In: Ecology of Hierarchical Landscapes:
Ed: Jiquan Chen et al., pp. 91-124
ISBN 1-60021-047-3
© 2006 Nova Science Publishers, Inc.
Chapter 4
WATER AND CARBON CYCLES IN HETEROGENEOUS
LANDSCAPES: AN ECOSYSTEM PERSPECTIVE
Asko Noormets1, Brent Ewers2, Ge Sun3, Scott Mackay4,
Daolan Zheng1, Steve McNulty3, Jiquan Chen1
1
University of Toledo
University of Wyoming
3
Southern Global Change Program, USDA Forest Service
4
State University of New York at Buffalo
2
1. ABSTRACT
Ecosystems, the elementary units in a landscape, determine landscape properties through
their interactions of with one another, with the environment, and the combined actions of
individual organisms within them. In this chapter, we discuss how water and carbon
cycles connect the organizational levels of organisms, ecosystem, and landscape, and
what we know of the mechanisms of their operation. We first describe the obstacles that
one faces trying to connect these different levels and the ways to tackle them. In the
second part of the chapter we use data from Chequamegon-Nicolet National Forest
(CNNF) in Wisconsin, USA, and examples from other published work to illustrate
current research questions and approaches. To date, greater progress has been made
connecting the plant and ecosystem levels, and more unanswered questions remain about
the relationships between ecosystem and landscape levels. The uniqueness of ecosystem
ecology among other life sciences is defined by its focus on the interactions between the
biotic elements of ecosystems and their abiotic environment, and the field has evolved
rapidly over the past two decades. Recognition of the dynamic and evolving nature of
ecosystems has caused ecologists to re-examine the basic assumptions behind the
concept. The current focus on spatiotemporal variability and the resultant changes in
current ecosystem science leads to linking ecosystem and landscape ecology. By
establishing the vocabulary and methodology to work across hierarchical levels and
taxonomic units, we are expanding the holistic understanding of the functioning of the
natural systems around us.
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2. INTRODUCTION
The distinction between an ecosystem and a landscape is more vague than that between
ecosystem ecology and landscape ecology. The scale of ecosystems and landscapes, as
defined in recent literature, is not necessarily mutually exclusive. Ecosystems, originally
defined as distinct units by Tansley (1935), are “spatially explicit units of Earth that include
all organisms, along with components of the abiotic environment within their boundaries”
(Turner et al., 2001). Landscape, on the other hand, was defined by Turner et al. (2001) as an
“area that is spatially heterogeneous in at least one factor of interest”. Thus, for different
questions, the spatial scope of these two terms may be vastly different. For a particular
question, however, we can still view “landscape” as the more comprehensive and inclusive
term, relative to “ecosystem”. These terms form the framework for this chapter, and we view
ecosystems as the elementary units of a landscape. As ecosystem ecology is the study of
trophic interactions that connect individuals (Vitousek, 1993), so is landscape ecology the
study of interactions that connect ecosystems. The composition of and the interactions
between individual ecosystems determine landscape properties and processes. Landscape
ecology addresses the spatial configuration of ecosystems relative to one another and the
different outcomes that result from different spatial configurations (Turner et al., 2001). In
order to understand the landscape-level processes we must be familiar with the hierarchical
relationships between different organizational levels as well as with interactions within a
level.
Let’s first look at how ecosystems are related to organizational levels above and below
them. Later we will discuss how ecosystems interact with one another and what are the
processes connecting them are. The properties of an ecosystem are determined by its
constituents: vegetation, climate, landform, soil, flora, and fauna, as well as the physical,
chemical and biological interactions among between these components. Understanding these
linkages is critical since we are faced with questions of larger scale for which we lack direct
data as well as clear understanding of their regulation. Since few measurements are possible
directly at the ecosystem level, we often rely on existing data collected at smaller spatial and
shorter temporal scales and draw inferences for larger scales (Miller et al., 2004). The
difficulties come not as much from quantifying all important processes but from integrating
processes that operate at different temporal and spatial scales and that interact with one
another. Frequently, the transitions from leaf and plant levels to that of an ecosystem center
around a canopy gas exchange model, as this represents the greatest fluxes of matter in the
system and can be used as a reference for other processes. Yet, the underlying assumptions of
the conceptual model developed for leaf level gas exchange (Farquhar et al., 1980; von
Caemmerer and Farquhar, 1981) have rarely been tested at the canopy level (DePury and
Farquhar, 1997). The additional processes of heterotrophic life, the structural complexity of
the plant canopy, and the modifying influence of ecosystems on the micro-environment lead
to interactions and feedbacks not present in lower level models. This inevitably leads to
greater variability in data and greater uncertainty in model estimates as (i) interactions may be
non-linear (Hu and Islam, 1997), (ii) the relationships may be scale-dependent (Walsh et al.,
1997), and (iii) measurement of model parameters and validation of model accuracy may not
be straightforward. The transposition of models to a different scale (specifically, from leaf
and canopy to ecosystem) rests on the simplifying and fragile assumption that the spatial
Water and Carbon Cycles in Heterogeneous Landscapes
93
effects can be explained by underlying and quantifiable gradients in geological, climatic and
edaphic features. Although this may initially seem counter-intuitive, hierarchy theory (Allen
and Starr, 1982; O'Neill et al., 1986; both as cited in Reynolds et al., 1993) suggests that the
predictive power of a model will not increase when we increase the number of lower levels of
organization in the model, since we are limited by the assumptions made on the first step
down towards finer spatiotemporal scales. However, the factors that exercise predominant
control over a process need not necessarily reside in the next hierarchical level of
organization, in which case hierarchical model structure may prove very powerful (Raupach
and Finnigan, 1988). For example, since in forested ecosystems the majority of
evapotranspiration (ET) comes from transpiration, changes in stomatal conductance have
direct effects on ecosystem-level flux. Thus, from the perspective of water and carbon cycles,
we need to understand the process of interest and determine the minimum amount of required
detail from the organ and species level to match the accuracy of models as defined by the
assumptions made when transcending from canopy to ecosystem level (Chen et al., 2004). In
some cases this may require almost no sub-canopy detail, while in others physiological
differences among species could be central to accurately predicting ecosystem processes.
The transition from ecosystem to landscape scale is one of aggregation (Table 1), even
though a simple summing approach may be inadequate as we will see later (Section 5.4.3).
Bradford and Reynolds (2006) and Gardner et al. (2001) emphasized that despite much of the
data from smaller scale experiments being potentially useful for landscape-level studies, the
majority of small-scale measurements are not amenable for scaling because of limitations of
experimental design and (lack of) consideration of factors that have relevance across different
scales. The specific requirements for spatial extrapolation of small-scale data regard adequate
characterization of larger-scale variability, which affects confidence with which predictions
can be made. Once the uncertainty of the data is known, their usefulness for making
predictions increases greatly (Law et al., 2006; Li and Wu, 2006; Wu, 1999).
