Simulation of evapotranspiration from Florida pine flatwoods

Ecological Modelling 114 (1998) 19 – 34

Simulation of evapotranspiration from Florida pine flatwoods Shuguang Liu *, Hans Riekerk, Henry L. Gholz School of Forest Resources and Conser6ation, Uni6ersity of Florida, Gaines6ille, FL 32611, USA Received 10 October 1997; accepted 20 May 1998

Abstract An evapotranspiration model (ETM), including three submodels of transpiration, rainfall interception and substrate (soil or water surface) evaporation, was developed for the simulation of evapotranspiration (ET) from cypress (Taxodium ascendens) wetlands and slash pine (Pinus elliottii ) uplands in Florida flatwoods. Transpiration was scaled up from the leaf level to the ecosystem level by incorporating information on ecosystem structure (e.g. species composition, tree density, leaf area index), turbulence transport in and from the canopy, stomatal conductance and meteorological information. The rainfall interception submodel was physically derived from the interception processes. The substrate evaporation submodels for the open surfaces in the wetlands and forest floors in the pine uplands were developed based on field observations. The model was tested with an independent data set acquired by eddy correlation. Daily and seasonal patterns of ET, bulk aerodynamic conductance, bulk stomatal conductance and canopy dryness index of these two ecosystems were simulated and the sensitivity of ET to environmental conditions was evaluated. © 1998 Elsevier Science B.V. All rights reserved. Keywords: Evapotranspiration; Modeling; Cypress wetlands; Slash pine uplands; North-central Florida

1. Introduction Slash pine (Pinus elliottii var elliottii ) uplands and cypress (Taxodium ascendens) wetlands are the two most important forest ecosystems in the flatwoods landscape in Florida (Ewel and Odum, * Corresponding author. Present address: Department of Botany, University of Wyoming, Laramie, WY 82071-3165, USA. Tel.: + 1 307 7663261; fax: + 1 307 7662851; e-mail: [email protected]

1984). Evapotranspiration from these ecosystems has been studied using various methods and techniques (Brown, 1981; Golkin, 1981; Heimburg, 1984; Riekerk, 1985, 1989; Ewel and Smith, 1992). However, it is still unknown as to which species (cypress vs slash pine) or ecosystem (cypress wetlands versus slash pine uplands) consumes more water in the landscape. The answer to this question has significant implications for the comprehensive management and landscape design of the Florida flatwoods (Odum, 1984; Ewel, 1990). To

0304-3800/98/$ - see front matter © 1998 Elsevier Science B.V. All rights reserved. PII S0304-3800(98)00103-3

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S. Liu et al. / Ecological Modelling 114 (1998) 19–34

Fig. 1. Structure of the evapotranspiration model ETM.

adequately answer this question and to predict ET for various management scenarios, it is necessary to understand the changing patterns of the three ET components (rainfall interception, transpiration, and evaporation from the substrate) under various environmental and managerial conditions. Many ET models have been developed for the estimation of ET from forests (Monteith, 1965; Shuttleworth, 1976; Brutsaert, 1982; Lhomme, 1988a,b). Among them, the Penman-Monteith combination equation (Monteith, 1965) is the most widely used. Although the Penman-Monteith equation has been applied successfully in many situations, it has been challenged conceptually (Steward and Thom, 1973; Shuttleworth, 1976; Finnigan and Raupach, 1987; Lhomme, 1991) and experimentally (Baldocchi et al., 1987). It has also been demonstrated that ET with significant below-canopy evaporation, such as that from a forested wetland, cannot be adequately described using such a single-layer combination equation (Lhomme, 1988a). To our knowledge, no existing ET model can be used to simulate ET

from both cypress wetlands and slash pine uplands in Florida flatwoods with consideration of detailed canopy and species information such as stomatal conductance (Liu et al., 1995), temporal and spatial variation of leaf area index (LAI) (Liu et al., 1997), leaf geometry of various species, stratification of canopy, surface water storage capacities of canopy elements (leaves/needles, branches and stems) (Liu, 1998), and water table fluctuations. Therefore, we developed a new ET model (ETM), which includes three submodels of transpiration, rainfall interception and substrate evaporation. We have also validated and applied this model for the simulation of ET for cypress wetlands and slash pine plantations in North-central Florida.

