The Systems Approach Framework SPICOSA - <a href="/glossary/index.shtml#SAF" title="Short for �Systems Approach Framework�, and comprising: 1) the use of General Systems Theory (GST) and Soft Systems Methodology (SSM) to understand and model problems in social-ecological systems; 2) the simulation of scenarios including problem management options; and 3) the engagement of stakeholders at the science-policy interface." class="gloss1">SAF</a>.eu

System Formulation

Data preparations

Introduction

In order to develop and run a simulation model of the Virtual System you need to prepare in advance the data sets that you will use. These data sets include imports or exports of quantities and information, externally or internally to the Virtual System, and test data in order to evaluate your model, here collectively called “inputs”.

Identify inputs and useful variables, assess relevance, and assemble metadata

The purpose of this task is to complete a Table of the primary Inputs needed to carry out numerical simulations and to analyse them, and non-modelled information, during the Appraisal Step. This table may have been drafted during the Design Step, in which the primary Inputs were probably identified. During the Formulation Step, this table is completed and documented in order to assist the development of the ESE model’s components and support the documentation process that will follow.

Before constructing this Table, you should review the information that you need to conduct the simulations and analyse their results. It is important to acknowledge that you have defined the Virtual System correctly in such a way that it includes all the primary functionality that links the changes in the forcing of the human activities to the changes in the Ecological Component Impact(s) (cause-&-effect chain), and, likewise, that links changes in the Impact(s) to changes in the response of the Economic and Social Component. The Table also assist you to examine the consequences of missing data or information that can affect the accuracy of your results in simulating and analyzing the scenarios that constitute your Policy Issue.

The focus here is on the primary inputs, which are functions or variables that vary in time, space and in specific interaction with the Virtual System. It is important to note that the adjectives, ‘external’ and ‘internal’, are not necessarily translated as spatial distinction, but they refer to whether or not they are included into the interactive dynamics described by the selected Virtual System. The focus is also on the primary set-up data information needed to conduct the Social and Economic Component analysis. The contents of this table are described below:

• Input type (manner of influence)
• Response Information (change of socio/economic status)
• Specific Identity (name & crucial characteristics)
• Interaction Level (external, internal or switched)
• Relevance Level (in relation to conceptual model)
• Availability status & ownership
• Data Purpose (validation, calibration, analysis, or simulation)

Input type
The Input information needs to be well characterized in order to provide the best possible resolution in the simulation analysis. This is because the complexity of simulation increases with the multiple Human Activities, the multiple cause-&-effect chains, and the multiple impact-response chains. Different Human Activities may contribute to the same impact through different cause-&-effect chains. In contrast, a single type of Human Activity may contribute to multiple Impacts through different cause-&-effect chains. Identifying specific Human Activities that are influencing an Impact is important to the public officials who are responsible for environmental management or environmental policy. Describing the Impact is important to scientific researchers. Both are important for the implementation of the SAF. Often scenarios will require the untangling of a web of links between multiple Human Activities and multiple Impacts. Otherwise it may be necessary to sort out the contributions of Human Activities relative to ecological functions having similar cause-&-effect chains, i.e. differentiating between nitrogen input from the marine source water and that from river runoff. Analogously, the Responses to a given Impact in the Ecological Component will differ in the Economic Component and in the Social Component and therefore they will require different types of Inputs for their assessments.

Response Information
Response Information is the information referring to the Economic and Social Components, necessary in order to conduct their respective simulations or interpretative analyses, in analogy to the input information needed for the simulation of the Ecological Component. The scope of the Response Information is defined by the division of these components between those used in the ESE model and those used in the Interpretive Analysis.

Specific Identity
This field provides key information related to the Input or Response Information. The “identity” is specified by providing i.e. name, location or other description. Each of the generic Human Activities that you identified in the Design Step will have a number of separate entities, which you will have to evaluate separately with respect to their interaction and relevance to the Policy Issue. For example the generic activity may be “agriculture”; depending on the relevance to the Policy Issue (and the relative scenarios) you may have to specify characteristics as the type of cultivation, the area occupied, the location, the soil type, the number of people employed, etc.

