Embed Size (px)
Citation preview
Plantation forest water use in southwest Victoria
PROJECT NUMBER: PNC064-0607 JUNE 2009
SUSTAINABILITY & RESOURCES
This report can also be viewed on the FWPA website
www.fwpa.com.auFWPA Level 4, 10-16 Queen Street,
Melbourne VIC 3000, AustraliaT +61 (0)3 9614 7544 F +61 (0)3 9614 6822
E [email protected] W www.fwpa.com.au
Plantation forest water use in southwest Victoria
Prepared for
Forest & Wood Products Australia
by
R.G. Benyon, T.M. Doody, S. Theiveyanathan and V. Koul
Publication: Plantation forest water use in southwest Victoria Project No: PNC064-0607 © 2009 Forest & Wood Products Australia Limited. All rights reserved. Forest & Wood Products Australia Limited (FWPA) makes no warranties or assurances with respect to this publication including merchantability, fitness for purpose or otherwise. FWPA and all persons associated with it exclude all liability (including liability for negligence) in relation to any opinion, advice or information contained in this publication or for any consequences arising from the use of such opinion, advice or information. This work is copyright and protected under the Copyright Act 1968 (Cth). All material except the FWPA logo may be reproduced in whole or in part, provided that it is not sold or used for commercial benefit and its source (Forest & Wood Products Australia Limited) is acknowledged. Reproduction or copying for other purposes, which is strictly reserved only for the owner or licensee of copyright under the Copyright Act, is prohibited without the prior written consent of Forest & Wood Products Australia Limited. ISBN: 978-1-920883-75-1 Researcher: R.G. Benyon, T.M. Doody, S. Theiveyanathan and V. Koul CSIRO Materials Science and Engineering Private Bag 10 CLAYTON SOUTH VIC 3169 Final report received by FWPA in June, 2009
Forest & Wood Products Australia Limited Level 4, 10-16 Queen St, Melbourne, Victoria, 3000 T +61 3 9614 7544 F +61 3 9614 6822 E [email protected] W www.fwpa.com.au
i
Contents
1 Executive summary........................................................................................... 6
1.1 Objectives ................................................................................................................. 6
1.2 Key Results............................................................................................................... 6
1.3 Recommendations.................................................................................................... 7
2 Background and methods ................................................................................ 9
2.1 Plot-scale monitoring of plantation water use ......................................................... 15 2.1.1 Field site selection and descriptions................................................................... 15 2.1.2 Monitoring methods and analyses ...................................................................... 16
2.2 Model descriptions and testing ............................................................................... 17 2.2.1 SoilFlux ............................................................................................................... 17 2.2.2 3PG+................................................................................................................... 18 2.2.3 CABALA.............................................................................................................. 18
3 Results............................................................................................................. 20
3.1 Water use and water balances at the six field sites................................................ 20 3.1.1 Macarthur............................................................................................................ 20 3.1.2 DPI...................................................................................................................... 23 3.1.3 Digby................................................................................................................... 24 3.1.4 Bessiebelle ......................................................................................................... 25 3.1.5 Dartmoor............................................................................................................. 27 3.1.6 Hurdle Flats ........................................................................................................ 29 3.1.7 Wood volume increment and water use efficiency ............................................. 32
3.2 Site factors influencing water use ........................................................................... 35 3.2.1 Groundwater ....................................................................................................... 35 3.2.2 Rainfall................................................................................................................ 36 3.2.3 Potential ET ........................................................................................................ 37 3.2.4 Influence of geology and soils on ET.................................................................. 39 3.2.5 Growth factors .................................................................................................... 40
3.3 Whole-of-rotation plantation water use ................................................................... 42
3.4 Testing model accuracy and precision ................................................................... 45 3.4.1 SoilFlux ............................................................................................................... 45 3.4.2 3PG+................................................................................................................... 48 3.4.3 Cabala ................................................................................................................ 52 3.4.4 Accuracy of 3PG+ and Cabala for growth predictions ........................................ 55
4 Discussion and recommendations ................................................................ 58
4.1 Comparison of the three models............................................................................. 58
4.2 Application of models to water resource management questions in the future ...... 60
4.3 Scaling from plot-based studies to whole catchments and regions and implications for interpretation of the results of WatLUC Stage 3................................................ 61
4.4 Effects of plantations on groundwater levels .......................................................... 65
4.5 Effectiveness of block plantings of trees in reducing groundwater recharge for salinity control.......................................................................................................... 67
5 Conclusions and recommendations.............................................................. 68
5.1 Water use and water yield from plantations with closed canopies.......................... 68
5.2 Accuracy of recent modelling studies of impacts of new plantations ...................... 69
5.3 Plantations for salinity control.................................................................................. 70
6 Acknowledgements......................................................................................... 70
References................................................................................................................ 71
Appendix A ............................................................................................................... 73
Appendix B ............................................................................................................... 77
Appendix C ............................................................................................................... 92
List of Figures
Figure 1. Depth to groundwater in the lower south east of South Australia.. .............................. 11
Figure 2. Map showing the location of field sites......................................................................... 14
Figure 3. Cumulative rainfall, ET and soil water deficit from 2002 to 2007 at the Macarthur site21
Figure 4. Change in soil water over time at three depths at the Macarthur site. ......................... 22
Figure 5. Cumulative rainfall, ET and soil water deficit from 2005 to 2007 at the DPI site ......... 24
Figure 6. Cumulative rainfall, ET and soil water deficit at the Digby site from February 2005 to May 2008.............................................................................................................................. 25
Figure 7. Cumulative rainfall, ET and soil water deficit at the Bessiebelle site from April 2005 to April 2008 ............................................................................................................................. 26
Figure 8. Cumulative rainfall, ET and soil water for periods before and after thinning at the Dartmoor site........................................................................................................................ 28
Figure 9. Cumulative rainfall, ET and soil water deficit at the Hurdle Flats site .......................... 29
Figure 10. Daily rainfall and daily transpiration at Hurdle Flats. A.. ............................................. 31
Figure 11. Relationship between annual volume increment and annual ET at the six monitoring sites...................................................................................................................................... 34
Figure 12. Variation in WUET over time at the six SW Victorian sites......................................... 34
Figure 13. Relationship between net water balance and depth to ground water at 18 sites for which DTW was accurately monitored................................................................................. 35
Figure 14. Relationship between annual ET and rainfall. ............................................................ 37
Figure 15. Relationship between ET and rainfall plus net change in soil water for sites not accessing groundwater. ....................................................................................................... 37
Figure 16. Relationship between annual ET and long-term mean annual potential ET. ............. 38
Figure 17. Relation ship between annual ET and LAI. ................................................................ 41
Figure 18. Relationship between mean annual ET and periodic annual increment (PAI) ........... 42
Figure 19. Predicted rainfall, ET, soil water deficit and the net runoff and/or recharge from two rotations of blue gums.......................................................................................................... 44
Figure 20. Comparison between observed mean annual ET at field sites in southeast SA and southwest Victoria with simulated annual ET using the SoilFlux model............................... 46
Figure 21. Comparison of observed and SoilFlux predicted mean monthly ET for sites without access to groundwater ......................................................................................................... 47
Figure 22 Comparison of observed and SoilFlux predicted mean monthly ET for sites with access to groundwater ......................................................................................................... 48
iv
Figure 23 Comparison between observed mean annual ET at field sites in southeast SA and southwest Victoria with simulated annual ET using the 3PG+ model.. ................................49
Figure 24. Comparison of observed and 3PG+ predicted mean monthly ET for sites without access to groundwater .........................................................................................................50
Figure 25. Comparison of observed monthly ET with that predicted by 3PG+ averaged across the 11 sites with access to groundwater ..............................................................................51
Figure 26. Comparison between net changes in soil water observed at each site and predicted by the 3PG+ model...............................................................................................................51
Figure 27. Comparison between observed annual ET and annual ET predicted using Cabala ..52
Figure 28. Comparison between observed and predicted monthly ET using Cabala, averaged for sites not accessing groundwater ..........................................................................................53
Figure 29. Comparison between observed and predicted monthly ET using Cabala, averaged across sites with access to groundwater..............................................................................54
Figure 30. Comparison between the simulated net change in soil water and observed net change in soil water for Cabala. ...........................................................................................54
Figure 31. Comparison of simulated and observed maximum LAI for the 3PG+ model .............55
Figure 32. Comparison between simulated and observed maximum LAI for Cabala .................55
Figure 33. Comparison of observed and simulated PAI for 3PG+ ..............................................56
Figure 34. Comparison between observed and simulated MAI for 3PG+ ...................................56
Figure 35. Comparison between predicted and observed PAI for the Cabala model..................57
Figure 36. Comparison of predicted and observed MAI for the Cabala model ...........................57
Figure 37. Predicted net change in stream flow each year from the Crawford River catchment (blue line) in response to the assumed change in plantation area (green line)....................65
Figure 38. Variation in depth to groundwater at a blue gum plantation site in the Wattle Range, southeast South Australia.....................................................................................................67
List of Tables
Table 1. Details of the field sites ................................................................................................. 15
Table 2. Mean annual water use, mean annual growth for the period of measurement at each site and water use efficiency based on evapotranspiration at the six monitoring sites. ....... 33
6
1 EXECUTIVE SUMMARY
1.1 Objectives
This project aimed to:
1. Quantify water use in four Tasmanian blue-gum (Eucalyptus globulus Labill.) and two radiata pine (Pinus radiata D.Don) plantations for 3 years using advanced tree and soil-based measurements. Sites were chosen that typify the hydrogeology of plantation areas in southwest Victoria, and which contrast with the extensive network of sites previously monitored by CSIRO in the karst areas of southeast South Australia (Benyon and Doody 2004).
2. Use the results to improve understanding and prediction of the regional effects of plantations on water yields through an iterative process of validation and refinement of extant water balance models currently being applied within the region. Three models were tested: the SoilFlux model used for the Water and Land Use Change Project (SKM 2005, 2008a and b); the 3PG+ forest growth model (Landsberg and Waring 1997); and CSIRO’s CABALA plantation growth and water use model (Battaglia et al. 2004).
3. Assess the likely effectiveness of block plantings of trees on recharge reduction for salinity control.
1.2 Key Results
Water use at the six southwest Victorian plantation sites
Plantation water use was monitored at four blue gum sites for approximately 3 to 5 years and at two radiata pine sites for 2 to 2.5 years. Monitoring was all in the period of closed canopy (ground almost totally shaded) in plantations aged between 4 and 13 years. Rainfall from late 2004 to mid autumn 2007 was 23% below the long-term mean. Rainfall was 4% above average during the final year of monitoring. All plantations used all of the available rainfall during the monitoring period, which would be expected given that in 2 of the 3 years rainfall was 23% below average.
Sources of water use
All plantations accessed soil water from at least 4 to 6 m depth in the soil profile and probably from deeper than 6 m. One blue gum plantation accessed groundwater from approximately 6 m depth. One pine plantation was also gaining additional water from >6 m depth, particularly during spring and early summer. The source of this additional water is not known.
Factors determining water use of closed-canopy plantations in the Green Triangle
Based on an analysis of data collected at various times in the past 9 years from 22 closed-canopy plantation sites across the Green Triangle, plantation annual water use in the region is largely determined by rainfall, except at sites where groundwater is accessible (usually if present with 6 m of the land surface), in which case annual water use is largely determined by potential evapotranspiration. Across the 22 sites, 94% of the variation in mean annual evapotranspiration could be account for by mean annual rainfall, accessibility of groundwater (whether the plantation accessed groundwater or not) and mean annual potential evapotranspiration as defined by Wang et al. 2001). Geology, topography and hydrogeology also have an indirect effect, by influencing groundwater accessibility. Soil texture had no detectable influence on plantation water use.
Accuracy of water use simulations by current models
Current models are able to produce unbiased predictions of annual water use of plantations with closed canopies at sites where tree roots do not access groundwater, but with poor accuracy at sites where the trees take up groundwater. The SoilFlux and 3PG+ models substantially under-estimated groundwater uptake, while CABALA tended to over-estimate groundwater uptake.
7
Implications for the conclusions from the WatLUC study
The SoilFlux model used by SKM in their southwest Victorian Water and Land Use Change Study (SKM 2005) gave accurate predictions of water use for closed-canopy plantations that were not accessing groundwater. For closed-canopy plantations accessing groundwater the model under estimated water use by an average of 29%. Modelling also needs to account for substantially lower water use in the early part of each rotation. This will reduce the expected long term impacts of plantations on water availability. New data from southeast South Australia indicates water use in the first two years of each rotation is lower and groundwater recharge higher than previously assumed (Appendix A) (Brown et al. 2006). Therefore, in the WatLUC study, the effect of plantations on catchment water balances was probably under-estimated in sub-catchments where plantations are being established over shallow water tables but may have been slightly over-estimated in sub-catchments where plantations generally do not have access to groundwater.
By combining estimates of water use from plantations with closed canopies and recently collected data in southeast South Australia on water use in the early part of the rotation, it was shown that averaged over a complete rotation, runoff or recharge of plantations grown on a 10 to 12 year rotation is about one-third that occurring from pasture estimated by Brown et al (2006) Taking into account the low water use in the first 2 years of a rotation, and the approximate rate of new plantation development in some of southwest Victoria’s catchments since 1998 (e.g. Crawford River), it is not expected that any change in cumulative total stream flow attributable to recent plantation developments in the region will be detectable until after 2006. This is consistent with recent observations (SKM 2008b). From about 2008 onwards, a reduction in mean annual streamflow of approximately 10,000 ML, or 29% is expected from the Crawford River Catchment following conversion of approximately 25% of the catchment from grazing land to plantations in the period from 1998 to 2007.
Benefits of commercial tree plantations for salinity control
The results indicate block planting of trees will be effective in reducing groundwater recharge. During the period of full canopy closure in each crop cycle, little or no recharge will occur during years of average to below average rainfall. However, recent data from southeast South Australia indicates that in commercial plantations which are periodically harvested and replanted, evapotranspiration is somewhat lower during the inter-rotation period. In the period between harvesting and replanting and in the first year or two after replanting, soil water quickly recharges over winter and substantial amounts of groundwater recharge occur. There will thus be a period at the beginning of each rotation when some groundwater recharge will occur, and consequently commercial tree plantations may not be quite as effective in reducing recharge to saline groundwater flow systems as re-vegetation with trees that are not periodically harvested.
1.3 Recommendations
Refining estimates of plantation water use in the Green Triangle
The research presented in this report indicates that in closed canopy plantations in the Green Triangle, annual water use is determined largely by rainfall, accessibility of groundwater, and where groundwater is accessible, by potential ET. An empirical regression relationship using these three factors to account for variation in mean annual ET between sites has an average error of ±3% for sites without access to groundwater and ±8% for sites with access to groundwater. This relationship could be used within the region to provide estimates of mean annual water use by plantations with closed canopies. It would provide more accurate predictions than current process models and than the current deemed rates in the Lower Limestone Coast prescribed Wells Area of South Australia, particularly at sites where the plantations are thought to be accessing groundwater. This model is only applicable to radiata pine and blue gum plantations with closed canopies in the Green Triangle where mean annual rainfall is <750 mm. Data or estimates of annual rainfall and potential ET are readily available from sources such as the Commonwealth Bureau of Meteorology and SILO. Accessibility of groundwater is determined by depth to groundwater and whether root impeding layers exist between the surface and the water table. Drilling of observation bores, or excavation of soil pits to determine depth to groundwater and identify root impeding layers, would enable assessment of whether groundwater is potentially accessible to, or currently being accessed by plantations. It is not known whether long-term declines or rises in ground water levels will influence groundwater uptake. There is a need for
8
additional long-term studies of plantation waters use at sites where water tables are falling or rising.
Long-term monitoring and analysis of rainfall and streamflow data needs to continue in southwest Victorian catchments that have been subject to substantial land use change in recent years and compared with rainfall runoff relationships in catchments not experiencing major land use change. This would enable verification of predictions of long-term impacts of land use change on streamflow. The full effects of large scale plantation developments that have occurred since 1998 may not become evident for several more years.
Improved accounting for lower water use prior to canopy closure
To estimate full-rotation water use, the substantially lower ET in the early part of the rotation needs to be taken into account. Data on water use rates between the fallow and canopy closure are limited, with no data available for plantations aged between 2 and 4 years. This is a critical time in the rotation; where ET is expected to increase rapidly, from approximately 310 mm year-1 at age 1 to 2 years, to the equivalent of rainfall or greater by canopy closure (age 2 to 4 years in blue gums and 4 to 8 years in radiata pines). Further studies of water use in the period between fallow and canopy closure are required.
Improving process-based models for simulating plantation water use
Existing process models do not simulate groundwater uptake well. Further work needs to be undertaken to understand the reasons for this and to improve their predictive ability at locations where groundwater is accessible. Effects on groundwater uptake rates of factors such as root length density above the water table, aquifer and soil hydraulic conductivity and aquifer transmissivity need to be studied. Long-term accumulation of salts in the root zone also need to be monitored to determine whether groundwater uptake by plantations results in increasing salt accumulation in the root zone, and reduced water use and growth rates. The maximum depth for groundwater uptake appears to be between 6 and 8 m. Hypotheses to explain why groundwater is not generally taken up from deeper than this need to be tested; for example a hypothesis that lack of oxygen below this depth prevents root survival.
Additional data on groundwater uptake by radiata pine plantations
For radiata pine plantations, only limited data exist on groundwater uptake. This species is currently grown on a 30 to 40 year rotation, but data on groundwater uptake have only been obtained from un-thinned plantations <10 years old. At sites where groundwater is accessible, water use rates in older stands, over a range of site productivity classes, needs to be examined in new field studies.
The importance of continuing to improve estimates of plantation water use
One proposal to account for plantations in the regional groundwater balance in South East South Australia would issue up to 280,000 ML of water to plantations in the Lower Limestone Coast Prescribed Wells Area, representing approximately 25% of available groundwater. With such large quantities of water involved, potentially worth >$200 million, it is important to ensure accuracy in estimates of plantation water use. The additional science necessary to improve current estimates would cost a small fraction of the potential value of the water involved.
9
2 BACKGROUND AND METHODS
Establishment of commercial crops of Pinus radiata D.Don (radiata pine) began in southwest Victoria during the middle part of the 20th century. This estate of approximately 67,000 ha has been increasing only slowly in area since the 1990s. In contrast, a realisation that the soils and climate of the region are well-suited to fast growth of Eucalyptus globulus Labill. (Tasmanian blue gum) and a forecast high demand for this species to provide feedstock for pulp mills in Asia, has resulted in rapid conversion of approximately 123,000 ha of agricultural land to blue gum plantations in south west Victoria in the past decade. Since 1995, the total area of plantations in southwest Victoria has increased by more than 200%, from approximately 60,000 ha to 190,000 ha. Although still approximately only 3% of the total land area in the Glenelg Hopkins and Corangamite CMA regions (SKM 2005), much of it has been concentrated in a few sub-catchments (SKM 2008). For example, between 1998 and 2005, approximately 18,000 ha, or 26% of the 70,000 ha Crawford River catchment was converted from grazing to Tasmanian blue gum plantations (SKM 2008). It has been predicted that once these recently established blue gums reach full canopy cover, combined long-term mean annual stream flow and groundwater recharge in this catchment will be 35% lower than before the change (SKM 2008).
It is known that, in general, for a given site and climate, a forest can use (evapotranspire) more water than grassland and therefore water yield from a forest is often less than from grassland (Hibbert 1967, Bosch and Hewlett 1982, Brown et al. 2005). Given the potential for land use change to influence evapotranspiration (ET) and therefore stream flow, it is important to understand how large-scale changes in land use influence water availability. To do this for particular catchments requires an accurate understanding of water use of different vegetation types under local conditions. Between 2002 and 2007, stakeholders in south west Victoria commissioned a three stage investigation by consultants Sinclair Knight Merz (SKM), of the potential effects of land use change on water resources in the region (known as the Water and Land Use Change Study, or WatLUC). The methods used and results of these investigations have been reported in detail by SKM (Sinclair Knight Merz 2005, 2008a, 2008b). The study predicted effects at the catchment scale by summing changes predicted at the point scale using a 1-dimensional vegetation water use model (SoilFlux). The model was run for representative soil types, climates and land use types to produce estimates of differences in evapotranspiration between different land uses at the point scale. These differences were summed to the landscape scale based on the area occupied by each land use x soil x climate combination in each catchment. Conversion of grazing land to plantations was one of the land use changes examined. To have confidence in the predictions it is essential to ensure model accuracy has been verified. Water balance models must be based on well tested physical and physiological principles to be able to reproduce the water use by different vegetation. Given the increase in the area of plantations in the region over the past decade, it is essential to confirm the accuracy of models of plantation water use that have been, or which can be applied across the region.
