Predicting the water-use of Eucalyptus nitens plantations in ...

RESOURCES 

PROJECT NUMBER: PNC143.0809 SEPTEMBER 2011 

Predicting the water-use of 

Eucalyptus nitens 

plantations in Tasmania 

using a Forest Estate Model 

This report can also be viewed on the FWPA website 

www.fwpa.com.au 

FWPA Level 4, 10-16 Queen Street, 

Melbourne VIC 3000, Australia 

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Predicting the water-use of Eucalyptus nitens 

plantations in Tasmania using a Forest Estate 

Model 

Prepared for 

Forest & Wood Products Australia 

by 

Sandra Roberts, Steve Read, Mike McLarin, Paul Adams

Publication: Predicting the water-use of Eucalyptus nitens 

plantations in Tasmania using a Forest Estate Model 

Project No: PNC143-0809 

This work is supported by funding provided to FWPA by the Australian Government Department of 

Agriculture, Fisheries and Forestry (DAFF). 

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ISBN: 978-1-921763-56-4 

Researcher: 

Sandra Roberts, Steve Read, Mike McLarin, Paul Adams 

Forestry Tasmania 

79 Melville St 

Hobart, Tasmania, 7000 

Forest & Wood Products Australia Limited 

Level 4, 10-16 Queen St, Melbourne, Victoria, 3000 

T +61 3 9927 3200 F +61 3 9927 3288 

E info@fwpa.com.au 

W www.fwpa.com.au

Executive Summary 

The objective of this project was to use a forest estate model to predict the annual water-use 

of Eucalyptus nitens plantations. This report describes measurements of water-use made in 

E. nitens plantations in Tasmania, the development of a model to predict water-use of E. 

nitens plantations and incorporation of this model in an estate model. 

An estate model that includes predicted water-use will enable forest managers to 

simultaneously assess the impacts of plantation management decisions on wood production, 

income and water-use. This capacity will be useful in limiting the impacts of plantation 

management on other water-users, ecosystems or threatened species, while maximising wood 

production and profits. 

Water-use was measured in 5 E. nitens plantations in the Florentine Valley, Tasmania from 

2008 to 2010, and in 1 plantation at Forestier during 2011. Plantations in the Florentine 

Valley were aged 9, 7, 4 and

A simple empirical model (below) was developed from the Florentine field data to predict 

annual water-use of E. nitens using plot basal area and annual rainfall. The adjusted Rsquared 

statistic indicates that the model explains 79% of the variability in water-use. 

Water-use (mm/yr) = 149 + 11.0*Basal Area (m 2 /ha) +0.361*Rainfall (mm/yr) 

The water-use measured at Forestier (792 mm), where rainfall was 854 mm and basal area 

was 30.2 m 2 /ha, fell within the 95% prediction interval (607 to 974 mm) for the model 

(above). The model predicted Forestier water-use of 790 mm, which provides some 

confidence that the Florentine model can be used at this location, and may be applicable for 

use elsewhere in Tasmania. 

The empirical model was successfully included in Forestry Tasmania’s Forest Estate Model 

(FEM). The FEM was used to generate estimates of water-use, the wood volume available to 

cut, and standing basal area for 1532 ha of E. nitens plantation in the Florentine Valley over a 

90 year period. The FEM showed that managing plantations to produce smooth water-use 

and smooth wood production is compatible – managing for one produces the other. This 

lends further support to the concept of a regulated forest minimising changes to hydrological 

systems (Hennes et al., 1971). 

The methods used to measure water-use and build the empirical water-use model are readily 

transferable to other plantation species and native forests. Managers of other plantation and 

forest types typically have FEMs and it is likely that with the collection of some additional 

data and use of process models that they could use similar methods to develop simple 

empirical models of water-use for the species and environments that they manage. 

The difference between rainfall and water-use represents the amount of water that is available 

either as streamflow or groundwater. Where forest management increases water-use, it can 

be deduced that there will be a reduction in water available either as streamflow or 

groundwater. This would give plantation (and forest managers) the power to consider 

plantation water-use and its affect on water availability during decision making processes. 

Further water-use studies could be undertaken in gauged catchments if a greater 

understanding is required of the relationship between water-use and streamflow, however, it 

is likely that this relationship will vary markedly between catchments based on their size, 

geology, topography, the mix of land uses, weather and rainfall patterns, and as such any 

relationships may not be readily transferable between sites.

Contents 

Executive Summary .................................................................................................................... i 

1. Introduction ........................................................................................................................ 1 

2. Florentine Study Summary ................................................................................................ 4 

3. Forestier Plantation Water-use ......................................................................................... 11 

3.1. Site description .......................................................................................................... 12 

3.2. Weather Station ......................................................................................................... 13 

3.3. Plantation basal area and sapwood area .................................................................... 13 

3.4. Instrumentation overview .......................................................................................... 14 

3.5. Soil evaporation......................................................................................................... 14 

3.6. Canopy Interception .................................................................................................. 14 

3.7. Transpiration ............................................................................................................. 17 

3.8. Forestier Water-use Summary ................................................................................... 18 

4. Comparison of Florentine and Forestier Results .............................................................. 19 

4.1. Predicting Forestier water-use with Equation 1 ........................................................ 21 

5. Process Model Comparisons ............................................................................................ 22 

5.1. ProMod ...................................................................................................................... 22 

5.2. CABALA .................................................................................................................. 22 

6. Final water-use function ................................................................................................... 25 

7. Modelling water-use of plantations using Forestry Tasmania’s Forest Estate Model ..... 26 

8. Future research needs ....................................................................................................... 34 

9. Discussion and Conclusions ............................................................................................. 36 

10. Recommendations ......................................................................................................... 38 

11. References ..................................................................................................................... 39 

12. Acknowledgements ....................................................................................................... 43 

13. Appendix 1. ProMod outputs for Florentine and Forestier .......................................... 44

1. Introduction 

Plantation development can result in changes to the availability of water (either as streamflow 

or in groundwater). Where grassland is planted with trees, water availability can decrease 

(Bosch and Hewlett, 1982: Vertessy, 2001: Bren et al., 2006: Best et al, 2003). Where forest 

is replaced with plantation there can be changes in water availability in either direction (Bren 

and Hopmans, 2007: Bren et al., 2006: Cornish and Vertessy, 2001). The changes in water 

availability are linked to changes in the capacity of vegetation to use water. 

In this paper, water-use is the term used to describe the total amount of water evaporated 

from an area of plantation due to transpiration, canopy interception and soil evaporation. 

However, it is common for other authors to use the terms evapotranspiration or interception 

to describe water-use. Water availability is the term used in this paper to describe the 

difference between rainfall and water-use. Available water either becomes streamflow, or 

moves into and through soil or aquifers. Other authors may refer to water availability as 

water yield or describe its components. 

In regions of Australia where water resources are in limited supply or where there is concern 

that aquatic, riparian or groundwater ecosystems could be impacted by reduced water 

availability, water licensing, codes of practice, regulations and policy have been developed to 

preserve water resources. These codes and regulations may limit plantation development or 

make it more expensive. 

The National Water Initiative represents an agreement by governments across Australia to 

achieve a more cohesive approach to the way Australia manages, measures, plans for, prices, 

and trades water (Council of Australian Governments, 2005). The National Water Initiative 

gives governments the power to manage interception by plantations either by limiting their 

development or by issuing water licences. 

In South Australia, the State government has policies to limit plantation development in some 

locations or to issue water licences to protect groundwater resources (Department for Water, 

2012). In some states, Forestry regulators and Codes of Forest Practice limit the extent of 

forest and plantation activity in some catchments. For example, catchments contributing to 

council or domestic water supplies, or catchments with threatened aquatic species, may have 

limits on the area of plantation developed during a nominated period (Forest Practices Board, 

2000). 

In rare cases, plantation development has been prevented by legal action from neighbours 

who fear that local surface and groundwater resources would be compromised by plantation 

development (e.g. Forest Practices Tribunal, 2009 - Clement v FPA and Curran, 2009). 

Restrictions on plantation development or a requirement to purchase water licences 

ultimately lead to greater costs for industry through greater dispersion of the plantation estate 

or through the purchase of water licences. 

1

It is important that water-use by plantations can be quantified and predicted so that catchment 

managers, water authorities and natural resource managers can ensure that water supplies, 

licensed allocations, threatened species, ecosystems and aquaculture are not compromised; so 

that forest managers can avoid unwanted impacts on water while continuing to produce 

wood; and so that regulators can develop and implement fair regulations and policies. 