Examples:

• for an Urban Waste Human Activity: the location of its discharge points and the amounts of outflow.
• for urban storm runoff: the drainage area and points of entry,
• for industries: the kind, the name of each,
• for fisheries: the species, the location, the methods, the legal restrictions controlling the activity,
• for employment: the categories, the population, the income,
• for exportation out of the Virtual System: the commodity, the amount, the price,
• for public statistics: the population group, the descriptive sector,
• for tourism: the category, the number of tourists, the seasonal distribution.

These examples are brief. If you need further information to identify an Input or a Response Information due to a Human Activity or to an Ecological Forcing, don’t hesitate to use further comments in the table.


Interaction Level
The Input data and Response Information that is necessary in order to conduct a simulation analysis is distinguished in several levels, which you have to classify in order to clarify their interaction with the Virtual System that you are analyzing.

Examples:

External level (Level 1): When there is no interaction between the Input and the Virtual System, i.e. the input is not changed by changes in the “virtual system or Impact”, it is independent. Certain policy scenarios may of course explore potential changes in this input, if it is necessary for the Policy Issue. For instance:

• atmospheric forcing (weather) is normally considered as an external Input because of the difference in scale with a coastal zone. That means that the meteorology is not changed by the Coastal Zone estuary or land.

• atmospheric deposition is also considered an external Input because it has a larger-scaled origin and thus can be described as being independent of the internal processes of the Virtual System [note:, if the environment that you want to simulate is exposed to a significant production of substances (of NOx, NH4, or polluted aerosols, etc), which enter as key variables or which enter via one of the policy scenarios, then you should consider the local production of these as an internal Input].

• if a policy scenario requests the effects of an improbable event (e.g. 100-yr storm), then a simulated storm can be added to the normal weather input in order to construct this scenario. This input remains as an external one because the storm is not created by any internal dynamic of the system.

External or Internal (Level 2): There is no interaction between the Input and the Virtual System, but there is at least one scenario concerning the manner of influence of the Input on the intensity of the Impact. For instance:

• if a scenario concerns the runoff of a river, then the Virtual System should include the land-use practice, its drainage to the river, and the substance loading. The external Inputs are now those forcing the land runoff to the river. But you must keep in mind that in this case the model would have land-use and river-components and the river outflow would be an internal Input from the river component to the estuary component. On the other hand, if there were no scenarios concerning land-use, the river effluent could be simply specified as an external Input.

External switches to Internal (Level 3): There is a dynamic threshold-interaction, between the Virtual System and an external Input, which converts it to an Internal Input. For instance:

• if the number of tourists is an Input, and the resulting increases in sewage outflow contribute to greater hypoxic conditions (Impact), this would be an external Input. However, if the Social Component is analyzing the negative response of tourists to hypoxic conditions in the beach, then the number of tourists must be considered as an internal Input.

• if the amount of investment capital available is fixed and insufficient to institute more sustainable practice, this amount would be an external Input, i.e. there is no interaction between the system and amount of money available. However, if a deferred payment is allowed, in which the long-term benefits are allowed to payback the initial capital-investment loan, then the investment capital would become an internal Input, i.e. a sum that is calculated during the simulation.


Relevance Level
Please note that although it is absolutely necessary to make a preliminary evaluation of the relevance level, you will need to do the final relevance evaluation after you have acquired the data. In the preliminary evaluation of the relevance of your Inputs you should use the Conceptual model you developed during the Design Step and try to identify the level of relevance between each input and variable with your Virtual System always regarding your Policy Issue. The most efficient way to do that is to follow a hierarchical approach: first you should select a set of Inputs that is the more relevant to your simulation, then a set representing the second level of relevance, and, regarding to the detail you are able to add to your simulation, a set of inputs representing the third level of relevance.

Availability
Important in your evaluation of relevance is the probable constraints of availability of sufficient data/information of the Inputs. For data evaluated as highly relevant, you have to explore plausible strategies to deal with insufficient or missing data that is essential for your simulation. Sometimes these strategies consume resources (time & money) and result in little gain in resolution. In other words, the extent to which you can acquire sufficient input data controls both the accuracy and feasibility of your simulation.

Acquiring the data may take time, which is why the process was initiated in the Design Step. At this point you must evaluate the availability of your Inputs, in order to identify the feasible formulation choices. In the Input Table, you can enter three levels of availability: OK, possible, not available. For your own facilitation, the ‘possible’ level should be explained in a footnote to the Table so that it can be further evaluated later.

