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An assessment of catchment condition in Australia
Ecological Indicators 6 (2006) 205–214 This article is also available online at: www.elsevier.com/locate/ecolind
An assessment of catchment condition in Australia Joe Walker a,*, Trevor Dowling a, Simon Veitch b b
a CSIRO Land and Water, P.O. Box 1666, Canberra, ACT 2611, Australia Department of Agriculture, Fisheries and Forestry, P.O. Box 858, Canberra, ACT 2601, Australia
Abstract The condition of Australia’s water catchments was assessed in a 2 million km2 intensive land-use zone (ILZ) as part of a National Land and Water Resources Audit (the Audit). The assessment used existing biophysical data obtained from satellite imagery, digital elevation models, computed or derived values, surrogates and existing spatial data from state and national databases. The data were used to derive indicators and an index of relative catchment condition for 3718 sub-catchments and 197 basins. The spatial patterns for catchment condition were then mapped across the ILZ. The term indicator is used consistently to mean a single attribute (even if it is derived from several variables), whereas a sub-index or an index is an aggregation of scaled indicator values. An inventory was made of all potentially relevant biophysical measures of the water, land and biota in catchments that existed in archives. Criteria to assess data quality including a national coverage, ease of interpretation, scale, relevance to policy development and catchment management were applied to some 110 measures from which 21 indicators were ultimately selected and used for tabulation, mapping and interpretation. The data were assembled at 250 m cell resolution and aggregated to 5 km cells as the base unit for analysis. To map the indicators or indices, a five-point classification (quintiles) was used, ranging from relatively poorer to better condition. The class boundaries were based on equal intervals or equal areas under the frequency curve of each indicator or index. Indicator values for each 5 km cell ranged from 1 to 5 (1 being poorer, 5 being better). Index values were calculated by adding the scores for a set of indicators, for example, all the 21 identified and taking a mean for all the 5 km cells within each catchment. Data manipulations and analysis used a multi-component decision support system (DSS), geographic information system (GIS) called catchment condition (CatCon). Numerous maps were generated for each indicator and for indices comprising different combinations of indicators, ranging from the complete set (21), a core set (14) to relatively few (6) and for different regionalisations. Cross-comparisons between catchment condition classes and some consumptive outputs from catchments were also carried out. Only maps for the index based on 14 indicators and a single cross-comparison are presented here as illustrations. The web site www.affa.gov.au/catcon/ has more examples. The main points to emerge were: clear interpretable patterns of better to poorer condition were defined for the intensive landuse zone; visualisations of catchment condition were markedly different for classes defined by equal area or equal interval; the broad patterns of relative condition could be captured with relatively few aggregated indicators; the cross-comparisons suggested areas where land-use changes are needed to develop sustainable land-use systems. The study illustrates that relatively low quality but extensive data can be used to provide a rational basis to identify priority areas for management action and for natural resource management policy development. We suggest that the development of a higher quality national data set in * Corresponding author. E-mail address: [email protected] (J. Walker). 1470-160X/$ – see front matter. Crown Copyright # 2005 Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.ecolind.2005.08.020
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Australia to assess the condition of the Nation’s catchments may be best achieved through the co-ordination and collection of core data sets (indicators) at regional scales. Crown Copyright # 2005 Published by Elsevier Ltd. All rights reserved. Keywords: Indicators; Index; Water catchment assessments; GIS
1. Introduction The catchment condition assessment described here was part of an Audit of Australia’s natural resources—the National Land and Water Resources Audit (NLWRA, 2002). The broad aim of the catchment study was to establish the current relative biophysical condition of Australia’s surface water catchments (sizes range from <100 to >5000 km2) within the intensive land-use zone, an area of approximately 2 million km2 (Fig. 1). Once condition has been established a monitoring program was envisaged to detect trends in catchment condition
over the next 10–20 years. The Audit was also tasked to provide an updated Australia-wide environmental database, the primary use being for decision-making and policy development relevant to environmental issues at national, state and regional levels. The focus on water catchments as reporting units in Australia recognises the fact that most land and stream degradation issues are associated with the water cycle, the low-nutrient and often structurally poor soils and the characteristic cycles of flood and drought. Thus, a water catchment is considered to be a useful management unit relevant to the key issues needed to develop integrated natural resource management
Fig. 1. The 2 million km2 intensive land-use zone (grey) defined by the National Land and Water Resources Audit (NLWRA, 2002) and used in this assessment.
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programs. In addition, a nested hierarchy of catchments using digital elevation models already exists in this case those derived by CRES (Hutchinson et al., 2000). In Australia, catchments are the main natural resource management-planning unit and were used by the nationwide Decade of Landcare Program 1988– 1998 (Campbell, 1994). The Landcare program fostered the establishment and maintenance of a network of some 3500 community-based catchment groups. Two major current programs have been created, the National Action Plan for Salinity and Water Quality, and the Natural Heritage Trust, which complements a successor National Landcare Program and continues the catchment focus on natural resource management. Catchment condition across the intensive agricultural zone is thus of interest to local Landcare groups as well as policy-makers in state and Australian Government departments. Catchment condition can be considered to reflect the interaction of a complex and multi-layered set of hydrological, geomorphic, ecological and bio-geochemical processes and the many human enterprises dependent on their use for the supply of goods and services. Here, we consider only the biophysical aspects, but recognise that any assessment of catchment condition will contain value judgments that depend on the biophysical attributes we can measure and their interactions with often conflicting societal values, land uses and economic factors. The spatial density, national availability and quality of data about catchments have limited our ability in Australia to define current catchment condition, and subsequent trends in a quantitative way. For many planning and policy purposes at the national scale, a relative assessment (poorer to better) to establish the major patterns was considered useful by the relevant steering committees responsible for the National Land and Water Audit. This biophysical assessment of Australia’s water catchments uses indicators of key catchment processes as a first step to establish the condition of our catchments. We reported on the condition of 3718 catchments and 197 basins in the ILZ to the Audit. The indicator approach closely follows Walker and Reuter (1996), Walker (1999, 2002) and Jones et al. (1997), and the hydrological aspects of catchment function are described by Zhang and Walker (1998). The assessment described here was a synthesis of available
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biophysical data obtained from satellite imagery, digital elevation models, computed or derived values, surrogates, other Audit projects and existing GIS data from state and national archives. The data used can be described as being readily available, spatially dense but of poor-medium quality. This kind of data set is often overlooked and one aspect of this study was to find out if plausible patterns of catchment condition can be identified from this quality of data for use by policy-makers and land-managers.