State agencies and water management authorities are considering how to account for and 

manage water use by plantations but the methods used to do this mostly require further 

development. The State Government of Victoria plans to use the Department of Primary 

Industries Land Use Information System to model the water use of different land uses based 

on previous studies of evapotranspiration, streamflow and groundwater (Department of 

Sustainability and Environment, 2011). South Australia uses an “annualised deemed value” 

for plantation water consumption to allocate licenses and identify plantation development 

thresholds (Harvey, 2009). This smoothes hydrologic impact over the full forest rotation 

period and expresses impacts as an annualised value for the full rotation of all plantations of 

the same species in the same groundwater management area. The Department of Primary 

Industries, Parks, Water and Environment in Tasmania have included plantation water use in 

some of their streamflow models by incorporating the TasLUCaS plantation streamflow 

change equation (Brown et al., 2006; Hydro Tasmania Consulting, 2007). The Tasmanian 

streamflow models are currently used to determine which catchments may have significant 

impacts from plantation development. 

Plantation owners have attempted to understand the impacts of plantations on water resources 

through paired catchment experiments (Cornish and Vertessy, 2001; Bren and Hopmans, 

2007 ), water use studies (Forrester et al., 2010; Hatton and Vertessy, 1990; McJannet et al., 

2000; Feikema et al., 2007; Benyon and Doody, 2004) and process models such as 3PG, 

CABALA (Battaglia et al., 2004), and Promod (Sands et al., 2000). While there is a general 

understanding of the direction and magnitude of water use or streamflow change associated 

with plantation development in some locations as a result of these studies, the development 

and implementation of tools that enable plantation owners to assess impacts of plantations 

and plan activities to minimise negative outcomes for water resources is ongoing. 

To quantify, predict and manage water-use by plantations, Forestry Tasmania commenced a 

program of research in 2007. The objective of the research was to develop a tool that enables 

assessment of plantation water-use during the wood-flow planning process. Early in the 

research program Forestry Tasmania’s Forest Estate Model (FEM) was identified as a tool 

that could be developed for water management. The FEM currently uses information on the 

location, type, management and growth of plantations to predict wood-flow. By including 

simple empirical plantation water-use functions in the FEM it would be able to 

simultaneously assess water-use and wood production, and explore different harvest and 

planting schedules to optimise wood production and/or water availability. 

Previous studies provide evidence of links between forest and plantation water use and 

growth parameters such as sapwood area, leaf area and basal area (Macfarlane et al., 2010; 

Forrester et al., 2010; Roberts et al., 2001; Vertessy et al., 2001). Creating plantation water- 

2

use functions based on growth parameters for use in the FEM requires further collection of 

site and species specific water-use and growth data, evaluation of existing growth and wateruse 

models, and use of previously published data. Forestry Tasmania chose to focus on 

developing a function for Eucalyptus nitens plantations in Tasmania – this being a widely 

planted species for which limited water-use information was available, and an ideal candidate 

for a pilot study given the uniform plantation stand structure. 

This report describes E. nitens water-use experiments conducted at two locations in 

Tasmania; the Florentine Valley, where water-use was measured in 5 plantations over a 3 

year period, and at Forestier, a drier site on the Tasman Peninsula, where water-use was 

measured in 1 plantation over a 1 year period. 

The experimental methods were described in Roberts and Barton-Johnson (2009). Much of 

the inspiration for the experimental methods came from Roberts (2001), Roberts et al. (2001), 

Benyon and Doody (2004), and Putuhena and Cordery (1996). 

The results of the Florentine water-use study were published in Roberts (2011A). A 

summary of the most important results from the Florentine study is provided in Roberts 

(2011A). A description of the Forestier plantation and the results of the Forestier study are 

provided in this report as they are not published elsewhere. Evaluation of existing models 

was described by Roberts (2011). Updated comparisons of two models, ProMod and 

CABALA, are presented in this report. 

The process of developing the E. nitens water-use function for the FEM is described in this 

report. Examples are provided of the outputs from the FEM when the water-use function 

derived from the experiments is included. 

3

2. Florentine Study Summary 

Figure 1. Location of Florentine study sites (Source - Google Maps and Forestry Tasmania 

GIS) 

Water-use and its constituent processes (transpiration, canopy interception and soil 

evaporation) were measured in five E. nitens plantations of different age (Table 1) situated 

close to each other in the Florentine Valley (Figure 1) in Tasmania from 2008 to 2010. The 

plantations occur at 375-425 m above sea level on Jurassic dolerite with some limestone 

outcrops. The soils are deep well-drained red/brown ferrosols (Grant et al., 1995). Annual 

rainfall in the Florentine Valley was between 984 and 1425 mm during the experiment; the 

long-term average rainfall at the nearest weather station is 1200 mm/yr (Maydena, Bureau of 

Meteorology). Potential evaporation (Silo Data Drill, QLD DERM) ranged between 887 and 

4

975 mm/yr during the experiment. The ratios of rainfall to potential evaporation suggest that 

the site is seasonally water-limited but anecdotal evidence suggests that, during dry summer 

periods, the trees have access to ground water, and that their overall annual water-use is 

consistent with that of plantations growing on a non-water-limited site. Roberts (2011A) 

provides a full description of the plantations and the results from this experiment. 

Estimates of annual and monthly basal area, canopy interception, soil evaporation and 

transpiration were made during the study (Roberts, 2011). The purpose of these estimates 

was to generate estimates of annual water-use for adjacent plantations of different age and 

basal area, with a view to developing water-use functions that could be used in the FEM. 

Only annual data are shown in this report as this is the temporal scale of the FEM. Data 

could be disaggregated to smaller time intervals if they were required for other uses. 

The amount of water-used by the plantations, and the proportions of this total used by the 

different water-use processes (transpiration, canopy interception and soil evaporation), varied 

with age (Figure 2). Figure 2 shows the annual canopy interception, soil evaporation and 

transpiration associated with each plantation for each year, and the thinning status of the 

plantation. 

In a plantation less than 1-year-old, soil evaporation was the dominant process, accounting 

for 376 mm/yr, while transpiration and canopy interception combined only accounted for 168 

mm/yr. In an 11-year-old unthinned plantation, transpiration (445 mm/yr) and canopy 

interception (379 mm/yr) were the dominant processes, with soil evaporation just 40 mm/yr. 

Thinning reduced canopy interception and transpiration, from around 1000 mm/yr in an 

unthinned stand to around 660 mm/yr, while markedly increasing soil evaporation, from an 

unthinned value of around 40 mm/yr to a thinned value of 120-200 mm/yr. 

Annual water-use was thus linked to the age of unthinned plantations (R 2 =0.68; Figure 3), 

and also thinning status (Figure 3). The thinned data set doesn’t span enough years to allow 

analysis over time. 

In 2010 all plantations recorded lower than anticipated water-use. This is probably because 

lower than average rainfall (984 mm) and lower than average potential evaporation (887 mm) 

occurred in 2010. Some of the variation in the relationship between annual water-use and age 

thus appears to be due to environmental conditions experienced during the year of 

measurement. Water-use of plantations of the same age could therefore vary between years. 

Furthermore, plantation growth is also a function of the conditions experienced throughout 

the entire life-cycle of the plantation, and some plantations originated during periods with 

better conditions than others and hence had greater density, leaf area and/or basal area for a 

given age. Thus, unthinned Plantation 20A had lower water-use in 2008 than younger and 

older unthinned plantations in that year, which may indicate that this plantation has a lower 

leaf area or growth rate than other plantations due either to site quality or the conditions 

experienced in its early life. 

5

Table 1. Plot information for E. nitens plantations in Florentine Valley 

Coupe ID FO021AC FO021AT FO020A FO023C FO022D 

Eucalypt Species E. nitens E. nitens E. nitens E. nitens E. nitens 

Date Planted 08/1999 08/1999 08/2001 05/2004 10/2007 

Coupe size (ha) 60 60 82 44 56 

Mean annual 

increment 

(m 3 /ha) 

Experimental 

Plot Area (m 2 ) 

Number of trees 

in plot (before 

and after 

thinning, where 

thinned) 

Regime 

27 27 24 26 25 

910 815 1104 1075 864 

96 86 / 27 125 / 47 110 120 

EC3 High 

prune 

EC3 High 

prune 

6 

EC3 

High 

prune 

EC3 High 

prune 

EC2 Mid 

Prune 

Easting 456323 456294 456366 455520 456102 

Northing 5282925 5282934 5282070 5283350 5283431 

Original stocking 1086 1086 1067 1100 1100 

Date/s of pruning 

Dates of thinning 

Pest control 

1/4/05 

8/2/07 

unthinned 

control 

24/12/03 

28/11/05 

1/4/05 

8/2/07 

1/3/06 

18/12/06 

1/2/08 

29/08/07 28/11/08 

24/12/03 

28/11/05 

24/12/03 

28/11/05 

30/12/07 

15/5/08 unpruned 

Plantation 

thinned, 

but 

research 

plot 

unthinned 

24/12/08 

30/12/07 

unthinned

Water Use (mm/yr) 

08 

09 

10 

08 

09 

10 08 

08 09 

10 

Figure 2. Annual canopy interception, soil evaporation and transpiration for each plantation, 

for each year of age, for thinned and unthinned plantations in the Florentine Valley. 

