Development of Decision Support Tools to Assess the Sustainability of Coastal Lakes

1Ticehurst, J.L., 2Rissik, D. 1Letcher, R.A., 1Newham, L.H.T., and 1Jakeman, A.J.

1Integrated Catchment Assessment and Management Centre, 2Department of Infrastructure, Planning and Natural Resources, E-Mail: [email protected]

Keywords: Coastal lakes, Sustainability assessment, Bayesian decision network, Decision support.

EXTENDED ABSTRACT The integration was completed using a Bayesian Coastal lakes in provide decision network (BDN). This approach was important ecological, social and economic benefit advantageous over other methods because it is for much of the state’s population. However they suited to the rapid accumulation and integration of are naturally sensitive to catchment inputs, existing information sourced from observed data, particularly when the lakes are periodically closed model simulation and expert opinion, at various to the ocean. Demand on the lakes and their scales, from many disciplines. The BDN framework catchment’s finite resources through: encroaching structure also inherently represents uncertainty in urban development; poor agricultural the input data but can be readily up-dated when management; the need to protect native flora and new information becomes available. fauna; a growing market for seafood; and increasing tourism; is increasing conflict over Consultation with local stakeholders was an their use and sustainable management. These imperative component throughout the whole issues are intricately linked, so the management process, but it played a particularly important role of coastal lake systems requires knowledge of the in the development of the BDN framework and in processes and interactions between all key defining the scenarios. The close interaction with components of the system. This is a complex future users of the tool meant that the product was problem requiring the integration of, often more likely to be adopted. minimal, information, from various disciplines. Few tools exist to assist catchment and lake Table 1. Method undertaken for a detailed managers to analyse the whole lake system and sustainable assessment of eight coastal lakes in make more informed management decisions. New South Wales, .

The New South Wales government recognized the Stage Phase threat to the coastal regions and issued a 1 build understanding of constraints, issues and ‘Statement of Intent’ in 2003, to conduct a targets for lake and catchment health Comprehensive Coastal Assessment for the state. 2 develop an initial conceptual framework for Stage one included a sustainability assessment of BDN and potential future scenarios eight coastal lakes in the state (Cudgen, Myall, Coila, Narrawallee Inlet, Burrill, Wollumboola, 3 review BDN framework with stakeholders and Back lakes). The research 4 revise initial framework presented in this paper was conducted to contribute to the Coastal Lake Assessments. 5 populate BDN links with data 6 incorporate the BDN model into a user friendly This paper describes the development of a software platform decision support tool to inform of the potential 7 review the interface and populated BDN with impacts of management decisions on all key stakeholders components of a coastal lake system. The method used to develop the decision support tool, in short, 8 revise interface and populated BDN to reflect stakeholder feedback involved collecting information about potential management decisions, and the key values of the 9 distribute the sustainability assessment tool to lake, and integrating this into a single, simple relevant stakeholders with appropriate training tool. A more detailed depiction of the process is in its use. presented in Table 1.