2. Indicators from catchment measures The term indicator is used consistently to mean a single measure (even if it is derived from several variables), whereas a sub-index or an index is an aggregation of indicator values (in this case class values). A systems model of catchment function was developed, and then simplified into land, water or biota components (Walker et al., 2002a and website www.affa.gov.au/catcon/). From 110 possible measures relevant to catchment condition, 21 measures were selected as indicators of catchment condition (Table 1). Measures were deleted on the basis of criteria that included: national coverage, data quality, unequivocal interpretation and applicable to policy or management issues. The selection criteria were designed to give ‘yes’ or ‘no’ answers, and the initial selection of 21 indicators was based on an assessment of meta-data descriptions by the catchment condition team. Most measures (70%) were eliminated on the basis that a national coverage did not exist, and some 10% on the basis that standard methods were not used. Maps were drawn for several regionalisations for all 21 indicators. For the development of indices the indicator data set was further reduced to 14. This was based partly on advice from the Audit and the relevant Australian Government departments, regarding difficulty interpreting some indicators and some resource data sets were removed because they were derived from the same data source (e.g. soils are often mapped from vegetation as seen on imagery). Cross-correlations between all data sets were performed, and closely correlated measures examined to ensure they were independent variables.
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Table 1 The list of measures that met the indicator selection criteria Water condition
Land condition
Biota condition
Rivers in salt hazard Road density Suspended sediment ratio* Agriculture on steep slopes Rivers through forests Pesticide hazard* Industrial point sources* Rivers in acid hazard Nutrient point sources* Impoundments*
2050 salinity risk * Soil acidification on hazard Soil structural hazard* Hillslope erosion ratio* Agriculture on steep slopes Native vegetation change
Native forest fragmentation* Native vegetation change* Intensive agriculture area Protected areas* Human population density Road density* Feral animal density* Weed density*
The 14 indicators, marked with an asterisk (*), were chosen for the overall catchment condition index.
The 21 indicators shown in Table 1 include individual indicators (e.g. change in the area of native vegetation), simple combinations as surrogates (e.g. agriculture on steep slopes), or more complex combinations of variables using process-based models (e.g. potential erosion). Note that three indicators appear in more than one catchment component giving an apparent total of 24 indicators.
3. Analysis and mapping Data can be obtained from a variety of sources (e.g. satellite imagery, digital elevation models, computed or derived indicators, surrogates, Audit or State/ Territory government data and existing GIS data in the national archive) and the quality and scale of collection is variable. Excellent indicator data do exist at smaller regional and State resolution, but often these could not be used because having a consistent national coverage was a project requirement. The original scales of maps and data sets available for the study varied. Resolution ranged from 250 m 250 m (e.g. digital elevation models, DEM) to approximately 10 km 10 km for soils and 50 km 50 km for weeds. All data were re-sampled to 250 m 250 m cells for analysis, and then aggregated to 5 km 5 km cells (115,000). These cells were also aggregated to sub-catchments of approximately 500 km2 (3718 sub-catchments) as defined by CRES (Hutchinson et al., 2000) or aggregated to the well-established Australian Water Resources Council Basins (AWRC Basins), 197 basins.
Mean values for individual indicators were tabulated for various regionalisations—sub-catchments, basins, local government areas, local catchment area, electorates and so on. Mean values can then be inspected for a particular catchment, local catchment area, etc. to obtain the raw data scores for each indicator. These large tabulations are not reported here, due to the volume of the data. Displayed this way, indicator values for catchments can be interpreted to identify a potential problem that requires further investigation. The indicator values give the base level from which to judge the impacts of particular remedial actions at the catchment or basin scales. To map the indicators or indices it was necessary to define classes on a five-point scale from relatively poorer to better condition. Individual indicators classes based on well-established threshold values are preferred, but few well-established thresholds exist for the indicators available (Walker and Reuter, 1996). The five classes (quintiles) were chosen as a convenient number for mapping, and the class boundaries for each indicator were based on frequency distributions (equal intervals or area under the curve). All individual indicators showed a skewed distribution in the frequency versus quantity plots, and for this reason quintiles based on area under the curve was used. The five classes were: (1) poor, (2) poor to moderate, (3) moderate, (4) moderate to good and (5) best. Placing continuous data into classes can pose a number of problems, in this case because recognised class thresholds are not available. Variation in class values between regions, for example, tropical versus
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temperate, can also be an issue but for this exercise had to be ignored. We defined classes using the shape of the indicator frequency distribution curves. Indicator classes can be defined either as equal intervals across the range of values or by equal areas occurring under the frequency curve. Equal areas were appropriate for indicators since most of the data had skewed distributions. Selecting an equal interval method would have resulted in most data falling into one or two classes. All indices had a distribution that approximated a normal bell shaped curve and equal intervals were used. In practical terms, using equal areas for a bell shaped distribution will exaggerate the end members. In summary integrating the suite of indicators into catchment index values proceeded as follows. Each indicator class score was compiled at 250 m 250 m and an average score calculated for each 5 km 5 km cells for the approximately 2 million km2 intensive land-use zone of Australia. The score per cell will be between 1 and 5. The cells were spatially aggregated into 3718 sub-catchments and 197 basins, and a mean score for each indicator calculated per catchment or basin (also ranging from 1 to 5). Combining a suite of indicators into a single value is overviewed by Andreasen et al. (2001). The method we used was the simplest available and follows Karr et al. (1986) who used it for a biological integrity index and Jones et al. (1997) who used it in the US-EMAP program for eastern USA. It has also been used by Walker et al. (2002b) who tested the scores against independent biophysical data for catchments in the Canberra region and Liu et al. (2002) who applied the method to part of the loess plateau in China. There are other more sophisticated methods available including fuzzy logic approaches (Dowling et al., 2003) and numerical taxonomic methods (Jones et al., 2001; Riitters et al., 2005). The various data manipulations used a multicomponent spatial decision support system called CatCon. CatCon was developed in-house from a GISbased system described by Veitch and Bowyer (1996). CatCon contains all the spatial data for the 21 indicators. It can be used to zoom into specific locations to examine the 5 km 5 km data or subcatchments, catchments or regions. See www.affa.gov. au/catcon/ for more details. CatCon was used to:
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view, manipulate and map indicators or indices; generate indices/sub-indices by adding ranked indicator scores to give a relative comparison of condition between parts of catchments or between catchments; compare biophysical catchment condition assessments with non-biophysical attributes (the crosscomparisons). Users of CatCon can: select their area of interest (National, state/territory, regional or catchment) and zoom in to that area of interest; set the scale for units of analysis and weight values if needed; select indicators from the overall set (by clicking on boxes) to generate water, land, biota and overall catchment indices; compare biophysical catchment condition with other catchment attributes.