This study aimed to improve knowledge of the water use of plantations in southwest Victoria to enable better understanding of the effects of recent and future plantation developments on water availability in the region and to undertake thorough testing of available models of plantation water use to be used at small and large scales. The SoilFlux model used by SKM in their Water and Land Use Change Study had not previously been tested in the region against real observations at the stand scale. The study reported here provided the real observations and undertook an analysis of the accuracy and precision of SoilFlux. It also tested the accuracy and precision of two
10
commonly used forest growth and water use models: a modified version of the public domain 3-PG model (Landsberg and Waring 1997) and the CABALA model (Battaglia et al. 2004).
The Green Triangle, including southwest Victoria, is part of the Otway Basin: one of about 20 large sedimentary basins acting as regional groundwater basins in Australia (Jacobsen et al. 1983). The Otway basin extends from the lower south-east of South Australia across the south-west of southern Victoria to the western edge of the Port Phillip basin. Although one of the smaller sedimentary basins in areal extent, the Otway Basin is eighth largest in Australia in terms of divertible groundwater resources. Within the Otway Basin, the water needs of the south-east of South Australia are met almost entirely from groundwater, while more than half the water needs of south-western Victoria are supplied by groundwater. In 2001, divertible groundwater in the Otway Basin was estimated to be 850 000 ML year–1, of which about 20% was being used (Barber 2001). Depth to groundwater across much of the lower southeast of South Australia is <20 m, and for much of the region north of Millicent it is <5 m (Figure 1). Depth to groundwater is more variable in south west Victoria. In the southern and western part of the region depth to groundwater is often shallow. For much of the Hawkesdale Ground Water Management Area, for example, depth to groundwater is <10 m. With below average rainfall since spring 2004, there has been a general downward trend in watertable levels across the region. Hopton et al. (2001) summarised the ecophysical features of the Green Triangle, as described below. Within this region, water resources are predominantly below ground in south-east South Australia, and a mix of surface and below ground in south-west Victoria. The annual flow of surface water from the Glenelg, Hopkins and Portland catchments averages 1 370 000 ML, of which 28% was divertible and 7% was currently being used in 2001 (Hopton et al. 2001).
Pine and blue gum plantations in the Green Triangle are largely planted over two broad landforms: 1. Karstic and sedimentary geological formations containing unconfined aquifers in the south-
east of South Australia and parts of south-western Victoria; 2. The volcanic plains of south-western Victoria.
The effects of plantation forestry on water resources of these two landforms may differ due to differences in topography, groundwater flow characteristics and soil and regolith depth and texture. In particular, the soils derived from basalt may be higher in clay content, with lower hydraulic conductivity. The karstic and sedimentary areas of the Green Triangle are characterised by permeable limestone beds or sediments laid down when the region was submerged under shallow seas, when the coastline was further inland than its present-day position. Water resources in this part of the region are almost entirely derived from shallow unconfined aquifers in the limestone or sediments. The gently undulating topography and permeable, sandy surface soils allow rain that falls to the ground to infiltrate the soil. There is very little surface runoff – recharge of groundwater occurs mainly via deep drainage. The unconfined aquifer acts as a storage reservoir from which water resources are drawn. In the lower south-east of South Australia there is a slow (2 to 100 m y–1) flow of water in a westerly or southerly direction through the aquifer to the sea (Fred Stadter, personal communication). Because the aquifer storage is currently full, any aquifer water that is not used for domestic, agricultural or industrial use will eventually enter the sea via coastal or off-shore springs, or the extensive network of drainage channels.
11
Figure 1. Depth to groundwater in the lower south east of South Australia. Map produced by the South East Resource Information Centre.
In the volcanic plains the hydrogeology is dominated more by surface water systems. Rainfall that is not evaporated, used by the vegetation or retained in the soil drains vertically or runs off laterally: either by overland or subsurface flow, or by intersection of groundwater by incised streams. Rain that moves past the roots of the vegetation may be used by vegetation further
12
down-slope. In lower parts of the landscape, the watertable may be within reach of deeper-rooted vegetation, so that recharge or runoff from higher up-slope can be used by vegetation further down-slope. Groundwater aquifers are generally less transmissive than the unconfined aquifer present in the karst limestone areas.
It is beyond the scope of this report to describe the physical features of the Green Triangle in more detail.
There are important differences between the two broad landforms of the Green Triangle that may interact with changes in vegetation to affect the water balance in different ways:
• Over large areas of the karst and sedimentary geology, plantations and other vegetation may use water directly from the unconfined aquifer if the capillary fringe of the watertable is within the root zone.
• These aquifers are generally highly transmissive and could supply water at high rates to large plantations.
• As this situation is uncommon in Australia, studies of forest hydrology from other parts of the country may not provide a good guide to water use by plantations on the karstic landform.
• In the surface water catchments of the volcanic plains, there will always be some surface runoff, especially from those areas that generate overland or subsurface lateral water flows.
• Due to generally greater depth to groundwater, watertables will generally be less accessible by trees on the volcanic plains than in the karstic and sedimentary landforms.
• Studies of forest hydrology conducted elsewhere in Australia and the world will probably provide a better guide to the effects of plantations on water balance in the volcanic plains, although this has yet to be confirmed from field studies.
• Even if groundwater is accessible to tree roots on the volcanic plains, the rate of extraction by large tree plantations may be lower, due to lower transmissivity of the aquifers compared with those in the karst areas.
Benyon (2002) reviewed the then current state of knowledge and made recommendations for additional research into plantation water use in southwest Victoria. The report concludes: “Uncertainty over the potential for groundwater uptake by plantations is the greatest gap in our knowledge of water use by plantations in the region. Current research will substantially fill this gap in the karst areas. These studies, however, are confined to a narrow range of soil types on a single type of groundwater system (highly transmissive, unconfined, freshwater aquifers in karst limestone). Parallel studies are needed to determine rates of water use by plantations in southwestern Victoria in areas underlain by shallow watertables but having heavier soils, less transmissive and more localised groundwater systems and perhaps higher salinities than are present in the karst limestone areas”. The report made five specific recommendations regarding additional research required to improve understanding of the impacts of plantations on the region’s water resources.
These were:
1. A monitoring site be established in south-western Victoria that is representative of some of the sites currently being planted with blue gums in that area. Soil textures in the volcanic plains areas of the Green Triangle are heavier than those in the karst areas where current studies are being conducted. The chosen site would ideally have a shallow watertable (less than 10 m) and a heavier textured soil than existing monitoring plots in karst areas. This
13
would help improve our understanding of the influence of soil texture on groundwater uptake.
2. Outputs from the Water and Land Use Change Study be used to identify common site types in the region based perhaps on watertable depth, type of groundwater system (particularly aquifer transmissivity), soil texture (light or heavy).
3. If commonly planted blue gum sites in the volcanic plains are substantially different from sites currently being monitored in the karst areas, in terms of aquifer transmissivity, soil type (particularly texture) and groundwater salinity, additional monitoring sites should be chosen to include all or some of the following: a site with a shallow aquifer of low transmissivity on a light-textured soil; sites with shallow aquifers of low and high transmissivity on heavy-textured soils; sites with shallow, highly transmissive aquifers of moderate salinity (say 1500 to 3000 mg L–1) with light- and heavy-textured soils.
4. At representative sites, water use should be measured early in the rotation and after thinning.
5. Results from monitoring of field sites should be compared with model predictions of water use to enable verification and refinement of model predictions.
The present study provides the data and analysis required to meet recommendations 1 and 5 and in part recommendations 3 and 4. Recommendation 4 is also being partly addressed by a concurrent project in south east South Australia on water use in the early part of the rotation. At the time the present study was established, there was insufficient data available at the local scale to enable recommendation 3 to be fully addressed.
In 2002 one field site was established for monitoring water use of a blue gum plantation in the basalt plains with a heavy clay soil and a watertable at about 8 m depth. Three additional blue gum field sites were established in late 2004 and early 2005. One of these was destroyed in a bushfire in April 2005, but a replacement site was established in May 2005 about 1 km from the original site. Two radiata pine sites were established in winter and spring 2005. Locations of CSIRO’s plantation water use field sites are shown in Figure 2
14
Figure 2. Map showing the location of field sites. E. globulus sites are indicated by blue circles and P. radiata sites by green circles
15
2.1 Plot-scale monitoring of plantation water use
2.1.1 Field site selection and descriptions
The six field sites in south west Victoria were chosen to represent the main groundwater flow system types in which plantations have been established. The data from these sites supplements data collected from another 16 plantation sites in south east South Australia between 1999 and 2007. Details of the 22 sites are given in Table 1.
Table 1. Details of the field sites
Site number &
name
Species Date
planted
Monitoring
started
Monitoring
ended
GFS1 DTW2 (m)
South west Victorian sites
1 Macarthur Blue gum Aug-98 Sep-02 Nov-07 Volc plains ~8
2 DPI Blue gum Jul-96 Feb-05 Nov-07 Volc plains ~10
3 Digby Blue gum Jul-98 Mar-05 May-08 Volc plains n.m.(>6)
4 Bessiebelle Blue gum Jul-98 Jun-05 May-08 PC limestone ~6
5 Dartmoor Pine Jul-94 Aug-05 May-08 Sand plains n.m.(>6)
6 Hurdle Pine Jul-96 Jul-05 Sep-07 Sand plains n.m.(>6)
South east South Australian sites
1 Julia Hill Pine 1970 Aug-99 Nov-00 Sand plains ~5
2. Miles Pine 1978 Sep-99 Aug-04 Sand plains ~9
3. Caroline Pine 1971 Dec-99 Oct-01 Sand plains ~22
4. Nangwarry Pine 1984 Mar-00 Mar-04 Sand plains ~9
5. Airport Pine 1996 Mar-00 Jan-06 Sand plains ~6
6. Struan Pine 1996 Apr-00 Apr-03 Sand plains ~4
7. Normans Pine 1969 Aug-05 Aug-06 Sand plains n.m.(>6)
8. Beachport Blue Gum 1994 Sep-99 Sep-04 Sand plains ~3
9. Padthaway Blue Gum 1995 Dec-99 Dec-01 Sand plains ~16
10. Airport Blue Gum 1996 Dec-00 Nov-05 Sand plains ~4
11. McCourt 1 Blue Gum 1996 Sep 01 Jul-05 Sand plains ~3
12. McCourt 2 Blue Gum 1996 Nov 01 Nov-07 Sand plains ~3
13 Willunga Blue Gum 1998 Mar 02 Sep-04 Sand plains ~2
14. McCourt 3 Blue Gum 1996 Mar 02 Jun-05 Sand plains ~3
15. McCourt 4 Blue Gum 1996 Mar 02 Jun-05 Sand plains ~10
16. Wandillo Blue Gum 1992 Nov-05 May-07 Sand plains ~4
1. GFS means ground water flow system
2. DTW means depth to the water table
n.m. means not measured
16
2.1.2 Monitoring methods and analyses
The monitoring methods used were the same as those described by Benyon and Doody (2004) and Benyon et al. (2006). In summary, periodic measurements of rainfall, evapotranspiration (the sum of interception, soil evaporation and transpiration) and changes in soil water were used in Equation 1 to estimate whether water was being lost from the study site (either as runoff or deep drainage: this method does not identify which), or whether the trees were using more water than available from rainfall and depletion of stored soil water.
Qwt = P - I - T - E - (Sc-Sp) [1]
where: Qwt was either net drainage or runoff (a positive value) or water uptake (a negative value) below the maximum depth of soil water measurement (~6 m at most sites).
P represents gross total precipitation for the period, measured in a rain gauge in an open area nearby.
I represents interception loss (rain that wets the exterior surfaces of the vegetation and evaporates without falling to the ground). Interception loss occurs during and immediately after rain.
T represents transpiration (water taken up through plant roots which travels through the plant to be lost to the atmosphere via evaporation through the leaves and other plant surfaces). T takes place on most days during daylight hours and sometimes also at night (Benyon 1999).
E represents evaporative losses from the soil surface and leaf litter. Evaporation from the soil surface is highest when the surface soil is wet, and decreases as it dries.
Sc represents the current volumetric water content of the root zone.
Sp represents the previous volumetric water content of the root zone.
Each site was visited at intervals of approximately 4 weeks. At each visit, total rainfall (P) collected in the nearby gauge was recorded. Soil water (Sc) was measured using a neutron probe (CPN Hydroprobe) at 0.3 m depth intervals in each of five stainless steel access tubes to a maximum depth of 5.85 m. The net change in soil water since the previous measurement was calculated.
Daily transpiration (T) was estimated based on the mean daily sap velocity estimated using the heat pulse technique, corrected for wounding (Swanson and Whitfield 1983), recorded using sapflow sensors (Greenspan) in six sample trees per plot, and an estimate of the total cross-sectional area of sapwood.
Evaporation from the surface soil (E) for the period between visits was determined using five mini-lysimeters per plot. Each lysimeter consisted of a rain gauge and a 0.3 m deep, 0.1 m diameter column of soil, suspended in a pit over a container so that the soil surface of the column was at ground level. The container collected any water that drained through the soil column. At each visit the total amount of rainfall and drainage collected was measured, and the soil column was weighed. Equation 2 was used to estimate the total evaporation from the surface soil for the period. All values in Equation 2 are in mm
17
E= P - D - (Wc-Wp) [2]
where: E represents evaporative loss from soil surface and leaf litter
P represents gross total precipitation for the period, measured in a rain gauge placed near the soil column but not so close as to intercept rain falling into the soil column.
D represents the volume of drainage collected beneath the soil column and converted to a depth in mm.
Wc- Wp represents the change in weight of the lysimeter during the period, expressed as an equivalent depth of water in mm, assuming water has a density of 1000 kg m-3.
Wood volume production in each plot was also measured periodically. The stem diameter at breast height (1.3 m above ground level) over bark (DBH) was initially measured on every tree in each plot. Band dendrometers were attached to each of the six sap flow sample trees per plot at breast height, and tree circumference was measured at each visit. Stem diameter was estimated from circumference, assuming a circular stem cross-section. For each measurement, a linear regression relating growth increment to DBH was estimated and applied to all trees in the plot. The height of the six sample trees was measured every 3 to 6 months using a Vertex and the mean height was calculated. Extrapolation between height measurements was made based on a regression relating mean height to mean DBH. Stand total stem volume over bark was estimated as the product of stand basal area and mean tree height, divided by three.
2.2 Model descriptions and testing
One of the most important outcomes of this research has been the comprehensive testing of several models for their ability to simulate plantation water use across the range of site conditions, and climates in which plantations of E. globulus and P. radiata have been established in the Green Triangle. One of these models (SoilFlux) has already been applied extensively in southwest Victoria to estimate water use of plantations and other land uses across the region as part of the Water and Land Use Change Study (SKM 2005). The 3-PG and Cabala models have been used extensively to predict growth rates of plantations in various regions. These two models can also be used to simulate evapotranspiration.
All three models estimate vegetation water use based on rainfall, estimated potential evapotranspiration, leaf area index (LAI), root zone depth, soil water holding capacity and other soil properties. In all three models water movement is only simulated in one dimension (the vertical) on a daily or monthly time-step. In the versions of each model used in this study the vegetation can access water from a watertable if it is present within the root zone.
2.2.1 SoilFlux
The SoilFlux model was developed by SKM and is described in more detail by SKM (2005). It runs on a daily time step and uses the Richard’s equation to simulate water movement in the soil profile. It also simulates solute transport in the soil using the convection-dispersion equation. The model predicts daily soil evaporation, transpiration and water loss from the soil profile (runoff and drainage below the root zone lumped as a single loss value, L). Inputs include daily rainfall, potential evapotranspiration as defined by Wang et al. (2001), LAI, root depth and root length
18
density, soil depth, water holding capacity. Some of these variables are derived from a soil classification (e.g. Northcote classification).
For this study, SKM was contracted to run the model for 21 plantation sites in southeast SA and southwest Victoria for which CSIRO had measured rainfall, interception, evaporation from the surface soil, transpiration and changes in soil water. For each site CSIRO provided the location, daily rainfall, soil depth, water holding capacity, soil classification, LAI and root depth. SKM ran the model and provided estimates of monthly total ET for the period for which CSIRO had measured ET. SKM provided two detailed reports describing the model, inputs and outputs: one for the southeast South Australian sites and another for the southwest Victorian sites (Appendix B and C). A separate spreadsheet detailing predicted monthly ET for each site was provided by SKM to CSIRO. CSIRO undertook an analysis of the comparison between predicted and observed annual and monthly ET.
2.2.2 3PG+
The 3-PG forest growth model was developed by Landsberg and Waring (1997). The model simulates canopy development, light capture, photosynthesis and net carbon assimilation. Simulated net carbon gain is partitioned between roots, stems and foliage, controlled by soil water and a user-defined soil fertility rating. Net carbon partitioning to foliage is multiplied by specific leaf area to estimate growth in leaf area index, which determines the amount of light capture and transpiration. Foliage death and losses are also simulated, allowing modelling of the change in LAI over time.
For this study we used a modified version of 3-PG called 3PG+ developed by Dr Jim Morris (Morris 2003). This model simulates interception loss separately as a function of LAI. The soil behaves as a simple bucket model. The size of the bucket is determined by root zone depth and plant available water capacity of the soil over this depth. Soil depth permitting, root zone depth can increase over time. Maximum potential ET (transpiration and soil evaporation) is modelled as a single variable using the Penman-Monteith equation based on weather conditions (solar radiation, air temperature, humidity and wind speed) and leaf area index. This estimated potential ET value is scaled down or modified if soil water is in short supply to give an estimate of actual ET.
Inputs include a set of variables describing physiology and allometric relationships derived from the literature or field studies for the species being modelled. Inputs also include monthly net radiation, rainfall, temperature, humidity, soil depth, water holding capacity and fertility rating. Outputs include monthly LAI, average stem size, interception loss and evapotranspiration. In the version we used, root zone depth increases over time, and groundwater uptake is simulated if the root zone reaches the water table. Soil texture is used to define soil hydraulic properties, which can restrict the rate of groundwater uptake. If the water holding capacity of the root zone is exceeded a single loss value combining runoff and deep drainage is produced.
2.2.3 CABALA
The Cabala model (Battaglia et al. 2004) is a process-based, mechanistic forest growth model developed by the CSIRO. It simulates light, nitrogen and water capture by trees and the use of
19
these resources to grow foliage, roots and stems. Partitioning of net carbon gain is optimised between these various organs to maximise resource capture. The interaction between plant functioning and the environment in which the trees grow is driven by various physiological and allometric relationships for the species or genotype being modelled. These species-specific parameters are obtained from the literature or field studies. For the present study, previously derived species parameter sets for E. globulus and P. radiata were used. Battaglia et al. (2004) provide a more detailed explanation of the various parameters used in the model.
As with the 3-PG model, the soil behaves as a single bucket, the size of which is determined by plant available water holding capacity and root zone depth. Water enters the bucket from rainfall and is removed by soil evaporation and transpiration. If the water holding capacity of the root zone is exceeded, there is a net loss called ‘runoff’ in the model, but which could be either runoff or deep drainage.
Model inputs include initial plant spacing and row orientation (which can influence light capture early in the development of the plantation), the set of species specific physiological parameters and allometric relationships, daily rainfall, daily minimum and maximum temperature, net radiation, humidity, soil depth, soil texture, soil water holding capacity, and total nitrogen in the top 0.5 m of soil. Model outputs include monthly LAI, stem size, interception, soil evaporation and transpiration, net runoff and/or deep drainage.