2. Model description The model consists of three submodels of transpiration, rainfall interception and substrate evap-

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Fig. 2. Comparison of the predicted ( ) and measured () evapotranspiration using eddy correlation method for a slash pine plantation.

oration (Fig. 1). Both the interception and the substrate submodels treat the canopy as a singlelayer, while the transpiration submodel treats it as a multi-layer.

2.1. Transpiration submodel The transpiration submodel was based on Lhomme’s general combination equation (GCE) (Lhomme 1988a,b): lE =





D(Rn − S)+rcp(VPD + DdTe) g%a g% D+ l 1 + a g%a

(1)

where D is the slope of the saturated vapor pressure curve (Pa K − 1), Rn is the net radiation above the canopy (W m − 2), S is soil heat flux density

(W m − 2), r is air density (kg m − 3), cp is specific heat of the air at constant pressure (J kg − 1 K − 1), VPD is the vapor pressure deficit of the air at the reference height above the canopy (Pa), g is psychometric constant (= 66 Pa K − 1), dTe is the difference of the canopy equivalent temperatures for sensible heat transfer Tec and for latent heat transfer Tev (Lhomme, 1988a,b), and g%c and g%a are the bulk stomatal canopy conductance and the bulk aerodynamic conductance, respectively. The GCE was originally developed to quantify the total latent heat exchange between a forest and the atmosphere. It is similar to the Penman-Monteith combination equation (Monteith, 1965). However, the meanings of the two conductances are different from those in the Penman-Monteith equation, and there is an extra term, dTe, in the

S. Liu et al. / Ecological Modelling 114 (1998) 19–34

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GCE. As indicated by numerical analysis, dTe is not negligible when the foliage is dry and the substrate is wet, and it is small when both are dry (Lhomme, 1988a). This extra term makes the application of the GCE difficult in forests with substantial amount of evaporation from the substrate. In order to get rid of dTe in Eq. (1), we separated substrate evaporation out from transpiration, and simulated the three components of ET separately. Only transpiration is simulated by GCE, with dTe being set to zero. This treatment is justified by the fact that transpiration is not dependent on the evaporation from the substrate in forests. The canopy is divided into several layers in the transpiration submodel. The profiles of net radiation Rn(z) and photosynthetically active radiation PAR(z) in the canopy were simulated with the Beer-Lambert Equation: Rn(z)=Rn(zh ) e

− k (SAI(z)/2 cos u)

PAR(z)=PAR(zh) e − k (SAI(z)/2 cos u)

(2) (3)

where u is the solar zenith angle, SAI(z) is cumulative surface area index from canopy top zh to the height z, k = 0.35. The extinction coefficient k

of 0.35 corresponding to half-SAI was derived from radiation measurements (Liu et al., 1997). If converted to total SAI, it would have been 0.175, which was close to the value of 0.15 used by Cropper and Gholz (1993) and observed empirically for a slash pine plantation by Gholz et al. (1991). Profiles of wind speed u(z), turbulent diffusivity K(z) and boundary-layer resistance rb(z) in the canopy were simulated using formulations from Perrier (1967, 1976, cited by Lhomme, 1991): u(z)= u(zh) e − b0L(z)

 n  

a0 du(z) l(z)2 dz

K(z)= gb(z)=

1 u(z) a w

(4) (5)

b

(6)

where w is the width of a leaf (m), a0 and b0 are 0.4 and 0.6, respectively, a and b are 150 and 0.5, respectively, l(z) is the leaf area density or leaf area per unit volume (m2 m − 3), and L(z) is the cumulative leaf area index (m2 m − 2) related to leaf area density by: L(z)=

&

zh

l(z) dz

(7)

z

Bulk stomatal conductance (g%c) and bulk aerodynamic conductance (g%a) are scaled up from the leaf/needle scale to the ecosystem scale by considering elementary conductances, including aerodynamic conductance between the top of the canopy to the reference height, the vertical diffusive conductance of latent heat in the canopy, leaf/needle boundary-layer conductance, and stomatal conductance at the leaf/needle scale. The horizontal conductances for sensible heat (leaf boundarylayer conductance, gb,i ) and latent heat (leaf stomatal conductance, gs,i ) in layer i, which are used for the calculation of the bulk conductances g%c and g%a (Lhomme, 1988b), were calculated as: gbi = LAIi gbi gs,i = LAIi Fig. 3. Comparison of the predicted and measured potential evaporation from the open water surfaces in cypress wetlands.

gsi gbi gsi + gbi

(8) (9)

where LAIi is the leaf area index in layer i, gsi and gbi are the stomatal conductance and leaf

Fig. 4. Daily patterns of photosynthetically active radiation (PAR), vapor pressure deficit (VPA), air temperature (Ta) and canopy dryness index in the C wetland on selected days during 1993.