Another component of uncertainty in scaling from lower levels to the ecosystem level
derives from the fact that ecosystems, as we understand them now (Chapin et al., 2002), are
partially open systems. The cycles of energy, water, and carbon are not constrained to
individual ecosystems, but operate on a continental or even global scale. The cycles of
mineral nutrients (e.g. N, P), on the other hand, are closed, and the finite amount of nutrients
is repeatedly cycled through the ecosystem’s various components (although long-distance
transport of nutrients can occur through either natural or anthropogenic phenomena (Goudie
and Middleton, 2001; Husar et al., 2001; Lelieveld et al., 2002; Prospero, 1999)). The nature
of these depletable resources leads to feedbacks by which different ecosystem processes are
related to one another and stabilize the system. It is important to note, however, that the
interplay between stabilizing (usually internal) and destabilizing (usually external) influences,
the mechanisms involved, and even the metrics of stability, are still a matter of active debate
(O'Neill, 2001; Wu, 2004; Wu and Loucks, 1995).
In this chapter we will discuss water and carbon fluxes in temperate forest ecosystems,
the methods of verifying and constraining various estimates, and what is known of the
variation in these fluxes between different ecosystems. Most of the examples are based on
work conducted in the Chequamegon-Nicolet National Forest (CNNF) in Wisconsin, USA.
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Table 1. Organizational levels above and below ecosystem as relevant for scaling carbon
and water fluxes (the abiotic components of ecosystem are not shown in this
representation). We differentiate between change in organizational level (shown with
arrows) and simple aggregation. Since ecosystem and landscape are on the same
organizational level, the scaling between them is relatively straightforward. Scaling to
an ecosystem from the lower level, on the other hand, includes transition of
organizational levels and is relatively more complex and vulnerable to error.
Organizational
level
Aggregate
Tissue
Organ (Leaf)
(Canopy)
Organism
Community
Ecosystem
Landscape
Region, Biome
Change of organization / hierarchical level
3. SCALING PLANT AND ECOSYSTEM PROCESSES
TO THE LANDSCAPE LEVEL
3.1. The Principles of Scaling
The need for scaling arises from our interest in answering questions at large spatial and
long temporal scales on the basis of information that is limited in both dimensions (Jarvis,
1995). Scaling has been argued to be of central importance in all aspects of ecology (Levin,
1992) as it helps us formalize our understanding of processes that drive the behavior of
broader systems and of interactions between processes acting at different spatiotemporal
dimensions. Without understanding the numerous interactions and feedbacks that constitute
and contribute to the behavior at higher organizational scales, our power to predict and
generalize is limited (Bradford and Reynolds, 2006; Norman, 1993).
When we think of scaling between different organizational levels, we start with a certain
verifiable conceptual model, a hypothesis of how the processes of interest are regulated. For
scaling water and carbon cycles, we assume that the chain of reactions proceeds as follows:
radiation input and energy balance set constraints to water balance, which in turn constrains
Water and Carbon Cycles in Heterogeneous Landscapes
95
carbon balance. The radiation balance of an ecosystem is modified by the albedo of the
vegetation, which, in turn, is determined by soil type and water availability but can be
modified by frequency of disturbance. These feedbacks that operate in both time and space
determine the behavior of the system. The extent to which a process can reach its full capacity
or biological potential depends upon feedbacks from other co-occurring processes. Thus, at
the ecosystem level we are interested in identifying the key feedbacks across the leaf-to-plantto-ecosystem-to-landscape hierarchy.
The simplest approach to scaling is summation. However, the greater the transition in
scale (either spatial or temporal), the greater the chance that this method will not suffice. In
Table 1 we have presented the organizational levels below and above the ecosystem and
shown the transitions of scale that are encountered. We have also highlighted within-scale
aggregation designs where the whole can be approximated as the sum of individual
components without invoking any rules of higher organization.
The generalized steps for scaling were outlined by Caldwell et al. (1993) as follows: (1)
assessing the scale of the phenomenon in question, (2) identifying the boundary conditions
and constraints, (3) searching for consistencies at different scales, (4) streamlining bottom-up
models to incorporate only the salient features, (5) incorporating feedbacks (both positive and
negative) that may operate on some scales but not necessarily on other scales, and (6) testing
the results on different scales with independent studies. Another aspect of scaling includes
iterative steps of formalizing, verifying and simplifying relationships that are known to
operate in the system.
3.2. Complexities of Ecosystem-to-Landscape Scaling
One of the first tasks when modeling processes across multiple scales is to find
dimensions that are common for different organizational levels and would lend a common
measure to the question of interest. In terms of water and carbon fluxes, both energy balance
and gas exchange are properties measurable at leaf, canopy and ecosystem scale.
Nevertheless, when transcending a level of organization (Table 1), additional components and
processes come into play and may alter the process of interest. For example, it is understood
that carbon exchange at the leaf level is driven by: (i) sink/source strength of the leaf, (ii) CO2
availability in the bulk media, and (iii) stomatal conductance (GS) (Baldocchi, 1993). At the
ecosystem level, however, a significant (if not the dominant) fraction of exchange occurs by
turbulent transfer, and molecular diffusion plays only a minor role (Jarvis and McNaughton,
1986; McNaughton and Jarvis, 1991). The importance of turbulent transfer of air is that this
method can transport CO2 and H2O against the concentration gradient, whereas at the leaf
level the exchange is driven by the concentration gradient. Boundary layer conductance
affects gas exchange at both leaf and canopy scales, even though the breaking of this layer
has a more dramatic effect on canopy than on leaf gas exchange. Likewise, the regulation of
leaf- and canopy-level energy balance is controlled by different factors. Leaf energy balance
is determined by the incoming and outgoing radiation, the balance between short- and longwave components, and the partitioning of incoming radiation to sensible and latent heat
fluxes. Canopy energy balance, on the other hand, additionally depends on the transfers of
heat and water vapor inside the canopy and across the landscape, which are affected by
advective flows, the energy balance of underlying soil and energy storage in vegetation and in
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the canopy air space. The energy balance of an ecosystem depends primarily on vertical
energy fluxes. Although the lateral transfer of energy in soil is possible, the vertical gradients
and fluxes dominate over lateral ones (Noormets et al., 2004).
The proper level of detail to include in the scaling process may vary with the question to
be answered. For modeling gas exchange, it has been suggested that since energy exchange
between vegetation and the atmosphere occurs at the top of the plant canopy, only the
dominant canopy species need to be characterized (Chapin, 1993). This approach is
exemplified by models that describe canopy-level behavior in reference to a single layer of
leaves (Ball and Berry, 1991; Collatz et al., 1991; Farquhar et al., 1980; Norman, 1993).