To run Cabala it is possible to input climate data as either daily, average monthly or observed monthly. For this study, real daily climate data were used. These data were obtained from a combination of in-situ weather stations and from silo patched point data (www.nrw.qld.gov.au/silo/datadrill/). Where silo data were used, the approximately monthly rainfall data collected in-situ was compared to the silo rainfall data for the corresponding period and a regression relationship developed to enable correction of the silo rainfall data to match periodic rainfall measured at or near the site.
Cabala incorporates a model of nitrogen mineralisation in the top 0.5 m of soil, which determines nitrogen availability to the trees. The soil N model in Cabala requires measurements of various surface soil properties, including pH, bulk density, total organic matter and C:N ratio. At each site, 20 soil samples were collected at depths of 0-10 cm, 10-20 cm and 20-50 cm. The samples were bulked into two groups of ten for each depth.
Soil pH was determined using a handheld pH meter. The soil samples were oven dried at 40°C and bulk density of each dried sample was calculated as per Miller and Donahue (1990). Each sample was sieved using a 2 mm sieve. The fraction >2 mm was sieved again using a 5 mm sieve. Chemical analysis was conducted on sub samples of <2 mm and the 2-5 mm fraction using a Leco Carbon Nitrogen Analyser to determine total nitrogen and total carbon. The larger soil fraction (greater than 2 mm but less than 5 mm) was ground to reduce the soil particle size for chemical analysis. The estimates of total C and total N were used to derive C:N ratios. Measurements of C:N ratio, total organic matter and soil bulk density were used to determine total soil N in the top 0.5 m.
20
3 RESULTS
3.1 Water use and water balances at the six field sites
3.1.1 Macarthur
The canopy had already closed when measurements began at the E. globulus plantation located about 10 km southeast of Macarthur. ET (the sum of I, T and E in Equation 1) was monitored from July 2002 (plantation aged ~4 years) to October 2007 and soil water from October 2002 to November 2007. Some data were not collected for the final month due to damage to equipment caused by cattle. The discussion below therefore refers to data collected from October 2002 to October 2007.
Cumulative rainfall, ET and soil water deficit (the difference between the maximum soil water observed over the 5 years of monitoring at each depth and the actual soil water measured at each site visit) are shown in Figure 3. Cumulative total ET exceeded cumulative rainfall for much of the 5 years, but during a wet winter in 2004, cumulative rainfall overtook and exceeded ET for about 6 months. During a drought from late spring 2004 to late autumn 2007, cumulative ET increased above cumulative rainfall again and the plantation drew on soil water stored during the wet 2004 winter. Above-average rain through late autumn and winter 2007 reduced the soil water deficit substantially and by the end of monitoring cumulative rainfall had almost caught up to cumulative ET.
Mean annual ET for the period (680 mm year-1) was statistically significantly higher than mean annual rainfall (640 mm year-1). However, this difference between supply of and demand for water could largely be accounted for by a net decrease of 169 mm in the amount of water stored in the top 6 m of the soil profile. Allowing for this, only 32 mm, or 1% of the total ET of 3428 mm, cannot be account for by rainfall or depletion of stored soil water. The statistical confidence limits for the estimate of mean annual ET are larger than this, and therefore this plantation did not generate any significant net runoff or groundwater recharge over the five year monitoring period, nor did it consume any significant amount of groundwater from the watertable present at about 8 to 10 m depth.
High rainfall and increases in soil water at all depths indicated a wetting front had moved all the way through the soil profile and the soil had fully wet up in winter 2004. It was assumed that the maximum soil water observed during this period represented the drained upper limit for soil water. For each measurement period a soil water deficit was calculated as the difference between the maximum soil water observed during the 5 years of monitoring and the actual soil water. Because this site had a clay soil, moisture content was always relatively high. Across all measurement depths, the average maximum volumetric soil water content was 0.42 and the average minimum 0.32.
From summer 2004/05 to autumn 2007, drought resulted in a soil water deficit consistently in the 300 to 450 mm range. By the final measurement of soil water in mid November 2007, the deficit
21
had reduced to 160 mm as a result of 563 mm rain between late April and early November (Figure 3).
0
500
1000
1500
2000
2500
3000
3500
Oct
-02
Dec
-02
Feb
-03
Apr
-03
Jun-
03
Aug
-03
Oct
-03
Dec
-03
Feb
-04
Apr
-04
Jun-
04
Aug
-04
Oct
-04
Dec
-04
Feb
-05
Apr
-05
Jun-
05
Aug
-05
Oct
-05
Dec
-05
Feb
-06
Apr
-06
Jun-
06
Aug
-06
Oct
-06
Dec
-06
Feb
-07
Apr
-07
Jun-
07
Date (month and year)
Cu
mu
lati
ve t
ota
l (m
m)
Cumulative rainfall
Cumulative ET
Soil water deficit
Figure 3. Cumulative rainfall, ET and soil water deficit from 2002 to 2007 at the Macarthur site
Patterns of change in water content over time at different depths in the soil indicate maximum root depth of the plantation increased from about 3.5 m at 4 years of age to >6 m by age 7.5 years. To illustrate this, Figure 4 depicts how the volumetric soil water content changed over time at 4.5 m, 5.4 m and 5.7 m depth compared with changes in the top 1 m. For these three soil depths, the drained upper limit for water holding capacity was approximately 0.40 to 0.43 m m-1, while the lower limit of plant available water was around 0.30 to 0.32 m m-1. Between these upper and lower limits there was a change in soil water content of about 0.08 to 0.10 m m-1, or 80 to 100 mm m-1.
For the first year of measurements, soil water content in the top 1 m averaged about 0.30 m m-1 compared with 0.40 to 0.43 m m-1 at the 4.5, 5.4 and 5.7 m depths. From early December 2003, soil water at 4.5 m depth began to decrease. During the wet winter of 2004, there was a sharp increase in soil water at all depths, which then drained rapidly back to pre-winter soil water contents. After this drainage event, water content continued to reduce at 4.5 m depth, to a new equilibrium of about 0.32 m m-1 by February 2005. From then on soil water at this depth remained almost constant and only slightly different from the average water content of the top 1 m.
Apart from the brief increase in soil water after the wet winter in 2004, soil water at 5.4 m remained almost constant at around 0.40 m m-1 until autumn 2005. It then decreased steadily
22
during winter and spring 2005 and summer 2005/06 before reaching a new equilibrium level of around 0.31 to 0.32 m m-1.
Similarly, at 5.7 m depth, soil water remained almost constant at about 0.42 m m-1 until December 2005. From then until December 2006 it reduced gradually to a new equilibrium of around 0.32 to 0.33 m m-1.
This pattern, of a plateau, followed by a gradual reduction to another plateau is evidence of increasing root depth. The first plateau indicates no roots are present to take up water, so apart from the pulse of water moving past each depth in winter 2004, soil water remains constant. The onset of a period of decreasing water content at each depth indicates the tree roots have reached this depth and are beginning to withdraw water. This period of gradual reduction occurs later with increasing depth. It appears the tree roots reached 4.5 m in late spring 2003. They reached 5.4 m in autumn 2005 and 5.7 m in summer 2005. The new, lower plateau at each depth occurs when most of the plant available water has been taken up.
0.0
0.1
0.2
0.3
0.4
0.5
0.6
Oct
-02
Jan-
03
Apr
-03
Jul-0
3
Oct
-03
Jan-
04
Apr
-04
Jul-0
4
Oct
-04
Jan-
05
Apr
-05
Jul-0
5
Oct
-05
Jan-
06
Apr
-06
Jul-0
6
Oct
-06
Jan-
07
Apr
-07
Month and year
Soi
l vol
umet
ric w
ater
con
tent
(m
m-1
)
top 1 m4.5 m5.4 m5.7 m
Figure 4. Change in soil water over time at four depths at the Macarthur site.
Based on interpretation of patterns of change in soil water with depth, as described above, it appears the maximum root depth was about 3.6 m at 4 years, indicating that in the first 4 years after tree planting, root depth increased by about 0.9 m year-1. Over the next 3.25 years, root depth increased to 5.7 m, representing a rate of increase of 0.65 m year-1. If root depth continued to increase at this rate and no root impeding layers were present deeper in the profile, the roots may have reached the watertable within another few years, which by early 2007, had dropped to 9.8 m, due probably, at least in part, to the drought from late 2004 to April 2007.
23
3.1.2 DPI
Measurements of ET in the E. globulus plantation at the Victorian Department of Primary Industry’s (DPI) research farm near Hamilton began in December 2004 (plantation aged ~8.5 years) and ceased in mid November 2007 (age ~11.5 years). However, measurement of soil water did not begin until February 2005.
Over ~2.9 years of complete measurements, the cumulative total rainfall and ET increased roughly in parallel (Figure 5). For much of the period, cumulative ET was slightly above cumulative rainfall, but during a wet winter and spring in 2007, cumulative rainfall overtook cumulative ET. When monitoring ceased, total rainfall of 1656 mm exceeded total ET of 1573 mm; however it is likely cumulative ET would have caught up with cumulative rainfall over the 2007/08 summer and autumn.
It was not possible to obtain an accurate measure of the cumulative soil water deficit because, due to below average rainfall for the majority of the measurement period, the soil profile probably never completely wet up and therefore the drained upper limit was unable to be determined. The soil water deficit shown in Figure 5 is the deficit relative to the lowest observed during the monitoring period. In relative terms, the soil water deficit was least at the start and end of monitoring (Figure 5). From autumn 2005 to autumn 2007, during 2 years of rainfall well-below the long-term mean, the soil water deficit to 5.85 m depth remained almost constant. During the wet winter and spring 2007, it reduced by about 100 mm compared with the maximum deficit reached in 2006.
Mean annual rainfall for the period (571 mm year-1) was slightly higher than mean annual ET (542 mm year-1). However, this difference can be explained by the high winter and spring rainfall in the final year of monitoring, when rainfall exceeded ET by almost 170 mm. Taking account of a net increase in soil water of 35 mm over the monitoring period, 49 mm, or 3% of the rainfall cannot be accounted for by ET and changes in soil water. This is less than the 95% statistical confidence limits for ET, and therefore, over the period, plantation water use is not statistically significantly different from rainfall and the net change in soil water.
The periodic water balance calculations indicate there was both deep drainage and water uptake deeper than the 5.85 m maximum depth of soil water measurement and that the roots were active beyond this depth before monitoring of soil water began at age 8.5 years. This is consistent with observations at the Macarthur site that the roots reached this depth at about age 7.25 years. If the rate of increase in root depth after age 4 was the same at both sites, the tree roots at the DPI site would have reached about 6.5 m depth by the time monitoring began in late 2004, and 8.5 m by the time it ceased in late 2007. A nearby groundwater observation well indicated a watertable was present at approximately 10 m depth. Up to the time measurements of ET ceased there was no evidence the plantation was accessing groundwater.
24
0
200
400
600
800
1000
1200
1400
1600
1800
Jan-
05
Ma
r-0
5
Ma
y-0
5
Jul-0
5
Se
p-0
5
No
v-0
5
Jan-
06
Ma
r-0
6
Ma
y-0
6
Jul-0
6
Se
p-0
6
No
v-0
6
Jan-
07
Ma
r-0
7
Ma
y-0
7
Jul-0
7
Se
p-0
7
Date (month and year)
Cum
ula
tive
tota
l (m
m)
Cumulative rainfallCumulative ETSoil water deficit
Figure 5. Cumulative rainfall, ET and soil water deficit from 2005 to 2007 at the DPI site
3.1.3 Digby
Measurements in the E. globulus plantation near Digby began in February 2005 (plantation aged ~6.6 years) and continued until May 2008 (age ~9.8 years). Depth to groundwater at this site was not measured, but based on depth to groundwater in a bore <1 km to the east, at a lower elevation, depth to groundwater is probably >15 m. Due to the presence of rock preventing drilling any deeper, soil water was only measured to 4 m depth at this site.
The cumulative total rainfall and ET initially increased in parallel (Figure 6). However, between January and April 2006, cumulative ET increased more rapidly than cumulative rainfall. During this time the soil water deficit to 4 m depth did not increase, indicating the trees were obtaining water from deeper in the soil profile. Data over the next 2 years indicate this additional source of deep soil water had been depleted, as from April 2006 to April 2007 cumulative ET increased in parallel with cumulative rainfall, and soil water deficit was almost constant. Thus, it appeared that during 2 years of relatively low rainfall the plantation was only using rainfall.
A wetter period between late autumn and late spring 2007 (680 mm rain in about 7 months) resulted in soil water refilling to the drained upper limit and cumulative rainfall rising more rapidly than cumulative ET. By July 2007, cumulative rainfall had overtaken cumulative ET. The gap continued to increase until November 2007. During summer 2007/08 and autumn 2008 the rate of increase in ET rose and the rate of increase in cumulative rainfall slowed, as is normal for this time of year, so that when measurements ceased in mid May 2008, the cumulative rainfall and ET lines had again converged.
25
Mean annual rainfall for the monitoring period (630 mm year-1) was similar to mean annual ET (622 mm year-1). However, allowing for a net increase in soil water of 39 mm between the start and end of the monitoring period, the trees must have obtained a small amount of water (2 mm year-1) from>4 m depth. However, this very small amount is less than the statistical confidence limits associated with the measurements.
In summary, the data for this site indicate the root zone of the plantation is > 4 m deep, which is consistent with observations at other plantation sites of this age in the region. Averaged over a 3 year period containing two relatively dry winters and one relatively wet winter, plantation water use was not significantly different from rainfall, after allowing for observed changes in soil water.
0
500
1000
1500
2000
2500
Feb
-05
Apr
-05
Jun-
05
Aug
-05
Oct
-05
Dec
-05
Feb
-06
Apr
-06
Jun-
06
Aug
-06
Oct
-06
Dec
-06
Feb
-07
Apr
-07
Jun-
07
Aug
-07
Oct
-07
Dec
-07
Feb
-08
Apr
-08
Date (month and year)
Cu
mu
lati
ve t
ota
l (m
m)
Cumulative rainfall
Cumulative ET
Soil water deficit
Figure 6. Cumulative rainfall, ET and soil water deficit at the Digby site from February 2005 to May 2008.
3.1.4 Bessiebelle
Measurements in the E. globulus plantation near Bessiebelle began in late April 2005 (plantation aged ~6.8 years) and continued until mid May 2008 (age ~9.8 years).
From late autumn to early spring 2005, cumulative total rainfall and ET increased in parallel (Figure 7). However, between early October 2005 and March 2006, cumulative ET increased far
26
more rapidly than cumulative rainfall. By 5 April 2006, total ET exceeded total rainfall by 395 mm. During this time the soil water to 5.85 m depth decreased by only 32 mm, indicating the trees obtained 363 mm of water from deeper in the soil profile or from groundwater. During installation of the neutron moisture meter access tubes a watertable was encountered at about 5.5 m depth. Thus, with the watertable located within the zone in which soil water was being measured; the additional water use by the plantation can only have been derived from groundwater. The topography for several km in all directions is almost flat, and the monitoring plot is 250 m from the closest plantation edge, making it unlikely the extra water use was derived from surface flow onto the plot. The plot itself is in a slightly elevated location.
0
500
1000
1500
2000
2500
3000
Apr
-05
Jun-
05
Aug
-05
Oct
-05
Dec
-05
Feb
-06
Apr
-06
Jun-
06
Aug
-06
Oct
-06
Dec
-06
Feb
-07
Apr
-07
Jun-
07
Aug
-07
Oct
-07
Dec
-07
Feb
-08
Apr
-08
Date (month and year)
Cu
mu
lativ
e to
tal (
mm
)
Cumulative rainfall
Cumulative ET
Soil water deficit
Figure 7. Cumulative rainfall, ET and soil water deficit at the Bessiebelle site from April 2005 to May 2008
From April 2006 to mid September 2006, cumulative rainfall and cumulative ET again increased in parallel and the soil water deficit remained almost constant indicating that for this period ET was about the same as rainfall.
There was then another period of about 7 months to late April 2007 when cumulative ET increased at a much greater rate than cumulative rainfall, with only a slight decrease in soil water. During this 2nd summer the net uptake of groundwater was estimated to be 340 mm. Reductions in soil water at >5 m depth (data not shown) indicate the watertable had dropped.
The wet late autumn, to late spring period in 2007 (664 mm rain in 7 months) resulted in soil water refilling to the drained upper limit. Between late April and late August, cumulative rainfall rose by almost 200 mm more than cumulative ET. Soil water increased by only 149 mm indicating
27
some groundwater recharge occurred. An estimated 75 mm of recharge occurred between late April and early November 2007.
Between mid November 2007 and mid May 2008, ET exceeded rainfall by 336 mm but soil water reduced by only 102 mm, indicating groundwater uptake of 234 mm.
Mean annual rainfall for the entire monitoring period (619 mm year-1) was substantially lower than mean annual ET (920 mm year-1). There was no net change in soil water over the 3 years, meaning net groundwater uptake averaged 301 mm year-1.
In summary, the data for this site indicate the root zone of the plantation extends to the watertable present at about 6 m depth. There is a clear pattern each winter of soil water replenishment, and even some recharge in a wet winter, but this is far exceeded at other times of the year by groundwater uptake, which usually begins in early to late spring and ends in mid to late autumn.
3.1.5 Dartmoor
Measurements in the P. radiata plantation near Dartmoor began in July 2005. They ceased temporarily between September and November 2006 while the plantation was thinned, and then continued to April 2008. Close spacing of the rows prevented access by our drilling rig for installation of deep neutron moisture meter access tubes in the measurement plot. Only three access tubes were installed by hand to 1 m depth. Another tube was installed to 5.7 m depth close to one of the internal roads in the plantation about 15 m from the monitoring plot. The presence of the road close to this access tube probably influenced the amount of net rainfall and hence soil water readings are considered to be indicative only at this site.
Figure 8 separates cumulative rainfall, ET and soil water deficit for the pre- and post-thinning periods. The pre-thinning period was entirely during the drought, which was not as severe as at the four blue gum sites. The post thinning period included late drought from November 2006 to April 2007 and then the wet late autumn, winter and spring period of 2007.
The soil water data from the one deep access tube close to the road indicates soil water deficit through the drought period increased slowly from 316 mm in November 2005 to a maximum of 377 mm in early April 2007. Rain totalling 689 mm between late April and early November 2007 resulted in a reduction in the soil water deficit to 70 mm. However, as noted above this is only indicative because the soil moisture around this access tube may have been influenced by the nearby road.
In the pre-thinning period, ET and rainfall initially increased in parallel, and then diverged in late spring and early summer 2005 when ET exceeded rainfall (Figure 8). The two then increased in parallel again until late autumn 2006 when rainfall increased rapidly to almost meet the cumulative ET line. The two then increased in parallel again until measurements ceased during the thinning operation. Total ET during the 1.2 years of monitoring prior to thinning was 850 mm compared with rainfall of 808 mm. Based on measurements from just outside the monitoring plot, soil water was probably depleted during this time, accounting for the difference.
28
0
100
200
300
400
500
600
700
800
900
1000
Jul-0
5
Sep
-05
Nov
-05
Jan-
06
Mar
-06
May
-06
Jul-0
6
Sep
-06
Nov
-06
Jan-
07
Mar
-07
May
-07
Jul-0
7
Sep
-07
Nov
-07
Jan-
08
Mar
-08
Date (month and year)
Cu
mu
lativ
e to
tal (
mm
)
Cumulative rainfall
Cumulative ET
Soil water deficit
Figure 8. Cumulative rainfall, ET and soil water for periods before and after thinning (September 2006) at the Dartmoor site
In the first 6 months after thinning, ET was slightly higher than rainfall (Figure 6). From late April to early November 2007, rainfall increased substantially more than ET and the soil water deficit was substantially reduced. From November 2007 to April 2008, ET was higher than rainfall bringing the cumulative totals into convergence. In 1.4 years since measurements resumed after thinning, total ET of 933 mm has been almost the same as total rainfall of 932 mm. The one deep neutron moisture meter access tube near the road indicates that by mid April 2008, the soil was still wetter than it had been at the end of the drought. This indicates that around this tube, net rainfall was higher, or ET was lower than in the measurement plot, or that the root zone of the plantation is deeper than 6 m.