Numbers above each column are the calendar year of measurement. Numbers at the base of 

columns are coupe names. 


1200 

1000 

800 

600 

400 

200 

0 

1100 

1000 

900 

800 

700 

600 

500 

400 

Unthinned 

Figure 3. The relationship between age and annual water-use of plantations in the Florentine 

Valley. Blue squares represent unthinned plantations, red squares represent thinned 

plantations. Labels show plot ID, year of measurement and plantation age. 

7 

09 

10 

08 09 

1 2 3 4 5 6 7 9 10 11 8 9 9 10 11 

Age 

Missing 

data 

Thinned 

22D 23C 20A 21AC 

20A 

21AT 

22D 2008 Age 1 

22D 2009 Age 2 

22D 2010 Age 3 

23C 2008 Age 4 

23C 2009 Age 5 

10 

Canopy interception 

Soil evaporation 

Transpiration 

21AC 2008 Age 9 

y = 130.93ln(x) + 559.49 

R² = 0.44 

21AC 2009 Age 10 

21AC 2010 Age 11 

21AT 2008 Age 9 Thinned 21AT 2009 Age 10 Thinned 

20A 2008 Age 7 

23C 2010 Age 6 

21AT 2010 Age 11 Thinned 

0 2 4 6 8 10 12 

Age (years)

Age could be used to predict water-use in the FEM (Figure 3); however, stand basal area 

(Figure 4) was a better predictor of water-use than age. 

Figure 4 shows the relationship between annual average basal area and annual total water-use 

for each plantation and year of the study – 2009 and 2010 data were missing for 20A thinned 

plots. There is a strong relationship between basal area and water-use (R 2 =0.59). 


1100 

1000 

900 

800 

700 

600 

500 

400 

22D 2008 Age 1 


22D 2009 Age 2 

23C 2008 Age 4 

22D 2010 Age 3 

23C 2009 Age 5 


20A 2008 Age 7 

23C 2010 Age 6 


Figure 4. Relationship between basal area and water-use for thinned and unthinned 

plantations in the Florentine Valley. Labels show plot ID, year of measurement, age, and 

thinning status. 

Mean error in the prediction of water-use from the basal area: water-use function of Figure 4 

was 10% (Table 2), with errors ranging from 1 to 22% for individual plantations and years 

(mean absolute error of 76 mm, Statgraphics). The model residuals indicate that the function 

is a good fit to the data. 

8 

y = 125.25ln(x) + 461.36 

R² = 0.59 

21AC 2008 Age 9 

21AC 2009 Age 10 

21AC 2010 Age 11 

0 5 10 15 20 

Basal Area (m2/ha) 

25 30 35 40

It is important to note that the basal area: water-use function (Figure 4) has an upper limit 

determined by potential evapotranspiration 1 (around 930 mm/yr), and a lower limit which 

represents the water-use of deforested land (around 500 mm/yr depending on weed/grass 

cover). It is likely that once the upper limit of water-use is achieved by a plantation, wateruse 

will continue at this level while the plantation remains in good health. 

The potential evaporation estimate for the Florentine Valley was obtained from Silo Data 

Drill and is calculated from air temperature, solar radiation and vapour pressure deficit data. 

Rayner (2006) concluded that the estimation method used by Silo Data Drill tends to 

underestimate solar radiation during windy conditions. It is likely that the annual potential 

evaporation estimate provided by Silo Data Drill is an underestimate for the site. This may 

explain why it was possible for measured water use to exceed 1000 mm/yr in the oldest 

unthinned plantation. 

Table 2. Summary of results for model fitting. 

Basal 

Area 

(m2/ha) 

Water 

Use 

Actual 

(mm/yr) 

Water Use 

Predicted 

(mm/yr) 

Water Use 

Residual 

(Actual- 

Predicted) 

Water 

Use Error 

(%) Year 

1.5 544.4 514.9 29.4 5.4 2008 

4.0 684.8 635.1 49.7 7.3 2009 

9.7 611.2 745.8 -134.7 22.0 2010 

8.3 746.0 725.8 20.2 2.7 2008 

13.2 909.1 784.5 124.5 13.7 2009 

18.7 753.3 828.0 -74.7 9.9 2010 

23.2 757.2 855.4 -98.2 13.0 2008 

32.2 1052.2 896.1 156.2 14.8 2008 

34.8 1022.2 906.1 116.1 11.4 2009 

37.3 864.4 914.5 -50.1 5.8 2010 

12.6 768.5 778.8 -10.3 1.3 2008 

14.5 779.8 796.4 -16.6 2.1 2009 

16.8 702.8 814.4 -111.6 15.9 2010 

Average Error 

1 Potential evapotranspiration (PET) is the loss expected over a surface with no limitation of water. It is a 

function of the atmospheric demand which depends primarily on the energy available from net radiation, the 

humidity gradient in the lower atmosphere, wind speed and surface roughness (Beven, 2005) or, is a 

representation of the environmental demand for evapotranspiration and represents the evapotranspiration rate of 

a short green crop, completely shading the ground, of uniform height and with adequate water status in the soil 

profile. It is a reflection of the energy available to evaporate water, and of the wind available to transport the 

water vapour from the ground up into the lower atmosphere. Actual evapotranspiration is said to equal potential 

evapotranspiration when there is ample water (Wikipedia - http://en.wikipedia.org/wiki/Evapotranspiration). 

9 

9.6

The basal area: water-use function calculated for the Florentine stands (Figure 4) tends to 

overestimate water-use during years of low rainfall and underestimate water-use during years 

of high rainfall. Table 2 shows that in 2010, when rainfall was 983 mm, actual minus 

predicted water-use was always negative. In 2008 which had 1259 mm and 2009 which had 

1425 mm of rain, actual minus predicted water-use was more likely to be positive. 

A multiple linear regression using basal area (m 2 /ha) and annual rainfall (mm/yr) was formed 

in Statgraphics to predict water-use (WU) (Equation 1). P values below 0.05 for the 

intercept, basal area (BA) and rainfall (R), and an R 2 of 0.82 compared to an R 2 of 0.59 for 

the regression using basal area alone (Figure 4), shows that the rainfall term is a useful 

addition to the function. Equation 1 reduces the estimate of mean absolute error to 50 mm in 

the WU estimate compared with 76 mm for Figure 4 (Statgraphics). The derived function is: 

WU = 149+11.0*BA+0.361*R Equation 1. 

Including rainfall in the function improves its ability to predict WU, and means that average 

rainfall can be included in predictions of water-use, that a stochastic rainfall model could be 

linked to the FEM to assess the effect of rainfall variability on water-use predictions, and that 

the FEM could assess scenarios where rainfall is reduced or increased in response to climate 

change (although evaluating climate change impacts is challenging because changes in wateruse 

due to higher atmospheric CO2 concentrations are still unknown). 

10

3. Forestier Plantation Water-use 

A plantation water-use research site was established in a plantation at Forestier in February 

2011 to measure transpiration, canopy interception and soil evaporation. Forestier was 

selected because it has lower rainfall and greater potential to experience water deficits than 

the Florentine Valley. A weather station was also established. The same measurement 

techniques were used as at the Florentine site (Roberts and Barton-Johnson, 2009). 

Data were collected from Forestier to see if Equation 1, which was developed from 

Florentine Valley plantation water-use data, can be successfully applied to other locations. 

Figure 5. Location Map for Forestier research with “A” indicating research site location 

(Source- Google Maps) 

11

3.1. Site description 

The Forestier plantation is located near Hylands Rd, approximately 45 km east of Hobart 

(Easting 575,043, Northing 5,243,134) (Figure 5). The plantation research plot is 

approximately 270 m above sea level. 

The E. nitens plantation was established in June 2005 at a density of 1117 stems per hectare. 

