Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments: Perspectives from the Catchment Characterisation Project

Transcription

1 Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments: Perspectives from the Catchment Characterisation Project Glen Walker, Lu Zhang, Warrick Dawes, Mat Gilfedder, Alice Brown, Klaus Hickel, Ray Evans CSIRO Land and Water Science Report 13/07 March 2007

2 Copyright and Disclaimer 2007 CSIRO To the extent permitted by law, all rights are reserved and no part of this publication covered by copyright may be reproduced or copied in any form or by any means except with the written permission of CSIRO Land and Water. 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. Cover Photograph: Description: Chiltern Mt Pilot National Park, Victoria. Photographer: Mat Gilfedder 2007 CSIRO ISSN:

3 Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments 3

4 Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments: Perspectives from the Catchment Characterisation Project Glen Walker 1, Lu Zhang 2, Warrick Dawes 3, Mat Gilfedder 4, Alice Brown 2, Klaus Hickel 2, Ray Evans 5 1. CSIRO Land and Water, Adelaide 2. CSIRO Land and Water, Canberra 3. CSIRO Land and Water, Perth 4. CSIRO Land and Water, Brisbane 5. Salient Solutions Australia, Jerrabomberra, NSW CSIRO Land and Water Science Report 13/07 March 2007 Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page i

5 Acknowledgements The authors acknowledge funding from the Murray-Darling Basin Commission through SI&E Grant Number D9004: Catchment characterisation and hydrogeological modelling to assess salinisation risk and effectiveness of management options, and also through Grant Number D2013: Integrated Assessment of the Effects of Land use Changes on Water Yield and Salt Loads. The authors also acknowledge the Water and Rivers Commission (Western Australia) for providing their hydrological data for the Collie catchment case studies (including Lemon Creek), and Thiess Environmental Services for their assistance in the provision of streamflow data for Pine Creek (Goulburn catchment, Victoria). Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page ii

6 Executive Summary This reports brings together work undertaken with the Catchment Characterisation projects on the impacts of afforestation and clearing on water and salt generation from upland dryland catchments in the Murray-Darling Basin. In particular how this work relates to the ability to predict the contributions of programmes of action is such as a afforestation to catchment water and salt targets at end-of-valley and in-valley sites. The main parts of the work and this report are focused on the impact of afforestation with respect to: Changes in stream flow. Both at a long-term mean annual level, as well as differing changes in the distribution of flows throughout the year. Changes in stream salt load and salinity: using groundwater information to predict time-lags in salt discharge to streams following land-use change These two areas of research have been integrated to provide tools, such as the BC2C catchment model, for assessing the impact of afforestation/clearing on water and salt across large catchments using available data. Background to MDBC Strategies The release of the Basin Salinity Management Strategy (BSMS) (MDBMC 2001) replaced the MDBC Salinity & Drainage Strategy (S&D) (MDBMC 1989). Key differences between the two strategies include: 1. The inclusion of the dryland areas in the MDB. The BSMS expands the program of engineering works (including salt interception schemes) to buy time, while longer-term, cost-effective land use changes (e.g. redesigned farming systems and revegetation) are implemented. 2. The use of targets at various locations around the MDB, in addition to Morgan. These include both end-of-valley targets and in-valley targets to assist in overall catchment management and reflect local priorities. 3. Recognition that in the Registers it is more difficult to estimate the salinity benefits and disbenefits from more diffuse land use changes than it is for localised actions under the S&D Strategy (MDBMC 1989). There may be long time-lags before impacts become apparent and short-term salinity disbenefits may become a benefit in the long term. The dynamic nature of flow and salinity at any point in the river means that long-term monitoring will be required to detect any significant effect at a target site. The BSMS will be operated through a virtual system in which hydrological modelling is used to decide whether targets are being met. The formation of this virtual system is a major technical challenge. It requires the ability to assess the impact of cumulative land use change up to sites at the endof-valley, in-valleys, and at Morgan. This requires an understanding of processes across a range of disciplines and scales. Consequently, the states, MDBC and other organisations have invested in a number of projects including collection of data, understanding of underlying processes and the development of hydrological and integrated modelling. This report summarises some of the major findings of two of these projects, funded by the MDBC, CRC for Catchment Hydrology and CSIRO. The report relates these findings to end-ofvalley modelling. An important part of the virtual system is the role of the Tributary models, which simulate the effects of managed river regulation. To account for changed land use in upland areas on downstream targets, it is necessary to use a model to generate the changes in water and salt from the unregulated catchments and use them as input to the Tributary models. As the diversion for irrigation dominates land use change impacts on end-of-valley targets, the greatest influence of upland areas in the medium term will be on in-valley targets. Both these impacts will be considered in this report. Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page iii

7 Impact of Afforestation/Clearing on Stream Flow It has been known for some time that forested catchments have a lower long-term average run-off (mean annual water yield) than non-forested catchments. However, the development of robust estimators for the impact of afforestation or tree clearing on mean annual water yield is a recent one (e.g. Zhang et al. 1999, 2001). However, in addition to estimates of changes in mean annual water yield, for regional and MDB-wide planning it is often important to know how afforestation or clearing affects the timing of water yield changes and the distribution of flow. For example, if large-scale afforestation is planned in water-yielding catchments, will this affect water security or environmental flows during extended dry periods? Analyses of paired-catchment studies have indicated that afforestation has a differential impact on flow regime, resulting in greater relative reductions in low flows (defined in this report to be the flows which are exceeded % of the time). Examples in this report illustrate that as rainfall becomes greater, the whole range of flows tend to respond to climatic and vegetation changes in unison with the changes in the mean flow. As rainfall becomes less, the low flows are more affected than the high flows. A method for modelling the impacts of afforestation on flow regime has been developed by Best et al. (2003). Where no gauging data are available, regional relationships are developed between the mean annual flow and the parameters used in the Best method. However, where flow records are available, the existing flow records, combined with knowledge of catchment properties, can be used to develop catchment specific relationships between the annual flows and the model parameters. The Best method can be used within water routing (tributary) models (such as REALM & IQQM) to generate time series of flows under changed land-use conditions for regulated systems. Water allocation policy, regional planning and assessment of in-valley and end-of-valley targets require an understanding of response times following land use change. Data from field experiments suggest that the time for a small upland catchment to reach a new overall hydrologic equilibrium following afforestation may be in the order of years. The time lags associated with different parts of the flow regime are also important to consider. Impact of Afforestation/Clearing on Salt Load In order to be able to predict the impact of afforestation/clearing on salt loads it is necessary to understand the relationship between stream flow, salt load and groundwater processes. Analysis of data from unregulated upland catchments showed that mean flow-weighted salinity was highly correlated with rainfall. For the main river valleys with a large rainfall change, this implies that the higher rainfall (higher water-yielding) catchments provide diluting flows, while catchments with lower rainfall (lower water-yielding) will tend to make the main tributaries more saline. The groundwater response time gives a measure of the time lag between a land-use change and the change in groundwater discharge (and hence changes in saline groundwater discharge). The data needed to develop the groundwater response time is usually only found in a few intensively studied catchments. Currently the main groundwater information available for modelling across broad areas is contained within the groundwater flow systems (GFS) framework. The basic concept behind GFS is that the landscape can be divided into areas that hydrogeologically behave in a similar fashion. Thus, while it is possible to distinguish between quick (a few years) and slow (>50 years) processes, it is more difficult to distinguish between responses of say 15 years and 60 years, with greater confidence. Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page iv

8 BC2C The Biophysical Capacity to Change model (BC2C) was developed in recognition that the application of a simplified tool to assess the prioritisation of catchments was a necessary early step in catchment planning exercises (Dawes et al. 2004c). BC2C uses the water balance and groundwater response theory developed within the Catchment Characterisation projects and brings them together into a single modelling tool. As such, BC2C attempts to: incorporate information from groundwater flow systems and the inherent time delays between a land use change and salinity outcome, provide a trade-off between water yield and salt load impacts from land use change, provide information spatially in a form where catchment boundaries, groundwater flow systems and rainfall are explicitly represented, replicate water and salt balances from gauging information, enable interactive sessions using different land use (afforestation/clearing) scenarios to be trialled, while allowing background parameters to be interrogated. The simplicity of the model enables users to come to grips with the scale of intervention required to change salinity of upland streams, the trade-offs between decreased water yield and salt load changes, and to understand the time delays inherent in the landscape. [Note: This project, and the BC2C model both pre-date the 2CSalt model development project. The 2CSalt model is not discussed in detail in this report.] Discussion The BSMS (MDBMC 2001) tries to separate climate variability from land use change by considering a benchmark period: , for which all land use changes are modelled. In using the Tributary models, it should be noted that the flow regime and salinity-flow relationships change throughout the modelling period. This will confound the use of the benchmark period. There are a large number of uncertainties involved with the modelling. The modelling used for the flow duration impacts is based on field measurements at the catchment scale. Unfortunately, there are a limited number of case studies, which have sufficient monitored data on the effects of major land use change. Improvement in the confidence in these results will come from further field studies, where land use change has been implemented. Ideally, other information can be collected to enable the catchment to be used for demonstration purposes as well as providing greater confidence in modelling results. The report mainly considers afforestation as the land use change. Most field data is associated with afforestation or tree clearing. The limited field data suggests other land use changes will impact in a similar fashion to afforestation, but to a different extent. Paddockscale water balance studies combined with bottom-up modelling approaches develop Zhang-like curves. As more field data becomes available, there will be greater confidence that the scaling issues are adequately accommodated. Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page v

9 Conclusions The response of the Flow Duration Curve (FDC) to afforestation is dependent on limiting conditions for evapotranspiration: In high rainfall areas, the response will be more uniform across all flows, while for lower rainfall areas (where evapotranspiration is water limited for some part of the year) there will be a greater impact at the low-flow end. This leads to a large increase in the number of zero-flow days. A five-parameter model has been developed to give a quantitative description of all percentiles of the Flow Duration Curve - the actual parameter values can be adjusted (on the basis of understanding derived from paired catchment studies) to give a prediction of the new FDC under alternative vegetation cover. However, data from more catchments is required for more robust predictions. This approach, together with adjustments for afforestation change, has been used in the REALM model to investigate a proposed Blue Gum planting on the security of water allocation for irrigation users in the Goulburn-Broken Catchment. The model results showed that significant changes in water security could occur through large areas of afforestation. This approach was also applied within IQQM (NSW DLWC 1999) by modifying the parameters of the catchment water balance model to reproduce the estimated effects on FDC. For the non-regulated upland catchments studied, (a) the flow/salinity relationship could be adequately represented by a straight line on a log-log plot, (b) majority of salt export occurred at high flows, (c) flow weighted mean salinity was most sensitive to average annual rainfall. Following land use changes these relationships remain the same, but the values (mean flow weighted salinity, slope of flow vs. EC line) change over time. There are two components to these predictions: (a) Groundwater response time to a change in recharge can be modelled simply using the physical attributes of the aquifer. Data on such properties can be sparse and in such situations, the application of a more sophisticated model will not improve estimates. (b) The salt balance concept can help describe the evolution of the flow-salinity relations and salt output-input ratios. The Biophysical Capacity to Change (BC2C) spatial modelling tool can help users understand the trade-offs between in-valley stream flow and salt load changes as a result of land-use change. It predicts the impact on stream water and salt as a result of landuse change at a sub-regional scale. BC2C uses groundwater response modelling theory described in Chapter 3. By using a water and salt balance for the river system, the tradeoffs can be placed in a spatial framework to allow prioritising catchments for management actions. The results in this report show that reliable modelling of the impacts of land use change on river flow and salinity, as may be expected for the Basin Salinity Management Strategy, is still a long way off. However, there should be enough reliability to select priority areas. Reliability will only improve with more monitoring. Land-use change in upland catchments will mainly affect in-valley targets, with end-of-valley targets often affected by diversions for irrigation. Because of their significance for the shared river and Basin target at Morgan, there is likely to be greater scrutiny of the end-of-valley targets than for in-valley targets. Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page vi