4. Results An individual map using the 5 km 5 km data set can be generated with CatCon in less than 20 s. Using various combinations of indicators and regionalisations a planner or decision-maker can quickly generate a quantity of scenarios. Here, we present but a few of the many shown in www.affa.gov.au/catcon/. We show maps (Fig. 2) prepared for the catchment condition index, using 14 indicators at three scales of regionalisation. Each map has the AWRC basins overlaid for context. The 5 km 5 km grid cell map shows considerable variation in the relative rating. In eastern Australia, from the east coast to the mountain range, condition changes from good to poorer, then from mountain ranges to plains poorer to better. An east–west trend is evident in the sub-catchments that comprise the AWRC basins. The trends from poorer to better are more clearly shown at the sub-catchment scale (500 km2). The basin scale map shows the relative condition of each basin, and suggests major issues in the eastern wheat belt. Some caution is needed in interpreting the basin scale aggregation. The classes are average conditions, thus an individual basin can include relatively good sub-catchments as well as poorer
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Fig. 2. Catchment condition depicted by the 14 indicators in Table 1, shown at three spatial scales (5 km 5 km cells, catchments of approximately 500 km2 and AWRC Basins).
areas. Nevertheless, aggregating the measures to different scales can give a range of insights depending on the nature of the query from policy or decisionmakers. At the smaller scales, regions can be examined in detail by zooming into the selected area and adding roads, towns, etc. to the maps. An index can be based on a few or many indicators and a relevant question refers to a possible minimum indicator data set that captures most of the spatial patterns. The indicator set was cut from 21 to 14 and the calculated catchment condition index values compared. The general spatial picture of poor to better catchment condition could be consistently recognised. Further reduction to only six indicators, two from each of the land, water and biota components, gave very similar results to the 14 or 21 indicator-based indexes. The indicators used were: water—industrial point sources and impoundments; land—agriculture on steep slopes and native vegetation change; biota—road density and native forest fragmentation. Space here does not permit the full analysis of these observations, but the tentative conclusion is that at large national scales, relative
catchment condition can be defined with few indicators. In this case, the six indicators were amongst the easiest to measure and had high spatial resolution. It suggests that national scale data sets, which are of poor-medium quality, can provide the context to enable the development of a better sampling strategy and monitoring program at smaller spatial scales. Mapped patterns based on equal interval classes versus an equal area classification gave markedly different patterns—much greater than those using different indicator sets. As one would expect the equal area ranking biased the mapping towards the tails of the approximately normal distribution, greatly increasing the areas depicted as better or poorer. The equal interval classes perhaps best represent the areas considered to be ‘hotspots’ that currently require urgent attention, whereas the equal area ranking may better outline the extent of potential deteriorating condition given current land-uses. Relative catchment condition can be compared with other factors, such as economic production or human well being, using a 3 3 table (Fig. 3). In this
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Fig. 3. Comparison of catchment condition with agricultural production interpreted as sustainability scenarios for current land-use patterns.
case, classes of catchment condition (good, moderate, poor) can be compared with classes of goods and services data, or response attributes (e.g. agricultural productivity, macro-invertebrates in stream). For the comparison of catchment condition versus agricultural production, an interpretation of each combination is suggested in Fig. 3. Maps resulting from this kind of comparison help define areas that may benefit from specific policy and management options for maintaining or improving present condition. A cross-comparison allows us to easily see where high (or low) ratings for one attribute or index coincide with high (or low) ratings for other attributes, and where they are mismatched. In most cases, it is the mismatches that give the most useful insights, e.g. a catchment with a poor condition score but apparently high production suggests the natural resource capital may be overused. No assumptions are made about cause and effect in interpreting the maps and the cross-comparisons are used only to suggest areas
where further and more detailed examination is warranted. A comparison of catchment condition with agricultural production showed that the main wheat, cattle and sheep belts in the ILZ fall into the category poor catchment condition with high production. This suggests widespread and potentially unsustainable land-use systems, and raises the question of where could alternative systems best be placed to reduce the possibility of further system decline. To illustrate the use of a simple cross-comparison, we developed an ‘agricultural flexibility’ index on a 1– 3 scale based on the potential range of land-use options that could be applied at a catchment scale. For example, in some areas sheep grazing is the only viable option, and therefore has a low-flexibility score. The cross-comparison map of catchment condition versus agricultural flexibility is shown in Fig. 4. The interpretation should ignore the darker green areas as these are primarily national parks. Capacity to
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Fig. 4. Cross-comparison between catchment condition and agricultural flexibility. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the article.)
change land-use (light green and yellow areas) is higher in the higher rainfall areas. These coincide with potentially high production areas where farmers are able to finance necessary change. Less flexibility (pink and red areas) is most evident where rainfall is less than 500 mm/year and variable. These areas are also the landscapes where salinisation is a major issue.