It has been recommended that model “mechanisms” be constrained to one organizational
level lower than the level of interest and that the individual drivers at the lower level be
expressed phenomenologically (Reynolds et al., 1993). Cleaning models of excessive detail of
lower-level variation (Bazzaz, 1993) and retaining only significant factors and processes is a
continuous process and represents the refinement of a model for a particular application. For
example, models of ecosystem productivity usually operate at an hourly or daily time scale
and use respective mean or maximum radiation levels as input. Yet we know that light can be
very heterogeneous in the forest canopy (Fladeland et al., 2003). All leaves, except those at
the topmost canopy positions, are exposed to light conditions that vary over a few seconds or
minutes. Whether it is important to include such details depends on if these factors contribute
to explaining potential feedback mechanisms that stabilize the system. Of course, the
structure and level of detail in the model should support its purpose. For example, the
significance of sunflecks is expected to be greater on photosynthesis than on transpiration, as
the sun-shade transition has greater implications on local radiation than on air vapor pressure
deficit (VPD). The level of required mechanistic detail also increases with increasing
structural complexity of the system (Meyers and Paw U, 1986), but in general, understanding
the negative feedbacks between system components provides the main mechanism for model
simplification (McNaughton and Jarvis, 1991).
4. WATER FLUXES
4.1. Ecosystem Water Balance
The hydrological cycle, describing the circulation of water between different pools, is
depicted in Figure 1. The primary storage compartments for water include the oceans,
permanent ice, ground water, soil water, fresh water bodies and rivers, the atmosphere, and
the biosphere (plants and animals). The processes of water transfer between the storage
compartments include precipitation, evaporation, transpiration, infiltration, runoff, and
groundwater flow. Detailed understanding of the dynamics of these pools and the mechanistic
regulation of the transformations among them are required to assess the quantity and quality
of regional and global water resources (Entekhabi et al., 1999; Hutjes et al., 1998). In this
section, we will focus on the components that link plant-level water use to ecosystem water
balance to landscape water balance and ultimately to the regional water cycle.
Water and Carbon Cycles in Heterogeneous Landscapes
97
Figure 1. Major water fluxes in a forested watershed. The insets describe components of biological control of
transpiration: (i) stomatal conductance (GS) increases in response to light levels (Q), (ii) GS declines with
increasing vapor pressure deficit (D) and (iii) decrease in hydraulic conductance (k) of the soil-plant
continuum in response to water potential (Ψ) gradients between soil and leaves (vulnerability curves).
The hydrologic cycle is driven primarily by large-scale motions of air masses, which are
caused by differences in the amount of heating from solar radiation at different latitudes and
between land and water. Air masses moving over land and water pick up some water from
evaporation from oceans and ET from continents, move this water in vapor form, and release
it as precipitation. The ET flux represents a combination of several water loss components
(ordered from high to low magnitude): plant transpiration, canopy and litter interception, and
evaporation from soil and vegetation surfaces. In addition to environmental factors, plant
transpiration rate is related to leaf area index (LAI) and sapwood area (Andreassian, 2004;
Gholz and Clark, 2002; Gholz et al., 1990). Canopy interception of precipitation can account
for up to 20% of total rainfall in North American forests (Helvey and Patric, 1988).
Interception is related to canopy and branch structures, LAI, and the intensity and frequency
of precipitation. Soil evaporation in forests is generally minimal unless the soil is saturated
(Currie, 1991). Next to precipitation, ET is the second largest water flux in the water balance
in terrestrial systems. On average, about two thirds of annual precipitation returns to the
atmosphere as ET (Baumgartner and Reichel, 1975). This flux is closely related to ecosystem
productivity (Law et al., 2002; Rosenzweig, 1968) and biodiversity (Currie, 1991).
Available water that is not evaporated contributes either to run-off (Q, 0-50% of
precipitation) or change in water storage pools (ΔS). Runoff includes both surface and
subsurface flows, and at a watershed scale can be quantified as streamflow. Surface flow
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rarely occurs and is uncommon in forested ecosystems because forest soils have high
infiltration capacity. Subsurface flow rates are controlled by soil texture and structure and
associated physical properties such as hydraulic conductivity, soil water retention, and
hydraulic gradients that are often dictated by surface and subsurface land topography. Runoff
is typically higher in moist than in dry ecosystems – in tropical rainforests (Shuttleworth,
1988) and temperate forests (Waring et al., 1981), about 50% of precipitation contributes to
runoff and 50% to evaporation, whereas in grasslands and steppes 100% of precipitation
leaves as ET (Floret et al., 1982; Massman, 1992). ET originates almost completely from
terrestrial surfaces, with only about 3% coming from lakes and rivers. Thus, at least 60% of
all circulating water within continental areas moves through the biosphere to the atmosphere.
Since only about one percent of this water is stored in plant or animal tissues at any given
time, the rates of water fluxes are high in comparison to the amount of water stored. The
residual term, change in storage pools, is negligible in the temperate zone over multiple years,
but on short temporal scales, it can change significantly. These changes may also be difficult
to quantify due to the heterogeneity of soil, geology and topography, and different subpools
of saturated and unsaturated zones.
4.2. Stomatal Control of Transpiration
In physical terms, the evaporation of water from wet surfaces is described with the
Penman-Monteith equation (Monteith, 1965) that combines earlier mass-transfer and energy
balance-based approaches:
VPD
ra
E=
r
ρ w λ ( Δ + γ (1 + c ))
ra
Δ R n + c pρ a
Eq. 1
where E is evapotranspiration, Δ is the slope of the saturation vapor pressure-temperature
curve, Rn is canopy net radiation, cp is the specific heat capacity of air, ρa is the density of air,
VPD is vapor pressure deficit from canopy to air, ra is the bulk vegetation aerodynamic
resistance, ρw is the density of water, λ is the latent heat of evaporation, γ is the psychrometric
constant, and rc is canopy resistance. Aerodynamic resistance, ra, is affected by canopy
properties and the flow of air through and above the canopy, while rc = (GSL)-1, where GS is
canopy average stomatal conductance and L is canopy leaf area.
While maximum potential ET is determined solely by physical conditions (water
availability and demand), actual ET from the leaf surface is regulated by stomatal guard cells
that help maintain plant water status even under high atmospheric water demand. In
physiological terms, this means that the hydraulic conductance of the soil-plant continuum
will be functionally linked to stomatal conductance (Sperry et al., 2002).
The response of stomatal conductance to environmental conditions is the key plant level
control over whole plant hydraulic conductance. Many studies going back to the early 1970s
(Lange et al., 1971; Schulze et al., 1972) have shown decreasing stomatal conductance with
Water and Carbon Cycles in Heterogeneous Landscapes
99
increasing VPD. Such a response leads to a non-linear saturating response between VPD and
transpiration (Ewers et al., 2005) and even a decrease at extremely high VPD (Jarvis, 1980;
Monteith, 1995; Pataki et al., 2000). The cue for this decline in stomatal conductance was
cleverly shown to be linked to transpiration rather than directly to VPD by Mott and
Parkhurst (1991). Although several empirical models have been developed (Ball et al., 1987;
Jarvis, 1976) and have confirmed the involvement of hydraulic feedback loop between guard
cell water potential and transpiration rate (Franks, 2004), the exact mechanism of regulation
remains elusive. Nevertheless, the control of transpiration by GS is heavily utilized in climate
(Avissar and Pielke, 1989; Sellers et al., 1997), ecosystem (Aber and Federer, 1992; Foley et
al., 1996, 2000; Running and Coughlan, 1988; Running and Hunt, 1993; Running et al., 1989)
and hydrologic modeling (Band et al., 1993; Famiglietti and Wood, 1994; Mackay and Band,
1997; Vertessy et al., 1996; Wigmosta et al., 1994).
