Coastal Lake Assessment And Management Clam Tool

Coastal lakes and lagoons often become closed off from the ocean for long periods. As such, they are highly susceptible to catchment inputs and, in New South Wales, Australia, are under increasing pressure from expanding populations. Coastal lake catchments provide a variety of economic, ecological and social values. However, given that the resources are finite, there is increasing conflict over their use and sustainable management. The issues are intricately linked and understanding the impact of making trade-offs and management decisions about coastal lakes and their catchments requires

Table 9.2. Results of CatchMODS and M2PM model stimulations, for a limited investment scenario (approximately $20 000) showing three hypothetical catchment management scenarios.

Response variable

Base case

Scenario 1

Scenario 2

Scenario 3

Remediation description

Current conditions of land use and riparian and gully management

Conversion of existing grazing areas to forestry use in an upper sub-catchment (Captain King's Creek)

Severe, moderate and minor gully revegetation in a mid sub-catchment (Campbells River 4)

Stream-bank revegetation in lower sub-catchment (Campbells River 2)

Remediation cost

Dollar value

-

$0 (fixed) $20 000 yr1 (ongoing)

$20 000 (fixed) $1 670 yr1 (ongoing)

$20 000 (fixed) $2000 yr1 (ongoing)

Catchment response (pollutant delivery to Ben Chifley reservoir)

Sediment load

55 1 69 x 1 03 kg.yr1

55 167 x 1 03 kg.yr1 0% reduction

54 628 x 103 kg.yr1 1.0% reduction

53 999 x 1 03 kg.yr1 2.1 % reduction

TN load

77 x 1 03 kg.yr1

0% reduction

1.2% reduction

1.2% reduction

TP load

1236 x 103 kg.yr1

0% reduction

1.0% reduction

2.1 % reduction

Reservoir phytoplankton response (annual mean concentration)

Cyanobacteria (represented by Microcystis)

no modelled change

1 % reduction

1 % reduction

Diatoms (represented by Aulacoseira)

-

no modelled change

1 % reduction

2% reduction

knowledge of the processes and interactions between all key components of the system. This can be highly complex and requires the integration of information - often minimal - from various disciplines.

The Coastal Lake Assessment and Management (CLAM) approach to developing decision support tools has been formulated to assist decision makers in managing their coastal lake catchments (Newham et al. 2004b; Ticehurst et al. 2005a, 2005b). CLAM uses a Bayesian decision network (BDN) approach to integrate social, economic and ecological values for the catchment and coastal lake being considered. The approach has been developed to make it, and its outcomes, accessible to managers in a way that any uncertainty associated with data or predictions can be ascertained and understood.

Bayesian networks conceptualise a system through a series of variables joined by causal links (Figure 9.5). Bayesian Decision Networks (BDNs) are Bayesian networks that allow the impacts of individual or cumulative management decisions or scenarios to be explored. Links within the framework represent the relationships between variables. The effects of management scenarios on variables are shown using probability distributions. Probability distributions reflect the likelihood that a particular decision will create a particular response of each variable. Probability distributions have the added benefit of explicitly representing the uncertainty in the relationship between each variable or in the response of each variable to decisions. This allows users to make judgements on the certainty of the model predictions and to assess the risk of making decisions.

The approach also enables the quality of the underlying data to be described and made clear to users. The two ways in which uncertainty is described make it clear to users where information needs to be upgraded or improved, or where acting on the predictions of the models may carry greater risk.

BDNs can efficiently incorporate social, economic and ecological values within the modelling framework because the approach lends itself to the easy incorporation of both qualitative and quantitative data. When observation data or model simulation are not available, expert opinion and local knowledge can be used. The BDN can be readily updated as new information becomes available.

The usefulness of BDNs is increased if they reflect the important processes that operate within each system, but also if the scenarios being assessed reflect the community's and stakeholders' aspirations. By following a specified process, which includes wide consultation with experts and community, it is possible to develop useful models. The process used to

Figure 9.5 Bayesian decision network for the Merimbula Lake CLAM decision support tool. Grey ellipses are decision variables, the dashed and solid lines are equal and are only to assist in the interpretation, 'Lake ANZECC' refers to the water quality guidelines developed by the Australian and New Zealand Environmental Conservation Council, 'Lake WQ' is lake water quality which includes total suspended sediment (TSS), total nitrogen (TN) and total phosphorus (TP).