5. Discussion Patterns in catchment condition were obtained from the chosen indicator set, and these highlighted a link with current land-uses. The main drivers of catchment condition are biophysical setting, vegetation cover changes, land-use patterns and intensity. It is changes to these drivers that cause major changes to the water cycle, soil chemistry and soil structures in Australia, and along with infrastructure developments and population density, affect the land, water, fauna and flora in catchments. The main conclusion is that the small set of indicators provides most of the broad scale informa-
tion needed by national and regional policy-makers. Thus, sufficient information already existed in Australia to carry out the initial step to identify priority areas and to develop scenarios to explain differences in condition. This broad type of assessment may be less relevant in smaller European countries where detailed spatial data sets exist. However, there are large areas of the world where rapid surveys are needed and sufficient imagery and modelled data can provide the first cut. Cross-comparisons between biophysical condition and consumptive and non-consumptive products are a useful approach to generating insights on sustainability, suitability and targeting funding to areas for rehabilitation and structural adjustment. Such comparisons are best viewed as hypotheses, to be tested by further investigation. They are a useful addition to the initial catchment condition assessment. A common perception is that the poor quality of data available severely limits attempts to carry out a national scale biophysical resource assessment of a large country like Australia. The reality is that sufficient data to cover all aspects will never be
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available, and a sub-set must suffice. Even the application of well-established process-based models to predict, for example, hillslope erosion, do not have the spatial resolution needed to plausibly map issues at a national scale. However, indicators derived from process knowledge obtained at a finer scale, are adequate to provide a first assessment of current biophysical conditions and guide more detailed data gathering. If detailed regional and local-scale assessments of catchment condition are needed they will require major new mapping expenditure across the country. A core question remains ‘‘how much environmental data do we need to make a policy or management decision at a national scale’’. As scientists we tend towards collecting data for more and more attributes, and this often fails to get into a form that is useful in decisionmaking. One can argue that at national scales we need fewer attributes, but better quality estimates for a core data set. In addition, investment may be better directed to focus on boosting the collection of regional or local environmental data, and interpreting the information at these scales. To do this requires the development of a sampling strategy and the collection of quantitative data over time to address relevant regional issues. The present catchment condition evaluation can be used to develop the necessary sampling strategy. Assuming there is a need for an overall index, there is always debate about how best to aggregate indicators or indeed to populate more complex models to accomplish an overall index. The main lessons from this experience are: first, use the simplest aggregations, and see if these provide the information needed for policy and decision makers; second, if more detail is needed, then focus on data readily obtained from imagery, or invest in quick field measures at the appropriate scale; third, the client needs to be aware that map visualisation depends heavily on how the data were classified and coloured. This highlights the need to retain access to the original data sets to check the actual values of the measures used. There is an ongoing need to coordinate, collect, manage and analyse environmental data. These are key components of the Audit program in Australia in its current phase. Effective future monitoring of catchment condition can be achieved by recording relatively few key attributes. A useful minimum
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indicator set, within a sound sampling strategy, would be: - percentage area of disturbed vegetation cover (including land-use types); - river/stream water quality (turbidity, salinity, nutrients, contaminant loading); - stream biota; - condition of riparian vegetation; - changes in soil properties (structure, fertility, salt and acidity).
Acknowledgements The original catchment condition team included the authors plus Rob Braaten, Lisa Guppy, Natasha Herron and Jim Tait and we acknowledge their significant contributions. Bruce Jones (USEPA-EMAP project) provided valuable insights in developing the general approach to indicator development. David Mackenzie and two anonymous reviewers provided many useful comments and these are appreciated.
References Andreasen, J.K., O’Neill, R.V., Noss, R., Slosser, N.C., 2001. Considerations for the development of a terrestrial index of ecological integrity. Ecol. Indicators 1, 21–35. Campbell, A., 1994. Landcare: Communities Shaping the Land and the Future. Allen and Unwin, St. Leonards, Australia. Dowling, T., Summerell, G.K., Walker, J., 2003. Soil wetness as an indicator of stream salinity: a landscape position approach. J. Environ. Model. Software 18, 587–593. Hutchinson, M.F., Stein, J.A., Stein, J.L., 2000. Upgrade of the 9 Second Australian Digital Elevation Model. ANU Centre for Resource and Environmental Studies, http://cres.anu.edu.au/ dem/. Jones, K.B., Neale, A.C., Nash, M.S., Van Remotel, R.D., Wickham, J.D., Riitters, K.H., O’Neill, R.V., 2001. Predicting nutrient and sediment loadings to streams from landscape metrics: a multiple watershed study from the United States Mid-Atlantic Region. Landscape Ecol. 16, 301–312. Jones, B.K., Ritters, K.H., Wickham, J.D., Tankersley, R.D., O’Neill, R.V., Chaloud, D.J., Smith, E.R., Neale, A.C., 1997. An Ecological Assessment of the United States Mid-Atlantic Region. United States Environmental Protection Agency, Office of Research and Development Washington, DC. EPA/600/R-97/ 130, pp. 1–105. Karr, J.R., Fausch, K.D., Angermeister, P.L., Yant, P.R., Schlosser, I.J., 1986. Assessing Biological Integrity in Running Waters: A
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method and its Rationale. Special Publication 5, Illinois Natural History Survey, Champaign, IL. Liu, G., Xu, M., Rui, L., Walker, J., Hu, W., 2002. Assessment of a small catchment on the Loess Plateau. In: McVicar, T.R., Rui, Li, Walker, J., Fitzpatrick, R.W., Changming, L. (Eds.), Regional Water and Soil Assessment for Managing Sustainable Agriculture in China and Australia, ACIAR Monograph No. 84, pp. 323–332. National Land and Water Resources Audit (NLWRA), 2002. Australian catchment, river and estuary assessment. National Land and Water Resources Audit, Canberra, www.environment. gov.au/atlas. Riitters, O’Neill, R.V., Hunsaker, C.T., Wickham, J.D., Yankee, D.H., Timmins, S.P., Jones, K.B., Jackson, B.L., 2005. A factor analysis of landscape pattern and structure metrics. Landscape Ecol. 10, 23–29. Veitch, S.M., Bowyer, J.K., 1996. ASSESS: a GIS-based system for selecting suitable sites. In: Morain, S., Lopez Baros, S. (Eds.), Raster Imagery in Geographic Information Systems. Onword Press, Santa Fe, NM, pp. 182–191. Walker, J., Reuter, D.J. (Eds.), 1996. Indicators of Catchment Health: A Technical Perspective. CSIRO Publishing, Collingwood, 174 pp.