2414 Myall, Wollumboola, Narrawallee, Burrill, Coila, 1. INTRODUCTION Merimbula and Back). The CLAM tool for The coastal regions of NSW are appreciated for the Merimbula lake is presented and discussed as a social, economic and ecological value they bring to case study. their resident and visiting populations. In recent times there has been an active migration of the 2. BAYESIAN DECISION NETWORK human population to coastal areas, as well as an APPROACH increase in coastal tourism. This creates a greater Many approaches exist for integrative modelling. need for urban development and tourist facilities. A review by Jakeman et al. (2005) presents various At the same time there has been an increased approaches such as expert systems, coupled pressure for the conservation of natural ecosystems component models, metamodels, agent-based in the coastal zone. In addition, agricultural models and risk assessment approaches. The production is commonly intensifying in an attempt methods vary in properties such as their ability to to remain profitable in the current market. Coastal account for quantitative verses qualitative data, lakes are naturally sensitive to catchment inputs, and their inherent ability to be used in decision- but the different use of land, flora, fauna and water making or to reflect uncertainty. A Bayesian within coastal catchments creates pressure and network approach was chosen for this analysis for conflict over the management of these limited reasons discussed below. resources. Bayesian networks conceptualise a system through In the past coastal management has focused on one a series of variables joined by causal links (Pearl or two facets of the system, such as fish species or 1988). Bayesian Decision Networks (BDN) are habitat management (e.g. Alder et al. 2000). structured with decision variables (scenario However, in order to manage the complex systems choices), interim variables (state indicators) and of coastal lakes, knowledge of all key processes utility variables (outputs or goals). within the whole catchment is required. In fact, Underwood (2002:487) commented that “Never Catchment planning is unavoidably complex has there been more need for urgent, integrated (Ewing et al. 2000) but the BDN approach offers a and coherent decision-making about coastal comparatively simple method of integration environmental issues”. Progress is being made because each process does not have to be explicitly towards a more holistic approach to catchment represented (Borsuk et al. 2004). management (e.g. Bennett and Lawrence 2002, Courtney and White 2000, Alder et al. 2000), but Causal links within the framework represent the even these can be dominated by one catchment relationship between variables using probability process. For example Ewing et al. (2000:449) distributions. Thus the uncertainty in the describes a hydrological and water quality model relationship between each variable can be with “provision for some basic socioeconomic explicitly represented (Varis and Kuikka, 1997), analysis”. allowing the user to make a judgement on the reliability of the model predictions. It is a ‘huge ask’ for catchment managers to have adequate knowledge across all facets of the BDNs can efficiently incorporate social, economic catchment system to make integrative and and ecological values within the modelling informed decisions. Decision support tools are a framework because the approach lends itself to the possible means to integrate such complex systems easy incorporation of both qualitative and and array of knowledge, and present it in a quantitative data (Varis and Kuikka 1997). So simplistic way for them (Ewing et al. 2000). when observation data or model simulation is not However, the acceptance and therefore the success available, expert and local knowledge can be of such tools depends strongly upon community utilised. As new information becomes available, consultation (Courtney and White 2000, Alder et the BDN can be readily updated (Walters 1986, al. 2000). Borsuk et al. 2004). Thus the BDN approach provided an efficient This paper presents the method for the method for the integration of social, economic and development of a decision support tool to assist in ecological values within a complex system, Coastal Lake Assessment and Management utilizing available quantitative and qualitative data (CLAM). The approach could be considered and explicitly representing the uncertainty in the another example of Adaptive Environmental model predictions. Assessment and Management (AEAM), as defined by Ewing et al. (2000). CLAM integrates social, economic and ecological values for the catchment considered. Eight separate tools were developed for eight coastal lake catchments in NSW (Cudgen,

2415 3. MODEL AND SOFTWARE assess the potential impacts of the DESCRIPTION management scenarios tested. The model development process is outlined in Table 2. Summary of features available in the Table 1. An initial effort was made to develop a CLAM software. relevant BDN framework but community consultation played an important role throughout the model development process, by providing Software Features available feedback on the representation of the catchment Page system and the scenario options. Welcome Project background, contacts and licensing agreements The probability distributions for each BDN variable (Stage 5, Table 1) were derived through Info Photograph gallery of the catchment, brief the analysis of observed data, model simulation, a list of facts about the catchment series of general assumptions or expert opinion. Maps Series of catchment properties which can be Community input was obtained from reports and overlaid, such as landuse protected areas, from direct consultation, and provided expert local erosion potential etc. knowledge. Approach Brief description of BDN approach and the Each variable within the BDN framework was BDN framework for the catchment calibrated if it was necessary and possible. For Inputs Description of how the probability variables with a probability distribution distributions were attained for each determined from on-site observed data, calibration variable, including the assumptions and was not necessary. For those variables that utilised weaknesses for each. model simulation, models were calibrated to local Scenario Each scenario choice option, plus a map values where the data was available. However, locating various scenarios and a text many of the coastal catchments did not have the description of the assumptions used for each appropriate information readily available to do scenario. this. Local experts reviewed variables that were Utility Change in the dollar value for the economic populated using qualitative data, to ensure that the variables within the model responses were appropriate for the local conditions. Report A summary of the inputs, scenario choices and the output probability distributions, The revised BDN model framework and the which can be exported and saved. probability distributions were coded into the Integrated Component Modelling System (ICMS) (Stage 6, Table 1). The CLAM interface is a Model verification of the BDN is yet to be simple computer software package that operates completed for any of the eight CLAM tools. Given through ICMS. The software consists of eight the integrative nature of the model, the most pages, summarized in Table 2. The supporting text appropriate method for model verification is for within the software aims to make the tool various members of the community to use the tool independent and easy to use. Notable features of and gauge the performance of the model the software package include: predictions. This should be completed as part of Stages 7 and 9 (Table 1) upon release of each of • Photographs, map layers of catchment the eight tools in October this year. properties and associated text, which provide the user with information to familiarise themselves with the 4. MERIMBULA CASE STUDY catchment, Merimbula Lake is approximately 4.5 sq. km, within a 43.4 sq. km catchment. Half of the • Descriptions of the methods used to catchment is still forested, and only 5% is under generate the probability distributions for urban development, but as with most coastal areas each variable, enabling the user to gauge in NSW, development is concentrated around the their level of certainty in the model lake and waterways. The key industries for the predictions. This make the tool a ‘white- catchment are tourism and oyster production, box’, rather than a ‘black-box’ model while forestry and cattle grazing also occur. (Ewing et al. 2000), as the latter is said to Within the Lake and its catchment there are have hidden assumptions, and wetlands protected under the NSW Governments • Display of output probability distributions SEPP 14 protection, as well as koala habitat for each variable and the function to save protection (SEPP 44) and coastal zone protection and export them, so the user can visually (SEPP71). In addition it contains an area of South