4.3. Potential Mechanisms Governing Stomatal Control of Transpiration
Stomatal closure under high VPD conditions is probably a response to low leaf water
potential, ΨL (Oren et al., 1999b), and reduced transpiration (Monteith, 1995) helps to
minimize potentially fatal xylem cavitation (Sperry, 2004; Tyree and Sperry, 1989). Stomatal
regulation provides the universal control point that regulates the benefits of CO2 uptake for
photosynthesis in exchange for transpired water, and it operates between the demands of VPD
and availability of water as determined by environmental parameters (soil water release
properties; Sperry, 1998) and plant hydraulic properties (Katul et al., 2003).
The regulation of ΨL is ultimately governed by the properties of the water conducting
xylem in the plant and the texture of the soil. It has been shown that hydraulic conductance
scales linearly with plant leaf area, a relationship that holds across many plant species and
environmental conditions. Yet, any change in environmental conditions that increases
resource availability decreases the efficiency of hydraulic conductance per unit leaf area
(Mencuccini, 2003), which will ultimately affect both the magnitude of transpiration and its
response to environmental conditions. Factors that may alter hydraulic conductivity include
changes in soil texture, vapor pressure deficit, CO2 concentration, and soil nutrients
(Mencuccini, 2003).
4.4. Modeling Stomatal Conductance Response to Internal and External
Signals
Quantifying the response of GS to VPD is not a trivial matter since the latter is closely
correlated with radiation and temperature regime of the underlying surface. However,
Rayment (2000) devised a statistically based method for data filtering that allows the VPD
effect on GS to be isolated from the effects of other environmental variables. Conditional
filtering was used to develop the concept of reference stomatal conductance (GSref; Ewers et
al., 2001), leading to significant simplification of the Jarvis (1976) stomatal conductance
model (Oren et al., 1999a):
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GS = GSref − m ⋅ ln VPD
Eq. 2
where -m is the logarithmic sensitivity of the GS response to VPD. GSref is defined as
maximum GS at VPD=1 kPa. This model is preferred over the Ball-Berry stomatal
conductance model (Ball et al., 1987) because of its use of relative humidity as the driving
factor instead of VPD. The Ball-Berry model is also outperformed by the Jarvis (1976)
approach in comprehensive multiple model comparisons (Katul et al., 2000). The advantage
of the Jarvis model variants (Oren et al., 1999b) for hydrologic processes is that it directly
addresses plant response to vapor pressure deficit as a proxy for water loss rate, which means
it works best when the rate of water loss is high and hence hydrologically significant.
Recently, Katul et al. (2003) presented a coupled water and carbon model that bridges the gap
between the carbon-oriented models using the Ball-Berry equation and the water-oriented
models using the Jarvis equation.
Oren et al. (1999) relationship
E. nevadensis, L.. tridentata
12 - 151 year old P. mariana
Seven tree species year 1
200
Seven tree species year 2
150
-2
-1
-1
m (mmol m s ln(kPa) )
250
100
50
0
0
100
200
300
-2
400
-1
GSref (mmol m s )
Figure 2. Relationship between reference canopy stomatal conductance (GSref, defined as GS at VPD=1 kPa)
and the sensitivity of GS to VPD (m; Eq. 1). The solid line (with slope 0.6) represents species that regulate
minimum leaf water potential (Oren et al., 1999b). Species that do not regulate minimum leaf water potential
(Ephedra nevadensis, Larrea tridentata and Picea mariana) have lower -m at any given GSref as a result of
declining leaf water potentials with changing sapwood:leaf area ratios. Seven tree species from the temperate
Chequamegon Ecosystem-Atmopshere Study (ChEAS) area from two contrasting years closely follow the 0.6
line despite defoliation, water level change, and leaf area dynamics.
Across a large range of species, and even environmental conditions within species, -m is
0.6 GSref (Figure 2). Since the original review of Oren et al. (1999b), many other species and
effects of environmental conditions with species have been analyzed (Addington et al., 2004;
Ewers et al., 2001, 2005; Gunderson et al., 2002; Oren et al., 1999a). The 0.6 proportionality
between -m and GSref results from the regulation of minimum ΨL to prevent excessive xylem
Water and Carbon Cycles in Heterogeneous Landscapes
101
cavitation. Species or individuals with high GSref have the disadvantage of having a
proportionally high -m and greater absolute reduction in GS with increasing VPD, while
species with low GSref have the advantage of having a low -m and smaller absolute reduction
in GS with increasing VPD. Important deviations from the 0.6 proportionality occur (i) in
species where the minimum ΨL decreases with increasing VPD, (ii) when the range of VPD
increases, or (iii) when the ratio of boundary layer conductance to stomatal conductance is
low (Oren et al., 1999b). The first two conditions result in a ratio of -m to GSref less than 0.6
as the result of plants that have less strict regulation of ΨL (Figure 2) such as drought tolerant
desert species Ephedra nevadensis and Larrea tridentata (Ogle and Reynolds, 2002; Oren et
al., 1999b) or trees that maintain a low sapwood-to-leaf area ratio as seen in Picea mariana
with increasing age (Ewers et al., 2005). The third condition results in a ratio of -m to GSref
that is greater than 0.6 (Oren et al., 1999b).
4.5. Scaling Transpiration Measurements and Models
The earlier summing approaches to canopy gas exchange required over 30 simultaneous
gas exchange measurements to be taken at any given point in time at different levels of the
canopy (Leverenz et al., 1982). Clearly, such a low efficiency is prohibitive for larger
domains. Data on stem sap flow can now provide a continuous record of a much greater
portion of plant water use (Ewers and Oren, 2000; Granier et al., 1996). For stand-level
applications, measurements of sap flow first must be scaled to plant level ET using radial and
circumferential measurements (Ewers et al., 2002; Ewers and Oren, 2000; James et al., 2002;
Lu et al., 2000; Lundblad et al., 2001; Oren et al., 1999b; Phillips et al., 1996) and then to
stand level through estimates of stand sapwood area (Oren et al., 1998). Such studies have
shown that radial and circumferential trends change both within and among species and may
change with time (Ford et al., 2004). Considering these trends, estimates from leaf-level gas
exchange and whole tree sap flux measurements show a good agreement (Figure 3).