10 Recommendations Knowledge gaps Monitoring: As large areas of land-use change are implemented under the NAP and other policies, there is a need for improved monitoring to allow data to be collected on the impacts of land use change on flow and salinity. Only through more data is it likely that more robust models can be developed in the future, and the level of confidence in model predictions increased. Application of tools The Best method for modifying flow duration curves (FDC) is available for use in water routing (tributary) models. The predictive capability of these tributary models can be enhanced through the implementation of the Best method. BC2C model is ready for use under a limited range of applications (upland dryland areas). Documentation is ready and the model is part of the CRCCH modelling toolkit. The recommendation is that use of the model is to be encouraged, particularly as a first step in preparing GFS information in a form for modelling and predicting catchment response. Some of the shortcomings of the BC2C model should be overcome with future work. This could include the extension of the approach to cover intermediate GFSs situated upstream from the main irrigation areas. These areas are not covered currently, but have the capacity to influence flow and salinity at in-valley sites. [Note: an example of this is the subsequent development of the 2CSalt model with the CRC Catchment Hydrology beyond the scope is this report] Management actions Climate change can significantly affect stream flow. It is necessary to address the issue of climate change in the natural resources management in conjunction with land-use change. The impacts are not likely to be additive. For many areas, the scale of intervention required to produce a significant salinity impact is very large. There are also areas which have potentially negative salinity impacts as a result of afforestation. Managers should consider as part of their planning processes, the implications of no-flow days on stream ecology and the decreased volumes of water available for diversions and environmental flows. Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page vii

11 Table of Contents 1. Introduction Objective of this report Use of targets for salinity management in the MDB Background Basin Salinity Management Strategy (BSMS) Targets Need for modelling How does upland management relate to BSMS targets? The Basin salinity target EOV targets In-valley sites Tributary models and river regulation Tributary models Water balance models Other land uses Structure of this report Impact of afforestation/clearing on stream flow Outline Changes in mean annual water yield due to afforestation Stream-flow regime and flow duration curve Flow regime Flow Duration Curve Removing the effect of rainfall variability on stream-flow regime Examples of afforestation affecting flow duration curves Number of no-flow days Modelling afforestation impacts on the FDC Case study - Ten Mile Creek Mean annual water yield Changes in flow duration curves Time to reach new flow equilibrium Discussion afforestation impacts on stream-flow regime Impact of afforestation/clearing on salt load Objectives Metrics for salt load and salinity Salinity non-exceedance curve Stream salinity, salt load and flow relationships Changing salinity-flow relationships over time Evolution of the salt output / input ratio Mean flow weighted salinity with respect to rainfall Impact of afforestation Groundwater responses and time scales Groundwater flow systems Groundwater response Time-scales Shape of the response curve Combining the groundwater time-scales Salt leaching processes Land use change impact on regional groundwater systems Summary - Salt mobilisation processes Bringing together the theory development of the BC2C model Background to BC2C Requirements for the BC2C model Using the BC2C model Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page viii

12 Sub-Regional example Little River Prioritisation of upland catchments for afforestation in the MDB BC2C - Summary Linking upland water and salt generation models to regulated river flow and salt routing models Role of different models in the context of the BSMS Implementing the Best method into tributary models Impact of blue gum plantation establishment on water supply Predicting salt loads from catchments Discussion, Conclusions and Recommendations Discussion Conclusions Recommendations Glossary References Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page ix

13 Questions Can observed flow and salt mobilisation processes be explained? Can future salinity impacts be predicted under a non-intervention scenario? Can the impacts of land use change on stream flow, salinity and salt loads be realistically predicted at specified locations? What scale of intervention is required to meaningfully impact on salinities within major valleys and which parts of the river valleys are most amenable to affecting this? Can plans be made to incorporate the trade-offs in siting land use change between water yield, salinity and productivity? How well are the combined impacts of land use change and climate variability on flow regime understood? How much data are required? Report Title Page 1

14 1. Introduction 1.1. Objective of this report This report summarises some of the major findings of two projects, funded by the MDBC, CRC for Catchment Hydrology and CSIRO and places the findings within the context of the Murray-Darling Basin (MDB) Basin Salinity Management Strategy (BSMS). These projects are: 1. Integrated Assessment of the Effects of Land Use Changes on Water Yield and Salt Loads, (MDBC: Project D2103), which has an overarching objective to predict the regional scale impacts of afforestation and other land use changes on mean annual and seasonal catchment water yield, groundwater recharge, and stream salinity. This project uses a so-called top-down approach to interpret monitoring data from paired catchments and other studies, rather than heavily-parameterised or lumped conceptual modelling to investigate the impacts of land use on water yield and salinity. 2. Catchment Characterisation and Hydrogeological Modelling to assess Salinisation Risk and Effectiveness of Management Options, (MDBC: Project D9004), which has the overarching objective to produce a framework and suitable outputs to ensure that funding and resources for salinity management is targeted towards appropriate management activities. This project is differentiated from other work through its focus on groundwater processes within the context of regional salinity planning. There have been two publications summarising earlier findings from these projects (Walker et al. 2003, Zhang et al. 2007). Those reports were focused on the relevance of project findings to regional planning, while this report is focused on their relevance for the BSMS and end-of-valley reporting. [Note: these projects pre-date the work within the CRC for Catchment Hydrology which developed the 2CSalt model] 1.2. Use of targets for salinity management in the MDB Background In the 20 years following the drought of 1967, there was a period of intense investigation into salinity processes and management options for the River Murray. These focused on changes in irrigation practices and engineering options for reducing salt loads to the River Murray. These eventually led to the 1988 Murray-Darling Basin (MDB) Salinity and Drainage (S&D) Strategy. The S&D Strategy retained this focus on irrigation and engineering options, while dryland processes were treated in only a nominal fashion. This sharp focus of the S&D Strategy and prior policies was vindicated by successfully reducing mean salinity in the River Murray at Morgan by approximately 80 EC (µs/cm) post However, there were indications that the salinity effects from non-irrigated areas were being underestimated. Allison and Schonfeldt (1989) provided a simple back-of-the-envelope analysis showing the magnitude of potential increases on the river of ~80 EC from the dryland component of the Riverine Plain. Evidence of upward trends in stream salinity from upland areas of the MDB (Williamson et al. 1997, Jolly et al. 1997a, Walker et al. 1998), together with salt imbalances (Jolly et al. 1997b) suggested that increasing land and stream salinisation was being experienced in upland areas with impacts on irrigation and water supplies. There was also evidence that the dryland areas of the Mallee Region (Allison et al. 1990) would be contributing increasing salt loads to the River Murray in SA. A review of the Salinity and Drainage Strategy in 1997 led to the 1999 Basin Salinity Audit (MDBMC 1999). This predicted that business as usual with respect to land and water management will cancel this reduction within 20 to 30 years. Further predictions included: Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 2

15 The median salinity levels at Morgan would exceed 800 EC within years Average river salinities in key tributary rivers will rise significantly within years affecting urban and irrigation purposes Serious impacts on floodplain wetlands of national and international importance Current annual impact costs of dryland salinity in eight tributary valleys of $247M, and Annual impact costs of $47M to consumptive users of River Murray water. The 2000 National Land and Water Resources Audit provided a similar analysis nationally Basin Salinity Management Strategy (BSMS) Subsequently, the 2001 Basin Salinity Management Strategy (BSMS) expanded the 1988 Salinity and Drainage Strategy to consider tributary streams and the effects of land salinisation and protection of in-valley assets. The Basin Salinity Management Strategy (BSMS) was released in 2001 with objectives of: maintaining the water quality of the shared water resources of the MDB, controlling rises in salt loads to tributary streams, controlling land degradation and protecting important agricultural land, terrestrial ecosystems, cultural heritage and built infrastructure, and maximising net benefits from salinity control. The BSMS is compatible with other salinity strategies including the 2000 National Action Plan for Salinity and Water Quality (NAP) and various state salinity initiatives ( ) in having a focus on protection of key assets and values, the use of regional targets and a shift to regional decision-making. These strategies required techniques to enable assessment of appropriate salinity management over large areas. Thus, a major difference between the BSMS and the S&D strategy is the incorporation of dryland areas of the MDB. It expands the program of engineering works to buy time, while longer-term, cost-effective land use changes (e.g. re-designed farming systems and revegetation) are put in place Targets Another key difference is the use of targets at various locations around the MDB: 1. The Basin salinity target is to maintain the salinity at Morgan at less than 800 EC for 95% of the time. 2. The end-of-valley (EOV) target is to maintain the salinity and salt load at stations representing conditions at the end of each major valley to less than the target value at least the given percentage of time (50%, 80%, 95%). 3. In-valley targets assist in overall catchment management and reflect local priorities. It should be noted that these targets are not necessarily ends in themselves, but are means for measuring progress towards achieving the BSMS s objectives. Each EOV target is effectively a cap on salinity. Within the valley, they help protect key values and assets by sending appropriate signals in the catchment while for the shared rivers, they contribute to protecting the water quality by encouraging states to meet obligations for the shared rivers. As in the 1988 Salinity and Drainage Strategy (S&D), the Basin salinity target is supported by a system of salinity credits and debits. This generates a consistent currency for which tradeoffs and Basin-wide accountability can be accommodated. The system of salinity credits and debits for achieving the Morgan target will be managed through registers. An important difference with the S&D register is the recognition that it was more difficult to estimate the salinity benefits and disbenefits from the more diffuse land use changes than it was for localised actions under the S&D Strategy. There may be long time lags before impacts are apparent and short-term salinity disbenefits may change to long-term benefits. Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 3

16 Need for modelling Ideally, monitoring could be used to assess whether targets are being met. However, the dynamic nature of flow and salinity at any point in the river means that long-term monitoring will be required before any significant effect can be detected. Instead, the BSMS will be implemented through a virtual system in which hydrological modelling is used to assess credits or debits on the registers and also determine whether targets are being met. Ultimately, monitoring and acquisition of other data as well as improvements in modelling will enable the virtual system to better reflect the real world. Difficult decisions need to be made with respect to trade-offs in salinity investment and the modelling needs to be credible enough to support these decisions. The implementation of this virtual system is a major technical challenge. It requires the ability to relate the cumulative land use change in various parts of the MDB to sites in-valleys and at the ends-of-valleys as well as Morgan. This requires an understanding of processes across a range of disciplines and scales. The underlying data to support this at appropriate scales is simply insufficient. Consequently, the States, MDBC and other organisations have invested in a number of projects including collection of data, understanding of underlying processes and the development of hydrological and integrated modelling. The two projects being summarised here are but a small component of this work. They focus on the role of changed land use in the non-irrigated upland catchments on meeting the BSMS s objectives and how they may modify the flow and salinity regime at different target sites. The next section provides an overview of the impacts changed land management on flow and salinity. The focus is on the likely sensitivity at locations for different targets in the MDB. Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 4