2416 East Forest National Park and has many important Department of Primary Industries (DPI) (NSW Aboriginal sites around the lakes edge. Fisheries and NSW Agriculture) and local oyster producers a BDN framework was developed. Conflicts in management for the Merimbula Lake Figure 1 shows the framework for Merimbula and its catchment include: Lake catchment, illustrating the integrative process • Encroachment of urban development and of many ecological (water dynamics and quality, tourist facilities upon the lake, and flora, fauna and ecology), social and economic values identified during consultation with the • Maintenance of seagrass beds and fish community. populations, • Erosion and sedimentation into the lake 4.2. Populating the BDN • Lake water quality with regard to oyster Data analysis, model simulation, general production, and assumptions and expert opinion were used to populate the variables with probability • Management of the airport, which is distributions. The method used for each variable is adjacent to the lake. summarised in Table 3. Where possible, the input data and assumptions were reviewed by a local 4.1. Merimbula BDN framework expert to check for consistency and accurate interpretation. Following consulation with the council, Department of Infrastructure, Planning and Natural Resources (DIPNR), Department of Environment and Conservation (National Parks and Wildlife Service) (DEC(NPWS)), the

Oyster industry Oyster yield Oyster sales Local revenue

Fish catch limit Fish catch

Toxic algal blooms ANZECC Sealevel rise Swimming Lake height Caravan park management Aquatic Water quality: fauna Sediment Cultural Values Acceptability Fishing Fish stock

Infrastructure Rural residential aquatic Mangroves pests Salt marsh Millingandi creek Council cost Seagrass beds

Airport Population Aquatic management Boat use weeds Wetland fauna Wetland management Wetlands Wetlands habitat Migratory Urban birds Bushland development Flora

Tourism Fire Terrestrial habitat Forest fauna

Figure 1. Bayesian Decision Network for the Merimbula Lake CLAM tool. SHAPES: Ellipse = decision variable, rectangle =state variable, diamond = utility variable. SHADING: Solid = scenario, spot = water dynamics and quality, vertical dash = social value, chequered= economic value, hashed shading = flora, fauna and ecology. LINES: lines for links are all equal, the dashed and solid lines are only to assist in interpretation of the plot. ANZECC = Australian and New Zealand Environmental Conservation Council guidelines, Lake water quality includes nitrogen, phosphorus, suspended sediment, pathogens, salinity and hydrocarbons.