S. Liu et al. / Ecological Modelling 114 (1998) 19–34 23

Fig. 5. Daily patterns of canopy stomatal conductance (gs), bulk boundary-layer conductance (gb), aerodynamic conductance (ga) and transpiration (Tr) in the C wetland on selected days during 1993.

24 S. Liu et al. / Ecological Modelling 114 (1998) 19–34

Fig. 6. Daily patterns of canopy stomatal conductance (gs), bulk boundary-layer conductance (gb), aerodynamic conductance (ga) and transpiration (Tr) in the C upland on selected days during 1993.

S. Liu et al. / Ecological Modelling 114 (1998) 19–34 25

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S. Liu et al. / Ecological Modelling 114 (1998) 19–34

Fig. 7. Daily variation of net radiation, air temperature, vapor pressure deficit and wind speed during 1993.

boundary conductance, respectively, of a unit LAI in layer i. The stomatal conductance of each layer was calculated based on simulation models developed for the dominant species (Liu et al., 1995). Boudary-layer conductance is the reciprocal of boundary-layer resistance.Aerodynamic conduc-

tance (ga) between the canopy top height H and the reference height z is calculated by (Lhomme, 1991): ga0 =



k u z− d ln H− d



(10)

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Fig. 8. Dynamics of leaf area index in the C wetalnds and C upland during 1993.

where k is the von Karman’s constant (0.41), d is the displacement height (m), and u  is the friction velocity (m s − 1), which can be interpreted physically as an index of the rate of rotation of the frictionally driven eddies in the air flow above the surface (Thom, 1972). The displacement height is commonly related to stand height (Webb, 1975; Brutsaert, 1982; Shuttleworth, 1989) and LAI of the canopy (Shaw and Pereira, 1982; Lindroth, 1993), and was calculated by (Lindroth, 1993): d= (0.858−0.408 e

− 0.328LAI

)H

(11)

The vertical diffusive conductance, gai, when the vertical flux of latent heat crosses layer i, is calculated based on the eddy diffusivity K(z) in layer i (Lhomme, 1991): gai

&

zi − 1

zi

dz K(z)

n

−1

#

Ki zi − 1 −zi

(12)

where Ki is the mean eddy diffusivity for heat and vapor for layer i (m2 s − 1).

2.2. Rainfall interception submodel The derivation and testing of the rainfall interception submodel is described elsewhere (Liu, 1997). Here only the general equation is presented: I= Cm(D0 − D)+

&

T

(1−D)edt

(13)

0

where I is rainfall interception (mm), Cm is the water retention capacity of the element surfaces (leaves, branches and stems) in the canopy (mm), D0 is canopy dryness before rainfall, D is canopy dryness after rainfall, T is rainfall duration, and e is the evaporation rate from a wetted canopy. Canopy dryness index is defined as the ratio of water retained on the surfaces of the canopy

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Fig. 9. Seasonal change of canopy stomatal conductance in the C wetland and C upland during 1993.

elements and the water retention capacity, Cm. Canopy dryness index (D) during a rainfall can be calculated as (Liu, 1997): D= Dexp(−kP/Cm)

(14)

where k is ground coverage, and P is the cumulative rainfall.

Eeq =

2.3. Substrate e6aporation submodel 2.3.1. E6aporation from open water surfaces in cypress wetlands

Es = 0.3Eeq

A(h) A

ing water (m2), which is a function of water table depth h (m), A is the delineated wetland area (m2), and 0.3 is a coefficient determined by field measurements from the three cypress wetlands used in this study (Liu, 1996), and Eeq is the equilibrium evaporation (mm h − 1):

(15)

where A(h) is the wetland area covered by stand-

o (R − S) o+1 n

(16)

where o= D/g.