17 1.3. How does upland management relate to BSMS targets? The Basin salinity target The Basin salinity target, of maintaining the salinity at Morgan at less than 800 EC for 95% of the time, is an extension of the goal used for the Salinity and Drainage Strategy (MDBMC 1989) that preceded it. That goal led to an extremely targeted set of actions at the lower end of the MDB. The likely rationale for this can be seen using data from around the MDB. Of the salt-load passing Morgan during the period , about 22 % was attributed to the very large area of the Darling catchment, 3 % to the Murrumbidgee and 42 % to the large Murray catchment upstream of Swan Hill, with more than 30% to the Lower Murray (Smitt et al. 2002). Of all the streams, only the Loddon and Campaspe have a higher mean salinity at their EOV stations than the River Murray at Morgan. Thus, most contributing streams dilute the River Murray. This suggests that one of the main levers on salinity at Morgan is salt load management in the Mallee and Loddon-Campaspe. Another key lever is control of diversions from fresher tributaries. The history of diversions across the MDB is shown in Figure 1.1. As can be seen, the annual diversions represent over 90% of the average natural flow to the sea. Given that most of the water diverted would be from fresher streams, this should lead to a significant reduction in the dilution of saline water in the Lower Murray and from the Loddon-Campaspe. This issue is explored in more detail in this report Average natural flow to the sea Annual Diversion (GL/year) Full development of existing entitlements Year Figure 1.1 The increase in diversions across the MDB with time, in comparison to the average natural flow to the sea. (adapted from MDBMC 2000) Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 5

18 EOV targets While the EOV targets plays a pivotal role within the BSMS, many of the EOV target sites have only been recently installed because of the remoteness of the ends-of-valley and the difficulty of setting up a gauging station for the sometime braided streams on the alluvial plains (Figure 1.2). Of the 32 end-of-eov target sites, there is only sufficient EC data at 16 of the sites to conduct salt balance and salinity trend analyses (Smitt et al. 2002). Smitt et al. (2002) also demonstrated that 15 years of gauging data was required to detect the influence of salt interception schemes on the stream salinity at Morgan. This suggests that it will be some time before the gauging records at most stations can be used to detect the salinity impact of changed land use and management under the BSMS, given the higher variability of some of the tributary streams, changes in river diversions, the more diffuse nature of land use change and the targets being related to the higher percentiles. Consequently, there is a strong reliance on modelling to detect land use impacts above the annual variability. Figure 1.2 Location of sites used to complete salt balance calculations, (this includes the End-of- Valley target stations plus additional sites) (from Smitt et al. 2002). Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 6

19 For the EOV sites where there is sufficient data to detect a salinity trend or estimate a salt balance, it is informative to further investigate those for which there is a detectable imbalance or trend. For the period , the only river tributaries where the salt exported from the tributary greatly exceeds that coming in through rainfall are the Barwon, Loddon and Campaspe (Smitt et al. 2002). Of these, only the Campaspe has significant rises in salinity. This suggests that the salt imbalances and rising trends that had been found by Williamson et al. (1997), Jolly et al. (1997a, 1997b), and Walker et al. (1998) for upland and alluvial irrigation areas, especially for southern New South Wales, are not detectable at the ends of the valley. One of the possible causes is the removal of salt load from the river in the water diverted for irrigation. Catchments storing salt are the Murrumbidgee, Mehi, Culgoa, Narran and the Murray Basin, as a whole, as measured at Morgan. Of these, the Culgoa, Narran and River Murray at Morgan have significant falls in salinity. The most likely cause for this is diversion of salt, through engineering interception of saline groundwater, diversion of saline drainage water to disposal basins, and re-use of irrigation drainage water In-valley sites The role of diversions for irrigation can be more clearly seen by contrasting the flow and salinity totals upstream of major diversions (e.g. Wagga on the Murrumbidgee) to those downstream, e.g. Hay or the EOV site, (Balranald). Jolly et al. (1997b) estimated that the average annual salt flow past Wagga for the period was t with t having been diverted, and ~ t coming into the catchment through rainfall, (a salt export ratio of ~4). By Hay, another t had been diverted, decreasing the salt flow in the river to t and halving the salt export ratio. By Balranald, the total salt diverted had increased to t. The high salt output-input ratio at stations such as Wagga indicate some potential for dryland land use change to decrease salinity in the river (see Chapter 3). The lower output-input ratios at stations such as Balranald indicate that diversions for irrigation will decrease the benefit for the shared rivers more than land use change in upland tributary areas. While EOV sites have a high profile because of the credit and debit system, the BSMS recognises the importance of the in-valley sites. The findings of the two projects, described in this report, with their emphasis on dryland areas, are likely to have much greater relevance to these in-valley targets than the EOV targets. There is likely to be a larger measurable response from in-valley sites than for EOV sites. Thus in the short-term these in-valley targets become a more important driver for change in upland areas. However, if the landuse change becomes large enough, and given there is a cap on diversions, dryland management will lead to a noticeable effect at EOV sites. To separate the land use impacts from diversions and variations, it is necessary to use tributary models, described in the next section. Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 7

20 1.4. Tributary models and river regulation Tributary models Tributary models are an important part of the BSMS EOV reporting. Because of the regulated nature of most tributaries of the Murray-Darling Basin, water allocation models are required to support the operation of the river. Irrigation requires storages and releases of water to meet the demand of irrigation. In southern Australia, the large water storages capture much of the runoff over the wetter winter period, and release this for irrigation of crops, often over the drier summer period. Broadly, streams can be separated into three main areas (Figure 1.3): 1. Unregulated sections of river, where the flow and salinity will be responsive to rainfall events. 2. Regulated sections below major storages, regulation evens out much of the natural variability, as water releases are managed to meet the needs of downstream users. 3. Closer to the End-of-valley sites, diversions will lead to lower flows. unregulated regulated irrigation unregulated within-valley site end-of-valley site Figure 1.3 Diagram of main areas. To separate the effects of land-use change from those of regulation, it is necessary to use models that replicate the regulation processes in order to analyse EOV targets. There are three major tributary models used in the MDB are: 1. IQQM - Integrated Quantity and Quality Model (NSW and Qld) 2. REALM - Resource Allocation Model (Victoria). 3. MSM/BIGMOD (MDBC Murray River flow model). These tributary models differ but generally contain operational rules for structures and simulation of flows and salt along rivers. Recently, they have been calibrated for all the major tributaries in the MDB for both water and salinity. The tributary models simulate the movement of water and salt through the system, but not the generation of these Water balance models Daily water balance models such as SACRAMENTO (Burnash and Ferral 1996) are used to simulate flows of water generated from unregulated catchments (i.e. those unaffected by dams). The outputs of these types of models are used as inputs to IQQM and REALM which then simulate water transport downstream. Water balance models are designed to replicate flows under current land-use through calibration with gauging data. These water balance models are not designed to predict the impact of land-use change and this limits the ability of tributary models to predict land-use impact at EOV sites. Some models simulate the generation of salt from catchments, although tributary models are mostly designed to use salinity-flow relationships from unregulated catchments. To simulate the effect of land-use change at EOV sites, it is necessary to provide time series of flow and salinity data from unregulated catchments to use as input to tributary models. Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 8

21 1.5. Other land uses While in general, models are needed which relate to a range of land uses, this report deals with the impacts from afforestation only. There are two reasons for this: 1. this report relies on field/experimental data at the catchment scale, and most of these studies deal with the impacts of either afforestation or tree clearing. 2. experimental data for other land-uses suggest that impacts are of a similar nature to that from afforestation, although often to a lesser extent. As such, afforestation forms a sensible end member for the amount of possible change. As more data becomes available dealing with the impact of different land-uses on catchment water and salt flows, specific predictions for these land-uses can be made. The results also do not distinguish between differing species of tree. Where water is the main limiting factor for tree growth, the species is only a minor effect on water transpiration compared to area covered with trees. The report also only considers situations where the trees will cover more than 30% of the catchment and hence become an important land use within that catchment Structure of this report This report addresses impacts of afforestation in upland areas on salt and water in streams, one of the issues that separate the BSMS from the previous S&D strategy. While in-valley targets are more likely to be a driver for land use change in upland catchments, there is also a need to address their impact on EOV targets. This will require tributary models, which in turn requires models that can provide a time series of flow and salinity as input. Thus, there are two broad outcomes of interest in: 1. in-valley targets predicting from unregulated streams 2. EOV targets - where regulation and diversion means that tributary models are required to make predictions. To address these issues, the Report is divided into the following chapters: 1. Chapter 2 describes how afforestation changes not only the mean annual water yield but also the flow regime. 2. Chapter 3 describes how afforestation affects salinity and salt load from unregulated catchments 3. Chapter 4 describes the BC2C model, which relates salinity and flow impacts to invalley targets. 4. Chapter 5 addresses the integration of water and salt impacts from unregulated catchments into the tributary models. 5. Chapter 6 discusses the implications of these results and provides both conclusions and recommendations. Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 9

22 2. Impact of afforestation/clearing on stream flow 2.1. Outline The objective of this Chapter is to summarise the findings from projects D2013 and D9004 on the effects of afforestation on stream-flow regime from upland non-irrigated catchments. As explained in Chapter 1, there are two broad foci: 1. to predict changes in flow and salinity in-valley sites in unregulated streams; 2. to provide time-series of flow and salinity as input to routing (tributary) models such as IQQM and REALM for EOV sites. The central component of the approach is to use the flow duration curve (FDC), which provides a statistical overview of the distribution of flows. FDCs are a simple and powerful way of displaying a complete range of flows for a catchment, allowing easier assessment of effects of land-use change on stream-flow regime. The emphasis of this chapter is to use available measured data from paired catchment studies. This restricts the discussion to the impacts of afforestation, although the methodology can be applied to other land use changes. Afforestation has been recognised as a major land use change in Australia in the coming decades. The impacts of afforestation on water security, salinity, and environmental flows need to be considered in catchment management planning. This builds on previous work which is already described in detail in Zhang et al. (2005) which examines the impact of afforestation on the total water generated from catchments (mean annual water yield). This chapter briefly discusses the mean annual water yield, and moves to a description of methods for predicting the changes in distribution of flow as a result of afforestation Changes in mean annual water yield due to afforestation Forested catchments have a lower mean annual water yield than non-forested catchments, and changes in vegetation cover (e.g. afforestation or clearing) will result in changes in mean annual water yield. Recent work of Zhang et al. (1999, 2001) has provided an estimator for the impact of afforestation or tree clearing on mean annual water yield (shown in Figure 2.1). Since the relationships shown in Figure 2.1 are based on observed data from over 250 catchments around the world with a variety of climates and vegetation types, they are robust when considered over such a range. These relationships have been implemented on a spatial (GIS-based) platform in order to capture the spatial pattern of land use in relation to catchment boundaries and rainfall (Bradford et al. 2001, Vertessy and Bessard 1999). The method of Zhang et al. (2001) describes the general relationships between water yield and rainfall and highlights the effects of vegetation cover on average water balance. Like any other modelling approach, the calibration of the model for local conditions, will improve the prediction. Potter et al. (2005) and Hickel and Zhang (2006) showed that the model works for different rainfall seasonalities, although this is a secondary factor for estimating average water yield. Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 10

23 1200 Mena annual water yield (mm) Grass Forest Mean annual rainfall (mm) Figure 2.1 Relationships between mean annual water yield and rainfall for catchments under different vegetation cover Stream-flow regime and flow duration curve Flow regime While change to mean annual water yield is important for the purpose of regional and MDBwide planning, the impacts of afforestation on seasonal stream-flows or flow regime can be more significant from both water security and environmental flow perspectives. For example, if there is a large area of afforestation in water-yielding catchments, will this affect water security or environmental flows during extended dry periods? However, understanding of the seasonal impacts was very limited and there were no effective tools available for quantifying the impact. A commonly used approach for making such predictions is to rely on detailed physically based models or statistical models derived from paired catchment studies (Sivapalan et al. 1996, Scott and Smith 1997). Use of physically based models in large catchments is problematic and impractical because of extensive data demands. In this study, impact of afforestation on stream-flow regime was predicted using data obtained from paired catchment studies. An important step in quantifying the impact of afforestation on stream-flow regime is to develop a measure of stream-flow regime and its change. The flow duration curve (FDC) has been used to do this Flow Duration Curve A flow duration curve (FDC) is a simple and powerful way of providing a statistical overview of the distribution of flow out of a catchment. It provides a graphical and statistical summary of the stream-flow variability and distribution, with the shape being determined by rainfall pattern, catchment size and the physiographic characteristics of the catchment. The shape of the flow duration curve is also influenced by water resources development and land use type (Smakhtin 1999). The FDC is widely used as a measure of the flow regime as it provides an easy way of displaying the complete range of flow as well as a useful measure of how the distribution of stream-flow may change as a result of afforestation. A flow duration curve can be constructed from daily stream-flow data by ranking the flow from the maximum to minimum with each flow against the percentage of time this flow is exceeded (Figure 2.2). Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 11