If new additions of stomatal conductance measurements confirm the GSref-VPD
relationship for plants that regulate minimum leaf water potential (Figure 2), they may open
new avenues of scaling in both time and space. Recent studies even suggest that species that
do not regulate minimum water potential can be successfully modeled by incorporating the
dropping water potentials with increasing VPD (Ewers et al., 2005; Ogle and Reynolds,
2002), further confirming the mechanistic underpinnings of Eq. 2. Since only GSref needs to
be quantified, relatively few measurements are required for populating a spatially
heterogeneous landscape (Figure 4). Landscape gradients could be quantified with the
efficient, spatially-explicit 2-D measurement design as proposed by Burrows et al. (2002).
This approach uses non-uniform, non-random location assignment of point-pairs, considering
underlying landscape gradients and heterogeneity, and is particularly well suited for
geostatistical analyses. The use of a sampling design that minimizes the number of required
point-pairs for acceptable confidence limits makes this method particularly suited for scaling
other sparsely sampled properties, including ecosystem-level fluxes of water and CO2.
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Asko Noormets, Brent Ewers, Ge Sun et al.
Pinus taeda
Scotland Co., NC
July 24, 29 1998
C
I
F
IF
-2
-1
GS (mmol m s )
250
125
0
0
125
250
gS (mmol m-2 s-1)
Figure 3. Comparison of porometry-based stomatal conductance (gs) and sap-flux based stomatal
conductance (GS) in control (C), irrigated (I), fertilized (F), and irrigated/fertilized (IF) Pinus taeda trees in
three positions: upper branches (open symbols), lower branches (gray symbols) and stems (closed symbols).
The dashed line represents the 1:1 line. Data reanalyzed from Ewers and Oren (2000).
Figure 4. Major cover types around the WLEF tower (Davis et al., 2003). Ecosystem transpiration flux
saturates with increasing vapor pressure deficit, whereas free evaporation from the saturated soils and open
water surfaces increases linearly. Comparison of measured eddy covariance evapotranspiration flux with
area-weighted sum of scaled-up sap-flux measurements and free evaporation at the WLEF site showed good
agreement, whereas generic biome-based scaling was inaccurate.
Water and Carbon Cycles in Heterogeneous Landscapes
103
In situations when underlying spatial gradients are not known, a simple non-spatial
scaling approach (Peters et al., 2004) can provide a first approximation of landscape
heterogeneity. Figures 4 and 5 provide two examples, depicting the patchiness of sap flow
and ecosystem ET, respectively, in 2×2 and 25×30 km landscapes. Both examples assume
constant values of the parameter of interest (sap flow and ET) within a given land cover type
as estimated from Landsat Thematic Mapper data. Also called a “paint-by-numbers”
approach, this method may suffice if the land cover classification adequately captures the
sources of process variation. To test this, MacKay et al. (2002) compared the landscapeaveraged sapflux data to ecosystem evapotranspiration data from an Ameriflux eddy
covariance tower at the center of the landscape. This scaling framework was further used to
test the efficacy of biome-based models that are scaled based on leaf area index. They found
that, due to differences in stomatal regulation of transpiration, the identity of vegetation type
was as important for simulating transpiration as overall leaf area. Furthermore, they
demonstrated the importance of distinguishing between free evaporation and transpiration
when scaling up evapotranspiration, because of linear (evaporation) and nonlinear
(transpiration) responses of these processes to environmental drivers.
The scaled-up estimate from a larger and more complex landscape (Figure 5) using areaweighted average flux shows that the relative contribution of ET to the landscape-level ET
from each ecosystem is very similar to the contribution in physical area (Table 2). Although
the timing and peak values of ET differed among the six measured ecosystems, the
cumulative ET over the growing season (June-September) was similar despite differences in
leaf area (not shown) and averaged about 250 mm across the landscape. From the
hydrological point of view, the small differences (<30 mm or 12%) in ET among the
ecosystems can be considered too small to affect water fluxes at the landscape scale.
Figure 5. Spatial display of growing season ecosystem evapotranspiration from eight ecosystems (mature red
pine (MRP, age 63 years), mature mixed hardwood (MHW, 65), young red pine (YRP, 8), young hardwood
clearcut (YHW, 3), intermediate red pine (IRP, 21), intermediate hardwood (IHW, 17) and pine barrens
(PB1,12 and PB2, 2)) at the Chequamegon-Nicolet National Forest (CNNF) in northern Wisconsin.
Figure 6. Topography (A) and ecosystem types (B) of a section of CNNF (the area shown represents 5.5 km2). The input data at 30-m spatial resolution was used to run
hydrological model MIKE SHE at 100-m resolution. Simulated spatial distribution of modeled daily ecosystem ET (C), groundwater table depth (D), soil moisture
content (E), and flow directions (F) are shown for August 11, 2002.
Water and Carbon Cycles in Heterogeneous Landscapes
105
Table 2. Growing season evapotranspiration for five ecosystems and their relative
contributions at a landscape scale in northern Wisconsin. MRP – mature red pine
plantation (age 63 years), MHW – mature mixed northern hardwood stand (65), YRP –
young mixed red and jack pine plantation (8), PB – pine barrens (12), YHW – young
hardwood clearcut (3).
Cover
MRP
MHW
YRP
PB
YHW
Summary
Area (ha)
1796
4688
283
1876
3058
Sum
11702
Area contribution
0.15
0.40
0.02
0.16
0.26
Sum
1.00
ET (mm)
271
251
270
265
239
Mean
253
ET contribution
0.16
0.40
0.03
0.17
0.25
Sum
1.00
4.6. Simulating Landscape Hydrology
Having quantified individual ecosystem-level ET, we can analyze its dynamic response to
site properties (topography, soil properties, climate, etc.) by simulating water movement and
vegetation-atmosphere interactions at different spatial and temporal scales (Chen et al., 2005).
On Figure 6 we show daily ecosystem evapotranspiration and modeled hydrological
properties of soil around the mature hardwood stand in Chequamegon-Nicolet National Forest
(CNNF), Wisconsin. The hydrological model, MIKE SHE (Abbott et al., 1986a, 1986b; DHI,
2004; Im et al., 2004), utilizes spatially explicit information on vegetation type and
properties, soil, and topography, and it simulates the full water cycle characteristic of a forest
ecosystem, including ET and vertical soil water movement in the unsaturated soil zone to the
underlying groundwater system. The ET sub-model of MIKE SHE interacts with soil
moisture content dynamically at multiple layers. The total ET flux was controlled by the
potential evapotranspiration (PET), which in this study was calculated externally using
Hamon’s method (Lu et al., 2005) as a function of measured air temperature and calculated
daytime length using site location (latitude) and date. Water loss through canopy interception
was modeled as a function of PET and of LAI, which varies on a daily basis. Plant
transpiration (T) is described in MIKE SHE as a function of PET, LAI, root distribution, and
soil moisture content in the rooting zone. Soil evaporation was simulated in the procedure as a
function of PET, LAI, soil moisture, and residual water availability (PET-T). Vertical
movement of water infiltration was modeled using 1-D Richard’s equation while groundwater
movement was simulated as 2-D flow. Groundwater and overland water flows link all the
simulation units, which in this case are 100×100 m cells. The model inputs were generalized
spatial soil and vegetation parameters (LAI and plant rooting depth), but daily climatic
variables of air temperature and rainfall were used to constrain model behavior with measured
data.