Figure 9.5 Bayesian decision network for the Merimbula Lake CLAM decision support tool. Grey ellipses are decision variables, the dashed and solid lines are equal and are only to assist in the interpretation, 'Lake ANZECC' refers to the water quality guidelines developed by the Australian and New Zealand Environmental Conservation Council, 'Lake WQ' is lake water quality which includes total suspended sediment (TSS), total nitrogen (TN) and total phosphorus (TP).

develop a Coastal Lake Assessment and Management (CLAM) tool for a lake or estuarine system is given in Table 9.3. An initial effort was made to develop a relevant BDN framework following a literature review of appropriate reports and research. Community consultation played an important role throughout the model development process, by providing feedback on the representation of the catchment system and the potential management scenarios to include.

The model input data can be sourced from observed data, model simulation, literature review, general assumptions and expert elicitation. The data are used to create probability distributions for each BDN variable (Stage 5, Table 9.3). The information in each variable of the framework should be calibrated where necessary and possible. Calibration is not necessary for variables with probability distributions determined from on-site observed data. For those variables based on model simulation, models should be calibrated

Table 9.3. Process that should be followed to develop CLAMs.

Stage

Phase

1.

build understanding of constraints, issues and targets for lake and catchment health

2.

develop an initial conceptual framework for BDN and potential future scenarios

3.

review BDN framework with stakeholders

4.

revise initial framework

5.

populate BDN links with data

6.

incorporate the BDN model into a user-friendly software platform

7.

review the interface and populated BDN with stakeholders

8.

revise interface and populated BDN to reflect stakeholder feedback

9.

distribute the sustainability assessment tool to relevant stakeholders with appropriate training in its use

to local values where data are available. Often data about the complex interactions in small coastal catchments are not readily available. In these cases, local experts are used to review variables populated using qualitative data, to ensure that the responses are appropriate for the local conditions.

The CLAM interface is a simple computer software package that has been built in the Integrated Component Modelling System (ICMS). The revised BDN model framework and the probability distributions are coded into ICMS (Stage 6, Table 9.3). The software consists of eight pages, summarised in Table 9.4. Notable features of the software package include:

• photographs, map layers of catchment and lake properties and associated text, which provide the users with information to familiarise themselves with the catchment. These also make the package unique to a particular catchment and help to increase the ownership of the models to local stakeholders and community. This is very important when determining management actions.

• descriptions of the methods and assumptions used to generate the probability distributions for each variable, enabling users to make judgements on the sources of uncertainty in the model input and predictions. This includes a dynamic copy of the BDN framework showing the conceptual structure used within the CLAM DST where users can click on a variable to pop-up a document detailing assumptions made in generating associated conditional probabilities (for example, Figure 9.5). In cases where there are conflicting views related to the impact on a particular variable or the BDN structure,

Table 9.4. Summary of features available in the CLAM software.

Software page

Features available

Welcome

Project background, contacts and licensing agreements

Info

Photograph gallery of the catchment, brief list of facts about the catchment

Maps

Series of catchment properties that can be overlaid, such as land-use protected areas, erosion potential

Approach

Brief description of BDN approach and the BDN framework for the catchment

Inputs

Description of how the probability distributions were attained for each variable, including the assumptions and weaknesses for each

Scenario

Each scenario choice option, plus a map locating various scenarios and a text description of the assumptions used for each scenario

Utility

Change in the dollar value for the economic variables within the model

Output

Resultant probability distribution for each state variable

Report

A summary of the inputs, scenario choices and the output probability distributions, which can be exported and saved

the most supported option is chosen, but both views can be documented in the DST. Therefore the CLAM DST is an example of a 'white-box', rather than a 'black-box' model, as the latter is said to have hidden assumptions.

• display of output probability distributions for each variable. This aspect of the model is important for when liaising with end users; and the function to export and save probability distributions so the user can visually assess the potential impacts of the management scenarios tested.

Given the integrative nature of the model, time series data do not exist to concurrently verify all the BDN variables under current conditions or alternative future scenarios. Instead, the most appropriate method for model verification is for various members of the community and other experts to use the tool and review the input assumptions and the performance of the model predictions and to document their comments.

The CLAM models do not make decisions, but help managers to simplify and depict the complexity of ecological, social and economic factors operating in a systems and its catchment. By using the models, decision makers can understand the potential ramifications of making a decision and can then use the two methods of representing uncertainty to establish the risk of making such decisions. Outcomes may include collecting better data to increase the certainty associated with the model's prediction, or making a decision with the inclusion of a number of additional management actions to reduce any risk. It is essential to remember that cutting corners during the development of such models, and in the collection of data to populate the models, will reduce their usefulness and effectiveness.

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