The soil water measurements (again only based on one access hole close to an internal road) indicates the excess rainfall in winter and spring 2007 only penetrated to about 4 m depth (data not shown).
Thinning appears to have had only a small effect on annual ET. Interception loss as a percentage of rainfall averaged 23.3% before thinning but only 18.6% after thinning. For a mean annual rainfall of 800 mm, this would equate to annual interception of 187 mm before thinning and 149 mm after thinning. However, soil evaporation increased from approximately 172 mm year-1 before thinning to 195 mm year-1 after thinning. Mean annual transpiration reduced only slightly from 449 mm before thinning to 441 mm after thinning. Based on annual rainfall of approximately 800 mm, annual ET would have been approximately 808 mm before thinning and approximately 785 mm after thinning. Actual annual ET was 715 mm and rainfall 680 mm in the period before thinning compared with actual annual ET of 707 mm and rainfall of 690 mm in the period after thinning
29
In summary, ET appears to be in balance with rainfall at this site. Based on very limited soil water data it is possible the root zone of the plantation is >6 m deep. Thinning may have resulted in a small reduction in annual ET, equivalent to about 3% of annual rainfall.
3.1.6 Hurdle Flats
Measurements in P. radiata at Hurdle Flats in the Kentbrook plantation began in July 2005 and continued until September 2007 when equipment was removed prior to a scheduled thinning. Unfortunately the thinning did not take place as planned and for several months it was uncertain as to whether and when the thinning would occur. Consequently the equipment was not reinstalled and no more data were collected after September 2007.
The site has a very deep sandy soil, with a relatively low moisture content compared with the other plantation sites in the study.
0
500
1000
1500
2000
2500
Jul-0
5
Aug
-05
Sep
-05
Oct
-05
Nov
-05
Dec
-05
Jan-
06
Feb
-06
Mar
-06
Apr
-06
May
-06
Jun-
06
Jul-0
6
Aug
-06
Sep
-06
Oct
-06
Nov
-06
Dec
-06
Jan-
07
Feb
-07
Mar
-07
Apr
-07
May
-07
Jun-
07
Jul-0
7
Aug
-07
Date (month and year)
Cu
mu
lativ
e to
tal (
mm
)
Cumulative rainfall
Cumulative ET
Soil water deficit
Figure 9. Cumulative rainfall, ET and soil water deficit at the Hurdle Flats site
Rainfall and ET were similar for the first few months of this study, and then rainfall was substantially less than ET during spring and early summer 2005 (Figure 9). From mid August 2005 to mid January 2006, total ET was 214 more than total rainfall. From mid January to early August 2006, rainfall and ET increased approximately in parallel, then during the following spring and summer (August 2006 to March 2007), total ET was again greater than total rainfall, by 304 mm. By late March 2007, cumulative ET exceeded cumulative rainfall by almost 442 mm. Only about 50 mm of this excess could be explained by a reduction in soil water in the top 6 m of the profile. The remainder must have come from deeper in the soil profile, suggesting these trees have a very deep root system. The high autumn to spring rainfall in 2007 (650 mm between late April and mid September) resulted in a closing of the gap between cumulative ET and cumulative rainfall to 211 mm.
30
Averaged over the 2.2 years of monitoring, mean annual rainfall was 827 mm compared with mean annual ET of 939 mm. Allowing for the net increase in soil water over this period, net water uptake from >6 m depth was 138 mm year-1. The 95% statistical confidence limits for mean annual ET at this site are ± 116 mm. Therefore the difference between ET and rainfall is statistically significant, indicating either the plantation was drawing on a deep source of water, or root depth was continuing to increase well beyond 6 m depth, using up substantial stores of deep soil water.
The pattern of variation in transpiration through spring and summer indicated that in spring summer and autumn, rainfall was used up very rapidly and once surface soil moisture was used, daily transpiration reduced very rapidly to only a few mm per week at times (Figure 10). For example, in 2005/06 the maximum daily transpiration (4.5 mm) occurred in early November, after which there was a gradual downward trend, with small peaks corresponding to occasional rainfall events (Figure 10 A). By late January daily transpiration had dropped to around 0.5 to 1 mm. Several small rain events in early February 2006 followed by a more substantial one resulted in a sudden jump in daily transpiration to almost 3 mm. Approximately 1 week later, daily transpiration began to drop rapidly to only 0.5 mm by early March. During this period, soil water was measured on 13 February and 14 March. Between these dates, total net rainfall of 10 mm compared with total transpiration and evaporation of 56 mm. Soil water reduced by 17 mm, leaving 29 mm, or 1 mm day-1 transpiration unaccounted for.
Similarly, in the next year, peak daily transpiration of just under 4 mm occurred in early October but within 4 weeks, transpiration had decreased by more than 90% to daily values of <0.3 mm in early November (Figure 10 B). Daily transpiration remained at around 0.5 mm until mid January when a substantial rain event stimulated a rapid rise in daily transpiration to around 3 mm. In the space of only 5 days from 6 February to 10 February daily transpiration reduced from around 2.5 mm to below 1 mm and by 27 February it had fallen to <0.3 mm. During this period soil water was measured on 27 February and 28 March. Net rainfall of 13 mm compared with evapotranspiration also of 13 mm. There was no net change in soil water and therefore all of the ET could be accounted for by rainfall. Soil water on these two occasions was the driest ever measured during the 2.2 years of monitoring.
The variation in transpiration indicates that although there were some periods when the trees were able to source substantial amounts of water from >6 m depth, there were other times that this was not the case. There are at least three possible explanations for this.
One possibility is that lateral flow of water under the plantation at depth >6m created a perched water table, which the trees were able to take water from in spring and early summer, but this dried up by late summer. Alternatively there may have been a permanent water table, but fluctuating height of the watertable meant the roots could only access it some of the time.
Another possibility is that the tree roots continued to grow deeper in spring each year, making additional soil water available from depth but root growth ceased each summer so no new soil water became available after spring until further root extension into wetter soil in spring the following year. However, this does not explain the very low transpiration rate in November 2006, unless by then the roots had reached a root impeding layer and could go no deeper.
31
B. October 2006 to April 2007
0.0
5.0
10.0
15.0
20.0
25.0
1/10
/200
6
15/1
0/20
06
29/1
0/20
06
12/1
1/20
06
26/1
1/20
06
10/1
2/20
06
24/1
2/20
06
7/01
/200
7
21/0
1/20
07
4/02
/200
7
18/0
2/20
07
4/03
/200
7
18/0
3/20
07
1/04
/200
7
15/0
4/20
07
29/0
4/20
07
Dai
ly n
et r
ainf
all (
mm
)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
Daily net rainfallDaily transpiration
Figure 10. Daily rainfall and daily transpiration at Hurdle Flats. A. October 2005 to April 2006. B October 2006 to April 2007.
It is also possible that the tree roots had permanent access to deep groundwater but as the upper soil profile dried out, the surface roots sensing the very dry soil transmitted a hormonal signal to the foliage to reduce stomatal conductance to minimise water loss. During periods of relatively
A. October 2005 to April 2006
0.0
5.0
10.0
15.0
20.0
25.0
1/10
/200
5
15/1
0/20
05
29/1
0/20
05
12/1
1/20
05
26/1
1/20
05
10/1
2/20
05
24/1
2/20
05
7/01
/200
6
21/0
1/20
06
4/02
/200
6
18/0
2/20
06
4/03
/200
6
18/0
3/20
06
1/04
/200
6
15/0
4/20
06
29/0
4/20
06
Dai
ly n
et r
ainf
all (
mm
)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
Dai
ly tr
ansp
iratio
n (m
m)
Daily rainfallDaily transpiration
32
low rainfall, the rate of water use from >6 m depth decreased as the surface soil dried. During early spring 2006, when the soil water deficit was only moderate and when rain averaging 0.5 to 1 mm day-1 would have been frequently wetting the surface soil, net uptake rates from >6 m depth were around 1.5 to 2.5 mm day-1. However, from late September to mid December 2006, when net rainfall totalled only 18 mm in 84 days, and soil water was close to the driest ever recorded, net uptake from >6 m dropped very rapidly from 2.5 mm day-1 to 0.2 mm day-1. However, after a substantial rainfall event in late January 2007, for the following month net uptake from >6 m increased to 1 mm day-1 again. When soil water again reduced to the driest ever recorded, net uptake from >6 m rapidly reduced to zero. As the surface soil becomes very dry, the stomata might become very sensitive to small changes in surface soil water.
It is possible transpiration rates reduced in summer if the stomata closed in response to high atmospheric vapour pressure deficits. However, analysis of the relationship between daily transpiration and maximum daily VPD for the periods 13/02/06 to 14/03/06 and 27/02/07 to 28/03/07 indicated no significant correlation (data not shown). The mean and range in daily maximum VPD were the same for both periods, yet daily total transpiration rates and daily maximum transpiration rates were much lower for the 27/02/07 to 29/03/07 period. During the 13/02/06 to 14/03/06 period, daily transpiration rates declined substantially, yet daily maximum VPD did not change substantially.
3.1.7 Wood volume increment and water use efficiency
Mean annual wood volume growth increment was correlated with mean annual ET (Figure 11). Among the four blue gum sites this correlation was strong, accounting for 99% of the between-site variation in mean annual stem volume increment during the monitoring period. On average for each 100 mm year-1 increase in mean annual ET there was almost a 7 m3 ha-1 year-1 increase in stem volume increment. This indicates that by locating plantations at sites where groundwater is accessible, provided there are no site factors causing major growth limitations, for each ML of groundwater used, an additional 7 m3 of stem volume growth is achieved. However, because sites accessing groundwater have a higher proportion of their water use during summer, when high atmospheric VPD results in low water use efficiency, this may not be the case. A more detailed analysis is presented in Section 3.2.5.
The two pine sites did not fall so neatly onto the regression line. The Dartmoor pine site fell above the regression line while the Hurdle Flats pine site fell below the line. Despite having the highest annual ET, periodic annual increment at the Hurdle Flats site was about 4 m3 ha-1 year-1 less than at the Bessiebelle blue gum site.
Among the four blue gum sites there was also a strong correlation between mean annual ET and the water use efficiency of wood production (WUEET, expressed as unit of wood volume increment per unit of evapotranspiration in m3 ML-1), which increased as ET increased (Table 2). WUEET of the Bessiebelle site was twice that of the DPI site, for example. The Bessiebelle site had the highest WUEET out of the six sites. WUEET at Hurdle Flats was also moderately high, but lower than at Dartmoor and Bessiebelle. At four of the six of sites WUEET dropped during the drought, but at the Digby site although it fluctuated over time it did not reduce consistently during the drought. At Dartmoor WUEET increased after thinning.
33
At all sites annual water use efficiency of transpiration (WUEt: wood volume produced per unit of water transpired by the trees only) varied considerably over time (Figure 12). At the Macarthur site, for example, WUEt reached as high as 6.1 m3 ML-1 during the wet year of 2004 but dropped to as low as 2.7 m3 ML-1 during the dry year of 2006. Similarly, at the DPI site, WUEt varied from as high as 6.2 m3 ML-1 in 2005 to as low as 3.3 m3 ML-1 in 2006/07. At Digby WUEt ranged from 8.8 m3 ML-1 to 6.0 m3 ML-1 and at Bessiebelle WUEt ranged from 7.9 m3 ML-1 to 5.1 m3 ML-1. At Hurdle Flats WUEt decreased from 7.5 m3 ML-1 in 2005/06 to 5.0 m3 ML-1 in 2006/07 while at Dartmoor WUEt increased from 5.3 m3 ML-1 before thinning to 6.4 m3 ML-1 after thinning.
Drought seemed to be associated with lower WUEET and WUEt. Growth rates were based on measurements of stem circumference at 1.3 m height, outside the bark. Drying of the bark during drought and rewetting during wetter periods may have caused some of the apparent variation in growth and water use efficiency. WUE is also expected to be lower at times when humidity is low and atmospheric vapour pressure deficit is high. Drought will also increase the proportion of total rainfall lost to interception and soil evaporation, meaning that per unit of rainfall, a lower proportion is available to the trees. During droughts the trees might invest more of the net primary production in deep root growth to access more water from deeper in the soil profile, meaning less stem growth per unit of net primary production.
Table 2. Mean annual water use, mean annual growth for the period of measurement at each site and water use efficiency based on evapotranspiration at the six monitoring sites.
Site ET (mm year) Increment (m3ha-1yr-1) WUE (m3 ML-1)
1 Macarthur 628 15.3 2.43
2 DPI 542 10.6 1.95
3 Digby 677 21.3 3.14
4 Bessiebelle 920 36.3 3.94
5 Dartmoor 696 25.7 3.69
6 Hurdle 943 32.4 3.44
34
0
1
2
3
4
5
6
7
8
9
10
03/03 09/03 03/04 09/04 03/05 09/05 03/06 09/06 03/07 09/07 03/08 09/08
Year ending
Ann
ual W
UE
T (
m3
ML-
1)
MacDPI JenkBessDartHurd
Figure 11. Relationship between annual volume increment and annual ET at the six monitoring sites
Figure 12. Variation in WUET over time at the six SW Victorian sites
0
5
10
15
20
25
30
35
40
400 500 600 700 800 900 1000
Evapotranspiration (mm year-1)
Vo
lum
e in
crem
ent
(m3 h
a-1 y
ear-1
)
blue gums
pines
35
3.2 Site factors influencing water use
This section presents an analysis of the relationship between water use of plantations in the Green Triangle and variables including depth to groundwater, rainfall, potential evapotranspiration, geology, soil texture, leaf area index and growth rates. This analysis draws on data collected from 16 research sites in southeast SA as well as the six southwest Victorian sites described in Section 3.1. Some of the southeast SA data have been reported previously (Benyon and Doody 2004, Benyon et al. 2006) and some additional data have been collected from several plantation sites in southeast SA over the past 3 years. Details of the 22 sites are given in Table 1.
3.2.1 Groundwater
At 11 of the 22 sites, annual ET significantly exceeded rainfall and the net change in soil water, indicating the availability of an additional water source. As discussed previously, the Hurdle Flats site only appeared to use additional water in spring and early summer. At ten other sites there was no doubt the plantations were sourcing groundwater in spring, summer and autumn.
-400
-200
0
200
400
600
800
0 5 10 15 20 25
Median DTW (m)
ET
min
us R
ain
(mm
yea
r-1)
Figure 13. Relationship between net water balance and depth to ground water at 18 sites for which DTW was accurately monitored.
Figure 13 indicates the relationship between ET minus rainfall and median depth to groundwater (DTW) for sites at which DTW was accurately monitored.
Of 11 sites where DTW was <6 m only one did not access groundwater. This site was known to have a root impeding layer present at 0.9 m depth. At the other 10 of these sites, mean annual ET significantly exceeded mean annual rainfall plus the net change in soil water. Two of these sites
36
were radiata pine and eight were blue gum. Mean annual ET for 11 sites not accessing groundwater was 625 mm, compared with 1035 mm for the 10 sites using groundwater. The difference is highly statistically significant (p<0.001). This excludes the Hurdle Flats site which used significantly more water than could be accounted for by rainfall and changes in soil water to 6 m depth but for which the additional source of water was not known and depth to the water table was not measured (Section 3.1.6).
Of the total between-site variance in annual ET, 66% could be explained by whether the plantation accessed groundwater. Grouping of all 22 sites into those with DTW < 6 m and those with DTW>6 m accounted for almost half of the total between-site variance in mean annual ET. Thus, for explaining spatial variation in annual ET of plantations across the Green Triangle, depth to groundwater is a very important factor.
3.2.2 Rainfall
Rainfall was the most important variable influencing annual ET for sites not accessing groundwater, accounting for 89% of the variation in mean annual ET between these sites (Figure 14). Excluding two sites where interception and soil evaporation were not measured accurately, rainfall and the net change in soil water explains 96% of the variation in mean annual ET between sites not accessing groundwater (Figure 15). For all of these sites, mean annual rainfall falls within the 95% statistical confidence limits for mean annual ET and there is a 1:1 relationship between ET and rainfall minus the net change in soil water, indicating these plantations in the closed canopy state were using all of the available rainfall.
y = 0.97xR2 = 0.89
y = 0.29x + 841.70
R2 = 0.05
200
400
600
800
1000
1200
1400
1600
300 400 500 600 700 800 900
Annual rain (mm)
Ann
ua
l ET
(m
m)
Not accessing groundwaterAccessing groundwater
Figure 14. Relationship between annual ET and rainfall
37
.
Figure 15. Relationship between ET and rainfall minus net change in soil water for sites not accessing groundwater.
In contrast, for sites accessing groundwater, rainfall alone accounted for only 5% of the variation in mean annual ET, which is not statistically significant (Figure 14). Thus, for sites with access to groundwater, their water use does not appear to be limited by rainfall.
3.2.3 Potential ET
For this analysis we used estimates of mean annual potential evapotranspiration (PET) obtained from the Silo website for the period of measurement at each site. PET is the theoretical maximum ET determined by the available energy. Various models exist for estimating PET. PET is higher for small plantations (<1 km2) surrounded by dry farmland (point PET) than for large continuous areas of plantation (areal PET), because of differences in the size of boundary layers and the amount of advected energy (Wang et al. 2001). For the study sites, we assumed the Wang et al. (2001) model of point PET applies to research plots in small plantations or within 50 m of the exposed edge of large plantations, and Wang et al. (2001) areal PET applies to sites >50 m from the exposed edge of large plantations.
y = 1.00x
R2 = 0.96
0
100
200
300
400
500
600
700
800
900
0 100 200 300 400 500 600 700 800
Annual rain minus change in soil water (mm)
An
nu
al E
T (
mm
)
38
y = 0.86xR2 = 0.46
200
400
600
800
1000
1200
1400
1600
300 500 700 900 1100 1300 1500 1700
Annual PET (mm)
Ann
ual E
T (
mm
)
Not accessing groundwaterAccessing groundwater
Figure 16. Relationship between annual actual ET and annual potential ET.
Figure 16 shows the relationship between actual annual ET and PET for sites accessing and not accessing groundwater. For sites with access to groundwater there is a significant correlation with PET, which accounts for almost half the between-site variation in annual ET. This is largely related to whether the site was classified as point PET or areal PET. For these sites, actual ET is equivalent to approximately 86% of potential ET, indicating actual ET is largely being limited by the available energy at these sites.
Within the group of sites not accessing groundwater, there is a negative correlation between PET and ET. However, this has been influenced by one site which, due to its location much further north than all the other sites, has higher PET and lower rainfall. The lower rainfall at this site is responsible for its low water use.
Within the group of five sites with access to groundwater and where point PET applies, there is a weak negative correlation between PET and ET (R2 0.44). With such a small number of points, however, the correlation could have arisen by chance. The main factor related to PET is whether the site was classified as fitting the definition of point or areal ET. When groundwater is accessible, sites in small plantations or close to the exposed edge of a large plantation have higher ET than sites away from the edge of a large plantation. This difference is highly statistically significant (P<0.001).
The Hurdle Flats site is in the middle of a large plantation and is classified as having areal potential ET. Mean annual ET of 943 mm was similar to the mean annual areal PET at this site of 1017 mm. However, as described in Section 3.1.6, there were periods in summer and autumn at this site when ET was very low and obviously well below potential ET. Thus, although this site appears to fit the model there are some periods of the year when actual ET is very much lower than potential ET. The possible reasons for this were discussed in Section 3.1.6.
39
For sites accessing groundwater, a multiple linear regression including PET and rainfall as independent variables explains 75% of the between-site variation in mean annual ET.
Multiple linear regression analysis including all 22 sites was used to derive Equation 3. This explains as much of the total between-site variation in mean annual ET as we can account for (94%) in a single equation based on site factors:
ET = 0.716 x R + 542 x D1 + 0.282 x D1 x R + 0.930 x D2 x PET – 552 [3]
Where: ET is mean annual ET, R is mean annual rainfall, D1 has a value of 1 if the plantation does not access groundwater or 0 if it does, PET is potential ET and D2 has a value of 1 if the plantation accesses groundwater and 0 if it does not.