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Asko Noormets, Brent Ewers, Ge Sun et al.
Figure 7. Seasonal dynamics of simulated and measured ecosystem evapotranspiration and volumetric soil
moisture content in the mature mixed hardwood stand in CNNF in northern Wisconsin. The MIKE SHE
hydrological model systematically overestimated measured eddy covariance ET values.
The spatial pattern of ET is complex during the summer months because it depends on
the differences in leaf area among the forest ecosystems and gradients of soil moisture. On
Figure 6 we show daily ecosystem evapotranspiration and modeled hydrological properties of
soil around a mature hardwood stand in CNNF in Wisconsin for a single day. The spatial
distributions of soil moisture and ground water table depth distributions are primarily
controlled by land topography, but actual ET also depends on vegetation type and properties.
Modeling suggested that the lowest areas in the study landscape may be seasonally saturated
due to high groundwater levels and the absence of perennial streams. As expected, the
groundwater table (the hydraulic head) follows the general topographic gradient but displays
a more complex pattern, and lateral groundwater flow magnitude and directions vary in space
(Figure 6) and time. The temporal dynamics of ET and soil moisture, which are controlled by
energy and water availability (assessed through PET and precipitation, respectively), are
shown in Figure 7. It is obvious that soil moisture varies greatly in complex terrain, but recent
landscape-level studies also suggest that ecosystem ET can vary significantly even on
seemingly flat landscapes, like northern Wisconsin or the Atlantic coastal plain. In these
regions, small variations in topography can cause distinct hydrologic regimes (wetland versus
upland), and may result in pronounced differences in forest communities with different
ecosystem ET (Clark et al., 1999; Ewers et al., 2002; MacKay et al., 2002). Therefore,
extrapolation of carbon and water fluxes to the landscape-level must consider the effects of
landforms on physical and biological processes at a broader scale (MacKay et al., 2002).
Water and Carbon Cycles in Heterogeneous Landscapes
107
5. CARBON FLUXES
5.1. Variation Among Ecosystems
For ecosystem processes to be integrated over landscape to global scales, spatially
continuous parameters must be estimated from remotely sensed (RS) information. It has been
shown that disparity of spatial resolution between ecosystem level heterogeneity (Law et al.,
2006) and landscape level patchiness on one hand and the coarseness of some RS data on the
other (Turner et al., 2003; Zheng et al., 2004) can significantly alter estimates of regionally
integrated carbon and water balances. Therefore, we must understand the sources of
landscape-level variability on carbon and water exchange and the mechanisms behind it.
Figure 8. Growing season cumulative NEE, ER, and GEP in stands of different ages in the CNNF in
Wisconsin, USA. Panel (A) depicts the age relationship across all stands, whereas panel (B) distinguishes
between deciduous (black) and coniferous stands (red). NEE is expressed by sign convention by which
negative values indicate fluxes from atmosphere towards the surface (uptake by vegetation) and positive
values indicate fluxes from surface to the atmosphere (release by vegetation and soil). ER and GEP are both
positive and NEE = ER – GEP.
In a managed forest landscape in northern Wisconsin, we found that the differences in net
ecosystem exchange of carbon (NEE) among 10 different forest stands were better explained
by mean tree age than by stand biomass, LAI, species composition or any environmental
variable measured (Figure 8). The age-dependence was strongest for NEE, which exhibited
both steeper slope and greater R2 than its two component fluxes, gross ecosystem productivity
(GEP) and ecosystem respiration (ER). In fact, the directional change with age was stronger
in ER than in GEP, suggesting that further increases in GEP with age may be limited by self-
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Asko Noormets, Brent Ewers, Ge Sun et al.
shading (Oker-Blom and Kellomäki, 1983) or edaphic constraints. The effect of standreplacing disturbance on ER lasted longer than that on GEP, possibly as the result of
disturbance-generated coarse woody debris as well as soil disturbance. It has to be noted,
however, that the age-dependent changes in carbon fluxes depend on the nature of the
disturbance. For example, a chronosequence study of burned black spruce stands in Canada
found that ER was lowest in the youngest stand and increased with age (Litvak et al., 2003).
This is attributable to the loss of coarse woody debris and litter in the fire, which does not
happen in the case of conventional timber harvest. Furthermore, the age-dependence may
vary with species composition of the stand (Ewers et al., 2005). In Figure 8 the separation of
forests into deciduous and coniferous types shows that while the nature of age-dependence of
NEE is similar between the two types, that of ER is stronger for deciduous than for coniferous
stands and that of GEP is stronger for coniferous than for deciduous stands. Thus, analyzing
the response of NEE alone would have given an incomplete picture, since similar pattern in
age-dependent change in NEE was due to two different mechanisms in these forest types.
Nevertheless, identifying the mechanistic connections behind landscape-level patterns in
ecosystem behavior may be difficult since several age-related parameters (e.g., stand basal
area, biomass, percent canopy cover, leaf area index, soil aeration, and post-disturbance
change in substrate availability and type) are confounding or autocorrelated.
In addition to functional differences, variability of carbon fluxes among ecosystems may
be conferred by other, less obvious factors. These include the effects of underlying terrain, the
activities of animals, and temporal shifts in the magnitudes of these and other predominant
processes. First, the underlying topography may contribute to functional heterogeneity in the
landscape. For example, Boerner and Kooser (1989) found that the lateral transfer of leaf
litter down-slope exhibited strong seasonality and contributed to maintaining pre-existing soil
fertility gradients. Given the time that is required for litter to become inoculated with fungi,
and the complex fungal succession in litter decay (Rosenbrock et al., 1995; Wardle, 1993),
the timing of such a transfer may have biogeochemical implications. In addition, lateral
transfer of material (litter, seeds, soil, nutrients and water) can be facilitated by wind and
water, which, in turn, may depend on the underlying topography of the landscape. Second,
animal activities may contribute significantly to ecosystem elemental cycles and, in fact, may
link the cycles of different ecosystems through transfer of matter. For example, the transport
and metabolism of organic matter by herbivores is thought to significantly modify elemental
cycling in ecosystems by short-circuiting the decomposition loop, releasing nutrients in
mineral form faster than by microbial decomposition alone (Chapin, 1993). Furthermore, the
homeothermy of large herbivores consumes more energy and may further contribute to faster
biogeochemical cycling. However, the significance of large herbivores in biogeochemical
cycling of elements is yet to be quantitatively demonstrated and is currently being contested
by different views. A modeling study by Pastor and Cohen (1997) concluded that since
herbivore choice of food is based on the same chemical properties that determine
decomposition rates (i.e. higher nutrient content and faster growth and decomposition rates
due to lower energy requirements), the nutrient and energy flow through the ecosystem may
remain unaltered by the presence of large herbivores. Thus, the effect of different functional
groups of organisms on ecosystem function remains an open question. Finally, the processes
in ecosystems and differences between them do change over time. For example, the role of
herbivores in affecting an ecosystem’s elemental cycles may depend on the seasonality of the
food source or suitability of the environment for shelter and breeding. The seasonality of ant
Water and Carbon Cycles in Heterogeneous Landscapes
109
and rodent activity has been implicated in affecting the species composition in desert plant
communities (Inouye et al., 1980) through both feeding preferences as well as by transfer of
seeds. Recognition of the number and complexity of interactions at the system level has
sparked interest in identifying the fate and metabolic history of resources as a potential tool
for understanding the functional stability of ecosystems.