2.3.2. E6aporation from the soil/litter surface in the pine uplands It is very difficult to simulate evaporation of soil water from forest floors. However, results based on a chamber method showed that daily

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Fig. 10. Seasonal change of transpiration in the C wetland and C upland during 1993.

soil evaporation rate from a flatwoods pine forest floor was only about 0.3 mm (Brown, 1978), and soil evaporation decreased exponentially with water table depth. When the water table in the pine uplands dropped below 0.7 m depth, surface (0 – 10 cm) soil moisture without further rainfall was reduced to its residual water content (Liu, 1996), which made soil evaporation very low. Based on these experimental results, soil evaporation (Es) is estimated by: Es = 0.3Eeq e − 4WT

(17)

where WT is the depth of water table from the soil surface (m). The input variables of the model include (Fig. 1): (1) ecosystem type (wetland or upland) and the time period for simulation; (2) ecosystem structure and species information: canopy height, tree density, number of canopy layers used in the simulation, number of species, LAI fractions of each species and each layer, width of leaves/ needles, and coefficients in the Jarvis’ type stomatal conductance model (Liu et al., 1995); (3) hourly meteorological data: net radiation, relative humidity, PAR, wind speed, and rainfall; and (4)

daily water table records. Output variables include bulk stomatal conductance, bulk aerodynamic conductance, transpiration, rainfall interception, evaporation during rainfall, canopy dryness index, LAI, water retention capacity of the canopy, and substrate evaporation. Output can be at the time intervals of hour, day, month or year.

3. Sites and methods Field measurements were collected on the industrial forest lands of the Georgia-Pacific Corporation at 82°15% W longitude and 29°47% N latitude, about 15 km northeast of Gainesville, FL. The climate is characterized by an annual average rainfall of about 1330 mm and average winter and summer temperatures of 14 and 27°C, respectively (Dohrenwend, 1978). The study site, a mosaic of slash pine plantations and cypress wetlands, was in a rather level and poorly-drained interfluvial area. Spodosol soils (Ultic alaquod) dominated in the pine uplands with spodic horizons between 30 and 75 cm depth, underlain at

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Table 1 Sensitivity analysis on the evapotranspiration model ETM Variable

Item

Cypress wetlands

Pine uplands

Base

+

−

Base

+

−

LAI

Value (m2 m−2) ET (mm year−1) Sensitivitya

6 1349

7 1402 24

5 1281 30

6 1109

7 1178 37

5 1020 48

Radiation (R)

Value (cal s−1 m−2) ET (mm year−1) Sensitivitya

68.7 1349

75.1 1429 64

61.9 1267 61

68.7 1109

75.1 1150 40

61.9 1067 38

Temperature (Ta)

Value (0C) ET (mm year−1) Sensitivitya

23.0 1349

24.0 1378 49

22.0 1316 56

23.0 1109

24.0 1135 54

22.0 1078 64

VPD

Value (mbar) ET (mm year−1) Sensitivitya

10.6 1349

12.6 1391 17

8.6 1266 33

10.6 1109

12.6 1221 54

8.6 951 76

Wind Speed

Value (m s−1) ET (mm year−1) Sensitivitya

2.3 1349

2.7 1357 3

1.8 1342 2

2.3 1109

2.7 1126 9

1.8 1090 8

R, Ta and VPD

ET (mm year−1) Sensitivityb

1349

1501 102

1156 130

1109

1290 148

a b

885 184

Sensitivity was calculated as: Sensitivity = [(ET change)/ETbase]/[(value change)/valuebase]*100. (Value change)/valuebase was considered as the average of the (value change)/valuebase for radiation, temperature and VPD.

100 to 200 cm by a discontinuous clay layer of variable thickness (Riekerk et al., 1995). The 30year-old slash pine plantations had an understory dominated by saw palmetto (Serenoa repens Bartr.) and gallberry (Ilex glabra L. (Gray)), had a history of prescribed fires, and was fifth-row thinned 6 years before. Pond cypress, black gum (Nyssa syl6atica var. biflora) and swamp bay (Persea palustris (Raf.) Sarg.) formed the canopy layer in the wetlands (Liu et al., 1997). Three small cypress wetlands, designated C, K and N, and adjacent pine uplands were selected for paired studies. One antenna tower was set up in each wetland and its adjacent pine upland. At least three trees could be reached from each tower for measuring transpiration and stomatal conductance at the leaf/needle scale, and the vertical distribution of photosynthetically active radiation (PAR) and leaf area index (LAI). Sea-

sonal patterns of LAI were reconstructed based on information of litterfall and phenology of foliage elongation (Liu et al., 1997). Hourly meteorological information was obtained from a nearby weather station. The fluctuation of the water table was continuously monitored using Stevens water table recorders. Eddy correlation measurements acquired in another study conducted in a nearby pine plantation (Gholz et al., 1997; in review) were used to calibrate the simulated evapotranspiration at the ecosystem scale. Rainfall interception measured in the three wetlands and uplands between April 1, 1993 and March 31, 1994, and evaporation from the water surfaces of the three cypress wetlands measured from April 1993 to March 1994 using floating Class A pans, were used to test the rainfall interception and substrate evaporation submodels.