Thus, 94% of the variation in mean annual ET of plantation with closed canopies in the Green Triangle can be explained by water supply (accessibility of groundwater and rainfall) and, when water is plentiful due to accessibility of groundwater, by potential ET. This means that other site factors have little influence on annual ET of the region’s plantations. Furthermore, this relationship can be applied to other closed canopy plantation sites in the region. The only input data required are the mean annual rainfall, PET and knowledge of whether the plantation is likely to have access to groundwater based on depth to groundwater and root impeding layers. This empirical relationship gives a mean prediction error of ±3% for sites without access to groundwater and ±8% for sites with access to groundwater.
3.2.4 Influence of geology and soils on ET
None of the variation in mean annual ET between sites could be explained directly by geology or soil factors. The majority of the sites had soils derived from sediments and were either deep sands, or sand over light to medium sandy clay. Three of the southwest Victorian sites had basalt-derived soils. Soil type and depth is expected to influence variables such as water availability and nutrient supply. With the exception of one site in southeast SA, soils at all of the sites were at least 3 m and usually >6 m deep and had sufficient water holding capacity to enable all of the excess rainfall in winter to remain in the root zone for subsequent uptake over summer. Similarly, all soils appeared to be sufficiently fertile to enable the plantations to maintain a high enough LAI to use all of the available rainfall and soil water. Most of the plantations had maximum LAI in the 3 to 8 range (Section 3.2.5).
Common practice in selecting land for plantation establishment, has been to choose locations with deep soils (>4 m), without obvious limitations to growth such as root impeding layers, water logged soils, or extreme physical or chemical properties. At only one site did soil depth appear to constrain root depth to any obvious degree. This was an older radiata pine site with a root impeding layer at 0.9 m depth.
Any relatively minor effects of soil type on plantation water use were less than the statistical confidence limits for ET and therefore undetectable. On average the 95% confidence limits were equivalent to 10% of the mean annual ET. In other words, the mean ET values reported here have an average error of ±10% and therefore the effects of soil-related factors on ET would have to be relatively large to be detectable.
40
The statistical confidence limits for mean annual ET were significantly larger at sites accessing groundwater, averaging 124 mm year-1 at these sites compared with only 57 mm year-1 at sites not accessing groundwater. This difference was highly statistically significant (p<0.001). At sites accessing groundwater, there was greater variation between individual trees in their mean sap velocities which might indicate a difference between individual trees in the degree to which the roots penetrated to the water table.
It is important to note that geology, hydrogeology and topography do indirectly influence ET because these determine the depth to groundwater and the rate at which groundwater can move to the tree roots to replenish root-zone water supplies.
3.2.5 Growth factors
Leaf Area Index
Vegetation leaf area index (LAI) is often considered to be an important determinant of evapotranspiration. Among the 22 sites, the maximum LAI varied considerably, ranging from 2.6 to 8.4 with a mean of 4.8 across all sites.
Among sites not accessing groundwater, there was a statistically significant correlation between mean annual ET and maximum LAI (Figure 17). For LAI of between 2.7 and 4, there was a linear increase in ET as LAI increased which accounted for 80% of the variation in ET between these sites. Above LAI of about 4, however, there was no correlation between ET and LAI (R2 = 0.00), possibly because above LAI 4 any additional leaf area is shaded and therefore the plantation cannot intercept any additional energy for evaporation.
For the sites with LAI ≤ 4.0 and not accessing groundwater, annual ET was determined largely by rainfall and LAI. A multiple linear regression analysis, using mean annual ET as the dependent variable and mean annual rainfall and maximum LAI as the independent variable accounted for 97% of the variation in ET between these seven sites.
For sites accessing groundwater there was a weak correlation between mean annual ET and LAI (R2=0.40). The two sites with the highest LAI had the highest ET, but otherwise there was no correlation between ET and LAI in this group (Figure 17). This is possibly because LAI was above the LAI 4 threshold and because ET at these sites was energy limited. The two sites which had the highest LAI were also close to a plantation edge, where greater advected energy may have increased ET.
LAI was an indicator of whether the plantation accessed groundwater only to the extent that none of the five sites with low maximum LAI (3.3 or less) accessed groundwater. The three sites with basalt-derived soils all fell in this group, indicating the possibility that these soils were less suited to growing plantations than were the soils of sedimentary origin.
For sites with LAI of 3.8 or above, LAI provided no indication as to whether the plantation accessed groundwater or not (Figure 17).
41
y = 59.88x + 715.16R2 = 0.40
y = 122.54x + 205.46R2 = 0.80
y = -1.21x + 689.22R2 = 0.00
0
200
400
600
800
1000
1200
1400
1600
0 1 2 3 4 5 6 7 8 9
LAI (m2 m-2)
Ann
ual E
T (
mm
)Not accessing groundwaterAccessing groundwater
Figure 17. Relation ship between annual ET and LAI.
Annual increment
The term current annual increment (CAI) refers to the annual rate of stem volume growth of a tree plantation over the previous year. It is a commonly used measure of the current growth rate and productivity of plantations. Annual growth rates averaged over several years are referred to as periodic annual increment (PAI). CAI and PAI are expected to be strongly dependent on water availability and soil fertility. Because they are routinely measured by plantation owners, if CAI or PAI are strongly correlated with ET, maps of CAI or PAI from across the plantation estate might provide useful indications of spatial variation in plantation water use across a catchment or region.
Research reported by Benyon and Doody (2004) found that CAI and ET were only moderately correlated (R2 0.74) and that CAI was not necessarily a good indicator of whether a plantation was accessing groundwater. We have extended this analysis to include additional sites and because we have averaged growth rates over several years for some sites we use PAI here.
PAI varied substantially between the 22 sites, ranging from as little as 10 m3 ha-1 year-1 to as much as 63 m3 ha-1 year-1. The average across all our sites was 31 m3 ha-1 year-1.
On average, PAI of sites accessing groundwater was 39 m3 ha-1 year-1 compared with 23 m3 ha-1 year-1 for sites not accessing groundwater. PAI provided some indication of whether a plantation was accessing groundwater or not (Figure 18). All five sites with PAI of <20 m3 ha-1 year-1 were not accessing groundwater, while all seven sites with PAI >35 m3 ha-1 year-1 were accessing groundwater. However, of ten sites with PAI between 25 and 33 m3 ha-1 year-1, four were accessing groundwater and six were not. Because the measurements were not concurrent at all sites, variation in climate will probably have resulted in some variation in the relationship between PAI and mean annual ET, perhaps meaning the correlation between growth and water
42
use appears weaker than if measurements had been collected from all sites over exactly the same period.
y = 177.96x0.48
R2 = 0.57
y = 300.12x0.24
R2 = 0.60
0
200
400
600
800
1000
1200
1400
1600
0 10 20 30 40 50 60 70
PAI (m3 ha-1 year-1)
ET
(m
m y
ear
-1)
Not accessing groundwaterAccessing groundwater
Figure 18. Relationship between mean annual ET and periodic annual increment (PAI)
3.3 Whole-of-rotation plantation water use
The observations of water use presented in Sections 3.1 and 3.2 are for plantations with closed canopies. Hardwoods grown for pulpwood are usually harvested every 10 to 12 years, while softwoods grown for a range of different products are harvested every 30 to 40 years. After each harvest, the site is usually left fallow for 6 to 18 months, and after replanting it takes another 2 to 3 years in hardwoods and 4 to 8 years in softwoods for the canopy to close. For a significant proportion of each rotation, therefore, the trees do not fully shade the ground and ET is expected to be lower than after canopy closure. To fully understand how plantations affect water availability in the long-term, information on water use prior to canopy closure needs to be taken into account. This section discusses how water use prior to canopy closure differs to the post-canopy closure period of the rotation.
Recent and proposed water allocations in southeast SA assume that in the early part of a forestry rotation, ET is less than from pasture. This is because in the first year, weeds are controlled and the newly planted trees are very small. Weed control usually continues in the second year and the maximum ET of the plantation is not expected until at least the 3rd or 4th year, and in some radiata pines not until the 5th year to 8th year. This has been partly confirmed by a current study in south east SA which shows that in the first 2 years of 2nd rotation radiata pine plantations, evapotranspiration is substantially lower than rainfall and a significant amount of recharge probably occurs, even in years when rainfall is below average. This preliminary and as yet unpublished data is detailed in a progress report reproduced in Appendix A.
43
In new plantations on ex-pasture, a typical establishment involves deep ripping and mounding along the planting lines, with partial or complete weed control for a period of at least 2 years, beginning before tree planting and continuing until the trees are large enough to shade out competing weeds.
The recent SA data indicates in the first year of a new rotation, ET is approximately 300 to 320 mm year-1. Given a mean annual rainfall of 700 mm, there is therefore an excess of rainfall over ET of up to 400 mm year-1 in the first 1 to 2 years of a rotation.
New plantations in southwest Victoria are being established on ex-pasture sites. Assuming a maximum root depth of <1 m in pasture, soil drying to <1 m might have occurred. Once the pasture has been removed prior to tree planting, a small amount of the excess of rainfall over ET would refill the soil profile. Plant available water in the top metre of the soil profile is usually 50 to 100 mm. The soil water deficit under pasture by autumn can therefore be assumed to be 50 to 100 mm. In the first year of a new plantation, once this deficit has been replenished due to rainfall in early winter, the remaining excess of rainfall over ET will potentially be available for runoff to streams or recharge to groundwater. Assuming a soil water deficit of 100 mm at the time of preparing the land in autumn for tree planting, and rainfall in the following 12 months of 700 mm compared with ET of only 300 mm, a net of 300 mm water would be available for stream flow or groundwater recharge. Estimates of groundwater recharge are approximately 150 mm year-1 under pasture in southeast SA for mean annual rainfall of 700 to 750 mm (Brown et al. 2006). Therefore, based on the above calculations, runoff or recharge in the first year of a newly established plantation is expected to be up to twice as great as that from the pasture the plantation replaced.
Extrapolating beyond this, and assuming canopy closure occurs in a blue gum plantation at about 3 years of age, ET of a newly established plantation in the 2nd year may still be lower than that from pasture, and because there is no net soil water deficit to make up, runoff or recharge might be almost as high in the 2nd year after pasture removal as in the first year. By year 3 the plantation will probably have higher ET than from pasture and by year 4 ET in a well managed plantation will probably account for all of the rainfall. For the rest of the rotation annual ET may exceed rainfall as the tree roots access stored soil water deeper in the soil profile. The data from the Macarthur site show that between age 4 and 9 years, this plantation drew an additional 169 mm from stored soil water. The soil water deficit reached a maximum of 449 mm in April 2007 at the end of the drought but it had decreased to 161 mm by October 2007. The amount of runoff or recharge occurring at the beginning of the next rotation will depend on the soil water deficit at the end of the current rotation, and the rainfall in the first 2 or 3 years after harvesting.
We made simple water balance calculations to predict the amount of runoff/deep drainage for two rotations of blue gums, initially established on ex-pasture. We used the mean monthly rainfall from the Macarthur site during the 5 years of monitoring, scaled up by 9% to give a mean annual rainfall of 700 mm. For the first year after planting we used observed monthly ET from recently harvested sites in southeast SA. We scaled these monthly ET estimates up by 50% in year 2 of each rotation and from year 3 we used the mean ET for each month observed at the Macarthur site, again scaled up by 9% to allow for a mean annual rainfall of 700 mm. We assume the pasture was removed in early autumn, with a soil water deficit commencing at 50 mm. For each month we calculated the difference between rainfall and ET. If rainfall exceeded ET the excess rain was assumed to enter the soil and reduce the soil water deficit by that amount. If this resulted
44
in a soil water deficit of zero, any additional excess of rainfall over ET was assumed to either runoff or drain.
The result of this simple water balance modelling exercise is illustrated in Figure 19. We predict runoff or recharge of about 300 mm in the first year after removal of the pasture at the beginning of the first rotation, 190 mm in the second year and 127 mm in the third year. The maximum soil water deficit reached in the final year of the first rotation is 380 mm. If the plantation is harvested in October when the plantation is just over 10 years old, the soil water deficit after the previous winter is 259 mm.
After harvesting of the first rotation the land is assumed to remain fallow and largely free of any vegetation until it is replanted the following July. Assuming ET similar to that observed at fallow pine sites in southeast SA, the soil water deficit reduces to zero by the time the seedlings for the second rotation are planted. Runoff/recharge in the 2nd rotation totals 185 mm in the first year, 189 mm in the second year and 137 mm in the third year.
Over the two rotations, total runoff/recharge is estimated to be 1140 mm, or an average of approximately 50 mm year-1 over the 22 year period. For comparison, in southeast SA, recharge from pasture under similar rainfall is estimated to be about 150 mm year-1 (Brown et al. 2006).
Figure 19. Predicted rainfall, ET, soil water deficit and the net runoff and/or recharge from two rotations of blue gums simulated using the simple water balance calculations described in the text and observed
monthly ET from plantation sites in south west Victoria and south east SA
This analysis is based on limited data on early-rotation ET and needs to be verified through further field studies. The recent observations from south east South Australia indicate that during the fallow period, once the soil profile has refilled after harvesting, the amount of runoff or
0
2000
4000
6000
8000
10000
12000
14000
16000
0 5 10 15 20 25
Years since conversion from pastuire to plantation
Tot
al r
ainf
all,
ET
and
run
off (
mm
)
0
200
400
600
800
1000
1200
1400
1600
Soi
l wat
er d
efic
it (m
m)
Cumulative RainfallCumulative ETCumulative RunoffSoil Water Deficit
45
groundwater recharge is probably equal to rainfall minus ET. Given ET of only 300 mm year-1 from fallow plantation sites, for rainfall of 700 mm year-1, each additional year of fallow is expected to generate 400 mm of runoff or groundwater recharge. Increasing a blue gum rotation from 11 to 12 years by allowing an extra year of fallow (assuming coppice and weeds are controlled), would generate an additional 400 mm water every 12 years, increasing mean annual runoff or recharge from the plantation land by about two-thirds. In catchments or groundwater recharge areas where water has been fully or over-allocated and water has a high market or environmental value, extending the period of fallow between rotations might be a viable option for reducing impacts of plantations on water availability. However, this would come at the cost of increased herbicide use to control weeds and coppice and reduced mean annual increment. Presumably extending the period of fallow would only be financially viable if the forest owner could trade the additional water to generate extra income.
3.4 Testing model accuracy and precision
A comprehensive set of plantation water use data from 22 sites across the Green Triangle was assembled (Table 1) and used to test the ability of three models to simulate ET. As far as possible, testing was blind: the models were not calibrated to the observed ET data. Only input data that would normally be available to the modellers were used.
3.4.1 SoilFlux
Annual ET
Predictions of annual ET by the SoilFlux model were accurate for sites where trees were not using groundwater (Figure 20). The predicted annual ET fell within the 95% statistical confidence limits associated with the observed annual ET for all of these sites. Observed mean annual ET of 647 mm was not statistically significantly different from predicted mean annual ET of 631 mm. An F-test indicated there was no significant difference in the sample variance between the observed and predicted values. Similarly, a two sample T-test, assuming equal variances, indicated there was no significant difference between the observed and predicted mean annual ET. Among these sites there was a strong correlation between observed and predicted annual ET (predicted ET = 0.97 x observed ET, R2 = 0.91 when the regression is forced through the origin).
For sites where trees were using groundwater, SoilFlux always under-estimated annual ET (Figure 20) and for seven of these sites predicted ET was less than the lower 95% statistical confidence limit for observed ET. The observed mean annual ET was 1061 mm, compared with predicted mean annual ET of 758 mm for these sites. Thus, for sites accessing groundwater, SoilFlux under-estimated ET by an average of 29%.
An F-test indicated variance was significantly higher for observed than for predicted annual ET for sites where trees were using groundwater (p<0.05). A T-test assuming unequal sample variances, indicated the observed mean annual ET was significantly greater than the predicted mean annual ET (p<0.001). There was only a weak correlation between observed and predicted ET for these sites (R2 = 0.10). Although predicted ET is higher than rainfall, indicating the model does predict some groundwater uptake, the model greatly underestimates the quantity of
46
groundwater used and accounts for only 10% of the variance in annual ET between sites accessing groundwater.
Across all sites accessing and not accessing groundwater, SoilFlux explained 56% of the variation in mean annual ET.
In conclusion, for predicting annual ET of plantations with closed canopies, SoilFlux performed well for plantations without access to groundwater, but poorly for sites with access to groundwater. This suggests that in the Water and Land Use Change Study (SKM 2005), predictions of the impacts of plantations on mean annual ET used to estimate the loss of water from the soil profile (the L term defined in that study) will be accurate for catchments in which the plantations generally do not have access to groundwater. However, in catchments where plantations have been established extensively where depth to groundwater is <6 m and hydraulic conductivity is high, ET will be underestimated, and therefore L over estimated.
y = 0.16x + 584.23
R2 = 0.10
y = 0.97x
R2 = 0.91
0
200
400
600
800
1000
1200
1400
1600
0 200 400 600 800 1000 1200 1400 1600Observed ET (mm year-1)
Pre
dic
ted
ET
(mm
ye
ar-1
)
Not using ground waterUsing ground waterOne to one line
Figure 20. Comparison between observed mean annual ET at field sites in southeast SA and southwest
Victoria with simulated annual ET using the SoilFlux model. Error bars indicate 95% statistical confidence
limits for the observed values.
Monthly ET
For sites without access to groundwater, SoilFlux tended to underestimate ET for the May to August period but overestimate ET in October, November and December (Figure 21). Averaged across these sites, model predictions were accurate in September and for the January to April period.
The over prediction in spring and early summer is likely to be the result of the under prediction in winter. SoilFlux uses both potential ET (ET driven by available energy) and available soil water to simulate ET. During winter, monthly rainfall usually exceeds monthly potential ET, and therefore
47
actual ET is usually limited by energy supply rather than water supply. If the estimate of ET is less than observed ET then the accumulation of soil water over winter will be over-estimated by the model. From September, monthly rainfall is usually lower than monthly potential ET. However, the trees can draw on soil water accumulated over winter for a time, and while this supply of soil water remains plentiful, actual ET can proceed at a rate determined by energy supply rather than water supply. By underestimating ET in winter, and thus over-estimating the accumulation of soil water over winter, SoilFlux makes too much soil water available in spring, and hence the model over estimates ET in spring and early summer. Because during this period soil water is being depleted at a faster rate than reality, by January, the simulated soil water will have reduced back to what soil water would actually be. Once this happens, the model correctly predicts that monthly ET will be limited by water availability. This situation continues until rainfall begins to exceed potential ET again in the following May.
Not accessing groundwater
0
20
40
60
80
100
120
140
160
A M J J A S O N D J F M
Month
Mon
thly
ET
(m
m)
Observed
SoilFlux
Figure 21. Comparison of observed and predicted mean monthly ET for sites without access to groundwater
However, this does not explain why actual ET is under-estimated by the model during the winter months. We hypothesis that the method used in the model to simulate ET when it is limited by available energy underestimates ET at times when rainfall interception is a large component of total ET. High rates of rainfall interception were observed at our field sites in the region in winter. Winter rainfall is characterised by frequent light showers. Between May and September the number of rainy days averages approximately 20 per month. Frequent wetting and drying of the canopy and leaf litter below the canopy results in high rates of total interception loss and soil evaporation, which appears to result in actual ET exceeding the theoretical potential ET.
48
For sites using groundwater, SoilFlux substantially under-estimated ET for every month except October and November (Figure 22), probably for the same reasons as hypothesised for sites not accessing groundwater, but with the additional error associated with under-estimating groundwater uptake.
Accessing groundwater
0
20
40
60
80
100
120
140
160
A M J J A S O N D J F M
Month
Mon
thly
ET
(m
m)
Observed
SoilFlux
Figure 22 Comparison of observed and predicted mean monthly ET for sites with access to groundwater
3.4.2 3PG+
Annual ET
For sites where trees were not using groundwater, predictions of annual ET by the 3PG+ model were less accurate and less precise than for the SoilFlux model (Figure 23). The predicted annual ET fell within the 95% statistical confidence limits associated with the observed annual ET for seven of the ten sites not accessing groundwater.