Spot cultivation was used to prepare the soil because the site is stony. The plantation is 

managed for pulp production and is not pruned or thinned, and there is no intention of these 

activities occurring in the future. 

The research plot is underlain with Jurassic dolerite, and boulders and cobbles are common 

on the surface (30-80%), and in the soil matrix (50-90%). The soil is described as well 

drained, with moderate potential for erosion and low fertiliser requirement. 

The nearest Bureau of Meteorology rain gauge is at Dunalley, approximately 6 km west of 

the site. Between 1974 and 2011, rainfall averaged 700 mm/yr at Dunalley, ranging from 511 

to 1029 mm/yr. There is no distinct rainfall pattern in the monthly records. Most months 

average more than 50 mm of rainfall (Figure 6). 

There are no local temperature data available – the nearest Bureau of Meteorology weather 

station is at Hobart airport, approximately 35 km to the west. 

Figure 6. Average monthly rainfall at Dunalley between 1974 and 2011 (Bureau of 

Meteorology) 

12

3.2. Weather Station 

An automatic weather station was placed in a clearing adjacent to the research plantation. It 

recorded rainfall, air temperature, ground temperature, humidity, solar radiation, wind speed 

and direction. Data prior to May 2011 were lost due to technical difficulties. The tippingbucket 

rain gauge recorded less rain than the bulk rain gauge (tipping bucket = 456 mm of 

rain between 7/5/2011 and 31/1/2012) and was deemed to be defective. The maximum 

temperature recorded was 29 o C, the minimum recorded was 0 o C. 

3.3. Plantation basal area and sapwood area 

A 20 m x 20 m (400 m 2 ) measurement plot containing 39 trees (some with multiple stems) 

was established on 26/2/2011. The diameter of every stem within the plot was measured at 

1.3 m. Stems were remeasured on 20/6/2011, 19/10/2011 and 7/2/2012. Plot basal area grew 

from 29.27 m 2 /ha on the 26/2/2011 to 32.52 m 2 /ha on 7/2/2012. 

Sapwood area was determined for a number of trees using an increment corer and assuming 

radial symmetry. Sapwood area was strongly correlated with basal area (R 2 =0.78) for 

individual trees (Figure 7). Plot sapwood area was estimated at 10.75 m 2 /ha at 

commencement and 11.77 m 2 /ha at completion of the experiment. 

Sapwood Area (cm 2 ) 

180 

160 

140 

120 

100 

80 

60 

40 

20 

0 

Figure 7. Relationship between stem basal area and stem sapwood area of E. nitens at 

Forestier 

13 

y = 0.3051x + 17.8 

R² = 0.78 

0 50 100 150 200 250 300 350 400 450 500 

Basal Area (cm 2 )

3.4. Instrumentation overview 

Mini lysimeters and bulk rain gauges were placed beneath the canopy to determine soil 

evaporation. A throughfall trough was placed under the canopy and another was placed in a 

nearby clearing. Stem flow collectors were placed on three trees in the plot. A bulk rain 

gauge was placed in the clearing alongside the weather station and throughfall trough to 

verify the results from these. Heat pulse sensors were deployed to measure sap velocity in a 

subsample of stems. 

3.5. Soil evaporation 

Soil evaporation was measured with 10 mini lysimeters, and 10 rain gauges, each placed at 

random distances and bearings from a central point in the plot. The lysimeters were installed 

on 28/2/2011, and removed on 30/1/12 – a period of 11 months. During this time, an average 

of 96 mm of soil evaporation was measured from the lysimeters. Soil evaporation for the 

lysimeters was scaled to the year by assuming the evaporation from the missing month was 

equivalent to the average rate over the whole period of measurement – giving a total of 105 

mm/year from the lysimeters. However, because approximately 50% of the plot soil surface 

is covered with boulders which do not allow evaporation, the soil evaporation of the plot was 

estimated at 52 mm/yr. 

The bulk rain gauges associated with the lysimeters collected a total of 738 mm of rainfall 

beneath the canopy, 5 additional rain gauges collected 601 mm – these values are useful for 

contrast with the throughfall troughs and the rain gauge in the open 

3.6. Canopy Interception 

Calculating canopy interception requires a good estimate of rainfall. The tipping bucket rain 

gauge with the Automatic Weather Station consistently underestimated rainfall when 

compared with the bulk rain gauge in the clearing, while the throughfall trough in the open 

collected amounts of rain that were mostly consistent with the bulk gauge. The throughfall 

trough operated from 3/5/11 to 30/1/12 so provides a good record of daily rainfall for this 

period. Rainfall data are available from Dunalley (Bureau of Meteorology), so rainfall at 

Dunalley from 1/1/11-3/5/11 was used to help generate an estimate of total rainfall for 2011 

of 854 mm. 

The throughfall trough under the canopy was plagued with technical problems – either the 

inlets were blocked by frass or the gauge was unbalanced or misaligned with the siphon. 

Periods of good data from the throughfall trough were identified by comparison with rainfall 

measured in the rain gauges beneath the canopy. Sixty-seven days of good throughfall data 

were collected simultaneously in the open and under the canopy. These days were used to 

determine the proportion of rainfall that is captured on the foliage on each rainfall day 

(Figure 8). Approximately 74% of rainfall passed through the canopy to the soil surface and 

26% of rainfall was intercepted. 

14

Rainfall Collected in Throughfall Trough Below Canopy (mm/day) 

30 

25 

20 

15 

10 

5 

0 

Figure 8. Relationship between rainfall collected in the throughfall troughs in the open and 

under the canopy at Forestier 

Stemflow was measured on 4 trees from 13/4/11 to 12/1/12, a period of 9 months. Stemflow 

was collected in 20 L containers for three trees, and 1 tree was fitted with a tipping bucket 

rain gauge. Tree 25 with the tipping bucket rain gauge collected 303.3 L, Tree 32 collected 

358.4 L, tree 34 collected 225.5 L and tree 37 collected 324.38 L during the nine months. 

To estimate annual stemflow, stemflow needs to be predicted for the first three months of 

2011. The relationship between rainfall collected in the open trough and stemflow measured 

on tree 25 by the tipping bucket rain gauge (Figure 9) was used to estimate stemflow for tree 

25 from the rainfall record for 1/1/11 to 12/4/11. Stemflow was estimated at 107.4 L for tree 

25 during this period. Stemflow for this period was estimated for the three other trees based 

on the proportional relationship to tree 25 during the measurement period. This gave total 

stemflow volumes for 2011 of 409.3, 480.9, 304.5, and 437.4 L for trees 25, 32, 34 and 37 

respectively. 

15 

y = 0.74x 

R² = 0.93 

0 5 10 15 20 25 30 35 

Rainfall Collected in Throughfall Trough in Open (mm/day)

Stemflow (L) of Tree 25 

35 

30 

25 

20 

15 

10 

5 

0 

Figure 9. Relationship between daily rainfall and stemflow for tree 25 

There was a close relationship (R 2 =0.99) between stem diameter and stemflow across the 4 

trees (Figure 10). This relationship was used to estimate the stemflow (L) of each tree in the 

plot, and these values were summed to estimate plot stemflow (16,059 L) then divided by the 

plot area (400 m 2 ) to give stemflow of 40.14 mm for the year. 

16 

y = 0.02x 2 + 0.18x - 0.01 

R² = 0.89 

0 5 10 15 20 25 30 35 40 

Rainfall (mm)

Stemflow (L) 

500 

450 

400 

350 

300 

250 

Figure 10. Relationship between stem diameter and stem flow for 4 trees at Forestier 

Canopy interception was then calculated to account for 182 mm of the rainfall occurring at 

the site: 

Canopy interception = Rainfall – Throughfall – Stemflow = 854 – 632 – 40 = 182 mm/yr. 

3.7. Transpiration 

Transpiration is the product of sapwood area and sap velocity. Plot sapwood area, plot sap 

velocity and plot transpiration were calculated for each day, and transpiration was summed 

for the period of the study. 

Plot sapwood area was estimated for each day by assuming linear growth in sapwood 

between the sapwood estimates derived from plot inventory. 

Sap velocity was measured at 4 locations in each of 4 trees from 24/2/2011 to 7/2/2012. 

Sensors were allocated to different depths below the cambium to generate sap velocity 

estimates that were representative of the range of flow rates observed in the sapwood band. 

Sap velocity was estimated for each day by taking the average value of all operating sensors 

for that day. There were very few days with no data. Data from 1/11/11 onwards were 

modified to account for the large step change in sap velocity that occurred when sensors were 

moved to new positions on this date – it appears that the potting mix that insulates the 

thermistors in the sensors had degraded and that moving the probes caused the potting mix to 

fracture leading to the erratic behaviour seen in some probes from this time onwards. Figure 

11 shows the daily average sap velocity values after the last months of data were modified. 