Walker, J., 1999. Environmental indicators of catchment and farm health. In: Williams, J., Hook, RA., Gascoigne, H. (Eds.), Farming Action Catchment Reaction. CSIRO Publishing, Collingwood, pp. 99–117. Walker, J., 2002. Environmental indicators and sustainable agriculture. In: McVicar, T.R., Rui, L., Walker, J., Fitzpatrick, R.W., Changming, L. (Eds.), Regional Water and Soil Assessment for Managing Sustainable Agriculture in China and Australia, ACIAR Monograph No. 84, pp. 323–332. Walker, J., Veitch, S., Dowling, T., Braaten, R., Guppy, L., Herron, N., 2002a. Assessment of Catchment Condition: The Intensive Land Use Zone in Australia. CSIRO Land and Water, Canberra, Australia, 37 pp. Walker, J., Dowling, T.I., Jones, B.K., Richardson, D.P., Riitters, K.H., Wickham, J.D., 2002b. Testing readily available catchment-scale indicators as measures of catchment salinity status. In: McVicar, T.R., Rui, L., Walker, J., Fitzpatrick, R.W., Changming, L. (Eds.), Regional Water and Soil Assessment for Managing Sustainable Agriculture in China and Australia, ACIAR Monograph No. 84, pp. 323–332. Zhang, L., Walker, G. (Eds.), 1998. Studies in Catchment Hydrology: The Basics of Recharge and Discharge. CSIRO Publishing, Collingwood, Australia.
An assessment of catchment condition in Australia Joe Walker a,*, Trevor Dowling a, Simon Veitch b b
a CSIRO Land and Water, P.O. Box 1666, Canberra, ACT 2611, Australia Department of Agriculture, Fisheries and Forestry, P.O. Box 858, Canberra, ACT 2601, Australia
Abstract The condition of Australia’s water catchments was assessed in a 2 million km2 intensive land-use zone (ILZ) as part of a National Land and Water Resources Audit (the Audit). The assessment used existing biophysical data obtained from satellite imagery, digital elevation models, computed or derived values, surrogates and existing spatial data from state and national databases. The data were used to derive indicators and an index of relative catchment condition for 3718 sub-catchments and 197 basins. The spatial patterns for catchment condition were then mapped across the ILZ. The term indicator is used consistently to mean a single attribute (even if it is derived from several variables), whereas a sub-index or an index is an aggregation of scaled indicator values. An inventory was made of all potentially relevant biophysical measures of the water, land and biota in catchments that existed in archives. Criteria to assess data quality including a national coverage, ease of interpretation, scale, relevance to policy development and catchment management were applied to some 110 measures from which 21 indicators were ultimately selected and used for tabulation, mapping and interpretation. The data were assembled at 250 m cell resolution and aggregated to 5 km cells as the base unit for analysis. To map the indicators or indices, a five-point classification (quintiles) was used, ranging from relatively poorer to better condition. The class boundaries were based on equal intervals or equal areas under the frequency curve of each indicator or index. Indicator values for each 5 km cell ranged from 1 to 5 (1 being poorer, 5 being better). Index values were calculated by adding the scores for a set of indicators, for example, all the 21 identified and taking a mean for all the 5 km cells within each catchment. Data manipulations and analysis used a multi-component decision support system (DSS), geographic information system (GIS) called catchment condition (CatCon). Numerous maps were generated for each indicator and for indices comprising different combinations of indicators, ranging from the complete set (21), a core set (14) to relatively few (6) and for different regionalisations. Cross-comparisons between catchment condition classes and some consumptive outputs from catchments were also carried out. Only maps for the index based on 14 indicators and a single cross-comparison are presented here as illustrations. The web site www.affa.gov.au/catcon/ has more examples. The main points to emerge were: clear interpretable patterns of better to poorer condition were defined for the intensive landuse zone; visualisations of catchment condition were markedly different for classes defined by equal area or equal interval; the broad patterns of relative condition could be captured with relatively few aggregated indicators; the cross-comparisons suggested areas where land-use changes are needed to develop sustainable land-use systems. The study illustrates that relatively low quality but extensive data can be used to provide a rational basis to identify priority areas for management action and for natural resource management policy development. We suggest that the development of a higher quality national data set in * Corresponding author. E-mail address: [email protected] (J. Walker). 1470-160X/$ – see front matter. Crown Copyright # 2005 Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.ecolind.2005.08.020
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Australia to assess the condition of the Nation’s catchments may be best achieved through the co-ordination and collection of core data sets (indicators) at regional scales. Crown Copyright # 2005 Published by Elsevier Ltd. All rights reserved. Keywords: Indicators; Index; Water catchment assessments; GIS
1. Introduction The catchment condition assessment described here was part of an Audit of Australia’s natural resources—the National Land and Water Resources Audit (NLWRA, 2002). The broad aim of the catchment study was to establish the current relative biophysical condition of Australia’s surface water catchments (sizes range from <100 to >5000 km2) within the intensive land-use zone, an area of approximately 2 million km2 (Fig. 1). Once condition has been established a monitoring program was envisaged to detect trends in catchment condition
over the next 10–20 years. The Audit was also tasked to provide an updated Australia-wide environmental database, the primary use being for decision-making and policy development relevant to environmental issues at national, state and regional levels. The focus on water catchments as reporting units in Australia recognises the fact that most land and stream degradation issues are associated with the water cycle, the low-nutrient and often structurally poor soils and the characteristic cycles of flood and drought. Thus, a water catchment is considered to be a useful management unit relevant to the key issues needed to develop integrated natural resource management
Fig. 1. The 2 million km2 intensive land-use zone (grey) defined by the National Land and Water Resources Audit (NLWRA, 2002) and used in this assessment.