For one site, 3PG+ under-estimated annual ET by 348 mm. This site was known to have a root impeding layer at about 0.9 m depth, so soil depth was set to only 0.9 m. The period of monitoring water use happened to commence shortly after a prolonged dry spell during which the model predicted that soil water dropped to low levels and root zone salinity rose. The combination of dry soil and a high simulated salinity caused the model to dramatically reduce the leaf are index, resulting in very low transpiration.
Excluding this site, the observed mean annual ET of 656 mm compared with predicted mean annual ET of 671 mm. An F-test indicated there was no significant difference in the sample
49
variance between the observed and predicted values. Similarly, a two sample T-test, assuming equal variances, indicated there was no significant difference between the observed and predicted mean annual ET. Among these sites the correlation between observed and predicted annual ET was statistically significant but weaker than for SoilFlux (predicted ET = 1.02 x observed ET, R2 = 0.81 when the regression is forced through the origin).
Similar to SoilFlux, for 11 sites with access to groundwater, 3PG+ always under-estimated annual ET (Figure 23) and for 10 of these sites predicted ET was less than the lower 95% statistical confidence limit for observed ET. The observed mean annual ET was 1032 mm, compared with predicted mean annual ET of only 665 mm for these sites. Thus, for sites accessing groundwater, 3PG under-estimated ET by an average of 36%.
An F-test indicated a higher variance among observed than predicted annual ET for sites where trees had access to groundwater (p<0.05). A T-test, assuming unequal sample variances, indicated the observed mean annual ET was significantly greater than the predicted mean annual ET (p<0.001). However, unlike with SoilFlux the weak correlation between observed and predicted ET for these sites was statistically significant (R2 = 0.31), indicating the model was accounting for some of the variance between these sites and suggesting that a modification to the model which increased ET at sites with access to groundwater will enable more accurate predictions of groundwater uptake.
y = 0.30x + 353.30R2 = 0.31
y = 1.02xR2 = 0.81
0
200
400
600
800
1000
1200
1400
1600
0 200 400 600 800 1000 1200 1400 1600Observed ET (mm year-1)
Pre
dict
ed E
T (
mm
yea
r-1)
not using groundwater
using groundwater
1 to 1 line
Figure 23 Comparison between observed mean annual ET at field sites in southeast SA and southwest Victoria with simulated annual ET using the 3PG+ model. Error bars indicate 95% statistical confidence limits for the observed values. The open circle indicates an outlier in which soil depth was set as very
shallow in 3PG+ due to the presence of a hardpan layer.
50
Across all sites, 3PG+ explained only 17% of the variation in mean annual ET, largely because of the large under-estimate of ET for plantation with access to groundwater.
In conclusion, for predicting annual ET, 3PG+ performs reasonably well for plantations without access to groundwater, except where the soil is very shallow, but poorly for sites with access to groundwater.
Monthly ET
For plantations without access to groundwater, 3PG+ tended to underestimate ET for the May to September period but overestimate ET from November to January (Figure 24). Averaged across these sites, model predictions were accurate for October and the February to April period. The deviations were not as great as for the SoilFlux model. As with SoilFlux, by under-estimating ET in winter and spring, simulated soil water is over-estimated by early summer, resulting in an over-estimate of ET for a period until the excess soil water is used, enabling more accurate estimates of ET in late summer and autumn.
Not accessing groundwater
0
20
40
60
80
100
120
140
160
A M J J A S O N D J F MMonth
Mon
thly
ET
(m
m)
Observed
3PG
Figure 24. Comparison of observed and predicted mean monthly ET for sites without access to groundwater
For sites where trees had access to groundwater, 3PG+ substantially under-estimated ET in all months (Figure 25). The pattern of variation in ET through the year was approximately right, with peak water use correctly simulated as being in late autumn and early summer, but the lowest water use predicted to occur in late autumn rather than early winter. The largest errors in absolute terms were in February and the smallest in July and November.
51
Accessing groundwater
0
20
40
60
80
100
120
140
160
A M J J A S O N D J F M
Month
Mon
thly
ET
(m
m)
Observed
3PG
Figure 25. Comparison of observed monthly ET with that predicted by 3PG+ averaged across the 11 sites with access to groundwater
Net change in soil water
3PG+ simulated the net change in soil water between start and end of monitoring poorly (Figure 26) accounting for only 22% of the between-site variation in the net change in soil water.
Figure 26. Comparison between net changes in soil water observed at each site and predicted by the 3PG+
model
-500
-400
-300
-200
-100
0
100
200
300
-500 -400 -300 -200 -100 0 100 200 300
Obsereved net change in soil water (mm)
Pre
dict
ed n
et c
hang
e in
soi
l wat
er (
mm
)
E. globulus, no groundwaterE. globulus with groundwaterP. radiata, no groundwaterP. radiata with groundwater1 to 1 line
52
3.4.3 Cabala
Annual ET
The Cabala model was least accurate and least precise for simulating annual ET at sites where trees did not have access to groundwater (Figure 27). For four of these 10 sites, the annual ET simulated by Cabala fell outside the 95% statistical confidence limit for the observed annual ET. The model underestimated annual ET at six sites and over-estimated at four, with a mean error of 33 mm or 5%. An F-test indicated there was no significant difference in sample variance between observed and predicted annual ET. A T-test, assuming equal variances, indicated the predicted mean annual ET was not statistically significantly different from the observed mean annual ET. A regression between observed and predicted ET, forced through the origin, explained only 59% of the between site variance (predicted ET = 0.95 x observed ET, R2 0.59).
For sites accessing groundwater, Cabala’s performance was different from the other two models. Unlike SoilFlux and 3PG+, Cabala tended to over-estimated annual ET at these sites (Figure 27). For six sites the model significantly over-estimated ET and for one site it significantly under-estimated ET. At four sites, predicted mean annual ET was within the 95% confidence interval for observed ET.
Based on an F-test, the variances of the observed and predicted annual ET were not significantly different (p=0.33) for sites where trees used groundwater. Using a T-test assuming equal variances, the predicted mean ET was statistically significantly higher than the observed mean ET for sites using groundwater (P<0.05). On average, Cabala over-estimated annual ET by 177 mm, or 17% (1220 mm year -1 predicted compared with 1043 mm year-1 observed). The model explained <1% of the variance in mean annual ET between sites accessing groundwater, which was not statistically significant.
y = 0.947xR2 = 0.5903
y = 0.1006x + 1115.3R2 = 0.0076
0
200
400
600
800
1000
1200
1400
1600
0 200 400 600 800 1000 1200 1400 1600Observed ET (mm year-1)
Pre
dict
ed E
T (
mm
yea
r-1)
not using groundwater
using groundwater
Figure 27. Comparison between observed annual ET and annual ET predicted using Cabala
53
Cabala was the best-performed model when it came to explaining the overall variation across all of the sites, accounting for 63% of the total between-site variance in annual ET.
Monthly ET
Cabala was the best performed of the three models for estimating monthly ET.
For sites not accessing groundwater, the pattern of variation through the year was simulated well, with Cabala correctly predicting ET would be lowest in April and May and highest in October (Figure 28). From April to November, the observed ET averaged 56.6 mm month-1 compared with a predicted mean of 57.3 mm month-1. Cabala under-estimated mean monthly ET for the December to March period: the observed mean of 54.1 mm month-1 compared with a predicted mean of only 43.2 mm month-1. It is this under-prediction during the summer and early autumn period which caused the model to slightly under predict mean annual ET.
Cabala also performed reasonably well at predicting monthly ET at sites where trees had access to groundwater (Figure 28). For the May to October period, predicted ET was 77.4 mm month-1 compared with 78.4 mm month-1 observed. The model correctly predicted that the lowest ET occurs from May to July and the highest in December, but for November through to April the model consistently substantially over-estimated ET. During this period the observed ET of 95.6 mm month-1 compared with predicted ET of 125.1 mm month-1.
Not accessing groundwater
0
20
40
60
80
100
120
140
160
A M J J A S O N D J F M
Month
Mon
thly
ET
(m
m)
Observed
Cabala
Figure 28. Comparison between observed and predicted monthly ET averaged for sites not accessing groundwater
54
Accessing groundwater
0
20
40
60
80
100
120
140
160
A M J J A S O N D J F M
Month
Mon
thly
ET
(m
m)
Observed
Cabala
Figure 29. Comparison between observed and predicted monthly ET averaged across sites with access to groundwater
Net change in soil water
Cabala simulated the net change in soil water between start and end of monitoring reasonably well (Figure 30). At one site (marked as X in Figure 30), the watertable dropped by 2 m between the start and end of the monitoring period, which the model would not have accounted for, explaining why the net change in soil water was simulated poorly at this site. Excluding this site from the analysis, the model accounted for 61% of the between-site variation in the net change in soil water.
Figure 30. Comparison between simulated and observed net change in soil water for Cabala. At the site
indicated by X, the watertable dropped by 2 m over the monitoring period.
-250
-200
-150
-100
-50
0
50
100
150
200
250
-250 -200 -150 -100 -50 0 50 100 150 200 250
Obsereved net change in soil water (mm)
Pre
dict
ed n
et c
hang
e in
soi
l wat
er (
mm
)
E. globulus, no groundwaterE. globulus, with groundwaterP. radiata, no groundwaterP. radiata with groundwater1 to 1 line
55
3.4.4 Accuracy of 3PG+ and Cabala for growth predictions
The 3PG+ and Cabala models were primarily designed to simulate forest growth. This section presents summary comparisons between predicted and observed growth for these two models.
Leaf area index
The version of 3PG+ used for this project simulated LAI poorly, particularly for blue gums (Figure 31). For this species, simulated maximum LAI was often unrealistically high and the model explained no significant between-site variation. The reasons for this have not yet been thoroughly investigated. The simulations were better for radiata pine, but still not good, explaining only 33% of the between-site variation.
Figure 31. Comparison of simulated and observed maximum LAI for the 3PG+ model
Cabala performed better than 3PG+, accounting for 44% of the between-site variation in maximum LAI. This model over-predicted the maximum LAI twice as often as under-predicting it.
Figure 32. Comparison between simulated and observed maximum LAI for Cabala
0
2
4
6
8
10
12
14
16
18
0 2 4 6 8 10Obsereved maximum LAI (m2 m-2)
Pre
dict
ed m
axim
um L
AI (
m2 m
-2)
E. globulus, no groundwaterE. globulus with groundwaterP. radiata, no groundwaterP. radiata with groundwater1 to 1 line
0
1
2
3
4
5
6
7
8
9
10
0 1 2 3 4 5 6 7 8 9 10Obsereved maximum LAI (m2 m-2)
Pre
dict
ed m
axim
um L
AI (
m2 m
-2)
E. globulus, no groundwaterE. globulus with groundwaterP. raidata, no groundwaterP. radiata with groundwater1 to 1 line
56
Stem volume increment
In commercial forestry plantations, the rate of increment in stem wood is an important variable as it determines the quantity of wood that can be harvested. The version of 3PG+ used in this project gave very poor predictions of both periodic annual increment (PAI) (Figure 33) and mean annual increment (MAI) (Figure 34). PAI is the total increase in stem volume per hectare during the monitoring period, while MAI is the mean annual stem volume increment for the period from planting up to the time measurements ceased at each site. For blue gum sites without access to groundwater, 3PG+ simulated PAI reasonably well but for blue gums accessing groundwater and for all radiata pine sites it substantially under estimated PAI, often by 50% or more (Figure 33). Across all sites, the mean PAI predicted by 3PG+ was 53% lower than the observed mean and 3PG+ accounted for none of the between-site variation.
Figure 33. Comparison of observed and simulated PAI for 3PG+
Figure 34. Comparison between observed and simulated MAI for 3PG+
0
10
20
30
40
50
60
70
0 10 20 30 40 50 60 70Obsereved PAI (m3 ha-1 year-1)
Pre
dict
ed P
AI (
m3 h
a-1 y
ear-1
)
E. globulus, no groundwaterE. globulus, with groundwaterP. radiata, no groundwaterP. radiata, with groundwater1 to 1 line
0
5
10
15
20
25
30
35
40
45
50
0 10 20 30 40 50Obsereved MAI (m3 ha-1 year-1)
Pre
dict
ed M
AI (
m3 h
a-1 y
ear-1
)
E. globulus, no groundwaterE. globulus with groundwaterP. radiata, no groundwaterP. radiata, with groundwater1 to 1 line
57
Simulations of MAI were slightly better, but for three blue gum and five pine sites, MAI was under-estimated by more than 25% and for two blue gum sites, MAI was over-estimated by more than 25%. Overall, 3PG+ under-estimated MAI by 23% and there was no statistically significant correlation between simulated and observed MAI (R2 0.02). Cabala performed considerably better than 3PG+ for simulating stem volume increment. Cabala explained 57% of the between-site variation in PAI (Figure 35) and 54% of the variation in MAI (Figure 36). Mean PAI was underestimated by 10% but mean MAI was over-estimated by 6%.
Figure 35. Comparison between predicted and observed PAI for the Cabala model
Figure 36. Comparison of predicted and observed MAI for the Cabala model
0
10
20
30
40
50
60
70
0 10 20 30 40 50 60 70
Obsereved PAI (m3 ha-1 year-1)
Pre
dict
ed P
AI (
m3 h
a-1 y
ear-1
)
E, globulus, no groundwaterE. globulus with groundwaterP. radiata, no groundwaterP. radiata with groundwater 1 to 1 line
0
5
10
15
20
25
30
35
40
45
50
0 10 20 30 40 50Obsereved MAI (m3 ha-1 year-1)
Pre
dict
ed M
AI (
m3 h
a-1 y
ear-1
)
E. globulus, no groundwaterE. glob ulus with groundwaterP. radiata, no groundwaterP. radiata with groundwater1 to 1 line
58
4 DISCUSSION AND RECOMMENDATIONS
4.1 Comparison of the three models
Each models tested had advantages and disadvantages for simulating ET. The SoilFlux model was the best of the three for simulating annual ET from plantations without access to groundwater but significantly under-estimated annual ET from plantations with access to groundwater. The 3PG+ model also did well for simulating ET at sites without access to groundwater (except at one site with shallow soil), and although it substantially under-estimated ET at sites with groundwater, it at least explained some of the variation between these sites. The Cabala model performed least-well for simulating annual ET at sites without access to groundwater and in contrast to the other two models, tended to over-estimate ET at sites with groundwater, although at some sites the ET estimates were accurate. Cabala explained the greatest amount of the total between-site variation in mean annual ET and was the best when it came to simulating monthly ET. It correctly predicted the pattern of variation through the year whereas SoilFlux, and to a lesser degree, 3PG, substantially under-estimated ET in winter and over-estimated in late spring and early summer.
For SoilFlux, LAI and root depth are input variables rather than modelled variables. For the 22 study sites, reasonable estimates of LAI were available and root depth was able to be inferred based on depth to groundwater and in some cases, patterns of variation in soil water over time. For broader-scale applications of SoilFlux, such plot-scale estimates of LAI and root depth would not normally be available. However, once LAI exceeds a threshold value of about 3, in environments in which annual ET is water limited rather than energy limited, inaccuracy in estimates of LAI should not make much difference to the accuracy of ET estimates. Therefore, even though some of the input data to SoilFlux was probably better than would normally be the case, it is reasonable to conclude the model accurately simulates annual ET of closed-canopy plantations without access to groundwater. Additional comments about the use of SoilFlux and its accuracy when applied across entire catchments are provided by SKM in their 2nd (2008) report of the SoilFLux testing attached to this report (Appendix C).
In 3PG+ and Cabala, LAI and root depth are simulated variables rather than input variables. These both influence ET in these models, so it is not surprising these models did not perform quite as well as SoilFlux for predicting mean annual ET at sites without access to groundwater. If these models are to be used to simulate water use of existing plantation areas in future, results might be improved if the models could be initialised using observed LAI (e.g. ground-based measurements or estimated from satellite images) rather than starting the simulations from planting.
None of the models performed well for simulating annual ET for plantations with access to groundwater. SoilFlux consistently under-estimated ET at these sites and the model was only able to explain 10% of the between-site variation. The 3PG+ model under-estimated ET by an even greater amount, but did explain a statistically significant, although still relatively small proportion (30%) of the between-site variation. In contrast Cabala tended to over-estimate ET at sites with access to groundwater and explained none of the between-site variation. These results suggest there is some bias in all three models when used for simulating ET from sites accessing groundwater. The occurrence of significant bias rather than only random error provides some promise for improvement in the capacity of the models to simulate mean annual ET at sites where groundwater is accessible if the reasons for the bias can be identified. However, the observation
59
that none of the models can account for much of the between-site variation in annual ET at such sites highlights the difficulty of accurately modelling ET from vegetation in environments where water availability is not necessarily the factor limiting ET.
Factors limiting water use when groundwater is accessible by the roots of the vegetation can include available energy, the rate at which groundwater can move through the aquifer or soil and be replaced by lateral inflow, the accumulation of salt in the root zone as the roots take up water but excludes most of the salt which may be present, and the LAI of the plantation, which itself can be controlled by many factors. However, in the case of LAI, the data presented in Section 3.2.5 indicates this does not explain any significant variation in ET in plantations with access to groundwater in the Green Triangle.
Processes occurring under the ground, associated with root growth, water movement and water uptake are very difficult to observe, let alone simulate in simple models. The SoilFlux and 3PG+ models may have over-estimated the limitation on water uptake imposed by soil properties, or by salt accumulation, thereby causing groundwater uptake to be under-estimated. In the Cabala model on the other hand, soil physical properties do not constrain water uptake, so if the simulated root zone intersects a water table, it is assumed water availability becomes non-limiting to water use. In this case the energy available for driving evapotranspiration determines water use.
The analysis of the variables most strongly correlated with annual ET indicated that for plantations accessing groundwater about half the variation between sites was explainable by whether point or areal potential ET applied. For sites accessing groundwater, the mean annual ET estimated by Cabala was similar to the mean point potential ET as defined by Wang et al. (2001); the error in the Cabala estimates of annual ET was much greater at sites in the middle of large plantations, where areal, rather than point potential ET would be expected to apply. The way Cabala estimates ET at sites where water availability is not limiting probably needs to be improved. The version of Cabala we tested does not place a restriction on the rate of groundwater uptake from a water table. Cabala does, however, use a simplified formulation of the Penman-Monteith model that does not include the effect of aerodynamic roughness. The tendency of Cabala to over-estimate ET at sites accessing groundwater may have been because simulated potential ET at some sites was too high.
The Soil Flux model does account for whether point or areal PET applies, and sensitivity analyses were undertaken at some sites to examine the effect of this variable on simulated ET. For sites accessing groundwater it did make a small difference, but even when point PET was used, the model still substantially under-estimated annual ET. The underestimation of ET by SoilFlux at these sites is probably therefore related to the simulated rate of water movement through the soil or aquifer to the roots, or the simulated effect of root zone salinity on water uptake. In SoilFlux, ET is influenced by root length density, which is assumed to reduce with increasing depth in the soil. Research by Falkiner et al. (2006) suggests that some tree species have greatly increased root length density in the capillary fringe above the water table. The study by Falkiner et al. (2006) provided circumstantial evidence that the amount of groundwater uptake was strongly influenced by root length density. A Corymbia maculata plantation used approximately 400 mm year-1 more groundwater than a nearby E. grandis plantation (Polglase et al. 2002) and also had a substantially higher root length density in the capillary fringe. The root length density function in SoilFlux might need to be revised for plantation sites with access to groundwater. This would need to be verified using field measurements of root length density and the model would need to be retested. This is well beyond the scope of the study reported here.
60
Similarly, 3PG+ may have been under-estimating annual ET at sites using groundwater by placing too great a restriction on the rate of water movement through the soil to the roots. Again this would need to be examined through further field studies and model testing.
To improve the three models would require substantial additional work. Changes to the method used to estimate potential ET, the root length density function in SoilFlux, or the effect of soil hydraulic properties and root zone salinity on groundwater uptake rates would need to be verified through field studies.