17 

y = 18.2x + 43.9 

R² = 0.99 

200 

10 12 14 16 18 

Stem diameter (cm) 

20 22 24 26

For the 333 days on which data were collected, sap velocity averaged 6.8 cm/hr. The daily 

value ranged from 0.29 to 16.43 cm/hr, with a median of 6.47 cm/hr and standard deviation 

of 3.51 cm/hr. 

Average daily sap velocity (cm/hr) 

18 

16 

14 

12 

10 

8 

6 

4 

2 

0 

24/02/2011 

5/03/2011 

20/03/2011 

29/03/2011 

7/04/2011 

16/04/2011 

25/04/2011 

4/05/2011 

13/05/2011 

22/05/2011 

31/05/2011 

9/06/2011 

18/06/2011 

27/06/2011 

6/07/2011 

15/07/2011 

24/07/2011 

2/08/2011 

11/08/2011 

20/08/2011 

29/08/2011 

16/09/2011 

25/09/2011 

4/10/2011 

13/10/2011 

22/10/2011 

31/10/2011 

9/11/2011 

18/11/2011 

27/11/2011 

6/12/2011 

15/12/2011 

24/12/2011 

2/01/2012 

11/01/2012 

20/01/2012 

29/01/2012 

7/02/2012 

Figure 11. Daily sap velocity averaged across all functioning sensors from the Forestier 

research site 

To provide an estimate of transpiration for a year, transpiration was estimated for 7/2/2011- 

24/2/2011 by taking the average of January and February data. 

Over the period of the study, daily plot transpiration averaged 1.8 mm, with a minimum of 

0.2 mm and a maximum of 3.9 mm/day. The sum of transpiration was 558 mm for the year. 

3.8. Forestier Water-use Summary 

Rainfall for 2011 was estimated at 854 mm. Canopy interception accounted for 182 mm or 

21% of the rainfall occurring at the site. Soil evaporation accounted for 52 mm or 6% of the 

rainfall occurring at the site. Transpiration accounted for 558 mm or 65% of the rainfall 

occurring at the site. Total water-use was 792 mm for 2011, which is 93% of rainfall. 

18 

Date

4. Comparison of Florentine and Forestier Results 

Figure 12 is a plot of basal area and sapwood area of individual trees in the Florentine Valley 

(aged > 5 years or < 5 years), and Forestier. The data were segregated according to age 

because the ratio of sapwood to basal area changes after the canopy of the plantation closes at 

around 5 years. The Forestier results fall within the cloud of data points generated in the 

Florentine Valley, and the lines of best fit overlay each other. Statgraphics “Comparison of 

regression lines” indicates that there is no significant difference between the two fitted 

models (α=0.05). This result supports the use of the Florentine sapwood area model in other 

locations or the inclusion of Forestier data in the Florentine model. 

Sapwood Area (cm 2 ) 

300 

250 

200 

150 

100 

50 

0 

y = 0.30x + 17.8 

R² = 0.78 

0 100 200 300 400 500 600 700 800 

Basal Area (cm 2 ) 

Figure 12. Relationship between basal area and sapwood area for trees aged >5 years at 

Florentine, aged 5years)

Table 3. Summary statistics for daily sap velocity (cm/hr) 

Plot ID Florentine Florentine Florentine Florentine Florentine Forestier 

22D 23C 20A 21AT 21AC 

Mean sap 

velocity 

6.41 6.54 5.66 8.12 5.92 6.8 

Median sap 

velocity 

5.94 6.53 5.04 7.37 5.81 6.47 

Minimum sap 

velocity 

1.45 2.24 0.95 2.16 1.83 0.29 

Maximum sap 

velocity 

15.05 14.15 14.26 20.11 11.91 16.72 

Standard 

deviation 

3.64 3.07 3.27 4.15 2.50 3.51 

The sap velocity records for each plot span different periods so represent sap velocity 

responses to different sets of environmental conditions as well as to different growth stage 

and management events (e.g. thinning in 21AT). Once the plantations exceed 2 years of age, 

there is no age related pattern to the mean daily sap velocity of the different plantations 

(Figure 13, R 2 =0.02) with all unthinned plantations exhibiting a mean daily sap velocity 

between 4 and 7 cm/hr. Thinned plantations exhibit a higher mean daily sap velocity of 7 to 

8 cm/hr. The 1 year old plantation exhibited a substantially higher mean daily sap velocity 

around 10 cm/hr. 

Average sap velocity (cm/hr) 

10 

9 

8 

7 

6 

5 

4 

2008 

2009 

2010 

2008 

2009 

Figure 13. Relationship between plantation age and annual average sap velocity (cm/hr), for 

Florentine unthinned > 2 years old (red squares), Forestier unthinned < 2 years old (green 

20 

2010 

2011 

y = -0.04x + 5.77 

R² = 0.02 

2008 

2008 

2008 

y = -0.21x + 9.71 

R² = 0.25 

2010 

0 2 4 6 

Plantation age (years) 

8 10 12 

unthinned>2 thinned unthinned

triangle), Florentine thinned (blue diamonds) and Forestier (purple circle). Year of 

measurement is shown for each data point 

Sap velocity is highest in the 1 -year-old trees, probably because a small cross-sectional area 

of stem is supporting a relatively large leaf area when compared with the older trees, and 

because the low height (short path length) and new wood vessels (higher conductivity) of the 

seedlings mean that they would have lower hydraulic resistance (Ryan and Yoder, 1997). 

4.1. Predicting Forestier water-use with Equation 1 

When the basal area (30.18 m 2 /ha) and rainfall (854 mm) values for Forestier are entered in 

Equation 1, the estimated annual water-use is 791 mm. The measured value was 792 mm. 

This result provides some confidence that Equation 1 derived from Florentine data can be 

applied to E. nitens at Forestier. 

21

5. Process Model Comparisons 

Even though the aim of this study was to develop a simple empirical model for water-use 

prediction, process models such as ProMod (Battaglia and Sands, 1997) and CABALA 

(Battaglia et al., 2004) can also be used to estimate both the basal area and water-use of 

plantations in different locations in Tasmania, and thus could possibly be used to test and 

refine the empirical relationship between these parameters for different regions. 

5.1. ProMod 

ProMod is a simple model that predicts the growth of forest following canopy closure 

(Battaglia and Sands, 1997). ProMod also predicts water-use based on a crop factor which is 

a function of soil water content, and water-use efficiency which is a function of vapour 

pressure deficit. It requires information about plantation species, location, climate and soil. 

It tabulates results for growth, summarises information about site conditions and charts 

growth parameters against plantation age. 

ProMod was run for the Florentine and Forestier sites using the Forestry Tasmania Toolbox 

(Appendix 1). While ProMod gives annual basal area as an output, it does not give as an 

output annual water-use. Instead, water-use is given as an average annual rate for the 

plantation when aged greater than 3 years. 

Roberts (2011) showed that ProMod basal area estimates were consistent with measurements 

made in the Florentine Valley. ProMod underestimated basal area for Forestier (20-25 m 2 /ha 

versus 30 m 2 /ha), however the measurement plot at Forestier was in the most productive part 

of the plantation, and ProMod is describing the average condition of a plantation in that 

region. 

Overall, the estimates of basal area from ProMod appear to be representative of the growth 

occurring at Florentine and Forestier. This is reassuring given that the ProMod growth 

models are an important component of the FEM. However, the water-use estimates cannot be 

properly evaluated in their current form. 

5.2. CABALA 

CABALA is a linked carbon, water and nutrient model designed to be a practical tool to aid 

forest managers with decision making (Battaglia et al., 2004). CABALA was designed for 

Eucalyptus globulus, but has been partially parameterised for E. nitens. 

CABALA was run using detailed regimes for each of the five research plots in the Florentine 

Valley – including accurate planting dates and local daily weather data (Roberts, 2011). 

Water-use and growth were over-predicted by CABALA. Even with the revised water-use 

results from the August 2011 report, CABALA continues to overestimate water-use (Figure 

15). This is potentially because CABALA is overestimating the basal area of unthinned 

forests (Figure 14). Benyon et al. (2009) also found that CABALA overestimated water use 

in the Green Triangle – particularly for non-water-limited plantations. Roberts (2011) 

reported that CABALA showed promise for estimating E. nitens basal area and water-use in 

22

the Florentine Valley, but that it does need further calibration. Data from the Florentine 

study were provided to the creators of CABALA in November 2011 to help facilitate further 

calibration. 