S. Liu et al. / Ecological Modelling 114 (1998) 19–34

4. Results and discussion

4.1. Model 6alidation Predicted half-hourly ET and its daily patterns agreed well with the eddy correlation measurements (Fig. 2). This agreement indicated that ETM successfully scaled up transpiration from the leaf/needle levels to the ecosystem level, because all those measurements were acquired under no-rain conditions, with dry canopies while evaporation from the forest floor was small. It also indicated that the stomatal conductance models developed for the major species in these ecosystems (Liu et al., 1995) could be used for scalingup purposes. The interception submodel is general, as all parameters in the model, including canopy storage capacity, Cm; canopy dryness index, D; and degree of canopy closure, k, have specific physical meanings. The model has been tested with rainfall interception measurements from six cypress wetlands and slash pine plantations acquired during this research and with data from the literature (Liu, 1997). Model simulation explained 89% (r 2 =0.89) of the variations of interception measurements obtained from the six sites (Liu, 1997). The substrate evaporation submodel was tested using data from the floating Class A pans located in the wetlands. This kind of measurement represents the potential evaporation from the open water surfaces in the wetlands, because water surface areas in the pans were maintained constant. Potential evaporation from the water surfaces of the three wetlands as measured between April 1993 and April 1994 was 50097 mm. The predicted evaporation during the same period was 484 mm, and the seasonal pattern of predicted and measured evaporation from the pans also agreed well (Fig. 3).

4.2. E6apotranspiration from cypress wetlands and slash pine uplands Selected daily (every 30 days) patterns of measured PAR, VPD, temperature and simulated canopy dryness index are shown in Fig. 4 to illustrate diurnal patterns in different seasons. For

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example, as reflected in the canopy dryness index, there was rainfall on days 90, 180, 240, 270 and 330. Rainfall occurred on the days previous to days 150, 180 and 240 leading to wet canopies in the early mornings. Sporadic cloudiness on days 30, 120, 150 and 240 resulted in considerable fluctuations in PAR. Simulated canopy stomatal conductance increased rapidly in the early morning with increasing PAR in cypress wetlands (Fig. 5) as well as slash pine uplands (Fig. 6). It generally peaked in the morning. This pattern agreed with the observed pattern of stomatal conductance at the leaf level (Lindroth, 1985; Liu, 1996). The daily pattern of transpiration was similar to that of canopy stomatal conductance, but in contrast, transpiration generally peaked in the early afternoon (Figs. 5 and 6), when the VPD and PAR were at their daily maximums. Transpiration, canopy stomatal conductance, and bulk boundary-layer conductance were all higher in the C upland than in the C wetland, although the daily patterns were similar (Figs. 5 and 6). Maximum incident radiation occurred in May when there were fewer clouds than during the rest of the growing season (Fig. 7 Ewel and Gholz, 1991). Air temperature and VPD peaked in late July at about 32°C and 25 mbar, respectively. The seasonal variation of radiation, air temperature and VPD showed higher values in summer and lower values during the winter. Higher wind speeds occurred during the spring and winter seasons. The temporal changes of LAI showed a major contrast between the C upland and the C wetland (Fig. 8). LAI peaked at 4.7 m2 m − 2 in July in the wetland, and 4.2 m2 m − 2 in September in the upland. Minimum LAI was just before the beginning of the growing season with values of 1.4 and 3.4 m2 m − 2 in the wetland and upland, respectively. The LAI in the wetland during the winter season did not go to zero because of the presence of some pine trees and other evergreens among the deciduous cypress trees. Maximum canopy stomatal conductance occurred in July and September in the C wetland and upland, respectively (Fig. 9), corresponding with the seasonal dynamics of LAI at these sites