24 Flow(mm/day) Flow(mm/day) Flow(mm/day) Days Days Percentage of time that flow is exceeded a) Flow(mm/day) Flow(mm/day) Flow(mm/day) b) Days Days Percentage of time that flow is exceeded Figure 2.2 Times series and flow duration curve (FDC) for a) ephemeral stream (dry 55% of the time), and b) perennial stream. From left to right, the 3 charts are: 1) time series, 2) time series plotted using log-scale, 3) FDC curve for the same period. The flow duration curve (FDC) for a given catchment represents several key characteristics of the stream-flow regime. For example, in Figure 2.3, the high-variability perennial stream will only exceed a 0.1 mm/day flow (averaged over the catchment) for about 50% of the time. For the ephemeral stream, there is no flow for about 43% of the time. By displaying flows in this fashion, a better appreciation of the complete range of stream-flow and its variability can be obtained. The general slope of the curve represents stream-flow variability, while the x- intercept indicates the perennial or ephemeral nature of the stream. For regulated catchments, flow duration curves will be relatively flat indicating more constant flows, while for catchments with highly variable rainfall and little water storage capacity the slopes of flow duration curves will be very steep. The perennial or ephemeral nature of a stream can be clearly identified by examining the x-intercept or the percentage of time the flow is greater than zero. The FDC can be used as a measure of two of the components of the flow regime, the magnitude and frequency of stream-flow and provides an easy way of displaying the complete range for flows and how they would be changed under different vegetation scenarios in different climatic zones. Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 12

25 Flow (mm/day) perennial stream with low variability perennial stream with high variability 0.01 Ephemeral stream Percentage of time flow is exceeded Figure 2.3 Typical flow duration curves for perennial and ephemeral streams. A FDC can be depicted for different time intervals such as monthly or daily flows. It can be based on all the flows in a given year (annual flow duration curve) or for a subset of annual flows (seasonal flow duration curve) Removing the effect of rainfall variability on stream-flow regime Stream-flow is affected by both rainfall distribution and catchment properties, including land use. In assessing the impact of land use change such as afforestation on stream-flow regime, it is necessary to remove the effect of rainfall variability. For this purpose, a long stream-flow record is needed so that the full effect of rainfall patterns on stream-flow can be assessed. This will enable us to construct a robust FDC covering extreme floods and droughts, as short periods may not include the wide range of rainfall patterns. There are three methods to separate the effects of rainfall variability from that of afforestation on stream flow data, and allow the impacts of land-use change to be estimated: 1. Comparison of data from years with similar rainfall before and after the afforestation (Burt and Swank 1992), 2. Comparison of flow data from similar catchments with differing land use (so-called paired catchment studies). However, such datasets are not available for most catchments, so another method needed to be developed as part of the MDBC-funded - D2013 project, 3. Using a statistical methodology to remove the effect of rainfall variability (Lane et al. 2003). All of these methods have been used in the following sections. In the modelling for end-ofvalley targets, there is also a need to consider rainfall variability. The BSMS explicitly allows for this with the use of an agreed climatic/hydrologic sequence ( benchmark period ) from The effects of all actions will be assessed against this period. Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 13

26 2.4. Examples of afforestation affecting flow duration curves In this section, data is presented from three studies (in NSW, WA, and New Zealand (NZ)) of the impacts of land use on flow regime. Unfortunately, there are very few measured datasets for studying the effect of land-use change on stream flow. Because of the limited number of studies, and the relatively small size of these case studies, the representativeness of their results across the MDB needs to be further investigated. Figure 2.4a depicts the change in flow regime due to afforestation with pines in the Red Hill catchment (located about 50 km west of Canberra, in the Murrumbidgee catchment). Red Hill has a catchment area of 195 ha with average annual rainfall of 866 mm and the rainfall is winter-dominant (Hickel 2001). Stream-flow data from year 1 and year 8 following planting were used in the analysis. The data from these two years were chosen as they represent pre- and post-treatment conditions and also these years have similar rainfalls (887 mm, 879 mm). The FDC indicated that there is approximately a 50 % reduction in high flows (0-10 % on FDC). Low flows have become zero - a 100% reduction. The FDC indicate that the Red Hill catchment goes from being a highly variable perennial stream to an ephemeral stream. 10 Pines (1 years after planting). Annual Rainfall = 887 mm Pines (8 years after planting). Annual Rainfall = 879mm 1 Flow (mm/day) Percentage of time flow is exceeded Figure 2.4a Flow duration curves (1 year period) for the Redhill catchment (near Tumut, NSW). 1- year-old pines, and 8-year-old pines. (after Vertessy 2000). Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 14

27 Figure 2.4b shows the response to conversion of native forest to pasture in the Wights catchment in south Western Australia. The Wights catchment is part of a series of paired catchment studies in south Western Australia. The interplay between the local groundwater system and vegetation plays in important role in the hydrological response of these catchments with the change in stream-flow observed when native forest was replaced by pasture being closely related to an increase in groundwater discharge area (Schofield 1996). As with Figure 2.4a, it can be seen that all sections of the FDC are affected by the change in vegetation type. Comparing the FDC for pasture ( ) with a period of similar climatic conditions for native vegetation ( ) a 50 % change in high flows can be expected when changing from forest to pasture and a 100 % change in low flows Flow (mm/day) Percentage of time flow is exceeded Average Annual Rainfall = 1002 mm Average Annual Rainfall = 800 mm Average Annual Rainfall = 963 mm Average Annual Rainfall = 1008 mm Average Annual Rainfall = 914 mm Average Annual Rainfall = 1020 mm Average Annual Rainfall = 884 mm Figure 2.4b Flow duration curves for Wights catchment in south-western Australia (Based on a water year from April-March). (data courtesy of WRC, WA). Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 15

28 Figure 2.4c depicts a different response to afforestation with pines than the previous examples. These data are from the Glendhu experimental catchments in New Zealand. The control and treated catchments have mean annual rainfalls of 1310 mm and 1290 mm respectively. The treatment involved the planting 67 % of the catchment with Pinus radiata (McLean 2001). Therefore the changes in high and low flows have been assessed through comparison the control to the treated catchment at various stages after treatment. The reductions in flows are uniform for all sections of the flow duration curve with ~30 % reduction in both low and high flows. This response is typical of many catchments including the Mountain Ash catchments in Victoria (Watson et al. 1999) Flow (mm/day) Percentage of time flow exceeded Control Control Control Treated Treated Treated Figure 2.4c Flow duration curve from Glendhu experimental catchments (NZ) during the calibration period (both catchments tussock) years after pine plantation established years after pine plantation established. (from McLean 2001). The response seen in the Redhill and Wights catchments are typical of drier areas where annual evapotranspiration of forests approaches annual precipitation, while the response seen in Glendhu is typical of wetter areas where annual precipitation is greater than the annual evapotranspiration. In the Mountain Ash catchments in southern Australia, Watson et al. (1999) noted that in wetter catchments all flows respond to climatic and vegetation changes in unison with the changes in the mean flow, however in the drier parts of the study area changes in low flows are accentuated. Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 16

29 2.5. Number of no-flow days In lower rainfall areas (as explained in the previous section), studies have indicated that afforestation has a differential impact on flow regime, resulting in greater relative reductions in low flows (Sikka et al. 2003, Scott and Smith 1997). Lane et al. (2003) used a statistical model to assess the effect of afforestation on no-flow days in three Australian catchments within the MDB. Figure 2.5 shows the increase in the number of no-flow days with time in these catchments. In all catchments the observed number of no-flow days increases substantially after afforestation with pines. The three catchments (Stewarts Creek (VIC), Red Hill (NSW) and Pine Creek (VIC)) are located in winter dominant rainfall areas, with annual rainfall of 1156 mm, 866 mm, and 775 mm respectively. 300 Adjusted number of zero-flow days per year Stewarts Creek Red Hill Pine Creek Years after afforestation Figure 2.5 Increase in the number of no-flow days with time in Stewarts Creek, Red Hill and Pine Creek catchments. (climate adjusted using the method of Lane et al. (2003)) 2.6. Modelling afforestation impacts on the FDC Best et al. (2003) developed a method which allows a flow duration curve to be modified to reflect a change in forest cover based on the current stream flow data. This method links changes in mean annual water yield to changes in forest cover. The method is not linked to any specific climate seasonality. Two main steps are involved: 1. parameterisation of the FDC using the median flow of the days when flow occurs, the cease to flow point (CTF) or the percentage of days when flow occurs and three curve fitting parameters. 2. linking these parameters to the current understanding of the impact of vegetation on the mean annual water balance (Zhang et al. 2001) and catchment characteristics. In estimating catchment average water yield, it is assumed that there is no net change in catchment water storage over a long period of time. As a result, catchment water yield can be calculated as the difference between long-term average rainfall and evapotranspiration. In order to predict changes in the FDC the parameterisation of the FDC has been done in such a way that the parameters can be easily linked to the outputs from the mean annual water balance model. Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 17

30 2.7. Case study - Ten Mile Creek The Ten Mile Creek catchment is a southern tributary to the Billabong Creek (NSW) with an area of ha. This catchment was used as a case study, reported in Cresswell et al. (2004). The township of Holbrook is wholly within the sub catchment, with the majority of the sub catchment lying to the south and east of the township (Figure 2.6). Annual average rainfall in the catchment ranges from 644 mm to 1166 mm. Mean annual temperature is in a range of 10.5 to 14.7 o C. The current land use of the catchment is mainly grazing (improved perennial and volunteer, naturalised improved pastures) with some annual cropping, often integrated into mixed farming operations Mean annual water yield The mean annual water yield from the catchment is 175 mm or ML per year. The gauging station for Ten Mile has a contributing area of 108 km 2, which is less than half of the total catchment area modelled. The measured mean annual water yield for the period of 1968 to 1976 is ML per year and the predicted water yield (upstream of the gauging station) is ML per year, (within 6% of the measured water yield). Modelled mean annual water yield using a GIS application of the Zhang curve (Bradford et al. 2001) showed large spatial variations ranging from less than 100 mm to over 200 mm (Figure 2.7a). Most of low water yield areas are associated with remnant vegetation, while the maximum water yield occurs in 800 mm rainfall zones with no remnant vegetation. The impact of vegetation change on water yield is modelled based on the integrated revegetation strategy designed under the Heartlands Program (that accounted for the collective set of land use considerations associated with the biodiversity, salinity, water yield and commodity production themes) (Cresswell et al. 2004). It is predicted that mean annual water yield from the catchment will decrease by 18 %, which is equivalent to a 7886 ML water yield reduction (Figure 2.7b). Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 18

31 Thugga Plain Cookardinnia Creek Billabong Creek " Ralvona" Highway Hume Yarra Yarra Creek Sandy Creek " Holbrook M o untain Creek Native Do g Creek Morven Back Creek Bow ils Creek Dev lers Road Morga n s Ridge Ten Mile Creek Serpentine Creek " Woomargama Re ddall Creek ± km Ten Mile Creek Catchment Extent " Towns Major Roads Minor Roads Streams Ten Mile Creek Figure 2.6 Ten Mile Creek sub-catchment, and its location within the Murray-Darling Basin (adapted from Cresswell et al. 2004). Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 19