At this stage, therefore, neither CABALA nor ProMod can lend further value to the 

modelling of E. nitens water-use; however they both have the capacity to do so in the future 

by changing the output format (ProMod) or better calibrating the growth model (CABALA). 

Basal Area (m 2 /ha) 

50 

45 

40 

35 

30 

25 

20 

15 

10 

5 

0 

0 2 4 6 

Age (years) 

8 10 12 

Figure 14. Plantation age and measured basal area (Florentine study, blue diamonds) or 

predicted basal area for each plot from CABALA. 

23 

Measured 

20A 

23C 

21AT 

21AC 

22D

Water use (mm/yr) 

1200 

1100 

1000 

900 

800 

700 

600 

500 

400 

Figure 15. CABALA basal area and water-use predictions (upper line, blue diamonds) 

compared with Florentine measurements (lower line, green triangles). 

24 

y = 113.02ln(x) + 654.39 

R² = 0.82 

y = 125.25ln(x) + 461.36 

R² = 0.59 

0 5 10 15 20 25 30 35 40 45 50 

Average Basal Area for year (m 2 /ha) 

Cabala Florentine Measured Log. (Cabala) Log. (Florentine Measured)

6. Final water-use function 

The recommended function for inclusion in the FEM to estimate E. nitens water-use in 

Tasmania is: 

WU = 149+11.0*BA+0.361*R Equation 1. 

Where WU is water-use in mm/yr, BA is plot basal area in m 2 /ha, and R is rainfall in mm/yr. 

This function is better than the Roberts (2011A) function (Figure 4.) because it allows wateruse 

to be varied in response to differences in annual rainfall. 

Maps of rainfall isohyets are available for Tasmania. Grids and shape files for rainfall can be 

downloaded from http://www.bom.gov.au/jsp/ncc/climate_averages/rainfall/index.jsp and 

could potentially be included in the FEM, or, if more detailed rainfall data are desired, actual 

data or synthetic rainfall data from Silo Data drill for specific sites could be included in the 

FEM. 

Inclusion of potential evaporation maps to set the upper limit for water-use would prevent 

estimates of water-use being made that exceed what is physically possible. Grids and shape 

files for potential evaporation can be downloaded from 

http://www.bom.gov.au/jsp/ncc/climate_averages/evaporation/index.jsp . 

25

7. Modelling water-use of plantations using Forestry 

Tasmania’s Forest Estate Model 

The purpose of collecting water-use data was to construct a simple empirical water-use model 

that could be included in FT’s Forest Estate Model (FEM). 

Forest Estate Models are used by forest companies to estimate volumes of standing and 

harvested timber through time and to predict and compare financial returns. They enable 

managers to schedule their activities based on constraints such as the need to operate on the 

basis of sustainable yield, and aid decision-making by allowing the effects of different 

management scenarios on wood production and income to be compared. 

Forestry Tasmania uses Woodstock (Remsoft Spatial Planning System) FEM. Woodstock 

dynamically models the growth of forests and plantations using yield tables. 

The FEM performs calculations for each defined planning unit, which is usually a coupe, but 

can provide analyses for a plot of land, property, compartment, cluster of coupes, catchment 

or the whole forest estate by combining results from multiple planning units. Inventory data, 

growth models, information about species, age, stocking, management regimes, site quality 

and the location and extent of each planning unit, along with constraints or production 

objectives, are used by the FEM to generate estimates of basal area for the planning unit 

(Figure 16). These in turn are converted to estimates of the volume of timber harvested and 

the volume standing. Financial information can be included to produce estimates of the value 

of sales and the standing timber. 

If Equation 1 can be included in the FEM, then water-use and wood production can be 

assessed simultaneously. 

Equation 1 was included in the FEM. For this study, a minimum water-use of 591 mm/yr 

was set (this represents minimum water-use from a site with no trees), and a maximum wateruse 

of 915 mm/yr was set (this represents mean annual PET for the Florentine Valley). 

Average rainfall for the Florentine Valley of 1222 mm/yr was used. 

To demonstrate how inclusion of Equation 1 in the FEM will work, predictions were made of 

wood volumes, basal area and water-use over a 90-year period for all of the E. nitens 

plantations occurring within the Florentine Valley (Figure 17). These plantations ranged in 

age from 0-19 years at the start of the assessment, and covered a total area of 1532 ha. 

Predictions were also made for a single coupe. 

26

Wood 

Volume 

Standing 

Wood 

Volume 

Cut 

Financial 

Analysis 

Inventory 

Data 

Growth 

Models 

Planning Unit 

Species, Age, 

Stocking 

Figure 16. Schematic diagram of FEM structure, where green segments indicate existing 

components, blue represents management objectives and constraints that can be changed in 

accordance with company policy decisions, orange and the orange white segments indicate 

processes that are being incorporated as a result of this study. 

27 

Site 

Quality 

FOREST ESTATE MODEL 

Basal Area 

Estimate 

Water-use 

function 

Rainfall and 

Potential 

Evaporation 

Management 

Regime 

Water 

Use 

Extent, 

Location 

Management 

Objectives & 

Constraints

Figure 17. Screen shot showing plantations in the FEM Florentine Valley assessment, 

colour-coded by year of planting 

The FEM ran three scenarios: a scenario where NPV of the plantations is maximised (Figure 

18), a scenario where the plantations are managed to smooth water-use (Figure 19), and a 

scenario to smooth wood supply (Figure 20). An example of the output for a single coupe is 

also provided (Figure 21). For each scenario, screenshots of charts taken directly from the 

FEM are provided. The first chart in each screenshot shows the volume of wood harvested 

each year, the second shows the basal area of the standing trees, and the third shows the 

water-use of the plantations. The charts in Figures 18, 19 and 20 represent the wood volume, 

basal area and water-use of the total 1532 ha. To get wood volume in m 3 /ha, and basal area 

in m 2 /ha, the values on the vertical axes of the charts need to be divided by the total number 

of hectares (1532). Water-use is given as an average annual value across the whole area. 

Annual statistics for wood volume, basal area and water-use of the plantations are readily 

obtained from the FEM in tabular form to aid in further analysis and decision making if 

required. 

The charts demonstrate that managing plantations to produce smooth water-use and smooth 

wood production is compatible (see similarity between Figure 19 and Figure 20) – managing 

for one produces the other. This lends further support to the concept of a regulated forest 

minimising changes to hydrological systems (Hennes et al., 1971). 

28

In addition to producing the charts and basal area, wood volume and water-use predictions, 

the FEM produces a schedule that shows how management objectives can best be met. The 

FEM indicates when and where activities such as planting, thinning and harvesting should 

occur. FEM output is readily transferred to Microsoft Access where specific queries can be 

made to generate management plans over whatever term is deemed practical (Table 4). 

Figure 18. FEM screenshot showing volume cut (m 3 /yr), standing basal area (m 2 ) and 

evapotranspiration (mm/yr) for each year of a 90-year simulation where objective is to 

maximise net present value for 1532 ha of E. nitens plantation in the Florentine Valley 

29


evapotranspiration (mm/yr) for each year of a 90-year simulation where objective is to have 

non-declining total volume cut and smoothed evapotranspiration (+/- 10%) scenario for 1532 

ha of E. nitens plantation in the Florentine Valley 

30


evapotranspiration (mm/yr) for each year of a 90-year simulation where objective is to 

maximise Net Present Value with non-declining total volume cut for 1532 ha of E. nitens 

plantation in the Florentine Valley 

31

Figure 21. FEM screenshot for an individual E. nitens plantation showing volume cut 

(m 3 /yr), basal area (m 2 ) and evapotranspiration (mm/yr) over 90 years (three rotations) 

32 

Thin 

Clearfell

Table 4. Schedule of activities that will maximise Net Present Value with non-declining total 

volume cut and smoothed water-use (+/- 10%). First 15 years of a 90-year scenario shown. 

Coupe ID Year Scheduled Activity Number of Hectares 

FO020A 2 Thin 44 


FO015B 4 Thin 23 

FO015D 4 Thin 4 

FO020G 4 Thin 36 

FO034F 4 Thin 7 


FO023C 5 Thin 12 




FO032X 7 Clearfell 17 

FO006E 8 Thin 17 


FO032X 8 Clearfell 7 


FO006E 9 Thin 25 



FO022I 9 Thin 32 











FO013W 11 Clearfell 17 

FO014Y 11 Clearfell 1 


FO030V 11 Thin 74 



FO026Z 12 Thin 98 


FO021A 13 Clearfell 29 





33

8. Future research needs 

The data collected at Florentine and Forestier support the use of a single water-use model 

which includes a rainfall term for E. nitens in these locations. Without studies in a sample of 

other locations experiencing vastly different climate and rainfall, or the use of calibrated 

models for other locations, conclusions cannot yet be made about the general applicability of 

the model to plantations in areas experiencing vastly different conditions. 

