Transpiration and soil evaporation

Hillslope

5.4 Vertical process representation

5.4.4 Transpiration and soil evaporation

The Macaque model represents transpiration from both the canopy and understorey vegetation, however in this chapter reference is only made to canopy processes since an understorey is largely absent in South African commercial plantations. There are 23 climatic and plant physiological parameters used in Macaque to reduce or enhance transpiration by the forest canopy, resulting in a far more complex representation of the transpiration process than that utilised by theAeR Umodel.

It has been shown that transpiration from coniferous forests is strongly dependent on vapour pressure deficit and canopy conductance (Whitehead and Jarvis, 1981). Other factors affecting the transpiration rate of plants include solar radiation and the soil water availability (Lindroth, 1993). In Macaque, transpiration is calculated using the Penman-Monteith equation (Equation 5.5) in which both these influences are represented as: .

max(O,Rtrans,c) 1

/). esat +Cp pair Dref - - -

dayl raero, c,ref

Ec=----~---:-1----

AptiJ(/).esat+y(l+ ))

raero, c, refgC

Equation 5.5

Where

E_c = canopy transpiration rate

desat = rate of change of saturation vapour pressure with temperature

Rtrans,c = net radiation absorbed by canopy and used for transpiration

Cp specific heat of air at constant pressure

Pair density of air including water vapour

Dref = mean daytime vapour pressure deficit at reference level above canopy boundary layer

raero, c, ref =

A =

pw y

gc

dayl =

aerodynamic resistance to vapour transfer between canopy and reference level latent heat of vaporisation of water

density of liquid water psychrometer constant

conductance to vapour transfer of entire canopy

number of seconds in a day for which the sun is above the horizon

The meteorological components of the equation are driven by huniidity (specified as the vapour pressure deficit) and net radiation. Estimates of vapour pressure deficit, are obtained from Linacre's (1992) temperature based equations. Solar radiation is derived from daily temperature ranges, the time of year and topography (slope and aspect) of the terrain while these together with absorbed long wave radiation, are used to calculate net radiation.

The plant physiological controls of transpiration used in the Penman-Monteith equation are canopy conductance and aerodynamic resistance of the canopy. There are a number of factors than restrain or enhance leaf conductance (canopy conductance divided by LAl) as summarized in Figure 5.5. The effect of decreasing soil water availability on leaf conductance, for example, is correlated by pre-dawn leaf water potential (LWP). As the soil dries out, the LWP is reduced and leaf conductance decreases. Leaf conductance is further scaled by factors such as VPD, solar radiation, temperature (temperature range, night mtrumum temperature and optimal temperature) and atmospheric carbon dioxide concentration, using what is known as the product method. According to this method, simple optimality functions (ranging from zero to one) are derived for each controlling variable. The functions are then multiplied together to form a single optimality value, which is used to increase or decrease the leaf conductance.

Transpiration

Net radiation Canopy conductance Vapour pressure deficit

~vp~D~m_U_lt_iP_li_er~_~-

- - - - I

- - - - I

LWP multiplier Min. Temp multiplier Temperature multiplier

~

^I

^A-

I LA!

~ -I.

Radiation multiplier

I-

Pre-dawn leaf water potential

Root multiplier CO2multiplier

Figure 5.5. Transpiration controls ofthe Macaque model (after Watson, 2000).

Unlike ACRU where evaporative demand is linked directly to LAI and reference potential evaporation, LAI values in Macaque only control transpiration indirectly through the

calculation of canopy conductance, which is the product of LAI and leaf conductance and used in the calculation of radiation absorption by the canopy.

This approach is highly complex with numerous controls on transpiration being considered.It has, however, been noted that feedback within the transpiration/conductance systems of large e:ucalypt forests may be present, with the result that the conductance is always such that transpiration occurs at some potential, or at least at solely radiation-determined rates (Watson,

I 999a).

Given the data limitations encountered in South Africa, the potential for using such a detailed approach to calculate evapotranspiration may not be feasible in South Africa Although tree transpiration is seasonally affected by radiation (daylength), it is most highly correlated to vapour pressure deficit (VPD). The response to VPD rapidly tends towards a relatively constant transpiration rate, which is maintained by changes in canopy conductance - unless limited by soil water availability (Dye, 2000, personal communication). Thus, the accurate simulation of VPD and soil water available to trees is of great importance. Dye (1996), however, found it impractical to simulate the water balance of deep rooting systems at a site in Mpumalanga, South Africa for the purposes of estimating non-potential transpiration rates, since there were uncertainties concerning the depth of the rooting system, the soil water recharge mechanisms and the water retention characteristics of the deep subsoil strata.

Inorder to evaluate the performance of the Macaque model and its suitability for application in South Africa, the model was applied to a selection of forested catchments.InChapter 6 the criteria for catchment selection are provided followed by a discussion on the model application and simulation results.

* * *

In Chapter 5 the Macaque model was described which served to illustrate the differences in modelling approaches between the Macaque model andACRU model. Although both models are physically-based distributed models it is evident that Macaque's process representation is more detailed than that of theACRU model. The Macaque model application to South African catchments described in the following chapter serves to highlight whether this additional model complexity canbe supported by the availability of data.

6. APPLYING THE MACAQUE MODEL TO SOUTH AFRICAN