32 Catchment Boundary Current Water Yield (mm/y) ± (a) Kilometres Catchment Boundary Water Yield Reduction (mm/y) No change ± Kilometres Figure 2.7 (a) Mean annual water yield in Ten Mile Creek under current vegetation cover as predicted by the water balance model. (b) Mean annual water yield reduction in Ten Mile Creek due to the integrated revegetation strategy (from Cresswell et al. 2004). (b) Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 20

33 Changes in flow duration curves The changes in the FDC for Ten Mile Creek were calculated based on regional relationships for catchments in the Murrumbidgee Basin. Empirical relationships between the mean daily flow and the FDC model parameters were developed using the method of Best et al. (2003). These relationships allow FDCs at the outlet of Ten Mile Creek to be predicted for current land use and the integrated revegetation strategy. This approach was adopted, as existing flow data are not available at the catchment outlet. The regional relationships developed proved adequate in describing the FDC for Billabong Aberfeldy which is considered to be the closest gauging station with good quality records. Figure 2.8 shows the predicted FDCs for the current land use and the integrated revegetation strategy. Under current land use, Ten Mile Creek flows 90% of the time and the model predicts this will be reduced to 85% under the integrated revegetation scenario. 10 Current Land Use Integrated Revegetation Scenario 1 Flow (mm) Percentage of time flow is exceeded Figure 2.8 Modelled changes in Ten Mile Creek s FDC due to the integrated revegetation strategy (from Cresswell et al. 2004). Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 21

34 2.8. Time to reach new flow equilibrium Understanding response times following afforestation is required as a basis for water allocation policy, regional planning and assessment of in-valley and end-of-valley targets. It is also important to remember that any given commercially harvested afforested catchment is unlikely to have uniform tree-cover of a uniform age - different parts of the catchment will be at different parts of the harvest cycle. Generally, response in stream-flow will be slower following afforestation compared to deforestation as it takes time for trees to reach maximum water use. Data from field experiments suggest that the time for catchment to reach new equilibrium following afforestation varies between 10 and 20 years as generalised by Figure 2.9 (Scott and Smith 1997). Independent estimates of response time are consistent with Figure 2.9 (Lane et al. 2003). Lane et al. (2003) also note that Australian plantations seldom grow past 20 years and if so, are likely to be thinned. These management practices will affect stream-flow and the response time. Another interesting feature of the relationships shown in Figure 2.9 is that the shape of the curves resembles the tree growth curves simulated by plant growth model 3PG. It is possible that the response time can be inferred from plant growth data (Zhang et al. 2003). Figure 2.9 Generalised curves from estimating the percentage reduction in total and low flow after 100% afforestation with pine and eucalypt (from Scott and Smith 1997). The time lags associated with different percentiles of the FDC are also considered. For example, if the 80 th percentile is required, then is the time lag associated with that the same as for the mean annual flow? The study of Lane et al. (2003) supports this. Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 22

35 2.9. Discussion afforestation impacts on stream-flow regime Much of the previous work on the impact of afforestation on stream-flow has concentrated on mean annual water yield. It is generally accepted that increased forest cover reduces mean annual water yield. Predictive tools are available for assessing the impact of vegetation change on mean annual water yield. It is also recognised that there is a need to make predictions at monthly and daily timescales, particularly for water security and ecosystem assessments. However, paired catchment studies are the only available measured data, and it is necessary to understand results from these studies in order to ensure the development of a robust model. Analyses of data from the paired catchment studies reveal that vegetation change affects flow regime differently: The lower the mean annual rainfall, increased forest cover is likely to lead to greater relative reduction in low flow than in high flow. As mean annual rainfall becomes higher, changes across the whole range of flows will become more uniform. One of the consequences of afforestation can be to increase the number of no-flow days. Data from the Stewart Ck catchment study showed an increase in no-flow days each year from ~50 days to over 170 days. The time for a catchment to reach a new hydrological equilibrium following afforestation is determined by climate, vegetation and soil properties. For the catchment studied, it takes between 10 to 20 years to reach new equilibrium. Information on the time delay in stream-flow response can help management authorities to evaluate economic and social impact of proposed afforestation expansion. One way to represent the effect of afforestation on stream-flow regime is to adopt the concept of the flow duration curve (FDC). A method has been developed that allows the daily FDC to be modulated for changes in vegetation (Best et al. 2003). This links the understanding of vegetation on mean annual water yield to the shape of the FDC and has been developed and tested on small experimental catchments undergoing a large percentage change in vegetation cover. However, this tool has not been tested thoroughly on larger catchments. In order to improve the tool, additional case-studies on larger catchments undergoing vegetation change are required, particularly in relation to the impact of change on low and no flows. One of the major advantages of using the FDC to assess the impact of vegetation on daily or monthly flows it the ability to link the modified FDC to a modified flow time series. This time series can then form the inputs to water routing (tributary) models (such as REALM or IQQM) for regulated systems. The FDC can be used to generate a new time series of flow, via either rainfall runoff models (e.g. Sacramento) calibrated to the FDC or a spatial interpolation method (such as the one outlined in Hughes and Smakhtin (1996)). Like the tools for predicting the impact of vegetation on the mean annual water balance, the modulation of the FDC is for the change from one equilibrium state to another and does not consider the time delays associated with the establishment of a hydrologic equilibrium under vegetation change. In order to more fully model the impact of afforestation on the FDC, a methodology is required that will allow the understanding of the groundwater time delays to be linked to the FDC during the transition period between one equilibrium and another. Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 23

36 3. Impact of afforestation/clearing on salt load 3.1. Objectives The objective of this chapter is to summarise the findings of D2013 and D9004 Projects, with respect to predicting the impacts of afforestation on changes to salt loads and salinity from unregulated upland dryland catchments The expectation is that the information will be directed to two outcomes: 1. prediction of impacts at in-valley sites predicting from unregulated streams, and 2. prediction of EOV targets - where regulation and diversion means that tributary models are required to make predictions. This Chapter deals with three key issues with respect to the major tributaries: 1. Development of a metric, analogous to the flow duration curve (FDC) for salinity: a salinity non-exceedance curve (SNC). 2. Description of a method for predicting time responses to changes in land management is of critical concern. 3. Recognition of individual groundwater systems which will contribute to the catchment, and hence standard groundwater models such as MODFLOW (McDonald and Harbaugh 1988) will not be applicable Metrics for salt load and salinity Chapter 2 described the use of the flow duration curve (FDC). The first two examples shown in this section are the exact equivalent to the FDC for salinity. These are used within the BSMS to set targets (e.g. salinity at Morgan to be less than 800 EC for 95% of the time). More detail on the work described in this chapter can be found in Dawes et al. (2004b) Salinity non-exceedance curve A similar concept to the FDC, but for salinity, is the salinity non-exceedance curve (SNC). An example for the Kiewa River (north east Victoria) is shown in Figure 3.1. This figure indicates that for 60% of the time the electrical conductivity is less than 28 EC (µs/cm). An SNC can be made wherever there is a sufficient record of flow and salinity data, and is equally valid for both regulated and unregulated catchments. It should be noted that historically, salinity is rarely measured as often as flow, so the salinity record may not be as complete Kiew a River W est U/S of O fftake 60 Salinity (EC) % 20% 40% 60% 80% 100% % Non-exceedence Figure 3.1 Salinity Non-exceedance Curve for Kiewa River West Branch. Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 24

37 Both end-of-valley and in-valley targets can also be expressed in salt load non-exceedance curves (SLNCs). Inspection of data from gauging stations around the Murray-Darling Basin show that for the majority, SLNC largely mirror the FDC. Because the SLNC seems to add very little information over the use of FDC, this report has focused on the relationship between flow and salinity / salt load instead of SLNC as this emphasises the additional information Stream salinity, salt load and flow relationships This section explores the factors leading to a close correlation between SLNC and FDC, by using the example of an unregulated upland catchment. Figure 3.2 shows the measured stream flow against stream salinity graphs for Jim Crow Creek in the upland part of the Loddon catchment (north of Daylesford in north-central Victoria), with an average annual rainfall of 854 mm (Peel et al. 2000). The figure shows scattered data at low flow rates, consistency at high flows, and degree of fit to a standard power-law relationship. Note that the figure is presented with logarithmic scales on both the X and Y axes, so a power-law fit appears as a straight line Jim Crow Yandoit Salt Concentration (EC) y = x R 2 = Flow (ML/d) Figure 3.2 Measured stream flow against stream salinity: Jim Crow Creek@Yandoit (station ), gauged area = 166 km 2. A similar close relationship can be seen for the salt load versus flow example in Figure 3.3. This figure presents data for the same catchment as in Figure 3.2. Also shown is a straight line representing the flow-weighted mean salinity (106 mg/l or 166 EC). Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 25

38 Jim Crow Yandoit Gauged Data Mean flow weighted salinity = 106 mg/l Saltload (t/d) 10 1 (a) Flow (ML/d) Saltload (t/d) Jim Crow Yandoit Gauged Data Mean flow weighted salinity = 106 mg/l 50 (b) Flow (ML/d) Figure 3.3 Flow versus Salt Load graph for Jim Crow Creek, station a) log-log plot, b) linear:linear plot. Figure 3.3a shows that this flow-weighted mean salinity line underestimates the salt load at low flow. However, the log-log plot accentuates the fraction of salt exiting at low flow. As most salt comes off the catchment at high flows, an estimate of the total salt exiting the catchment can be represented by assuming a straight line between salt load and flow. In this case, the error in doing so was less than 3%, hence using a constant salinity would not introduce much error in estimating salt load from the FDC. It might be expected that at high flow, the salt load may plateau due to physical constraints in mobilising salt. However, this linear relationship is maintained even at high flows, which suggests a very large and easily mobilised store of catchment salt. Perhaps surprisingly, this type of relationship is found at many gauging stations. Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 26

39 A flow/salinity relationship is a very useful one, which can be, 1. easily derived from measured data, 2. used within water allocation models to model salt mobilisation processes 3. used to generate a SLNC from a FDC, and 4. used to generate a salt load Simple estimates of annual totals of water and salt flux can be obtained if there is a clear understanding of the relationships between stream flow, salinity and salt load. The robustness of these estimates varies, depending on specific aspects of a catchment (such as perennial or ephemeral stream flow, and the length of monitoring). In ungauged catchments the use of a single stream salinity value is adequate to describe the flow-salt load relationship, as long as this measurement is during a period of relative hydrologic equilibrium Changing salinity-flow relationships over time Lemon Creek Case Study One of the important features is that the shape of the salinity-flow relationship is preserved in moving from one equilibrium state to another, as a result of change in land-use. To see whether this is the case, existing data from the Lemon Creek catchment study (in the southwest of Western Australia) is used. This catchment was specifically monitored for over two decades to look at any measurable changes in stream flow volume and salinity following a substantial land use change. The previously forested 344 ha catchment had the lower 50% completely cleared in 1976/77, with groundwater levels and stream gauging measurements extending from 1974 to Graphs summarising the detailed measurements of stream flow, salinity and salt load are presented in Figures 3.4a and 3.4b. Mean annual rainfall for Lemon catchment is 710 mm (Mauger et al. 2001). The first ten years are reasonably similar and include the pre clearing period, while the period 1985 to 1989 represents a transitional period when water levels first reached the land surface. The final decade of measurements shows a continuing rise in both stream flow and salinity, as saline groundwater becomes more accessible to the catchment surface. 10,000 Salinity (EC - micros/cm) 1, ,000 10, ,000 Flow (ML/d) Figure 3.4a Lemon Creek stream flow versus stream salinity summary graph. Data are broken into 5-year blocks and fitted separately. Clearing occurred in 1976/77 and groundwater reached the land surface in (Ruprecht and Schofield, 1991). Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 27