E. nitens was selected for this study because it is a widely planted species, for which little 

water-use data have been collected, and was thus deemed to be a good species for a pilot 

study. The sites measured had E. nitens growing in uniform stands with sparse understorey 

which reduced the complexity of measurement. The methods used in E. nitens to measure 

and model water-use are likely to be successful for other tree-dominated ecosystems 

including plantations and native forests, although it is possible that further parameters such as 

age, understorey density, soil water-holding capacity, or access to groundwater (Benyon et 

al., 2009) may need to be added to the model to make it more widely applicable. Existing 

data on streamflow and water-use for other vegetation types may allow basal area: water-use 

models to be constructed for them, or new studies or calibration of process models may be 

required. 

Being able to predict the water-use of an array of commercial forest and plantation types will 

enable their influence on water resources to be evaluated. This is useful for forest managers 

who are exchanging trees for trees, but less useful for forest managers that are converting 

other vegetation types such as grassland to plantation, and less useful for catchment managers 

who may want to know the influence of all land management activities occurring in a 

catchment. Being able to quantify the water-use of all land use types for inclusion in the 

FEM would allow an even more useful tool to be developed. 

The data collected in the Florentine and Forestier studies have only been studied at the annual 

level. The data can potentially be used for a range of other purposes such as comparison to 

streamflow studies, physiological studies, linkage to leaf area measurements made by 

University of Tasmania, and comparison to LIDAR data as collected at Florentine during the 

study period for validation of estimates of plantation parameters made from LIDAR. Daily, 

monthly and seasonal values can be compared to climate to better understand the influence of 

climatic variables on plantation water-use. The associations between plantation water-use, 

streamflow, and groundwater need further exploration. 

Uncertainties have not been calculated for any of the water-use measurements. Identifying 

and quantifying all of the potential sources of error in the water-use measurements would 

help to define the certainty of the results. 

Any FEM water-use model is only as good as the basal area estimates that it is based upon. 

ProMod is the model that underpins FT’s FEM. ProMod predicted basal area to within 10% 

of the measured value in the Florentine Valley. ProMod underestimated basal area at the 

34

single Forestier site of age 6 -7 years (20-25 m 2 /ha predicted versus a value of around 30 

m 2 /ha measured at the site), however the research plot is located in a better part of the stand 

so it is probable that ProMod represents the average basal area of the stand rather than the 

basal area of the best quality part of the stand. Growth models should continue to be 

developed and refined as new data are collected. 

35

9. Discussion and Conclusions 

Annual plantation water-use was strongly linked to plantation basal area. It is not common 

for researchers to compare plot water-use to plot basal area, although they frequently identify 

strong relationships between stem diameter and stem transpiration (Vertessy et al., 1995; 

Moore, 1993; O’Grady et al., 1999; Kalma et al., 1997). It is also possible for there to be no 

identifiable relationship between stem diameter and stem transpiration (Forrester et al., 2010). 

Basal area is a function of stem diameter, so where relationships are found between 

transpiration and stem diameter, they will also exist with basal area. 

There was a strong relationship between sapwood area and stem diameter (or basal area 

calculated from stem diameter) in E. nitens and this was consistent between sites. It is typical 

for these parameters to be strongly linked (Roberts, 2001; Macfarlane et al., 2010; Benyon 

and Doody, 2004; Forrester et al., 2010). 

Rainfall in the year of measurement also influenced water-use in the Florentine Valley. Wet 

rough surfaces such as forests will , in general, have actual evapotranspiration rates at the 

potential rate until the water supply for the soil becomes limiting (Beven, 2005). The amount 

and seasonal distribution of rainfall will therefore affect annual water-use. 

For the plantation ages measured, basal area appears to provide an adequate description of 

site quality to predict water-use. Basal area reflects the influence of the suite of biotic and 

abiotic influences on growth and water-use for the lifetime of the plantation. Leaf area 

(which may be available for the plantation estate in the future after collection and processing 

of LIDaR) may also be useful for determining water-use of plantations in the future. Leaf 

area was an important determinant of plantation water-use (or transpiration) at other locations 

(Hatton and Wu, 1995; Forrester et al., 2010). Leaf area will be the subject of further study. 

ProMod and CABALA were the process models that are most likely to be able to predict 

basal area and water-use of E. nitens in Tasmania. ProMod needs to be modified to give 

annual water-use estimates. CABALA needs further calibration for E. nitens. These models, 

when correctly calibrated will potentially be useful for refining the basal area: water-use 

model and expanding it to other regions. 

The data collected so far do not indicate a need to use separate basal area: water-use functions 

for E. nitens in different parts of Tasmania. However it would be wise to conduct further 

testing to show that this is true. Use of the model on mainland Australia or in regions 

experiencing substantially different conditions would require testing to determine the effects 

of different productivity, rainfall, soil types and PET. 

Predicting plantation water-use from forest estate models makes sense for a number of 

reasons: 

• Most sizeable forest companies already have FEMs. They are vital tools for 

forest management planning. 

36

• FEMs represent the best available source of information about the plantation 

and forest estate, including species, structure, management and growth. The 

quality of this information is improving all the time as new data are 

incorporated. 

• FEMs are optimisation tools that allow a range of scenarios to be assessed 

given a host of constraints and they are able to compare the financial 

outcomes, wood production, product mix and standing volumes that will result 

from various scenarios. 

• FEMs can schedule events over long periods to best allow management 

objectives to be achieved. 

A simple empirical model links E. nitens plantation water-use to plantation basal area. Most 

Australian attempts to quantify the effects of plantations on water resources have focussed on 

streamflow rather than water-use and none have attempted to link with existing forest 

management tools. Forest companies do not manage entire catchments, do not know where 

all streams are or how much water is draining from them and do not have the tools or 

capacity to operate catchment models for anything other than small-scale studies. This makes 

streamflow an impractical basis for planning forest management to benefit water resources. 

The National Water Initiative recognises that large-scale plantation forestry may intercept 

significant volumes of water, and makes provision for the regulation of large-scale plantation 

forestry to reduce the risk to water access entitlements or to achieve environmental 

objectives. Forest industry, land managers and regulators are not currently in a position to 

quantify large-scale plantation interception in a way that takes account of the variation that 

occurs in interception in response to plantation health and growth. Incorporation of water-use 

data in FEMs could produce water interception predictions for plantations to inform these 

decisions. 

37

10. Recommendations 

Based on the results of this study, we recommend that: 

• Spatial rainfall data are included in Forestry Tasmania’s FEM so that the wateruse 

function can be easily applied to all E.nitens plantations in Tasmania. 

• Existing water-use and streamflow data for other plantation species and native 

forests are reviewed to see if basal area: water-use functions can be created for 

them with minimal collection of new data. 

• The outputs from ProMod are tweaked so that annual water-use estimates are 

provided for comparison to measured values. 

• CABALA is calibrated for E.nitens using data collected during this study. 

• Plantation owners that need or choose to consider plantation water-use during 

their planning processes consider including water-use functions in their forest 

estate models to aid in decision making. 

• Further data collection, use of process models and creation of empirical models is 

undertaken to enable the development of water-use functions for a wider range of 

plantation species. 

• When appropriate, regulators consider evaluating the impacts of proposed 

management on water resources from a water-use rather than streamflow 

perspective. 

38

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Macfarlane, C., Bond, C., White, D.A., Grigg, A.H., Ogden, G.N. and Silberstein, R. 2010. 

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Australia. Forest Ecology and Management 260: 96-105. 

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estate modelling. Australian Forestry 69(2):79-82. 

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Canberra, pp. 97. 

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patterns of tree water use in eucalypt open forests of northern Australia. Tree Physiology 19: 

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age. PhD Thesis, Department of Civil and Environmental Engineering, The University of 

Melbourne. 

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42

12. Acknowledgements 

This research was funded by The Tasmanian Community Forestry Agreement, the FWPA 

and Forestry Tasmania. Rebecca Barton-Johnson, Karl Abetz and Crispen Marunda regularly 

braved the elements to collect field data. The steering committee - Don White, Tony 

O’Grady, Stephen Elms, and Chris Lafferty - provided useful advice. 