2417 The results presented here seek to illustrate how Table 3. Variables and method for determining the the CLAM tool can be used when exploring the probability distribution (PD) for the Merimbula question of what changes can be made to improve Lake CLAM tool. or degrade the Merimbula Lake water quality, and what are the consequences for migratory birds, Variable PD method seagrass cover and local revenue. The results from Acceptability Data analysis 4 model runs are shown in Figure 2 with the ANZECC Data analysis details of the scenario options given in Table 4. The scenario options were chosen to represent the Aquatic fauna Expert opinion current conditions (1), high development with Aquatic pests Expert opinion current regulations (2), active management for water quality (3), and active management for water Boat use Expert opinion quality with urban development with increased Bushland Data analysis regulations (4). Table 5 shows the change in dollar value for local revenue for each scenario. Council cost Data analysis Table 4. Scenario options chosen for the model Cultural values Expert opinion simulations 1,2,3 & 4. Current conditions were Fire Expert opinion used where an alternate option is not shown. Fish catch Expert opinion Decision Scenario option 1 2 3 4 Fishing Expert opinion variable Fish stock Expert opinion Rural 50m buffer X Flora Expert opinion residential Forest fauna Expert opinion 150m buffer X Infrastructure Assumptions Urban Low density, X development 150m buffer Lake height Model simulation Medium density, X Local revenue Data analysis 50m buffer Mangroves Expert opinion Caravan park Increased X Migratory birds Expert opinion management capacity on septic tanks Oyster sales Data analysis & Assumptions Increased X X capacity, sewered Oyster yield Data analysis Tourism 20% increase in X Population Data analysis visitors Salt marsh Expert opinion Airport Install artificial X X Seagrass Expert opinion management wetlands Sediment Assumptions Millingandi 50m riparian X X Creek buffer Swimming Expert opinion Wetland Remove X X Terrestrial habitat Expert opinion management domestic stock Toxic algal blooms Expert opinion

Water quality Model simulation Wetlands Data analysis Table 5. Change in local revenue for each scenario given in Table 4. Wetland habitat Expert opinion Wetland fauna Expert opinion Scenario Change in local revenue ($) 1 3800 2 25364 4.3. Results and discussion 3 25250 The CLAM tool can be used in many ways to answer various questions. One of the most 4 32710 straightforward being ‘what if?’ type questions.

2418 Nitrogen (t/ha/yr) Pathogens (cfu/100ml) Local revenue Migratory birds (%)

1

2

3 Scenario number 4 <14 <7.5 >12.5 14-150 <1000 7.5-10 10-12.5 150-1000 Decrease Increase No change No change >10 decrease <10 decrease <10 increase >10 increase Figure 2. Probability distributions for 4 scenario runs, detailed in Table 4, using the Merimbula Lake CLAM tool.

Figure 2 shows that there is not any significant management, and the likely impact upon the lakes’ change in the expected distributions for any of the water quality is minimal and may even improve. selected variables between scenarios 1 and 2. That Given that the land proposed for urban is the current conditions, given by scenario 1, development is already cleared the planting of would not significantly change if development vegetation required for the regulation buffers occurred within the catchment and tourism would actually increase the vegetation compared to increased (Scenario 2). This may be an indication the current conditions. that the impacts of additional development are not The scenarios explored here showed little change substantial in their own right, or it may be that the in the local revenue, migratory birds, or seagrass current pressures on the lake are so high that the cover (not shown due to space), which is most additional pressure from the changes of Scenario 2 likely due to the scenario options selected for this are comparatively small. analysis. Table 5 shows variation in the change in The water quality (nitrogen (TN) and pathogens) the local revenue, which are possibly too small to shows a marked improvement between scenarios 1 see in Figure 2. Scenarios 2 and 3 have a similar and 2, and 3 and 4. Note that phosphorus and change in local revenue (approximately $25,000), suspended sediment show a similar change as but Scenario 3 shows an active improvement in the nitrogen, although the results are not shown here. lake water quality. Thus indicating that This indicates that there is high potential for management decisions can be made that result in improving the water quality of Merimbula Lake both economic and environmental benefit to the through active management methods such as community. riparian plantings, used in Scenario 3. The large It is important to note that the results presented change in the lake pathogens between scenarios 1 here are for a select few variables within the and 2, and 3 and 4 is most likely due to changing Merimbula Lake CLAM tool, and they do not the sewerage management system of the caravan necessarily reflect the likely changes in other park from septics (the current system) to sewered model variables. Also, the reader should be on the Merimbula town system. This change in reminded that the data used to determine the management is currently in progress in the probability densities vary in method and reliability. Merimbula catchment, which should see a Of the variables discussed above, the water quality considerable decrease in the lake’s pathogen (nitrogen and pathogens) data was determined concentration. through model simulation using inputs values for The similarity in the water quality results from Merimbula Lake catchment, where available. Scenarios 3 and 4 also indicates that urban However, the models used were simple, run at a expansion can occur within the catchment with course scale, and were not spatially explicit to appropriate regulations and additional catchment landuse changes (i.e. landuse was accumulated for