40 Saltload (t/d) (1949EC, R2=0.58) (1629EC, R2=0.61) (375EC, R2=0.49) (182EC, R2=0.96) (141EC, R2=0.95) Flow (ML/d) Figure 3.4b Lemon Creek stream flow versus salt load summary graph. Data are broken into 5-year blocks and fitted separately. Clearing occurred in 1976/77 and groundwater reached the land surface in (Ruprecht and Schofield, 1991). At Lemon Creek, the average stream salinity has increased from 141 EC to 1949 EC or a factor of However, the change in salinity only accounts for 63% of the increase in salt load from the catchment. In addition to the increase in salinity, has been an increase in stream flow, which together have increased catchment salt export by a factor of 22. Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 28

41 3.3. Evolution of the salt output / input ratio As can be seen from the Lemon Creek example, catchment clearing often leads to an increase in the export of salt. However, after some time, it is expected that salt export will eventually decline again as salt is leached from the catchment. This is generally conceptualised through a salt balance in which the salt export (O) is compared to the salt coming in through rainfall (I). The ratio (O/I) reflects the salt balance. Under pre-cleared conditions, smaller upland catchments would be generally considered to be in a salt equilibrium, i.e. O/I is equal to 1. Following clearing this increases as the result of increased groundwater discharge into streams. As salt is leached, this should lead to the (O/I) decreasing until a new salt equilibrium is reached (O/I is again equal to 1). This process is schematically shown in Figure 3.5. For larger catchments and lower recharge rates, the time scales for leaching are expected to be longer. For the larger sedimentary basins, this whole process is estimated to take hundreds to thousands of years. Hence the O/I ratio may not have been 1 for these systems prior to clearing for agriculture. However, for smaller upland catchments with little weathering and higher recharge, this process may take only a few decades. Most studies have found that the principal source of salt in streams varies from weathering in fresh catchments to cyclic salt (from atmospheric deposition) in saline catchments. For the MDB as a whole, the overwhelming source of salt is cyclic. Where weathering is the principal source of salt, the O/I ratio may be greater than 1 for equilibrium conditions. Salt Output Salt Input Rising hydraulic heads Equilibration between discharge and recharge achieved Leaching of salt from catchment New salt equilibrium Years since clearing Figure 3.5 Idealised representation of possible variation in catchment Salt Output/Salt Input (O/I) ratio with time since vegetation clearing (from Jolly et al. 2001). The mean-flow weighted salinity is the ratio of the salt exported from the catchment to the water exported from the catchment. Since flow increases following clearing, the mean-flow weighted salinity will not exactly reflect the evolution of the O/I ratio shown above, but would be expected to go to a maximum and then eventually return to less than the original value. Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 29

42 3.4. Mean flow weighted salinity with respect to rainfall In this section, the distribution of mean flow weighted salinities for 13 unregulated catchments in central and northern Victoria is investigated. These are plotted against rainfall in Figure 3.6 and as a comparison, the salt equilibrium lines for both native vegetation and post-clearing states. This is presented in the same manner as the stream flow versus stream salinity data in Figure 3.2 for ease of comparison. Note however that the x-axis has rainfall expressed in metres rather than millimetres. In the previous section, the expectation would be that the mean weighted salinity would increase post-clearing (move vertically up on the graph) and then decrease (move vertically down) until it reached the post-clearing salt equilibrium line. As can be seen, the mean flow-weighted salinity increases as rainfall decreases. For the main river valleys with a large rainfall change, this implies that the higher rainfall, higher-yielding catchments provide diluting flows, while lower rainfall, lower yielding catchments will tend to make the main tributaries more saline. The most likely explanation for this sensitivity to rainfall is that the relative change in recharge for cleared conditions compared to native vegetation is greater for lower rainfall (500 mm/yr), than for higher rainfall (1000 mm/yr). Thus, the peak in the O/I ratio and the mean flow-weighted salinity, when plotted against time, is expected to be greater for lower rainfall catchments than for higher rainfall catchments. Stream Salinity (mg/l) y = 103x R 2 = 0.93 Mean Annual Observed Forest Cleared Rainfall (mm / 1000) Figure 3.6 Average annual rainfall and average annual stream flow salinity for 13 unregulated catchments in central and northern Victoria (black dots), with a fitted trendline (thin black line). The curves are for mean annual forested (black curve), and cleared (grey curve) at equilibrium. Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 30

43 3.5. Impact of afforestation In addition to the impacts of afforestation, the impacts of clearing are also relevant. At Pine Creek (Goulburn Catchment in central Victoria) this mainly farmland catchment was entirely afforested with pine trees (total area 320 ha) in 1986/87. Stream flow and salinity, and groundwater depth and salinity, were monitored from the time of replanting until Figure 3.7 shows the annual summary data for the monitoring period. This result illustrates an important point with revegetation strategies: that the loss of surface water due to tree growth is much quicker than the reduction in saline groundwater discharge, thus the stream can increase in salinity before reducing again. The stream salinity returned to a level similar to the pre-experiment value. 200 Flow (mm) 700 Salt load EC 600 Flow (mm) or Salt load (t) Stream salinity (EC) Year Figure 3.7 Pine Creek annual summary graph, showing the decrease in flow, salt load and stream salinity since afforestation. In Pine Creek, the ratio of salt output in the stream to salt input with rainfall has also changed, dropping from approximately 8.0 in 1989 and 1990 to 2.0 in 1993 and to less than 1 by With the total stream flow volume reducing by a factor of 3.0, this indicates a reduction in the relative contribution of groundwater to the stream Groundwater responses and time scales Groundwater flow systems The data needed to predict groundwater response time-scales is usually only found in a few intensively studied catchments. The main groundwater information available across broad areas is that associated with groundwater flow systems (GFS). The basic concept behind the groundwater flow systems is that the landscape can be divided into areas that hydrogeologically behave in a similar fashion. Nonetheless, there is usually sufficient variation in parameters within any hydrogeological province for estimates of time-scales to be variable. While it is likely that Local GFS will respond more quickly than Regional GFS, this is not always the case. However, it is feasible to distinguish between fast responding flow systems (that occur within a matter of a few years) and very slow responding systems (hundreds of years), but it is difficult to distinguish between 15 years and 60 years, which perhaps is important for decision-making. Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 31

44 Groundwater response Apart from the salt balances, other biophysical considerations include the time lag between implementation of a land use change and the salinity response. In addition to the time delays imposed by planting/harvesting schedules and by tree growth times, groundwater systems can also impose significant time delays between management action, and salinity outcome. While a change in groundwater recharge will eventually lead to a corresponding change in groundwater discharge, the amount of time taken to reach this new state of equilibrium will vary considerably. A groundwater response function, F(t), has been defined as: F(t) = (Change in groundwater discharge at time t after land use change) (Change in catchment recharge) This groundwater response function will differ across catchments, depending on many factors such as flow-length, groundwater slope, recharge rate, and hydrogeological properties such as aquifer thickness and conductivity. However, it must start at zero (as there will be no immediate salinity impact), and must go to unity (as eventually all water goes in must come out). The need to use only readily available data places a strong constraint on the type of approach in defining F(t). Across large areas, data that are more readily available include topographic (sub-catchment disaggregation, gradient, flow-length), climate, soils and land use (recharge), and stream gauging (yield). Rarely will detailed hydrogeological and aquifer geometry information be known for any particular groundwater flow system (GFS), yet this is precisely the information which would be required to drive complex computer models such as MODFLOW (McDonald and Harbaugh 1988). It would be useful to relate groundwater response times to characteristics such as flow length, permeability, specific yield, aquifer thickness and slope, without needing specific spatial information within each and every GFS. This will allow differences in groundwater response time to be estimated across large areas in a consistent manner Time-scales Dimensional analysis is a classical physical technique, which has been frequently used in other areas of physics and fluid mechanics for exploring scaling relationships between time and other parameters. This technique can be used to derive response time-scales, which relate to different groundwater processes from the flow equations. Such a methodology allows the estimation of the time-scales associated with groundwater response for a range of different GFS with different geologies, slopes and flow lengths. A scaling argument is used to relate time-scale of groundwater response to key parameters (hydraulic conductivity, specific yield, aquifer thickness, flow-length, catchment gradient, and recharge rate). Three different processes contribute to the overall groundwater response: 1. vertical filling: t 1 = d S/ R (3.1) 2. lateral movement: t 2 = S L 2 / K d (3.2) 3. gradient driven lateral movement: t 3 = S L 2 / K h (3.3) where: d unsaturated zone representative thickness (m) d representative aquifer thickness (m) S representative storativity (specific yield) R change in recharge rate (m/yr) L flow length of the GFS (m) K representative hydraulic conductivity of the aquifer (m/yr) h The change in groundwater elevation along the flow length (m) Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 32

45 These time-scales have already been recognised in many previous studies (e.g. Glover and Balmer 1954, Kraijenhoff van de Leur 1958, Gelhar and Wilson 1974, Erskine and Papaioannou 1997, Manga 1999, Knight et al. 2005). For a flat thick aquifer, the time-scale approaches (2), while for a steep thin aquifer the timescale approached (3). In cases where changes in recharge rate mean that vertical-filling of the aquifer will occur quickly then (1) will dominate. Walker et al. (2005) showed that as the catchment parameters move between the two lateral movement types, the time response associated with the groundwater discharge function moves smoothly between these two time-scales Shape of the response curve While a dimensional analysis does provide relevant time-scales for the processes involved, it does this at the expense of not providing information on, for example, areas at risk of salinisation. This will require solving equations that are used in MODFLOW or FLOWTUBE. The analysis itself does not provide measures of fluxes, only the time to go from one equilibrium state to another. It also does not provide the functional form in going from one equilibrium state to another, for example exponential decay, lognormal function or a logistic function. However, knowing such shapes from solving the flow equations in MODFLOW or equivalent usually will show a narrowly defined number of shapes. For example, Figure 3.8 shows output from FLOWTUBE analyses of the case studies from the National Land and Water Resources Audit and the MDBC case studies: Brymaroo (QLD: Smitt et al. 2003a), Popes catchment (Wanilla, SA: Dawes et al. 2002), Whipstick (Kamarooka, VIC: Hekmeijer et al. 2001). Response bry 1-10mm/yr 0.3 bry 1-2mm/yr popes 1-10mm/yr 0.2 pope 1-2 mm/yr 0.1 w hip 1-10 mm/yr w hip 1-2mm/yr Time (yr) Response bry-1-10mm/yr bry 1-2mm/yr popes 1-10mm/yr popes 1-2mm/yr w hip 1-10mm/yr whip 1-2mm/yr Time (yr) Response bry 1-10mm/yr bry 1-2mm/yr popes 1-10mm/yr pope 1-2 mm/yr w hip 1-2mm/yr w hip 1-10 mm/yr Tim e (yr) Response bry 1-10mm/yr bry 1-2mm/yr popes 1-10mm/yr pope 1-2 mm/yr whip 1-2mm/yr whip 1-10 mm/yr Norm alised tim e (t/harm onic sum of 3 tim e- scales) (a) (b) Figure 3.8 Modelled response time to an increase in recharge from 1 mm/yr to 2&10 mm/yr (using the Flowtube model). (a) response over time, (b) response over normalised time. [bry=brymaroo, popes=popes (Wanilla), whip=whipstick (Kamarooka)] (from Gilfedder et al. 2003) Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 33