5.2. Response to Environmental Cues
The factors causing differences between ecosystem carbon fluxes may vary regionally,
and may include primary environmental factors that limit biological productivity
(temperature, precipitation, light or nutrients) as well as ecosystem properties (LAI, rooting
depth, phenology). Factors that are currently viewed as potential drivers of ecosystem carbon
fluxes and recorded as standard parameters include photosynthetically active radiation (PAR),
air and soil temperature, VPD, and soil moisture content or matric potential. Measures of site
fertility are generally assessed on a more limited scale. Although physical properties and
carbon and nutrient content of the soil change slowly in time, the spatial heterogeneity of
these parameters is significant (Bjørnlund and Christensen, 2005; Wijesinghe et al., 2005) and
not well understood. It is this heterogeneity of soil conditions that makes terrestrial
ecosystems some of the hardest to describe with models (Jørgensen et al., 1996). Yet, some
ecosystem functional properties are better related to soil than to biological characteristics. For
example, Reich et al. (1997) concluded that the relationship between aboveground NPP and
nitrogen mineralization across 50 temperate forest stands was explained better by soil type
and parent material than by stand type or species composition.
While the theory addressing ecosystem interactions in landscapes is still being developed,
applying leaf-level gas exchange models at a canopy or ecosystem level and basic models of
ecosystem respiration has enabled researchers to construct reasonable estimates of annual
ecosystem carbon balances (Falge et al., 2001; Ruimy et al., 1996). Even though the variation
in the light response parameters of assimilation and temperature response parameters of
respiration is much greater at the ecosystem than at the leaf level (Figure 9A), weekly model
fits can yield surprisingly tight and stable parameter estimates. The dynamic change in Pmax
and conservative standard errors of the estimates in five contrasting ecosystems at the CNNF
(Figure 9B) suggest that the phenological and climatological patterns did not interfere with
the parameter estimates at this time scale. Nevertheless, the cross-site differences in the
parameters of light and temperature response functions were not as clear as differences in the
fluxes themselves. The parameters of the temperature response function of ecosystem
respiration showed no unidirectional age dependence (Noormets et al., In press).
5.3. Spatial Heterogeneity
Integration of individual ecosystem data across landscapes requires spatial information
about the factors affecting between-ecosystem variation. In our study area, the most important
scaling parameter was stand age. Until recently, age could not be assessed from remote
sensing data and presented a serious hindrance to extrapolating ecosystem-level carbon fluxes
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Asko Noormets, Brent Ewers, Ge Sun et al.
Figure 9. Net ecosystem exchange of carbon (NEE) as a function of ambient photosynthetically active
radiation (PAR) in a mature red pine plantation in CNNF in Wisconsin over the entire growing season (A)
and seasonal dynamics of light-saturated photosynthesis (Pmax) in five different forest stands (B)
(abbreviations as in Figure 5).
to broader regions. It was not until recently that methods for deriving stand age from satellite
data (Landsat Thematic Mapper) have been developed (Cohen et al., 2001; Zheng et al.,
2004). The approach of Zheng et al. (2004) was further used to link remotely sensed land
cover information (Bresee et al., 2004) with ecosystem carbon fluxes, measured by eddy
covariance. The resulting spatial representations of carbon fluxes (Figure 10) can be analyzed
for regional patterns and the area’s carbon sequestration potential. The fluxes were measured
using the eddy covariance method (Baldocchi, 2003); ER was estimated from the response of
nighttime fluxes to temperature, and GEP was estimated as the difference between ER and
Water and Carbon Cycles in Heterogeneous Landscapes
111
NEE. Negative NEE indicates net accumulation of carbon in the ecosystem and positive NEE
marks net release to the atmosphere. The white areas in Figure 10 are ecosystems for which
we currently lack flux data, and the colored areas represent the ecosystems depicted in Figure
8. This example illustrates the significance of landscape heterogeneity and the overwhelming
dominance of small patches in this managed landscape. Omission of the fine-scale patchiness
could potentially lead to significant differences in area-integrated carbon and water balances,
as mentioned earlier (Turner et al., 2003).
A)
g C/m^2/GS
1 - 200
200 - 400
400 - 800
800 - 1000
1000 - 1200
NoData/Water
B)
C)
N
W
E
S
1:625,000
g C/m^2/GS
-800 - -600
-600 - -400
-400 - -200
-200 - -1
1 - 200
NoData/Water
Figure 10. Landscape-level variation in gross ecosystem productivity (GEP, A), ecosystem respiration (ER,
B) and net ecosystem exchange of carbon (NEE, C). Note that GEP ranges only 400-1200 and ER ranges 1400 g C m-2 per growing season. Sign convention as in Figure 8.
This approach represents non-spatial scaling as defined by Peters et al. (2004). It means
that stands of similar age and species composition as determined from the Landsat Thematic
Mapper satellite data are assumed to have similar carbon fluxes regardless of potential
differences in soil conditions, topographic position and spatial context of neighboring
ecosystems. Scaling-up to the landscape scale would be done using weighted averages,
analogous to Table 2 and as conceptually outlined in Table 1. In this approach, the locations
of ecosystems and underlying gradients are assumed insignificant. The approach presented in
Figure 6F, on the other hand, represents a spatially explicit approach, where the movement of
water depends on the vegetation, topography, climate, and water balance of neighboring
areas. The spatially implicit scaling would fall between these two approaches, considering the
spatially heterogeneous properties of individual grid cells but not including the exchange of
materials or energy with neighboring cells. As discussed by Peters et al. (2006), each of these
approaches has its place, depending on the questions being addressed, the availability and
detail of input and validation data, and the level of acceptable uncertainty in the final results.
For example, the study of the water cycle calls for the spatially explicit approach because the
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Asko Noormets, Brent Ewers, Ge Sun et al.
water table at a location is tied to the water table in neighboring locations, horizontal flux is
tied to underlying topography, and the lateral exchange cannot be ignored. Study of the
carbon cycle, however, can be successfully implemented using a spatially implicit approach,
since horizontal exchange of the material may occur but is much smaller in magnitude than
the fluxes determined by the intrinsic properties of individual ecosystems – assimilating leaf
area, allocation and metabolic rates of assimilated carbon. However, special situations may
exist (like transport of litter downhill, described in section 5.1.), when the identity and
exchange with the neighboring locations are significant. Thus, the choice of the modeling
framework must be based on detailed analysis of the processes of interest.