43

13. Appendix 

ProMod outputs for Florentine and Forestier 

44

Site productivity 

FFT v4.158 Saved 29/3/2011 

FlorentineHydrologyStudy E. nitens 

Location: Easting = 456323 Northing = 5282925 Aspect = W Elevation (m) = 400 

Soil: Slope % = 1 Depth (cm) = 200 Stoniness % = 20 

Fertility: not limited Texture: well-structured clay-loam Drainage: good drainage 

Climate: Month Temp(max) Temp(min) Rainfall Radiation Pan Evap. Rain Days 

(°C) (°C) (mm) (MJ/m²/day) (mm) 

Jan 21.0 8.0 82 22.0 4.0 18.0 

Feb 21.0 8.0 66 19.0 4.0 16.0 

Mar 18.0 7.0 85 15.0 3.0 19.0 

April 15.0 5.0 111 10.0 2.0 23.0 

May 12.0 4.0 134 7.0 1.0 26.0 

June 10.0 2.0 124 6.0 1.0 25.0 

July 9.0 1.0 147 6.0 1.0 27.0 

Aug 10.0 2.0 155 9.0 1.0 27.0 

Sep 12.0 3.0 159 12.0 2.0 25.0 

Oct 15.0 4.0 145 17.0 3.0 24.0 

Nov 17.0 6.0 116 20.0 3.0 21.0 

Dec 19.0 7.0 110 21.0 4.0 20.0 

Site: SQ index (ProMod) = 30.0 m Predicted peak MAI = 34 m³/ha/yr 

Age at peak MAI = 20 yr 

Total pan evaporation = 879 mm/yr 

Mean maximum temp. = 14.9 °C Soil water capacity = 272 mm 

Mean minimum temp. = 4.8 °C Total runoff+drainage = 741 mm/yr 

Mean daily temp. = 9.8 °C Drought stress [0-1] = 0.95 

Total solar radiation = 5.0 GJ/m²/yr Closed canopy LAI = 6.12 

Annual rain days = 271 days/yr Light use efficiency = 0.79 gDM/MJ 

Total rainfall = 1434 mm/yr Total stand water use = 695 mm/yr 

Net Primary Production = 37.53 t/ha/yr

Growth: Age MDH BA Vol MAI 

(yr) (m) (m²/ha) (m³/ha) (m³/ha/yr) 

3 10 6 20 7 

4 12 10 46 12 

5 14 15 78 16 

6 16 20 114 19 

7 18 24 153 22 

8 20 28 196 24 

9 22 32 239 27 

10 23 35 279 28 

11 25 38 325 30 

12 26 41 367 31 

13 27 43 407 31 

14 29 45 451 32 

15 30 47 490 33 

16 31 49 528 33 

17 32 51 568 33 

18 33 52 604 34 

19 34 54 642 34 

20 35 55 680 34 

21 36 56 712 34 

22 37 57 747 34 

23 38 59 782 34 

24 39 60 810 34 

25 40 61 843 34 

26 40 61 874 34 

27 41 62 899 33 

28 42 63 928 33 

29 42 64 957 33 

30 43 65 985 33 

31 44 65 1006 32 

32 44 66 1032 32 

33 45 67 1058 32 

34 45 67 1083 32 

35 46 68 1107 32 

36 46 68 1123 31 

37 47 69 1146 31 

38 47 69 1167 31 

39 48 70 1189 30 

40 48 70 1209 30 

41 49 71 1229 30 

42 49 71 1241 30 

43 50 72 1260 29 

44 50 72 1278 29 

45 50 72 1296 29 

46 51 73 1313 29 

47 51 73 1329 28 

48 51 73 1345 28 

49 52 74 1361 28 

50 52 74 1368 27

SQ is Site Quality Vol is the sum of all tree stem wood volumes under bark from ground to top 

SQ index is MDH at age 15 yrs BA is Basal Area (cross-sectional area of all trees over bark at 1.3m high) 

MDH is Mean Dominant Height MAI is Mean Annual Increment of Vol 

LAI is Leaf Area Index (ratio of leaf area to ground area) 

NOTE: Growth data are for an unthinned stand planted at 1000 stems/ha. 

WARNING: Results shown are based on assumptions regarding input costs, 

revenues. Product yields from growth models may be indicative only. 

Professional advice should be sought before acting on any output model.

Site productivity 

FFT v4.158 Saved 15/3/2012 

ForestierHydrologyStudy E. nitens 

Location: Easting = 575043 Northing = 5243134 Aspect = E Elevation (m) = 270 

Soil: Slope % = 2 Depth (cm) = 150 Stoniness % = 50 

Fertility: good natural site Texture: well-structured clay-loam Drainage: good drainage 

Climate: Month Temp(max) Temp(min) Rainfall Radiation Pan Evap. Rain Days 

(°C) (°C) (mm) (MJ/m²/day) (mm) 

Jan 20.5 10.9 60 23.5 4.5 10.0 

Feb 20.8 10.8 62 19.7 4.2 9.6 

Mar 19.0 9.5 71 14.2 2.9 12.1 

April 16.6 7.8 81 9.6 2.0 12.5 

May 13.3 5.7 84 6.1 1.2 14.5 

June 11.3 3.8 89 4.9 0.7 13.1 

July 10.8 3.3 87 5.7 0.8 14.7 

Aug 11.5 3.8 83 8.5 1.3 15.3 

Sep 13.4 4.8 64 12.8 2.2 14.3 

Oct 15.5 5.7 81 17.2 2.8 14.8 

Nov 16.7 7.5 74 20.0 3.5 15.2 

Dec 18.5 8.8 91 22.5 4.2 13.6 

Site: SQ index (ProMod) = 30.1 m Predicted peak MAI = 34 m³/ha/yr 

Age at peak MAI = 20 yr 

Total pan evaporation = 918 mm/yr 

Mean maximum temp. = 15.7 °C Soil water capacity = 127.5 mm 

Mean minimum temp. = 6.9 °C Total runoff+drainage = 278 mm/yr 

Mean daily temp. = 11.3 °C Drought stress [0-1] = 0.90 

Total solar radiation = 5.0 GJ/m²/yr Closed canopy LAI = 5.50 

Annual rain days = 160 days/yr Light use efficiency = 0.80 gDM/MJ 

Total rainfall = 928 mm/yr Total stand water use = 655 mm/yr 

Net Primary Production = 37.61 t/ha/yr

Growth: Age MDH BA Vol MAI 

(yr) (m) (m²/ha) (m³/ha) (m³/ha/yr) 

3 10 6 21 7 

4 12 10 47 12 

5 14 15 79 16 

6 16 20 116 19 

7 18 25 155 22 

8 20 28 198 25 

9 22 32 239 27 

10 23 35 282 28 

11 25 38 328 30 

12 26 41 371 31 

13 28 43 412 32 

14 29 45 456 33 

15 30 47 495 33 

16 31 49 533 33 

17 32 51 574 34 

18 33 52 610 34 

19 34 54 649 34 

20 35 55 687 34 

21 36 57 719 34 

22 37 58 755 34 

23 38 59 784 34 

24 39 60 818 34 

25 40 61 850 34 

26 40 62 882 34 

27 41 63 907 34 

28 42 63 937 33 

29 43 64 966 33 

30 43 65 995 33 

31 44 66 1015 33 

32 44 66 1042 33 

33 45 67 1067 32 

34 46 68 1092 32 

35 46 68 1110 32 

36 47 69 1133 31 

37 47 69 1155 31 

38 48 70 1177 31 

39 48 70 1198 31 

40 48 71 1219 30 

41 49 71 1239 30 

42 49 71 1251 30 

43 50 72 1270 30 

44 50 72 1288 29 

45 50 73 1306 29 

46 51 73 1323 29 

47 51 73 1340 29 

48 51 74 1356 28 

49 52 74 1364 28 

50 52 74 1379 28

SQ is Site Quality Vol is the sum of all tree stem wood volumes under bark from ground to top 

SQ index is MDH at age 15 yrs BA is Basal Area (cross-sectional area of all trees over bark at 1.3m high) 

MDH is Mean Dominant Height MAI is Mean Annual Increment of Vol 

LAI is Leaf Area Index (ratio of leaf area to ground area) 

NOTE: Growth data are for an unthinned stand planted at 1000 stems/ha. 

WARNING: Results shown are based on assumptions regarding input costs, 

revenues. Product yields from growth models may be indicative only. 

Professional advice should be sought before acting on any output model.

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