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S. Liu et al. / Ecological Modelling 114 (1998) 19–34

(Fig. 8). Canopy stomatal conductance in the C wetland was higher than that in the upland during summer, but the relationship reversed during the winter months, again consistent with the dynamic difference of LAI between these two ecosystems. Transpiration reached its maximum value in May (around Julian day 150) (Fig. 10) when net radiation peaked. Transpiration was reduced in July (around Julian day 200) due to low VPD and low wind speed conditions. The latter decreased the leaf boundary-layer conductance and also canopy aerodynamic conductance. The maximum daily transpiration in summer was around 5.5 mm day − 1 for both ecosystems, while the maximum in winter was about 0.6 and 1.5 mm day − 1 in the wetland and upland, respectively. In general, the C wetland had higher transpiration in summer than the C upland, with the relationship reversed in winter. Again, the dynamics of LAI primarily led to the contrasts in transpiration between the wetland and upland, not differences in stomatal conductance at the leaf/needle scale (Liu, 1996). Simulated ET from these three wetlands from 1991 to 1993 averaged 974986 mm, which was not significantly (a =0.05) more than that from their adjacent slash pine uplands with an average of 795920 mm.

4.3. Sensiti6ity analysis Water level fluctuations in the K wetland during the wet year of 1992 (Liu, 1996) and the meteorological information of 1993 (which had less missing data) were used for a sensitivity analysis of the ETM model. We called 1992 a wet year because the mean annual water level in the wetlands was the highest in 1992 during the study period. The use of water level records collected during a wet year in sensitivity analysis may give us the maximum possible ET from these ecosystems by varying other conditions such as radiation and LAI, because the contribution of evaporation from the substrate is close to its maximum. As expected, LAI was an important factor in controlling ET (Table 1), with ET increasing at a steadily increasing rate with LAI and a limit after about 8 m2 m − 2. The difference in the ET-LAI

relationship between the wetlands and uplands was nearly constant over a wide range of LAI. The difference was mainly dictated by the magnitude of evaporation from the open water surfaces in the wetlands. ET from cypress wetlands was more sensitive to a change in incident radiation than ET from pine uplands (Table 1), likely a result of the more vertical distribution of cypress leaves in this region (Liu et al., 1997). ET from both ecosystems was also quite sensitive to changes in air temperature, perhaps with implications for future conditions. ET from pine uplands was more sensitive to changes in relative humidity or VPD than ET from cypress wetlands. Aerodynamic conductance in both ecosystems did not limit ET because the sensitivity of ET to wind speed was very low. Obviously ET from both ecosystems would increase or decrease greatly when radiation, air temperature and VPD increased or decreased simultaneously, although ET from the uplands tended to be more sensitive to this kind of change.

5. Summary It should be noticed that there is an imbalance between the sophistication of the different submodels. The substrate evaporation submodels were empirically derived from field measurements. We also tried to develop a physical model based on radiation and/or VPD. However, because the wetlands are quite small and are encircled by slash pine uplands with different species composition (evergreen versus deciduous), leaf area indices, and therefore energy reaching the forest floor or open water surfaces, energy advection between these ecosystems was likely, making the development of such a model difficult. If the current submodels were modified to include the impact of seasonal leaf area dynamics on substrate evaporation and the magnitude of energy advection in the landscape, a more mechanistic formulation may be possible. On the other hand, ETM is not limited in terms of the number of species included or the number of layers for the specification of the canopy. Other canopy processes such as sensible heat, CO2 and

S. Liu et al. / Ecological Modelling 114 (1998) 19–34

other trace gas exchanges between the canopy and the atmosphere could be easily incorporated into ETM. ETM can also be interfaced with other hydrological and ecological models to facilitate the prediction of runoff, subsurface flow, impacts of forest management practices on the hydrology and ecology at various scales. ET from both ecosystems was sensitive to changes in leaf area index, incident radiation, vapor pressure deficit, and air temperature, but not to wind speed. The difference in ET between the wetlands and uplands for a given LAI was mainly determined by the evaporation from the open water surfaces in the wetlands.

Acknowledgements We thank the Georgia-Pacific Corporation for the use of its forested lands. This research was funded by USDA Forest Service Grant Contract No. A8FS-91961 to H. Riekerk and H.L. Gholz, and a DOE/NIGEC South-eastern Regional Center grant to H.L. Gholz.

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