Across all 22 closed-canopy plantation sites in which water use has been monitored in the Green Triangle over the past 9 years, 94% of the between-site variation in mean annual water use was explained by an empirical relationship including rainfall, accessibility of groundwater, and potential ET (Section 3.2). By comparison, the best model overall (Cabala) only accounted for 63% of the between-site variation. The performance of the process models was poor compared to the empirical model because none of the three models simulated groundwater use well. However, while the empirical model explained much more of the between-site variation in the Green Triangle, empirical models are often not accurate beyond the original data set from which they were derived. The advantage of process-based models is their applicability to other situations.
Cabala and 3-PG are primarily used to simulate growth rates of forestry plantations. However, the version of 3PG+ used in this study simulated growth rates very poorly. The reasons for this have not been investigated. We used a version of 3-PG modified to enable simulation of groundwater uptake and the accumulation of salt in the root zone over time. Simulated groundwater uptake rates were very much lower than observed. Low simulated water use probably partly explains low growth rates predicted by the model. If process models are to be used to accurately simulate growth and water use of plantations with access to groundwater, further model development will be necessary.
4.2 Application of models to water resource management questions in the future
The national Water Initiative (NWI), Paragraph 55 states “… a number of land use change activities have potential to intercept significant volumes of surface and/or ground water now and in the future” and identifies “ large-scale” plantation forestry as one possible intercepting activity. The NWI requires that fully allocated or over-allocated systems be identified and any intercepting activities assessed as being significant, be recorded, through a licensing system for example. Even in systems determined not to be fully allocated, significant water intercepting activities need to be identified and the amount of water likely to be intercepted needs to be estimated.
The WatLUC study (SKM 2005) has provided estimates of the impacts of land use change, including recent and potential future plantation forestry developments, on water availability in southwest Victoria. The analysis presented in Section 3.4 demonstrates the SoilFlux model provides accurate stand-scale estimates of plantation water use for closed-canopy plantations without access to groundwater. However it was also demonstrated that the model did not simulate the monthly variation in ET very well. In unregulated catchments in southwest Victoria, the impacts of land use change on low flows might be equally or more important then impacts on mean annual flows.
61
Further analysis is needed to determine how well one-dimensional models such as SoilFlux, 3PG+ and Cabala can be scaled to whole catchments. As discussed in Section 4.3 below, it is probably too soon for impacts of recent plantation developments in SW Victoria to be detectable as changes in stream flow. If these effects are going to become evident, this should occur within the next few years. Long-term stream flow records provide the best direct evidence of impacts of land-use change on water availability at the catchment scale. To ensure conclusions about catchment-scale effects of ‘intercepting activities’ drawn from the plot-scale monitoring and 1-dimensional modelling are sound, further analysis of stream flow data will be required over the next 3 to 5 years for southwest Victorian catchments in which large-scale land use changes have taken place. For example, in the past decade, approximately 25% of the Crawford River catchment has been converted from broad-acre grazing to hardwood plantations. Application of the SoilFlux model to this catchment by SKM predicted that mean annual stream flows from the catchment would decline by 30% or more as a result of this land use change (SKM 2008a).
Given the scale and timing of land use change in some south west Victorian catchments, investment in accurate monitoring of stream flows from these catchments over the next few years will be worthwhile to determine whether changes in stream flow predicted in the WatLUC study eventuate. This will provide valuable information on how well results from one-dimensional evapotranspiration models can be scaled to whole catchments. This monitoring will need to continue for a sufficient period to enable separation of the effects of land use change from the effects of inter-annual variation in rainfall.
4.3 Scaling from plot-based studies to whole catchments and regions and implications for interpretation of the results of WatLUC Stage 3
CSIRO’s research on plantation water use in the Green Triangle over the past decade has produced stand-scale direct measurements of plantation ET. These provide an understanding of the climatic and site factors influencing ET. An important question for water resource managers and policy makers is how well these plot-based measurements scale to whole catchments and regions. In particular, can stand-scale measurements simply be summed over entire catchments and regions to come up with accurate predictions of cumulative impacts at these larger scales?
As noted in Section 2, between 2002 and 2007 consultants SKM investigated the potential effects of land use change on water resources in south west Victoria (Sinclair Knight Merz 2005, 2008a, 2008b). The study predicted effects at the catchment scale by summing changes predicted at the point scale using a 1-dimensional vegetation water use model (SoilFlux). The model was run for representative soil types, climates and land use types to produce estimates of differences in evapotranspiration between different land uses at the point scale. These differences were summed to the landscape scale based on the area occupied by each land use x soil x climate combination in each catchment.
As discussed in Section 3.4.1 the SoilFlux model gave accurate estimates of closed-canopy plantation water use at sites without access to groundwater, but under-estimated plantation water use at sites where the tree roots had unimpeded access to groundwater in a highly transmissive, unconfined aquifer. With the exception of the Hawkesdale ground water management area, most of the plantations in southwest Victoria probably do not have such access to groundwater in a highly transmissive aquifer. Therefore we would expect the SKM modelling of plantation water
62
use to be accurate across most of the areas to which it was applied and if scaling can be achieved by summing point-scale effects based on land area, the predictions of catchment-scale effects would also be accurate. If this is true, should the effects of new plantations on stream flow in southwest Victoria already be evident?
As part of Stage 3 of the WatLUC study, SKM used double-mass plots (an X-Y plot of the accumulated total of one variable against the accumulated total of another) of the relationship between rainfall and stream flow in several individual catchments to look for indications of changes in this relationship. Using such plots, if the relationship between the two variables remains constant, the slope of the plot will also remain constant. Deviation in the slope indicates a change has occurred in one variable relative to the other. SKM also compiled double mass plots showing cumulative stream flow in pairs of catchments to determine whether the amount of runoff generated from catchments subjected to recent plantation development had changed compared to catchments with relatively low amounts of recent land use change. Their results indicated that while the relationship between rainfall and runoff has changed in the region since the mid 1990s, this change had occurred in all catchments, irrespective of recent changes in land use. Furthermore, up to 2006, the relationship between catchments with and without major land use change had not deviated from straight lines. These observations indicate land use change in several southwest Victorian catchments over the past decade has not yet resulted in detectable changes in stream flow.
Some stakeholders appear to have made several important inferences from this analysis including:
• That increases in the area of plantation forestry in the Green Triangle in the past decade are not resulting in changes in water availability.
• That stand-scale measurements of ET based on measurements of water use by individual trees cannot be scaled to whole catchments or regions accurately.
This section examines the validity of such conclusions.
It could be argued that if, based on plot-scale studies or models, changes in stream flow in southwest Victoria were expected, but have not actually occurred, plot-scale data cannot be accurately scaled to whole catchments. The important question is, ‘based on a plot-scale understanding of the difference in water use between plantations and pasture, and given the recent change from pasture to plantations in parts of some southwest Victorian catchments, would changes in stream flow have been expected in these catchments between 1998 (the year large-scale hardwood planting began) and 2006 (the most recent year for which stream flow data are available)?’
In the discussion below the example of the Crawford River Catchment is used. Approximate data on the increase in plantation area between 1990 and 2005 and annual rainfall and stream flow from this catchment, as presented in the recent study by SKM (2008a) are examined. In 1990, the catchment of about 70,000 ha in total area was approximately 65% agricultural land (mostly grazing) and 35% forest and woodland. Due mainly to establishment of hardwood plantation since 1998, by 2005 this had changed to about 40% agricultural land and 60% forests and woodland. Data on the area of new hardwood plantations established in the catchment each year are not publically available, however, between 1998 and 2005, the average rate of conversion of grazing land to plantations was about 2000 ha year-1.
63
A land use change from agriculture to plantations would be expected to result in a substantial reduction in stream flow (Bosch and Hewlett 1982, Zhang et al. 1999, Brown et al 2005). However, SKM’s analysis demonstrated that up to 2006, there was not yet any evidence of a change in stream flow due to this land use change in the Crawford River catchment.
Does this mean that the assumption that change from pasture to forestry reduces stream flow is wrong? The analysis below indicates that, based on knowledge of plantation water use at the stand scale, taking account of changes within the rotation, the approximate timing of the recent land use change in the Crawford River catchment, and low rainfall in the past few years, a net reduction in cumulative stream flow would not be expected to become evident until after 2006.
Totalled over a pulpwood rotation nominally of 10 to 12 years, the reduced water use early in the rotation is expected to be more than offset by higher water use after canopy closure, so over a full rotation a net increase in ET compared to pasture occurs and a net reduction in stream flow or groundwater recharge is the expected result. In the case of the Crawford River catchment, hardwood plantation development only began in 1998 and has continued to the present. The low water use in the early part of the rotation (Section 3.3) is expected to have had a significant impact on the time taken for net effects of the new plantation on stream flow to become evident.
Making the following assumptions about the area of plantations established each year and the changes in ET of plantation relative to pasture through the rotation, the net effect of the new plantations on stream flow can be estimated and it can be shown that up to 2006, the cumulative net effect would be expected to be close to zero. Only after 2006 will there begin to be a substantial net reduction in cumulative stream flow.
Assumptions:
• 2000 ha of new plantations established per year from 1998 to 2008 • All changes in ET are evident as changes in stream flow • In rotation year 1, plantation ET is 140 mm lower than from pasture • In rotation year 2, plantation ET is 40 mm lower than from pasture • In rotation year 3, plantation ET is 30 mm higher than from pasture • From rotation year 4 until harvest at the end of the 11th year, the difference in ET between
plantation and pasture is assumed equivalent to the difference predicted by Zhang et al. (1999) based on actual rainfall for that year. Applying this assumption gives annual ET of plantation with closed canopy averaging 80 mm higher than from pasture over the 9 years from 1998 to 2006.
Planting began in 1998, so in that year all 2000 ha of new plantations were in year 1 of the rotation, with an expected ET of 140 mm lower than from the pasture they replaced. Across 2000 ha this amounts to a reduction in ET from the catchment and an assumed increase in stream flow of 2,800 ML.
It is assumed that in 1999 another 2000 ha of new plantations was established, so we have 2000 ha of year 2 plantations (planted in 1998) and 2000 ha of year 1 plantations (planted in 1999). From the 2000 ha of 1998 plantations we expect 800 Ml of extra stream flow and from the 1999 plantations 2,800 Ml of extra stream flow, giving a total of 3,600 Ml of additional stream flow from the 4,000 ha of plantations present in 1999.
It is assumed another 2000 ha was planted in the year 2000, giving a total of 6,000 ha. From this we expect 2,800 Ml additional stream flow from the 2000 planting, 800 ML from the 1999 planting
64
but a reduction in stream flow of 600 Ml from the 1998 plantings. The net effect across the 6,000 ha is 3,000 Ml more stream flow (2,800 + 800 – 600).
In 2001, another 2,000 ha is planted. Rainfall in that year was about 800 mm. This would give a predicted reduction in stream flow from the 1998 plantings of 96 mm, or 1925 Ml across the 2,000 ha of 1998 plantings. Across all 8,000 ha of plantings now established we would still expect a net increase in stream flow of 1075 Ml (2,800 + 800 – 600 – 1925).
In 2002 ET is at its maximum for both the 1998 and 1999 plantings. However, rainfall was low (just over 600 mm), so the expected reduction in stream flow from these two age classes compared with pasture is only 58 mm in that year. Summing across all five age classes now, the net effect on stream flow is still a gain of 664 Ml (2,800 + 800 – 600 – 1168 – 1168). So, despite 5 years of planting, and 10,000 ha of plantations now in the ground (14% of the catchment), because of the short-term increase in water yield expected in the first 2 years of the rotation, stream flow is still expected to be higher than if no new plantations had been established.
It is not until after 2002 that any net reduction in stream flow from the catchment would be expected. Based on the assumptions above, the reduction in flow would be 1675 Ml in 2003, 4700 Ml in 2004, but due to low rainfall, only 2650 Ml in 2005 and 2060 Ml in 2006. During a period when annual rainfall varied between about 520 and 800 mm, and annual stream flow varied between 3,000 Ml and 60,000 Ml, these relatively small reductions would be difficult to detect in a stream flow record with such high inter-annual variation.
Summing over the 9 years (1998 to 2006), the net effect of the new plantations on cumulative stream flow up to 2006 is expected to have been close to zero. In other words, up until 2006 we would not expect to be able to detect any change in cumulative stream flow in the Crawford River catchment attributable to land use change. This means that a double mass plot of stream flow in the Crawford River catchment against a nearby catchment in which no land use change had taken place, would have showed no consistent deviation from a longer term trend. This is consistent with what was observed by SKM (2008b) and therefore there is no inconsistency between our understanding of plantation ET at the plot or stand scale, and the observed effects of plantations on stream flow at the catchment scale to date.
It is only after 2006, as the majority of the new plantations are in the closed-canopy phase, that we would expect substantial reductions in stream flow (Figure 37). Continuing the analysis presented above to 2019, and assuming annual rainfall each year equal to the average rainfall for the 1990-2006 period (715 mm), from 2008 onwards stream flow would be expected to average approximately 9,600 Ml year-1 less than if no land use change had occurred. This is a reduction of 29% compared with the 1990 to 2006 observed mean annual flow.
To be able to detect real changes in stream flow in the Crawford River catchment will therefore require at least several more years of stream flow monitoring. It would thus be wrong to conclude based on analysis of stream flows up to 2006 that land use change is not going to significantly affect stream flows in the future and that stand-scale measurements of water use cannot be scaled to whole catchments and regions. In fact, as shown in this section, when our stand-scale understanding of plantation water use is applied at the catchment scale, the results are entirely consistent with the observed trends in stream flow. It is worrying that some stakeholders appear to be implying that because substantial reductions in stream flow observed in the past 15 years in southwest Victoria are attributable largely to reduced rainfall, higher water use of some land uses compared with others does not need to be accounted for in regional water sharing plans. If recent
65
rainfall trends continue, the region will have substantially less water to share and therefore it will be even more important to understand how different land uses affect water availability.
Figure 37. Predicted net change in stream flow each year from the Crawford River catchment (blue line) in
response to the assumed change in plantation area (green line)
4.4 Effects of plantations on groundwater levels
Our research indicates plantations in the Green Triangle, particularly in southeast South Australia, can take up groundwater at locations where the watertable is at a median depth less than about 6 m. In areas where plantations are accessing groundwater, a draw-down effect might be expected under the plantation itself and beyond the edges of the plantation. The depth and areal extent of this draw-dawn will depend on various factors, including the size of the plantation accessing groundwater, the amount of groundwater uptake per unit area of plantation and the transmissivity of the aquifer.
The term transmissivity refers to the quantity of water that can pass through an aquifer in a given period of time, as determined by the saturated hydraulic conductivity of the aquifer and its thickness. Aquifer thickness and hydraulic conductivity can both vary by several orders of magnitude, and therefore transmissivity can also vary greatly between and within aquifers.
At 10 of the 11 sites where groundwater use was evident, the plantation was established over an unconfined aquifer present in sandy clay or karst limestone. The limestone aquifer in southeast SA is generally known to be of very high transmissivity because it is relatively thick (tens of m to >100 m in places) and has a high hydraulic conductivity. The Port Campbell limestone also has high transmissivity.
Pumping of groundwater from an aquifer induces a cone of depression around the pumping well. The depth and extent of this cone depends on the pumping rate and the transmissivity. As the latter increases, the cone is shallower but extends over a larger area.
-15000
-10000
-5000
0
5000
10000
15000
20000
25000
1995 2000 2005 2010 2015 2020
Are
a (h
a) o
r ch
ange
in s
trea
mfl
ow (
Ml)
Hardwood plantation area
Predicted net change instreamflow
66
Plantations using water from an aquifer will also cause a cone of depression, but because the plantation is spread out over a large area rather than extraction taking place at a single point, the cone of depression will have a different shape.
Another difference between mechanical pumps and trees is the ability of the pump to take water from deeper as the watertable is drawn down. If pumping lowers the watertable, the pump can be lowered down the well and pumping rates maintained. If uptake of groundwater by trees lowers the watertable, the tree roots must grow deeper if groundwater uptake is to continue. As discussed in Section 3.1.1, the root zone at one plantation appeared to increase in depth at around 0.6 to 0.9 m year-1 but this increase can only continue if there are no root impeding layers and if conditions in the regolith are otherwise suitable for root survival and growth. It is expected that plantations taking up groundwater would initially lower the water table but a new equilibrium groundwater level would be reached at which a lower rate of groundwater uptake will be balanced by groundwater inflow under the plantation.
Extraction of water from an aquifer of high transmissivity will create a shallow draw-down over a large area. Natural rates of water movement in the unconfined aquifer in southeast South Australia are 2 to 100 m year-1 (Fred Stadter, pers comm.). For example, for a flow rate of 36.5 m year-1 (0.1 m day-1), with an average effective porosity of 10%, a 1 m width of a 30 m thick aquifer could supply 0.3 m3 day-1 without any change in aquifer levels. Maximum rates of groundwater uptake measured at the 10 sites known to be accessing groundwater were about 3 mm day-1 for plantations located away from an exposed edge. Therefore, this hypothetical aquifer could supply a 100 m wide plantation with its maximum daily rate of groundwater uptake without any aquifer drawdown. To supply wider plantations than this would result in some draw-down. For example, if the natural gradient in the aquifer was 0.0005 m m-1 (0.5 m km-1), maximum draw-down 200 m inside the plantation would need to be 0.05 m, or 5 cm for the aquifer to supply 3 mm groundwater uptake day-1 to the whole plantation. These are very rough figures but give an indication that if groundwater uptake is from an aquifer of high transmissivity (600 m2 day-1 in this example), plantations have to be hundreds of m wide before any substantial draw-down would occur.
To make any firm conclusions about the potential effect of tree plantations on groundwater levels in different parts of the region, detailed information on aquifer properties and the depth of root impeding layers would be required. Predictions of the effects of plantations on groundwater levels would need to be verified by long-term monitoring at various locations within and at various distances away from plantations.
Recent data from southeast South Australia on groundwater levels in blue gum plantations established since 1999 are inconclusive on the impacts of these plantations on groundwater levels because the timing of plantation development has coincided with a period first of above average, then of below average rainfall.
As discussed in Sections 3.3 and 4.3, ET rates are substantially lower at the start of each rotation and it may take several years before the maximum ET of a plantation is reached. In the case of blue gums established over shallow groundwater in south east South Australia, this means plantations established between 1999 and 2002 would not have been expected to begin to draw down ground water levels until about 2003 or 2004. However, annual rainfall from 2002/03 to 2004/05 was 11% above average and therefore higher groundwater recharge than normal in surrounding agricultural land may have offset any groundwater uptake by the new blue gum plantations. Mean annual rainfall from 2005/06 to 2007/08 was 19% below average. An
67
observation well monitored by CSIRO in a blue gum plantation in the Wattle Range in south east South Australia between 2001 and 2008 indicates groundwater levels dropped substantially over that period (Figure 38), but it is impossible to be certain whether this drop is the result of the surrounding plantations using groundwater or the period of lower rainfall or a combination of both. During the wetter period from 2002 to 2004, groundwater levels fluctuated each year from about 0.5 m depth in spring to about 2.5 m in autumn. Levels were lowest in late autumn and rose rapidly in winter to their peak in spring. From 2005 to 2008 there was a drop in groundwater levels coinciding with the period of lower rainfall. This is also the period when it is expected the majority of new blue gum plantations in this area would have reached their maximum rate of groundwater uptake. It is not possible to conclude at this stage whether the drop in ground water levels between 2005 and 2008 was the result of 4 years of low rainfall or the effect of the new plantations, or a combination of both influences.
As is the case with the Crawford River catchment in south west Victoria, at least several more years of monitoring ground water levels will be needed before the long-term effects of plantations on the local water resource can be determined.
Date (month/year)
-5.0
-4.5
-4.0
-3.5
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
02
/02
06
/02
10
/02
02
/03
06
/03
10
/03
02
/04
06
/04
10
/04
02
/05
06
/05
10
/05
02
/06
06
/06
10
/06
02
/07
06
/07
10
/07
02
/08
06
/08
Wat
er ta
ble
hei
gh
t rel
ativ
e to
gro
un
d le
vel (
m)
Figure 38. Variation in depth to groundwater at a blue gum plantation site in the Wattle Range, southeast South Australia.