The transition from individual ecosystem to spatially-explicit framework introduces
additional complexity as the analysis of spatially-heterogeneous data departs from common
ecological methodology. Spatial autocorrelation and cross-correlation of various
environmental and ecosystem parameters calls for geostatistical and spatially-explicit
analyses (Legendre, 1993; Rossi et al., 1992), which are increasingly being used in ecological
research.
In addition to creating spatial heterogeneity, the boundaries delineating different
ecosystems have sometimes been viewed as distinctly different units in the landscape (e.g.,
see Chen and Saunders, Chapter 1, this volume; Ripple et al., 1991; Spies et al., 1994). Chen
et al. (1995) showed that distinct microclimatic gradients exist across the boundaries of
different ecosystems. Light levels decreased relatively rapidly from clearcut-forest boundary
into the forest, reaching the mean value of the ecosystem interior at 30-60 m, whereas
gradients in air humidity, wind speed and air temperature were significant over a 240 m zone.
Air and soil temperature showed intermediate depth of edge influence. Some variables have
been shown to be uniquely dependent on the forest-to-open area light gradient (VPD,
temperature and litter moisture), whereas edge-dependent gradients in shrub cover were
independent of direct beam radiation (Matlack, 1993). We have also found that the effect of
edges is further complicated by aspect and topographic position (Chen et al., 1995), which
require complex spatial analysis tools and a spatially-explicit sampling scheme (Quattrochi
and Goodchild, 1997). Our studies have shown significant changes in the distribution of
carbon pools across ecosystem edges (Chen et al., 1992; Rademacher, 2004) and these, in
combination with the gradients in microclimate, are likely to affect ecosystem carbon fluxes
in these zones. The depth of edge influence was found to be greater for above- than for
below-ground carbon pools (Rademacher, 2004).
The complexities of scaling the heterogeneous pattern of ecosystems to the landscape
level (Figure 10) have been recognized (Smithwick et al., 2003), even though the
methodology for addressing the neighbor-specific edge effects is still being developed (Law
et al., 2006; Peters et al., 2004, 2006; Turner et al., 2003). Understanding the mechanisms and
constraining scaled-up estimates with top-down measurements and modeling are the keys to
reliable transitions from ecosystem to landscape levels. Using such a multilevel approach is
expected to help us answer questions like the one facing the Chequamegon EcosystemAtmosphere Study (ChEAS) network. Here the regionally averaged eddy covariance
measurements from a very tall tower (highest measurement level 396 m) and atmospheric 13C
isotope concentrations suggest that the relevant area of northern Wisconsin is a source of
carbon to the atmosphere (Davis et al., 2003), whereas all ecosystem-level eddy covariance
data suggest the opposite – that the area is decidedly a carbon sink (Desai et al., 2005, In
press; Noormets et al., In press). With a long-term data record of atmospheric trace gas
Water and Carbon Cycles in Heterogeneous Landscapes
113
concentrations and fluxes (Bakwin et al., 1995, 1998, 2004), constrained by
evapotranspiration analyses (Ewers et al., 2002; MacKay et al., 2002), some of the highest
densities of eddy covariance measurement stations in the world, and parameterized plant
growth (Baker et al., 2003) and atmospheric transport models (Denning et al., 2003), the
answers to the above challenges are bound to emerge.
6. CONCLUSION
Recognition of the urgent need to understand the operation of the background
biogeochemical cycles and the magnitude of human impacts on those cycles has led to rapid
development of mechanistically based models, from microscopic to global scales. Most
relevant for issues discussed in this chapter are leaf- and canopy-level gas exchange models,
radiation transfer models, atmospheric transport models and the general circulation models.
Modern models are increasingly complex and often modular and hierarchical in nature – finer
scale processes are represented by individual sub-models that operate either independently,
iteratively with one another, or are constrained by direct measurements, thus simulating
natural feedback processes. Quantifying known uncertainties of modeling, as well as those of
field measurements used in calibration and validation is becoming increasingly common
(Houghton et al., 2001) and represents an important development in quantitative earth system
science. Knowing the limits of prediction further aids in choosing aspects for further and
more detailed study (Baldocchi, 1993).
Current challenges in scaling ecosystem processes to landscapes include more dynamic
representation of spatial gradients in environmental drivers and the flux responses they
invoke. The traditional approach of measuring fluxes in centers of stands, deriving model
parameters from these fluxes, and then assigning these parameters to the entire stand can be
questioned because of the known underlying gradients. In section 4.6. we discussed the
significance of horizontal exchange for the water cycle through the effects of geological and
topographic influences on ground water and surface water flow. Thus, while traditional
approaches may yield parameter differences based on crisp boundaries between generalized
patches of a landscape (Figure 11A), new spatially-explicit approaches may allow for these
parameters to be characterized as transitions across the landscape (Figure 11B). This may be
key if the underlying spatial variability of environmental drivers is not well-represented by
the patches or if evapotranspiration responses are nonlinearly related to these drivers. For
example, hydrologists have for years recognized the importance of nonlinearity of hydrologic
responses at the hillslope scale to spatial gradients in soil moisture, which have been
characterized with soil-topography indices (Beven et al., 1984; O'Loughlan, 1981) and
explicit measurements of soil moisture (Grayson and Bloschl, 2000; Seyfried and Wilcox,
1995). Gradients of nutrient cycling (Jackson and Caldwell, 1993a, 1993b), leaf area index
(Burrows et al., 2002), and vapor pressure deficit (Mackay, 2001) all appear to be important
for representing physiological responses to environmental drivers.
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Asko Noormets, Brent Ewers, Ge Sun et al.
Figure 11. The traditional approach (A) to landscape level integration assumes uniform properties across a
patch that is characterized at a “representative” central location. In (A-i) the black regions represent one
forest type with a sharp boundary with another forest or non-forest patch. This sharp boundary is a
representational convenience, and may not occur in natural systems. An alternative spatial gradient approach
(B-i) explicitly tests this assumption by quantifying spatial gradients of processes within the stands. A
potential application of this approach using the concept of reference stomatal conductance, GSref, is
illustrated. The response of GS to VPD can be simplified (Eq. 2, Figure 2) because the sensitivity, -m, of GS
to VPD is proportional to the magnitude of GS at low VPD (GSref) (A and B-ii). This simplification results in
a linear relationship between GSref and m (A and B-iii). In the traditional approach, a single point on the linear
relationship would describe each forest stand or other patch type (A-iii). The spatial gradient approach would
allow GSref and -m to vary in response to spatial gradients (e.g., soil properties, micrometeorology, leaf
nitrogen) within and between stands (B-iii). Proper representation of spatial variability is increasingly
important as landscapes become more fragmented, with large areas falling into transitional edge zones.
7. ACKNOLWEDGEMENTS
Support for this work was provided by NSF (JC, BEE, DSM), NASA (DSM) and USDA
FS Southern Global Change Program (JC, AN, SG). We gratefully acknowledge the help
from graduate and undergraduate students and technical personnel who contributed to various
components of the study.
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