46 The FLOWTUBE outputs in Figure 3.8a shows a wide range in groundwater response as a result of land use change (2-300 years). This relates to the variety of catchment sizes, difference in permeability and gradients. However, using a dimensionless time on the x-axis, most of the curves can be seen to collapse into similar functions (response between 1 and 6). In the absence of the groundwater information used in the modelling studies, a simple function, which had a similar shape to those in Figure 3.8b, captures most of the key information. The S-shaped logistic curve (Figure 3.9) gives a reasonable approximation for a response to an increase in recharge, particularly where the aquifer starts to discharge to the land surface in addition to the stream at the aquifer outlet. This simple model of response to change weights changes in recharge to changes in discharge, according to a time scale and rate of change. 1 D(t) = (3.4) 1 + exp { 4 ( 4t )} t half where t half is the time until 50% of the recharge has passed through the system. This function is a convenient one, although any functional shape could be used in its place if it was considered more suitable (Dawes et al. 2004a). Response (0=no, 1=full) Time (years) Figure 3.9 Example showing the smooth shape of the response (from 0=no change, to 1=fully responded) of the logistic function (Eqn. 3.4). For a DECREASE in recharge, the aquifer response appears to be better represented by a classical exponential decay curve. The BC2C model uses: t* ln(2) t half = 1 e D(t) (3.5) where the ln(2) is included to allow the response function to pass through a response time of 0.50 for a given t half. Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 34

47 1.00 Response (0=none, 1=fully) t half =5yr thalf =10yr t =20yr half Time (yrs) Figure 3.10 Example showing the shape of the response (from 0=no change, to 1=fully responded) of the exponential function (Eqn. 3.5) Combining the groundwater time-scales The minimum of the three time-scales will tend to dominate the overall response. In considering a range of catchments where the relative importance of the three processes may vary, a function is needed which can approximate this behaviour in a smooth manner. Using a value equal to the harmonic mean is a simple way of approximating this behaviour, as it combines the time-scales in such a way that it allows the minimum time-scale to dominate the response when that given process is the fastest. For an INCREASE in recharge, the harmonic mean of the three time-scales is: t harmonic mean 3 = (3.6a) t t t 1 For a DECREASE in recharge, the timescale relating to aquifer filling (t 1 ) is not relevant, and so the harmonic mean of the two lateral time-scales is used. t harmonic mean = (3.6b) t t The time taken for a 50% response (t half ) can be approximated using a value of one third of the harmonic mean (t half =t harmonic mean /3). This provides an approximation of a 50% timeresponse over a range of conditions. It provides a simple way of combining the three timescales smoothly, and in the absence of detailed modelling, can be used to parameterise a simple functional relationship which exhibits appropriate behaviour (Dawes et al. 2004c) Salt leaching processes The groundwater processes described in the previous section not only discharge salt into streams, they replace the more saline groundwater in the catchment with fresher groundwater. Eventually, a new salt balance or equilibrium will be reached, between recharge and discharge. Generally, this leaching process will be very much slower than the increase in groundwater fluxes. In most groundwater models, the salt discharge flux is estimated by multiplying the groundwater discharge by the salinity of the groundwater, thus ignoring changes in salt storage within the catchment. For larger catchments this may be a reasonable approximation, and so for many catchments the salt leaching process can be ignored. However for small upland catchments this leaching process may occur within 100 years (and therefore be of consideration under the BSMS). Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 35

48 Peck and Hurle (1973) found that O/I for eight forested catchments in WA ranged from , while the range for eight cleared farmland catchments was This matches with expectations that clearing will lead to a leaching of stored salt. Their data for forested catchments (mean annual rainfall of 970 mm) had stream salinity between EC, while the agricultural catchments (mean annual rainfall of 680 mm) had much higher stream salinity of EC. They estimated that the cleared catchments would take between 200 and 400 years to leach. Given the slow leaching times associated with a deeper groundwater store of salt, it is often not necessary to include salt balances in modelling studies. However, it is important to consider salt balances as small upland catchments may have already reached a new equilibrium (Hatton et al. 2002) Land use change impact on regional groundwater systems Apart from changes in land use in upland areas, changes in the irrigated areas over the very large regional groundwater systems will also be important in changing salt loads in rivers. For major irrigation areas, this is often simulated with the use of a groundwater model, such as MODFLOW (McDonald and Harbaugh, 1988). The unit response method (URE) of Knight et al. (2002, 2005) uses the same philosophical approach as that in the BC2C model (Dawes et al, 2004c). The URE has been used within the South Australian SIMPACT model (Miles et al. 2001) for estimating the salinity impacts of irrigation in the Mallee region as a basis for irrigation planning. The approach of Knight et al. (2005) was to simplify the description of the aquifer, and provide an analytical solution for the response times in a very large, almost flat and homogenous aquifer. This approach can provide approximate lead-in and lag times for the impacts of recharge change on groundwater discharge and salt load to rivers, and has already been applied in Regional Systems such as the Murray Geological Basin. The method predicts the change in discharge of groundwater to a straight river, some distance away from the location of recharge (Figure 3.11). The solution can easily be implemented in a spreadsheet, and replicates MODFLOW results (for the same simplified aquifer description). A response curve (such as in Figure 3.12) can be produced for any distance away from the river, given any aquifer properties, as long as basic assumptions of aquifer extent, connection to river, gradient, and homogeneity are not violated. The simplicity of this solution makes it more accessible for regional assessments than other existing methods. The form of the solution readily allows it to be applied within spreadsheetbased analyses. river Figure 3.11 The URE predicts the effect of a recharge change over a small area (green), expressed as the impact of groundwater discharge from a regional groundwater system into the river. Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 36

49 Flux ( f 3 ) Time [ t] (yr) Figure 3.12 Example of a change in groundwater discharge (f 3 ) to the river in response to a unit step change in recharge. In this case about half of the eventual effect of the new recharge is seen in the river after 170 years Summary - Salt mobilisation processes This chapter described many of the concepts behind understanding salt mobilisation from unregulated catchments: The flow salinity relationship (FSR) and catchment salt output/input ratio (O/I) represent the salinity state at any given time and change slowly with time. After a land use change, the Lemon creek data suggests that the shape of the functional relationships do not change with time, but many of the key parameters do. For the hydrological modelling described in the next section, information such as the FDCs and flow-salinity relationships are often required. To simulate the information required for targets, it is important to understand how these relationships change in time and space. The change in key parameters in time as a result of land use change can be simulated from historical catchment data (as was done with the FDCs), although there is extremely limited data on salt mobilisation processes to allow this method to be used in practice. It was possible to show the dependence of flow-weighted mean salinity of catchment stream flow on mean annual rainfall, but that is all. Currently, process understanding must be relied upon. There are two key processes: 1) groundwater response to increased recharge and 2) salt leaching process. Despite the limited available data (locations and length of record) it is possible to provide general time-scales associated with each of these processes Currently available information is across large areas is not perfect, which limits the ability to undertake exact and detailed modelling. Modelling can still aid decision-making and help provide guidance on physical processes and sensitivities. Understanding the time-responses and salt imbalances are two key issues in prioritising areas for land use change. Chapter 4 now describes modelling work which brings together this theory on water and salt mobilisation into a spatial model (BC2C) to examine specific issues within catchments. Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 37

50 4. Bringing together the theory development of the BC2C model Chapter 1 discussed the need for methods which are able to predict the impacts of land-use changes on stream salinity as measured at specified target sites within the valleys. BC2C (Biophysical Capacity to Change) is such a model which does this simply with respect to stream salinity. BC2C is aimed at prioritising areas which will have the greatest impact as measured at a reference point in the river (such as at a downstream gauging station). The BC2C model uses and brings together the work described regarding impacts of land-use change on water yield (Chapter 2) and salt load (Chapter 3). BC2C builds on this work by integrating it within a salt and water balance framework. Within the BC2C model, water-balance components, pathway time delays, potential mixing, and discharge processes are estimated. Efforts have been made to keep data requirements low and to use only data that is available for large areas. The work of Zhang et al. (1999, 2001) and Dawes et al. (2002) for water balance, Dawes et al. (2004c) for groundwater time delay, Gilfedder et al. (2003) and Smitt et al. (2003b) for response shape, and Coram et al. (2000) for groundwater flow system linkage, provide the basis for the procedures within the BC2C model. This builds on modelling work (Dawes et al. 2001, 2004a) which describes the SALSA model used by ABARE, another simple model which precedes the BC2C model Background to BC2C The background chapter (Chapter 1) has already discussed the need for methods which predict the impacts of land-use changes on stream salinity (as measured at specified target sites) within the valleys. The Basin Salinity Management Strategy (BSMS) requires stream salinity targets, and there is an expectation that regional groups will specify investment strategies for prioritising land-use management actions with respect to their impact on stream water and salt (salt loads and salinities) at these sites. Regional groups will need to be able to understand the complex spatial trade-offs between water and salt generation across catchments in order to help justify decisions for on-ground actions. For planning purposes, and the sorts of decisions required, the impact of these actions need to be predicted in terms of, the scale of management intervention required in particular locations, which locations will provide the most impact on water and salt, how soon it will be before the full effect of an action is realised as a stream impact, being able to investigate the trade-offs between salt load and water yield impacts, and combining these tradeoffs with other issues (social/economic/environmental). There is a need for a model: which can provide this information across large areas using relatively readily available catchment data, where the data can be easily interacted with, where it is easy to see how the answers have been calculated, which will allow quick interaction with parameters, and allow scenario changing, which can be used as a rapid assessment tool to cover large areas, often where there is very little available data, and which can be used to prioritise areas within the catchment where benefits are likely and where more detailed work can be focused such as more complex models and field investigations across these smaller areas. The BC2C (Biophysical Capacity to Change) model was developed to meet these needs. Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 38

51 4.2. Requirements for the BC2C model BC2C is a conceptual model that links changes in land-use to changes in stream flow and salt load. A tool has been developed for implementing this model that will allow the prioritising of areas for afforestation within large catchments. The BC2C tool has two important design characteristics. Firstly, it must be technically sound enough to give confidence that the salt and water balances can be reasonably fitted, and that modelled results following land-use change are believable. Secondly, it must be simple enough that changes can be made and new results displayed quickly, fulfilling an educational role. There are a number of desirable attributes for any apparently new model, first and foremost not reinventing previous work. In the salinity community though, there are other imperatives, and BC2C has the following features: complements other modelling activities in the various states such as CATSALT, LAMPS, IQQM, REALM, SHAM, etc, builds directly upon the GFS concept and the mapped units at the appropriate scale, explicitly includes timing of impact, uses readily available data, can be part of a package that would be flexible enough to incorporate changes and able to be workshopped at a regional level, and can be aggregated to provide a Basin wide picture. Using Groundwater Flow Systems (GFS) as a basis, readily available data sets and robust long-term water and salt relationships, it is possible to estimate the salt and water yield from catchments. A graphic-user interface model has been developed for BC2C within the CRCCH Toolkit Project, although variants have used EXCEL spreadsheets (for crude screening work of whole catchments) or GIS (for spatial applications and finer work). The data requirements are: annual average rainfall, average rainfall salinity, tree cover, and groundwater flow systems. Within the GFS layer there are several other parameters: groundwater recharge coefficient, groundwater response time and groundwater discharge salinity. BC2C considers only unregulated catchments (i.e. those unaffected by engineering water storages and diversions). The reasons for this are that diversions of water for domestic and commercial purposes distort the natural flow and salinity patterns. It is often difficult enough to establish the effect of an altered land-use pattern with annual climate variability the only complication. The addition of anthropogenic stream flow and salinity modifications not only makes the task much more difficult, but negates the ability to extrapolate the finding s to other catchments. Finally, BC2C uses an annual time step which allows some simplification of process and response representation. While the obvious advantage is that computations are simpler and fewer, there is a loss of detail at sub-annual time scales. The Biophysical Capacity to Change (BC2C) model was developed to help answer broad questions such as: how much difference can planting or clearing trees really make to the area of dryland salinity in a catchment, or the salt load of a stream? How long will it take until the full effects of clearing of woody-cover is seen? These are important questions, given current and proposed salinity management strategies that involve broad-scale reafforestation, coupled with the clearing of native vegetation in some areas. To answer these questions, a tool was needed with three requirements: Spatially explicit: able to produce maps showing areas that improve or degrade after a change. Temporally explicit: the response time of catchments following a change will affect the prediction. Produces a number: this moves beyond just producing a risk map with high or low associated risk, the required solution produces a number in relevant units that indicates the magnitude of change expected. Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 39