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programs. In addition, a nested hierarchy of catchments using digital elevation models already exists in this case those derived by CRES (Hutchinson et al., 2000). In Australia, catchments are the main natural resource management-planning unit and were used by the nationwide Decade of Landcare Program 1988– 1998 (Campbell, 1994). The Landcare program fostered the establishment and maintenance of a network of some 3500 community-based catchment groups. Two major current programs have been created, the National Action Plan for Salinity and Water Quality, and the Natural Heritage Trust, which complements a successor National Landcare Program and continues the catchment focus on natural resource management. Catchment condition across the intensive agricultural zone is thus of interest to local Landcare groups as well as policy-makers in state and Australian Government departments. Catchment condition can be considered to reflect the interaction of a complex and multi-layered set of hydrological, geomorphic, ecological and bio-geochemical processes and the many human enterprises dependent on their use for the supply of goods and services. Here, we consider only the biophysical aspects, but recognise that any assessment of catchment condition will contain value judgments that depend on the biophysical attributes we can measure and their interactions with often conflicting societal values, land uses and economic factors. The spatial density, national availability and quality of data about catchments have limited our ability in Australia to define current catchment condition, and subsequent trends in a quantitative way. For many planning and policy purposes at the national scale, a relative assessment (poorer to better) to establish the major patterns was considered useful by the relevant steering committees responsible for the National Land and Water Audit. This biophysical assessment of Australia’s water catchments uses indicators of key catchment processes as a first step to establish the condition of our catchments. We reported on the condition of 3718 catchments and 197 basins in the ILZ to the Audit. The indicator approach closely follows Walker and Reuter (1996), Walker (1999, 2002) and Jones et al. (1997), and the hydrological aspects of catchment function are described by Zhang and Walker (1998). The assessment described here was a synthesis of available
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biophysical data obtained from satellite imagery, digital elevation models, computed or derived values, surrogates, other Audit projects and existing GIS data from state and national archives. The data used can be described as being readily available, spatially dense but of poor-medium quality. This kind of data set is often overlooked and one aspect of this study was to find out if plausible patterns of catchment condition can be identified from this quality of data for use by policy-makers and land-managers.
2. Indicators from catchment measures The term indicator is used consistently to mean a single measure (even if it is derived from several variables), whereas a sub-index or an index is an aggregation of indicator values (in this case class values). A systems model of catchment function was developed, and then simplified into land, water or biota components (Walker et al., 2002a and website www.affa.gov.au/catcon/). From 110 possible measures relevant to catchment condition, 21 measures were selected as indicators of catchment condition (Table 1). Measures were deleted on the basis of criteria that included: national coverage, data quality, unequivocal interpretation and applicable to policy or management issues. The selection criteria were designed to give ‘yes’ or ‘no’ answers, and the initial selection of 21 indicators was based on an assessment of meta-data descriptions by the catchment condition team. Most measures (70%) were eliminated on the basis that a national coverage did not exist, and some 10% on the basis that standard methods were not used. Maps were drawn for several regionalisations for all 21 indicators. For the development of indices the indicator data set was further reduced to 14. This was based partly on advice from the Audit and the relevant Australian Government departments, regarding difficulty interpreting some indicators and some resource data sets were removed because they were derived from the same data source (e.g. soils are often mapped from vegetation as seen on imagery). Cross-correlations between all data sets were performed, and closely correlated measures examined to ensure they were independent variables.
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Table 1 The list of measures that met the indicator selection criteria Water condition
Land condition
Biota condition
Rivers in salt hazard Road density Suspended sediment ratio* Agriculture on steep slopes Rivers through forests Pesticide hazard* Industrial point sources* Rivers in acid hazard Nutrient point sources* Impoundments*
2050 salinity risk * Soil acidification on hazard Soil structural hazard* Hillslope erosion ratio* Agriculture on steep slopes Native vegetation change
Native forest fragmentation* Native vegetation change* Intensive agriculture area Protected areas* Human population density Road density* Feral animal density* Weed density*
The 14 indicators, marked with an asterisk (*), were chosen for the overall catchment condition index.
The 21 indicators shown in Table 1 include individual indicators (e.g. change in the area of native vegetation), simple combinations as surrogates (e.g. agriculture on steep slopes), or more complex combinations of variables using process-based models (e.g. potential erosion). Note that three indicators appear in more than one catchment component giving an apparent total of 24 indicators.
3. Analysis and mapping Data can be obtained from a variety of sources (e.g. satellite imagery, digital elevation models, computed or derived indicators, surrogates, Audit or State/ Territory government data and existing GIS data in the national archive) and the quality and scale of collection is variable. Excellent indicator data do exist at smaller regional and State resolution, but often these could not be used because having a consistent national coverage was a project requirement. The original scales of maps and data sets available for the study varied. Resolution ranged from 250 m 250 m (e.g. digital elevation models, DEM) to approximately 10 km 10 km for soils and 50 km 50 km for weeds. All data were re-sampled to 250 m 250 m cells for analysis, and then aggregated to 5 km 5 km cells (115,000). These cells were also aggregated to sub-catchments of approximately 500 km2 (3718 sub-catchments) as defined by CRES (Hutchinson et al., 2000) or aggregated to the well-established Australian Water Resources Council Basins (AWRC Basins), 197 basins.
