
vegetation characteristics on the climate via the feedback
processes. Integrated environmental modelling approaches
are being adopted to gain insight into various
environmental issues. At the catchment or river basin
scale, the dynamic representation of vegetation in
hydrological models offers a range of opportunities to
understand and predict the behaviour of ecosystems as
they influence water, carbon, and nutrient cycling.
Appendix
A.1. The biochemical model of leaf photosynthesis
The C3 and C4 photosynthesis model structure shown here
is based on the work of Collatz et al. [1991, 1992] and as
applied by Sellers et al. [1996b] and Cox [2001]. The
gross leaf photosynthetic rate is calculated in terms of
three potentially limiting assimilation rates:
(i)
Jc represents the gross photosynthetic rate limited
by the photosynthetic enzyme Rubisco.
 
ci − Γ

Vm 

c + K c (1 + Oa / K o ) 
Jc =   i
V
î m
for
C3
plants
for
C4
plants
(A1)
where Vm (mol CO2 m-2 s-1) is the maximum
catalytic capacity of Rubisco, Oa (Pa) is the
partial pressure of atmospheric oxygen, Γ is the
CO2 compensation point, and Kc and Ko (Pa) are
the Michaelis-Menten constants for CO2 and O2,
respectively. Vm, Γ , Kc, and Ko are all
temperature dependent functions (see equations
A4, A6, and A7).
(ii)
20
Je represents the gross photosynthetic rate limited
by the amount of available light.

 ci − Γ 
α (1 − ϖ ) I par 


 ci + 2Γ 
Je = 
α (1 − ϖ ) I
par
î
for C3 plants
A.2. Converting stomatal conductance units
(A2)
Stomatal conductance can be converted from its molar
units to that used traditionally in SVAT schemes and
hydrological models using the following equation [Sellers
et al., 1996b].
for C4 plants
where α is the quantum efficiency and values of
0.08 and 0.04 are used for C3 and C4 plants,
respectively, ! is the leaf scattering coefficient
and values of 0.15 and 0.17 are used for C3 and
C4 plants, respectively, and Ipar is the incident
PAR.
(iii)
g s ( ms −1 ) = 0.0224
for
C3
plants
for
C4
plants
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