2419 the catchment as a whole and was not sensitive to comments that such tools are only to assist in the proximity of the landuse change to the lake and decision making as they do “not substitute for the streams). Local revenue was also calculated at a complex processes of judgement and the many course scale because little information was political realities of planning”. This is true, but at available for the Merimbula Lake Catchment. Thus the same time tools such as CLAM, can show the assumptions were made on the proportion of the impact of management decisions on social, Bega Valley Shire revenue raised within the economic and ecological values important to a Merimbula Lake catchment. The direction of community, and thus stand to highlight decisions change in revenue is believed to be correct, but the made primarily for personal gain by those in absolute values need to be reviewed. More certain charge, as has been the case in the past. values can be added following the completion of a complementary project on the economy generated 6. ACKNOWLEDGEMENTS from Merimbula Lake by the Department of I would like to thank the many Shire council, Environment and Conservation. Information on DIPNR, DEC (NPWS), DPI (NSW agriculture and migratory birds and change in seagrass cover are NSW Fisheries) and other farmers and groups who currently only based upon general assumptions have given their time and input into the derived from the literature. The certainty of the development of BDN frameworks, and the results will be increased following a review by generation and review of model data. experts, which is currently underway. Thus the CLAM model for Merimbula is yet to be 7. REFERENCES verified by members of the community and other Alder, J., R. Hilliard and G. Pobar (2000), experts, so the results presented here are only Integrated marine planning for Cocos (keeling), an preliminary. isolated Australian Atoll (Indian Ocean), Coastal Management, 28:109-117. 5. SUMMARY AND CONCLUSIONS Bennett, J. and P. Lawrence (2002), Adaptive management framework for catchment and coastal The sustainable management of the coastal lakes in management decision making, Coastal CRC NSW is under pressure from increasing urban conference, 2002, 24-27 sourced development and tourism, intensification of http://www.coastal.crc.org.au/coast2coast/proceedi agriculture, and the growing importance for the ngs/Theme2/Adaptive-management- conservation of flora and fauna. An integrative framework.pdf approach is necessary to be able to manage for all these often conflicting interests. Borsuk, M.E., Stow, C.A. and Reckhow, K.H. (2004) A Bayesian network of eutrophication This paper presented a tool, called the CLAM models for synthesis prediction, and uncertainty (Coastal Lakes Assessment and Management) tool, analysis. Ecological Modelling, 173:219-239. which uses a Bayesian Decision Network to identify the likely impacts of management decision Courtney, C.A. and A.T. White (2000), Integrated on social, economic and ecological variables coastal management in the Philipines: Testing within a catchment. Community consultation was New Paradigms, Coastal Management, 28:39-53. an imperative component of the model Ewing, S.A., R.B. Grayson and R.M. Argent development, and will also be pursued for model (2000), Science, citizens, and catchments: verification in the future. Decision support for catchment planning in A CLAM tool for the Merimbula Lake was Australia, Society and Natural Resources, 13:443- presented as a case study. The brief analysis of the 459. model results given showed that active Underwood. T (2002), Our coastal environments management of the Merimbula Lake catchment is are on the rocks: Research needs for sustainable likely to significantly improve the lake water coastal management, Coastal CRC conference, quality. It also showed that urban development can 2002, 487-490 sourced proceed within the catchment without negatively http://www.coastal.crc.org.au/coast2coast2002/pro impacting upon the lake water quality, if ceedings/Plenary/Our-coastal-environments-are- appropriate regulations are imposed and catchment on-the-rocks.pdf management occurs. Various management options were shown to increase the local revenue, but not Varis, O. (1995) Belief networks for modelling all of them did so while improving the lake’s water and assessment of environmental change. quality. Environmetrics, 6, pp. 439-444. The CLAM tool is believed to be a useful tool and Walters, C.J. (1986) Adaptive management of a dynamic approach, to assist catchment managers renewable resources. MacMillan, New York. in making decisions. Ewing et al. (2000:456)

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