Mean values for individual indicators were tabulated for various regionalisations—sub-catchments, basins, local government areas, local catchment area, electorates and so on. Mean values can then be inspected for a particular catchment, local catchment area, etc. to obtain the raw data scores for each indicator. These large tabulations are not reported here, due to the volume of the data. Displayed this way, indicator values for catchments can be interpreted to identify a potential problem that requires further investigation. The indicator values give the base level from which to judge the impacts of particular remedial actions at the catchment or basin scales. To map the indicators or indices it was necessary to define classes on a five-point scale from relatively poorer to better condition. Individual indicators classes based on well-established threshold values are preferred, but few well-established thresholds exist for the indicators available (Walker and Reuter, 1996). The five classes (quintiles) were chosen as a convenient number for mapping, and the class boundaries for each indicator were based on frequency distributions (equal intervals or area under the curve). All individual indicators showed a skewed distribution in the frequency versus quantity plots, and for this reason quintiles based on area under the curve was used. The five classes were: (1) poor, (2) poor to moderate, (3) moderate, (4) moderate to good and (5) best. Placing continuous data into classes can pose a number of problems, in this case because recognised class thresholds are not available. Variation in class values between regions, for example, tropical versus
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temperate, can also be an issue but for this exercise had to be ignored. We defined classes using the shape of the indicator frequency distribution curves. Indicator classes can be defined either as equal intervals across the range of values or by equal areas occurring under the frequency curve. Equal areas were appropriate for indicators since most of the data had skewed distributions. Selecting an equal interval method would have resulted in most data falling into one or two classes. All indices had a distribution that approximated a normal bell shaped curve and equal intervals were used. In practical terms, using equal areas for a bell shaped distribution will exaggerate the end members. In summary integrating the suite of indicators into catchment index values proceeded as follows. Each indicator class score was compiled at 250 m 250 m and an average score calculated for each 5 km 5 km cells for the approximately 2 million km2 intensive land-use zone of Australia. The score per cell will be between 1 and 5. The cells were spatially aggregated into 3718 sub-catchments and 197 basins, and a mean score for each indicator calculated per catchment or basin (also ranging from 1 to 5). Combining a suite of indicators into a single value is overviewed by Andreasen et al. (2001). The method we used was the simplest available and follows Karr et al. (1986) who used it for a biological integrity index and Jones et al. (1997) who used it in the US-EMAP program for eastern USA. It has also been used by Walker et al. (2002b) who tested the scores against independent biophysical data for catchments in the Canberra region and Liu et al. (2002) who applied the method to part of the loess plateau in China. There are other more sophisticated methods available including fuzzy logic approaches (Dowling et al., 2003) and numerical taxonomic methods (Jones et al., 2001; Riitters et al., 2005). The various data manipulations used a multicomponent spatial decision support system called CatCon. CatCon was developed in-house from a GISbased system described by Veitch and Bowyer (1996). CatCon contains all the spatial data for the 21 indicators. It can be used to zoom into specific locations to examine the 5 km 5 km data or subcatchments, catchments or regions. See www.affa.gov. au/catcon/ for more details. CatCon was used to:
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view, manipulate and map indicators or indices; generate indices/sub-indices by adding ranked indicator scores to give a relative comparison of condition between parts of catchments or between catchments; compare biophysical catchment condition assessments with non-biophysical attributes (the crosscomparisons). Users of CatCon can: select their area of interest (National, state/territory, regional or catchment) and zoom in to that area of interest; set the scale for units of analysis and weight values if needed; select indicators from the overall set (by clicking on boxes) to generate water, land, biota and overall catchment indices; compare biophysical catchment condition with other catchment attributes.
4. Results An individual map using the 5 km 5 km data set can be generated with CatCon in less than 20 s. Using various combinations of indicators and regionalisations a planner or decision-maker can quickly generate a quantity of scenarios. Here, we present but a few of the many shown in www.affa.gov.au/catcon/. We show maps (Fig. 2) prepared for the catchment condition index, using 14 indicators at three scales of regionalisation. Each map has the AWRC basins overlaid for context. The 5 km 5 km grid cell map shows considerable variation in the relative rating. In eastern Australia, from the east coast to the mountain range, condition changes from good to poorer, then from mountain ranges to plains poorer to better. An east–west trend is evident in the sub-catchments that comprise the AWRC basins. The trends from poorer to better are more clearly shown at the sub-catchment scale (500 km2). The basin scale map shows the relative condition of each basin, and suggests major issues in the eastern wheat belt. Some caution is needed in interpreting the basin scale aggregation. The classes are average conditions, thus an individual basin can include relatively good sub-catchments as well as poorer
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Fig. 2. Catchment condition depicted by the 14 indicators in Table 1, shown at three spatial scales (5 km 5 km cells, catchments of approximately 500 km2 and AWRC Basins).
areas. Nevertheless, aggregating the measures to different scales can give a range of insights depending on the nature of the query from policy or decisionmakers. At the smaller scales, regions can be examined in detail by zooming into the selected area and adding roads, towns, etc. to the maps. An index can be based on a few or many indicators and a relevant question refers to a possible minimum indicator data set that captures most of the spatial patterns. The indicator set was cut from 21 to 14 and the calculated catchment condition index values compared. The general spatial picture of poor to better catchment condition could be consistently recognised. Further reduction to only six indicators, two from each of the land, water and biota components, gave very similar results to the 14 or 21 indicator-based indexes. The indicators used were: water—industrial point sources and impoundments; land—agriculture on steep slopes and native vegetation change; biota—road density and native forest fragmentation. Space here does not permit the full analysis of these observations, but the tentative conclusion is that at large national scales, relative
catchment condition can be defined with few indicators. In this case, the six indicators were amongst the easiest to measure and had high spatial resolution. It suggests that national scale data sets, which are of poor-medium quality, can provide the context to enable the development of a better sampling strategy and monitoring program at smaller spatial scales. Mapped patterns based on equal interval classes versus an equal area classification gave markedly different patterns—much greater than those using different indicator sets. As one would expect the equal area ranking biased the mapping towards the tails of the approximately normal distribution, greatly increasing the areas depicted as better or poorer. The equal interval classes perhaps best represent the areas considered to be ‘hotspots’ that currently require urgent attention, whereas the equal area ranking may better outline the extent of potential deteriorating condition given current land-uses. Relative catchment condition can be compared with other factors, such as economic production or human well being, using a 3 3 table (Fig. 3). In this
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Fig. 3. Comparison of catchment condition with agricultural production interpreted as sustainability scenarios for current land-use patterns.