52 4.3. Using the BC2C model Sub-Regional example Little River BC2C was applied to the Little River catchment (2325 km 2 ), part of the Macquarie catchment in NSW, near Dubbo (Evans et al. 2004) (Figure 4.1). The Little River catchment has an average annual rainfall of 700 mm, and is currently approximately 75% cleared for cropping/grazing. Dubbo Macquarie Highway Wellington Newell Little Obley gauge River Yeoval Highway River Cumnock Mitchell Little River Catchment Watercourse Highways km Molong Figure 4.1 Map of the Little River Catchment (from Evans et al. 2004) In order to run the BC2C model, a number of steps are required after the collation of data layers (GFS map, hydrogeological data, rainfall, land-use, etc). These are: 1. map GFS units, 2. calculate groundwater response times, and 3. predict impact of land-use change on water and salt Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 40

53 Step 1: Map GFS Units The first step is to determine the location and size of the individual groundwater units (GFS). The Little River catchment has been mapped as consisting of several different local scale GFS types (<5 km in hydraulic length). Local GFS are generally coincident with surface topography, so a Digital Elevation Model (DEM) can be used to identify the sub-catchment areas (this example uses the Auslig (2000) 9 second DEM). These areas are the fundamental unit on which the BC2C calculations are performed. The size of the units can be changed using a flow-accumulation parameter. This is a common topographic method for determining stream-lines from digital elevation models. It involves looking uphill from each cell and determining the catchment area of each pixel in the DEM. A flow-accumulation threshold is then used to determine whether a particular cell has sufficient contributing area to become stream pixel. Decreasing the area of the flow-accumulation parameter results in a finer stream network. Local knowledge is useful in deciding upon a suitable disaggregation of the catchment. In this example, maps were initially made for three different sizes of GFS. It was decided to use a flow accumulation parameter set to 1000ha, which resulted in 90 GFS units (Figure 4.2). Legend Kilometres Rivers Obley Catchment Figure 4.2 Disaggregation of Little River into sub-catchment areas. These have been used to define the location/size of the groundwater units in BC2C. The units upstream of the Obley gauge are shown in bold. Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 41

54 Step 2: Calculate Groundwater Response Time The second step is to obtain parameters for each GFS type, and use these to estimate groundwater response times for each of the mapped GFS units. These parameters are best obtained following discussions with local hydrogeologists and salinity researchers. An important thing to remember is that these parameters are easy to change within BC2C, so the sensitivity of the overall results to changes in individual parameters can be undertaken if required. Groundwater response times are calculated using the timescales described in Section 3.6. This requires the following steps for each GFS unit: 1. area-weighted values of aquifer properties (hydraulic conductivity: K, aquifer thickness: d, aquifer storage term: S), are calculated using the salinity province map, 2. topographic properties (height drop: h, and flow length: L) are calculated from the DEM, and 3. groundwater response time are calculated for each GFS unit (results shown in Figure 4.3). Little River T-Half Legend T_HALF Kilometres Figure 4.3 Estimated groundwater response times given as the time in years for 50% of the change in water-balance to appear in the stream following a change (t half in years) for each GFS unit. BC2C predicts that darker areas will respond more slowly than the lighter areas. The distribution of the groundwater response estimates for each of the GFS units in the Little River is shown in Figure 4.3. The information contained in the figure can be used to help incorporate groundwater information into an overall prioritisation of areas for land use changes, in terms of effect on stream salt load. This information has been used by NSW Agriculture to provide groundwater information for their farm-scale economic model (Andrew Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 42

55 Bathgate, pers. comm.). Interpretation of this type of information is best used as an initial tool to investigate relative differences across the catchment, rather than as a precise or accurate prediction of the exact response time. Step 3: Predict impact of land-use change on water and salt The third step is to predict the impact of a land-use change on water and salt export from the catchment. BC2C uses the Zhang curves (Zhang et al. 2001) to provide water balance terms. The current implementation is for mean-annual conditions under either woody cover and non-woody cover. Linear interpolation between the curves allowed estimation of the changes in excess water as a result of a change in woody cover over an area of catchment. BC2C also incorporates a simple tree growth function, whereby the full impact of afforestation is delayed by a canopy closure function (Gilfedder et al., 2005). This relationship allows for a further delay between afforestation and catchment response in addition to the groundwater response time. The catchment excess water is partitioned into a groundwater component, and a quick-flow component. The main difference between these is that the groundwater component is delayed by a groundwater response time and given the salinity of groundwater discharge, while the quick-flow component is not delayed, but its salinity is assigned a fraction of the groundwater discharge salinity. BC2C then calculates an annual time-series of the estimated changes in water and salt yield from each of the individual GFS units. These are then aggregated up to provide estimates from the whole catchment or area of interest. The BC2C model aims to predict changes in water and salt yield as a result of changed land-use. In the first instance, the model would typically be run with scenarios showing the changes between very large changes such as from fully forested to current, or current to fully forested. The idea is to estimate the impacts of very large catchment changes, to get an idea of the maximum possible impact. This can help as part of the exploration of the sensitivity of the model parameters to land-use change. A common first scenario is to show the impact of afforestation across an entire subcatchment this is a maximum impact scenario (and extremely unlikely in all but the smallest catchments). Its purpose is to provide an estimate of the maximum possible impact on water and salt load. For example, Figure 4.4 shows the response of the part of the Little River catchment (upstream of the Obley gauge: this sub-catchment takes up 25% of the total area of the Little River) to changes from: (a) fully forested to current, and (b) back again ( current to fully forested ). The results show predictions in the change in water and salt following such a large land-use change. Importantly, they show how long before the full impact of change is expected to express itself in the stream. Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 43

56 100% Change compared to current (%) 75% 50% water salt a) 25% 100% Time (yr) Change compared to current (%) 75% 50% water salt b) 25% Time (yr) Figure 4.4 Estimated water and salt load response upstream of the Obley gauge, following change from: a) fully afforested to current land-use, and b) current land-use to fully afforested. Change has been shown as % when compared to the current land-use. Other land-use change scenarios, such as increasing tree cover by 10% across part of the catchment can also be run. BC2C allows the impacts from different scenarios to be estimated relatively quickly and easily. Figure 4.5 shows the changes in water and salt (% of current amounts) for the catchment upstream of the Obley gauge (25% of area of the Little River catchment) following an increase in forest cover of 10% in all of the GFS units upstream of the gauge. Figure 4.5a shows the predicted impact at the Obley gauge, while Figure 4.5b shows the predicted impact at the outlet of the whole Little River catchment. Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 44

57 100% Change relative to "current" 75% 50% water salt a) 25% Time (yr) 100% Change relative to "current" 75% 50% water salt 25% Time (yr) b) Figure 4.5 Estimated water and salt load response following afforestation of a further 10% of each GFS in the area upstream of the Obley gauge: a) at the Obley gauge, and b) for whole Little River catchment. Change has been shown as % when compared to current land-use. In summary, BC2C provides a framework for combining known information about groundwater parameters in such a way as to investigate both the change and the timing of possible land-use change on water and salt export from catchments. A key feature of BC2C is the fast run times, and the ability to change parameters quickly, and investigate many different scenarios in a workshop setting. As such, BC2C provides a tool for improved understanding of catchment water and salt export, useful for prioritising afforestation efforts to focus on areas where more detailed modelling/monitoring can take place. Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 45

58 Prioritisation of upland catchments for afforestation in the MDB The need for prioritisation of areas does not only occur at the regional and sub-regional scale, but also at the much large Murray-Darling Basin (MDB) scale. While a simplified version of BC2C can be used at this scale, it should be noted that data at the MDB scale would generally be too coarse for specific land-use decisions. A simplified modification of BC2C was implemented on a GIS to estimate the impact of afforestation (explained in detail in Dowling et al. 2004). Three different impacts were considered: water yield (mm), salt load (t/km 2 /yr) and in-valley flow-weighted mean salinity before diversions (µs/cm/km 2 ). Because this information is spatial, the model provides a sense of where afforestation may make the largest impacts on water yield and salt loads. The mean flow-weighted salinity methodology relies on using a target stream site, but estimates whether the impact will be an increase or decrease in water and salt, and the magnitude of the change. While finer-scale information will be required to provide absolute numbers, it is clear that when considering the impacts of afforestation within the MDB: afforestation in many areas will produce no significant impact on stream salinity in the timeframe of 30 years, there may be an increase in stream salinity levels as a result of reduction in water yield, and where a reduction in salinity is predicted, this may not be in locations currently viewed as suitable for commercially viable plantations. Example results from this simplified version of BC2C are presented in the following figures. Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 46

59 Example of Results Figure 4.6 illustrates an example of the possible change in modelled excess water if the current land-use was changed to trees. In upland areas, this excess water can be thought of as water yield. The only areas with zero change are those that already have 100% trees, and although a forest appears as a coherent mass, these patches are scattered across the MDB. The greatest differences in total water yield are found in the high rainfall areas, with values quickly dropping from east to west and the lower rainfall inland areas. Figure 4.6 Examples of the types of maps of simplified BC2C output for the MDB: change in excess water as a result of changing from current cover to forest cover (Source: Dowling et al. 2004). [Note that Regional GFS types are not included in the analysis and have been masked out.] Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 47

60 Figure 4.7 shows an estimate of the effect on salt generation in 15 years time, as a result of changing current land-use to trees. The map allows the differences in response across the Basin to be estimated and compared in a broad manner, before investigating the details using finer resolution data and local knowledge where available. Figure 4.7 Examples of the types of maps of simplified BC2C output for the MDB: salt reduction after 15 years with change from current cover to forest cover. (note that the black areas are Regional-scale GFS and the methods applied are not appropriate to properly describe them or the irrigation areas they contain) (Source: Dowling et al. 2004). Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 48

61 Figure 4.8 shows the predicted impact of afforestation in 50 years time on in valley salinity. Orange areas are those where afforestation is predicted to have an increase in in-valley stream salinity, areas coloured in blue indicate a decrease, while white areas are either currently forested or are predicted to have almost no impact on in-valley stream salinity. Figure 4.8 Examples of the types of maps of simplified BC2C output for the MDB. In-valley impact on stream EC after 50 years with change from current cover to forest cover. Note that black shaded areas are Regional-scale GFS and the methods applied are not appropriate to properly describe them or the irrigation areas they contain. (Source: Dowling et al. 2004) BC2C - Summary The BC2C model uses and brings together the theory described on impacts of afforestation/clearing on water yield (Chapter 2) and on salt load (Chapter 3). The model is intended as a first-cut prioritisation tool which helps collate available data, and run scenarios to estimate impacts. The results of the model can be used to prioritise areas based on the likely impacts of changes, and help lead to areas where more detailed modelling and on-ground monitoring can be focused. The BC2C model allows the expected impact of afforestation change on water and salt export from catchments to be simply and quickly estimated in broad terms. The spatial platform used for running BC2C enables users to come to grips with the groundwater data before moving to more complex models. In many areas there is very little groundwater information, so the model is relatively simple by necessity. BC2C is designed to make use of the groundwater data which is available over large areas. [Note: BC2C pre-dates the 2CSalt model, which is not discussed in this report] Flow and Salinity Impacts of Afforestation in Upland Dryland Catchments Page 49