4.5 Effectiveness of block plantings of trees in reducing groundwater recharge for salinity control
The results indicate block planting of trees will be effective in reducing groundwater recharge. During the period of full canopy closure in each crop cycle, little or no recharge will occur, at least during years of average to below average rainfall. The analysis presented in Section 3.2.2 indicates that plantations with closed canopies that don’t access groundwater were using all of the available rainfall (taking into account changes in soil water content). Water use of plantations that
68
do have access to the water table may exceed rainfall and approach potential evapotranspiration rates, depending on the salinity of the groundwater.
As noted in previous sections, recent data from southeast South Australia indicates that the evapotranspiration of commercial plantations which are periodically harvested and replanted is reduced during the inter-rotation period. During the inter-rotation, the soil profile quickly refills from winter rainfall, and substantial amounts of groundwater recharge can occur. During this period when groundwater recharge appears to be greater than under pasture, salt might be mobilised, although this has not been confirmed by any measurements. Consequently, permanent revegetation with woody native species might be more effective for long-term salinity control than periodically harvested plantations. Across large areas, plantations occur as a mix of age classes from early rotation through to harvest age. Because plantation water use is higher, on average, than pasture water use, and groundwater recharge is lower, plantations will be effective in reducing groundwater recharge. In the right kinds of groundwater flow systems, large block plantings of trees are expected to be effective in controlling salinity.
Where plantations access groundwater, salt in the groundwater may become concentrated in the root zone over time. It is possible that when the plantation is harvested, salt that has accumulated over the previous rotation will be flushed into the groundwater, which may adversely effect other groundwater dependent vegetation nearby. Again, however, this has not been verified by any observations and the likelihood of this risk is unknown. A study of chloride concentrations under various land uses in southeast South Australia recorded higher levels of chloride accumulation under plantations than beneath other land uses (Leaney et al. 2006) which is consistent with lower groundwater recharge, and in some cases was probably indicative of groundwater uptake. Longer-term monitoring of salt levels under plantations and other land uses should be undertaken, including during the inter-rotation period after harvesting.
5 CONCLUSIONS AND RECOMMENDATIONS
5.1 Water use and water yield from plantations with closed canopies
In the Green Triangle, closed-canopy plantations without access to groundwater use all of the rainfall. This conclusion appears to be valid for mean annual rainfall ranging from ~500 mm to ~750 mm. During years of above average rainfall, soil water accumulates. Plantation root depth is great enough at most sites to enable the plantations to use up this store of soil water during drier years.
None of the data included a period of several consecutive wet years. The observation that at some sites soil wetting to > 6 m depth occurred after only a single wet winter indicates that after a sequence of two or more consecutive wet years, the soil profile may wet up sufficiently to allow runoff or deep drainage. Several long-term plantation water balance monitoring sites should be established to enable determination of whether consecutive wet years would result in net runoff or recharge from closed-canopy plantations.
Closed-canopy plantations with access to groundwater in an unconfined aquifer of low salinity and high transmissivity are able to take up groundwater at a rate almost equal to the difference
69
between potential evapotranspiration and rainfall. As potential ET is higher in small plantations or near to exposed plantation edges, groundwater uptake is higher in these situations than in the middle of large plantations. To determine the local and regional effects of plantations on ground water levels as a result of groundwater uptake, more observation wells in and near plantations are needed, as well as a longer period of monitoring in existing groundwater observation wells. Long-term monitoring of salinity under plantations accessing groundwater is also required to determine whether salt accumulates and the risk this posses to the health of the plantations and any nearby water dependent ecosystems.
Across all 22 closed-canopy plantation sites in which water use has been monitored in the Green Triangle over the past 9 years, 94% of the between-site variation in mean annual water use was explained by rainfall, accessibility of groundwater, and potential ET. Groundwater accessibility is determined by hydrogeology, topography, geology and the presence of root impeding layers. An empirical, regression model including rainfall, groundwater accessibility and potential ET accounted for more of the between-site variation in closed-canopy plantation mean annual water use than three process-based models. However the empirical relationship developed in this study was not tested against any independent data. Empirical models are often not accurate beyond the original data set from which they were derived and, as in this study, require a large amount of data from which to derive the empirical relationships. Process-based models can be run without the requirement to collect large data sets and have the advantage that being based on physical processes, enabling application to other situations. For example, the empirical model in Equation 3 (Section 3.2.3) would not be expected to give accurate predictions of plantation annual water use at locations where mean annual rainfall exceeds mean annual PET or at locations where aquifer transmissivity is low.
5.2 Accuracy of recent modelling studies of impacts of new plantations
Current models give accurate predictions of mean water use by closed-canopy plantations without access to groundwater but poor predictions of water use by plantations accessing groundwater.
The SoilFlux model used by SKM in the Water and Land Use Change study (SKM 2005), is accurate for closed-canopy plantations without access to groundwater but under-estimates water use by ~29% for closed-canopy plantations with access to groundwater.
Existing models have not been tested for plantations prior to canopy closure. Recent data from southeast South Australia indicates water use in the early part of each rotation is substantially lower than after canopy closure and lower than expected pasture water use. This lower water use needs to be taken into account in modelling studies if long term effects of plantations on water resources are to be accurately accounted for. More data on water use prior to canopy closure is needed.
When lower water use in the first year or two after conversion from grazing land to plantations is factored in, and given the timing of recent plantation developments in southwest Victoria, it is not surprising stream flow records up to 2006 do not indicate a detectable effect of new plantations established since 1998. However, it is expected effects on stream flow in catchments such as Crawford River will become evident over the next few years, as predicted in the Water and Land Use Change Study (SKM 2008a), particularly if annual rainfall returns to the long-term mean. Monitoring and analysis of stream flow records from catchments with and without major land use
70
change needs to continue for at least 3-5 more years to verify predicted effects of land use change.
Limited data from one site indicates thinning had little impact on plantation water use. However, more data would need to be collected from other sites before this observation could be turned into a general conclusion and the effect of thinning incorporated into plantation water use models.
5.3 Plantations for salinity control
Plantations will be effective for reducing groundwater recharge to help control rising water tables in areas where this is causing dry-land salinity. However, because of the low water use at the start of each rotation, resulting in substantial groundwater recharge, plantations may not be as effective as more permanent revegetation that is not periodically harvested.
6 ACKNOWLEDGEMENTS
We thank the Glenelg Hopkins Catchment Management Authority (especially Richard Murphy and Don Arnold), Forest and Wood Products Australia and CSIRO for supporting and providing the funding for this study. Green Triangle Forest Products (especially Andrew Moore), Timbercorp (especially Ben Bradshaw), The Victorian Department of Primary Industries, (especially Malcolm McCaskill) provided access to sites and other in-kind assistance. Jonathan Wearne partnered with us in developing the original plan and funding applications and convinced many stakeholders of the importance of the project. Michele Michael, Colleen Bernie, David Gritton, Leroy Stewart and John Larmour have ably assisted with fieldwork at various times. Dr Jenny Carter and Dr Auro Almeida reviewed a draft of the report and provided many helpful comments.
71
REFERENCES
Barber, C (2001) Introduction to groundwater resources and issues in Australia. In: Barber, C. and Armstrong, D. (eds) (2001) Fundamentals of Groundwater Science, Technology and Management. Edition 4. Centre for Groundwater Studies, Adelaide.
Battaglia M, Sands PJ, White DA, Mummery D (2004) CABALA: a linked carbon, water and nitrogen model of forest growth for silvicultural decision support. Forest Ecology and Management 193, 251-182.
Benyon RG (1999) Nighttime water use in an irrigated Eucalyptus grandis plantation. Tree Physiology 19, 853-859.
Benyon RG (2002) ‘Water Use by Tree Plantations in the Green Triangle: A Review of Current Knowledge.’ Glenelg Hopkins Catchment Management Authority, Hamilton Vic and CSIRO Forestry and Forest Products, Mt Gambier SA.
Benyon RG, Doody TM (2004) ‘Water Use by Tree Plantations in South East South Australia.’ CSIRO Forestry and Forest Products Technical Report Number 148. CSIRO Mount Gambier SA.
Benyon R, Theiveyanathan T and Doody T (2006) Impacts of Plantations on groundwater in south-eastern Australia. Australian Journal of Botany 54, 181-192.
Bosch JM, Hewlett JD (1982) A review of catchment experiments to determine the effects of vegetation changes on water yield and evaporation. Journal of Hydrology 55, 3–23.
Brown AE, Zhang L, McMahon TA, Western AW, Vertessy RA (2005) A review of paired catchment studies for determining changes in water yield resulting from alterations in vegetation. Journal of Hydrology 310, 28-61.
Brown K, Harrington G, Lawson J (2006) Review of groundwater resource condition and management principles for the Tertiary Limestone Aquifer in the South East of South Australia. SA Department of Water, Land and Biodiversity Conservation, Report DWLBC 2006/02.
Dahlhaus P, Heislers D, Dyson P (2002) Glenelg Hopkins Catchment Management Authority Groundwater Flow Systems. GHCMA Report No. 02/02. Dahlhaus Environmental Geology Pty Ltd. Buninyong Victoria.
Falkiner RA, Nambiar ESK, Polglase P, Theiveyanathan S, Stewart L (2006) Root distribution of Eucalyptus grandis and Corymbia maculata in degraded saline soils of south-eastern Australia. Agroforestry Systems 67, 279-291.
Hibbert AR (1967) Forest treatment effects on water yield. In ‘International Symposium on forest hydrology’. (Eds WE Sopper, HW Lull). Pergamon, Oxford.
Hopton H, Schmidt L, Stadter F, Dunkley C (2001) Forestry, land use change and water management: a Green Triangle perspective. In Nambiar ESK, Brown AG (Editors) (2001) Plantations, Farm Forestry and Water. Water and salinity issues in agroforestry No. 7. Agriculture, Fisheries and Forestry – Australia; Joint Venture Agroforestry Program; CSIRO Forestry and Forest Products. Rural Industries Research and Development Corporation, Canberra.
Jacobsen G, Habermehl MA, Lau JE (1983) Water 2000: Consultants Report No. 2. Australia’s Groundwater Resources. Department of Resources and Energy. Australian Government Publishing Service, Canberra.
Landsberg JJ, Waring RH (1997) A generalised model of forest productivity using simplified concepts of radiation-use efficiency, carbon balance and partitioning. Forest Ecology and Management 95, 209-228.
72
Leaney FW, Mustafa S, Lawson J (2006) Salt Accumulation and Water Balance Under Different Land Use in Bakers Range Area. CSIRO Land and Water Science Report 05/06.
Miller RW, Donahue RL (1990). Soils, an Introduction to Soils and Plant Growth, 6th Edition. Prentice-Hall Inc, Englewood Cliffs, New Jersey.
Morris JD (2003) Predicting the environmental interactions of eucalypt plantations using a process-based forest model. In Turnbull WJ (ed. 2003) Eucalypts in Asia. Proceedings of an international conference held in Zhanjiang, Guangdong, China, 7-11 April 2003. ACIAR Proceedings No. 111. Australian Centre for International Agricultural Research.
Polglase PJ, Theiveyanathan S, Benyon RG, Falkiner RA (2002) Irrigation management and groundwater uptake in young tree plantations growing over high watertables. Publication No. 02/146. Rural Industries Research and Development Corporation, Canberra, Australia.
SKM (2005) Water and land use change study. Land use and hydrologic change in south-west Victoria. Report to Glenelg Hopkins Catchment Management Authority and Water and Land Use Change Steering Committee. Sinclair Knight Merz Project VW002032.
SKM (2008a) Water and Land Use Change Study: Stage 3. Water and Land Use Change in the Catchment of the Crawford River. Report to Glenelg Hopkins Catchment Management Authority and Water and Land Use Change Steering Committee. Project VW03647.
SKM (2008b) Water and Land Use Change Study: Stage 3 Case Studies. Water and Land Use Change in the Catchments of: Darlots Creek, Eumeralla River, Moyne River, Merri River. Sinclair Knight Merz, Report to Glenelg Hopkins Catchment Management Authority and Water and Land Use Change Steering Committee. Project VW03647.
Swanson RH, Whitfield DWA (1981) A numerical analysis of heat pulse velocity theory and practice. Journal of Experimental Botany 32, 221-239.
Wang QJ, Chiew FHS, McConachy FLN, James R, de Hoedt GC, Wright WJ (2001) Climatic atlas of Australia: evapotranspiration. Bureau of Meteorology, Australia.
Zhang L, Dawes WR, Walker GR (1999) Predicting the effect of vegetation changes on catchment average water balance. Co-operative Research Centre for Catchment Hydrology, Technical Report 99/12.
73
APPENDIX A
Inter-rotation Recharge Project,
South East South Australia
Newsletter No. 2, January 2008
Richard Benyon1 and Tanya Doody2
1. CSIRO Forest Biosciences, PO Box 946 Mount Gambier SA
2. CSIRO Forest Biosciences, Waite Road, Urrbrae SA
Summary
In July 2005, the CSIRO, the South East Natural Resources Management Board and the Green Triangle Regional Plantation Committee commenced a study to investigate groundwater recharge under plantation land during the inter-rotation period. Monitoring of rainfall, evapotranspiration and changes in soil water began at three sites in the spring of 2005 and has continued to the present. Site 1 (P. radiata) was harvested in autumn 2005 and replanted in winter 2006. However, very low rainfall in 2006 (only 404 mm) and early 2007, resulted in poor survival. A 2nd replanting was undertaken in winter 2007. Despite the low rainfall in 2006, groundwater recharge was estimated to be 118 mm. Higher rainfall in 2007 (668 mm) gave an estimated 344 mm of recharge. Almost a full year of water use measurements were collected from Site 2 (also P. radiata) before it was harvested in spring 2006. The pre-harvesting data indicate water use of the plantation was approximately in balance with rainfall. Despite a dry spring and summer in 2006/07, there was sufficient rainfall in late autumn and early winter 2007 to re-wet the soil profile so that groundwater recharge began in late June. Good rains from then until early November resulted in an estimated 379 mm of recharge for 2007 from net rainfall of 770 mm. The third site (E. globulus) was not harvested until winter 2007. This site has a watertable present at about 4 m depth. Prior to harvesting, annual water use of the plantation was about 954 mm and annual net groundwater uptake was estimated to be 340 mm. In the 3 months since the monitoring equipment was re-installed, 73 mm of recharge has occurred. Monitoring at all three sites will continue until at least spring 2008.
74
Issues being addressed
• This project aims to improve our understanding of groundwater recharge under radiata pine and Tasmanian blue gum plantations in South East South Australia.
• Until this project, research to quantify plantation water use in the region had all been in plantations with closed canopies.
• Water use during fallow between rotations and before canopy closure is expected to be lower due to small tree size and because weeds are controlled.
• Current regulations in the region assume there is recharge in the inter-rotation period (pre-canopy closure), but this has never actually been measured.
Methods
• At three plantation sites, we monitored rainfall (rain gauges), tree water use (using sapflow sensors - up until harvesting only), interception (using throughfall gauges), evaporation from the soil (lysimeters), changes in soil water to 6 m depth (neutron probe) and deep drainage (drain gauges).
• We will test whether existing forest growth and water use models can provide accurate predictions of water use and recharge under a range of weather conditions and plantation ages to provide added confidence that the results of plot-based studies can be extrapolated to other sites and different weather conditions.
• Site 1 (P. radiata ~10 km E of Tarpeena, watertable at ~5 m depth) was harvested in autumn 2005. We began our measurements in spring 2005. Replanting took place in winter 2006, but due to drought a 2nd replanting was required in winter 2007.
• At site 2 (P. radiata ~10 km SE of Mt Gambier, deep watertable) we monitored water use for almost 1 year before harvesting took place in spring 2006. The site was replanted in winter 2007.
• At Site 3 (E. globulus near Wandillo, watertable at ~4 m) we monitored water use for 18 months before harvesting took place in winter 2007.
75
Results to date
We now have more than 2 years of post-harvesting data from Site One and one full year from Site Two. Significant recharge has occurred at both these sites. At Site 3 we only have 3 months of post-harvesting data so far, but this also indicates recharge has occurred. Water use prior to harvesting Prior to harvesting, the annual water use of the radiata pine plantation at Site Two was approximately in balance with rainfall. Total evapotranspiration was not significantly different from rainfall after allowing for a small net change in stored soil water. This is consistent with previous studies indicating that for closed-canopy plantations with deep water tables, water use is approximately in balance with rainfall.
At Site Three, the 18 months of water use data collected before harvesting was during a period of low annual rainfall (505 mm observed cf. ~700 mm long-term mean). Annual water use averaged 954 mm. Taking account of a substantial net reduction in stored soil water, annual net groundwater uptake of 340 mm was estimated. Water use and groundwater recharge after harvesting The table below provides summary data on post-harvesting water balances for Sites One and Two. At Site One in 2006, despite very low rainfall, there was recharge of 118 mm. Above average rainfall for the period from late April to early November 2007 resulted in substantial groundwater recharge at both sites. Evapotranspiration was consistent between years and between sites, averaging around 290 to 300 mm at both sites and in both years.
Patterns of change in soil water at different depths indicate that once the soil has re-wet after harvesting, below a depth of about 0.5 m there is little soil drying and below1 m almost none. Once the soil has re-wet, any rainfall reaching 1 m depth in the soil will probably continue to drain to the watertable. At Site One, re-wetting of the soil profile had occurred by the first spring after harvesting, despite a relatively dry winter, whereas at Site Two, with a deeper profile and with harvesting talking place in spring rather than autumn, re-wetting was not completed until early in the following winter.
Table detailing post-harvesting water balances and recharge at Sites One and Two
Site One Site Two
Year 2006 2007 2007
Net rain (mm) 404 668 770
ET (mm) 294 292 304
Change in soil water (mm)
-8 32 87
Recharge (mm) 118 344 379
The recharge observed at these sites is substantially higher than that which would be expected under pasture in these locations. This is probably because both sites have been largely free of vegetation since harvesting, due to low rainfall up to late April 2007, the small size of the trees planted in 2006 and 2007 and due to good weed control at both sites. At pasture sites, the roots of the grasses can access soil water to a greater depth in the soil profile (perhaps 1 m). Hence the amount of soil drying under
76
pasture would be expected to be greater than under the almost bare land in the fallow and early pre-canopy closure period of a plantation rotation. Therefore, evapotranspiration will be higher from pasture than from fallow or recently re-planted plantation land.
Preliminary conclusions to date
• The pre-harvesting water use data is consistent with previous observations in plantations with closed canopies
• In radiata pine plantations, evapotranspiration in the period of fallow following harvesting and in the first year after replanting averages only ~300 mm per year
• Once the soil profile re-wets after harvesting there is little soil drying over summer because evaporation only occurs from the surface soil.
• In a year of approximately average rainfall, groundwater recharge under plantation land during the fallow period and in the first 12 months after replanting is substantially more than expected under pasture, being about 360 mm per year at the two radiata pine study sites, compared with ~150 mm per year expected under pasture.
• Data collection at these sites should continue if changes in water use and recharge are to be monitored as the new canopy develops.
Acknowledgements
We thank the South East Natural Resources Management Board, CSIRO and the Green Triangle Regional Plantation Committee for providing the funding for this study and Green Triangle Forest Products (especially Andrew Moore) and ForestrySA (especially Don McGuire) for providing access to sites and other in-kind assistance. Michele Michael, Colleen Bernie, David Gritton, Leroy Stewart, John Larmour and Randall Falkner have assisted with fieldwork at various times.
Enquiries
For further information please contact: Richard Benyon CSIRO Forest Biosciences PO Box 946, Mount Gambier 5290 Mob.: 0419 495 144, Email: [email protected]
77
APPENDIX B
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
APPENDIX C
93
94
95
96
97
98
99
Important Disclaimer: CSIRO advises that the information contained in this publication comprises general statements based on scientific research. The reader is advised and needs to be aware that such information may be incomplete or unable to be used in any specific situation. No reliance or actions must therefore be made on that information without seeking prior expert professional, scientific and technical advice. To the extent permitted by law, CSIRO (including its employees and consultants) excludes all liability to any person for any consequences, including but not limited to all losses, damages, costs, expenses and any other compensation, arising directly or indirectly from using this publication (in part or in whole) and any information or material contained in it.