case, classes of catchment condition (good, moderate, poor) can be compared with classes of goods and services data, or response attributes (e.g. agricultural productivity, macro-invertebrates in stream). For the comparison of catchment condition versus agricultural production, an interpretation of each combination is suggested in Fig. 3. Maps resulting from this kind of comparison help define areas that may benefit from specific policy and management options for maintaining or improving present condition. A cross-comparison allows us to easily see where high (or low) ratings for one attribute or index coincide with high (or low) ratings for other attributes, and where they are mismatched. In most cases, it is the mismatches that give the most useful insights, e.g. a catchment with a poor condition score but apparently high production suggests the natural resource capital may be overused. No assumptions are made about cause and effect in interpreting the maps and the cross-comparisons are used only to suggest areas
where further and more detailed examination is warranted. A comparison of catchment condition with agricultural production showed that the main wheat, cattle and sheep belts in the ILZ fall into the category poor catchment condition with high production. This suggests widespread and potentially unsustainable land-use systems, and raises the question of where could alternative systems best be placed to reduce the possibility of further system decline. To illustrate the use of a simple cross-comparison, we developed an ‘agricultural flexibility’ index on a 1– 3 scale based on the potential range of land-use options that could be applied at a catchment scale. For example, in some areas sheep grazing is the only viable option, and therefore has a low-flexibility score. The cross-comparison map of catchment condition versus agricultural flexibility is shown in Fig. 4. The interpretation should ignore the darker green areas as these are primarily national parks. Capacity to
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Fig. 4. Cross-comparison between catchment condition and agricultural flexibility. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the article.)
change land-use (light green and yellow areas) is higher in the higher rainfall areas. These coincide with potentially high production areas where farmers are able to finance necessary change. Less flexibility (pink and red areas) is most evident where rainfall is less than 500 mm/year and variable. These areas are also the landscapes where salinisation is a major issue.
5. Discussion Patterns in catchment condition were obtained from the chosen indicator set, and these highlighted a link with current land-uses. The main drivers of catchment condition are biophysical setting, vegetation cover changes, land-use patterns and intensity. It is changes to these drivers that cause major changes to the water cycle, soil chemistry and soil structures in Australia, and along with infrastructure developments and population density, affect the land, water, fauna and flora in catchments. The main conclusion is that the small set of indicators provides most of the broad scale informa-
tion needed by national and regional policy-makers. Thus, sufficient information already existed in Australia to carry out the initial step to identify priority areas and to develop scenarios to explain differences in condition. This broad type of assessment may be less relevant in smaller European countries where detailed spatial data sets exist. However, there are large areas of the world where rapid surveys are needed and sufficient imagery and modelled data can provide the first cut. Cross-comparisons between biophysical condition and consumptive and non-consumptive products are a useful approach to generating insights on sustainability, suitability and targeting funding to areas for rehabilitation and structural adjustment. Such comparisons are best viewed as hypotheses, to be tested by further investigation. They are a useful addition to the initial catchment condition assessment. A common perception is that the poor quality of data available severely limits attempts to carry out a national scale biophysical resource assessment of a large country like Australia. The reality is that sufficient data to cover all aspects will never be
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available, and a sub-set must suffice. Even the application of well-established process-based models to predict, for example, hillslope erosion, do not have the spatial resolution needed to plausibly map issues at a national scale. However, indicators derived from process knowledge obtained at a finer scale, are adequate to provide a first assessment of current biophysical conditions and guide more detailed data gathering. If detailed regional and local-scale assessments of catchment condition are needed they will require major new mapping expenditure across the country. A core question remains ‘‘how much environmental data do we need to make a policy or management decision at a national scale’’. As scientists we tend towards collecting data for more and more attributes, and this often fails to get into a form that is useful in decisionmaking. One can argue that at national scales we need fewer attributes, but better quality estimates for a core data set. In addition, investment may be better directed to focus on boosting the collection of regional or local environmental data, and interpreting the information at these scales. To do this requires the development of a sampling strategy and the collection of quantitative data over time to address relevant regional issues. The present catchment condition evaluation can be used to develop the necessary sampling strategy. Assuming there is a need for an overall index, there is always debate about how best to aggregate indicators or indeed to populate more complex models to accomplish an overall index. The main lessons from this experience are: first, use the simplest aggregations, and see if these provide the information needed for policy and decision makers; second, if more detail is needed, then focus on data readily obtained from imagery, or invest in quick field measures at the appropriate scale; third, the client needs to be aware that map visualisation depends heavily on how the data were classified and coloured. This highlights the need to retain access to the original data sets to check the actual values of the measures used. There is an ongoing need to coordinate, collect, manage and analyse environmental data. These are key components of the Audit program in Australia in its current phase. Effective future monitoring of catchment condition can be achieved by recording relatively few key attributes. A useful minimum
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indicator set, within a sound sampling strategy, would be: - percentage area of disturbed vegetation cover (including land-use types); - river/stream water quality (turbidity, salinity, nutrients, contaminant loading); - stream biota; - condition of riparian vegetation; - changes in soil properties (structure, fertility, salt and acidity).
Acknowledgements The original catchment condition team included the authors plus Rob Braaten, Lisa Guppy, Natasha Herron and Jim Tait and we acknowledge their significant contributions. Bruce Jones (USEPA-EMAP project) provided valuable insights in developing the general approach to indicator development. David Mackenzie and two anonymous reviewers provided many useful comments and these are appreciated.
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