Data Purpose
Finally as a check on completeness, you should categorize the different data/information according to its use within your simulation analysis. More specifically:

Each of these categories will probably have different Input requirements.  

Examples:

Meteorological data may require daily weather forecasts as inputs for quasi-real time simulations; whereas for long-term forecasts, a statistical composite might be used, e.g. seasonal mean data with typical daily variability superimposed.

Tourist data would require scenario simulations based on available statistics e.g. growth in number, origin, or type of touristic activity

Acquire, analyse and use the Input data

Acquiring Data
The order in which Input Data are acquired should be based on their relevance. If it is not possible to categorize them as ‘OK’ in the Input Table you should begin the preparations to obtain them. After labelling them as ‘possible’ with notes, one should move to the next most relevant Input. Obviously, this approach may give way to get what is easy first. This is not a problem, but you must pay attention not to overlook the Inputs that are of high relevance for your simulation analysis. After you obtain the data it is useful to add some extra columns in the Input Table you constructed in the previous task in order to determine the units of the raw data you acquired, the duration for which you have these data, the deltaT, etc.


Determining Statistics

A minimum statistical analysis of all the numerical Input data should be made, e.g. calculate the mean and standard deviation for the times series that is available. This information is essential to evaluate the events or ecological cycles occurring in the data, which then will help you in evaluating the temporal resolution needed in the simulation. For example if an Input presents high variability at periodicities less than a day (for example, as in the case of tidal fluctuations or diurnal light variations), then the simulation should resolve on a time-scale of hours unless the modellers intend to smooth out this high frequency variability. (Note that the model time-scale is not the same as the time-step used in numerical integration of model equations during simulation. The time-step should not exceed the time-scale, and may often need to be much smaller).

Re-evaluate the relevance level.
In order for you to re-evaluate the relevance of your inputs, some general criteria will be established here. The accuracy-to-efficiency ratio of the simulation is enhanced if you chose only those inputs that are most relevant to the functionality of the selected Virtual System. The relevance definition here includes both the intensity (magnitude) and the quality (information) of the Input. Mass inputs are rated by magnitude and information inputs are rated based on the estimated extent that they control key functionalities within the system.

Examples:

The information you need to assess the relevance of the Inputs to the Virtual System, requires an evaluation of the forcing variables and their connection to the Virtual System. The examples below are illustrative and not definitive. The Inputs that concern mass (or energy) are classified according to their importance by using their magnitude respectively to other Inputs. The Inputs that concern Information are classified according to their importance using the level of the change that is stimulated relative to no such information. This is probably something you should keep in mind as it can be best evaluated later, during the multiple sensitivity analysis tests.

Magnitude:

For the category of Level 1 mass Inputs:

• discharge of water or substance by the Raging River: For a first-order evaluation of the magnitude, you would require the mean values of fluxes of those substances designated as key variables for your Virtual System (e.g. H2O, N, P, etc). As a second evaluation, you would need the sampling interval and the standard deviation of these variables. If they differ in deltaT, you will have to do some data conversion before calculating fluxes. If you do this, you could then make a comparison between the Raging River and another River of the Virtual System making sure, of course, that the different fluxes are dimensionally comparable.

Perch Catch from a river or lake: It is possible that the catch of these fishes might constitute the Impact of the simulation and one of the outputs of the Ecological Component model. Nevertheless, the catch data are needed for calibration of the model and thereby are identified as an input for the simulation. To evaluate the magnitude of the total catch, you would probably need the commercial, recreational, and illegal catches. The key variable with respect to the Virtual System might be the dry weight of carbon represented by the catch. The sampling time might be longer than the time step used for the simulation (monthly, or yearly) such that a data-analysis method should be used to convert to a shorter time interval, in order to resolve life cycle effects, etc. For the Economic Component, one would need the market value, which also may have some seasonal variability, and would also need other input information as the disposition, the size of the fish, etc.

For the category of Level 2 Mass Inputs:

Raging River Watershed: If a scenario requires a comparison of two types of land-use in the watershed, you must expand your Virtual System in order to include the water drainage from at least the portion of the watershed indicated by the scenario. Now your external Inputs are the rain and the agricultural practices. The Raging River then becomes an internal Input of which a certain, calculated part derives from the selected land-use.

For the category of Level 3 Mass Inputs:

• Foreign Tourists: For your application, the mass of tourists entering or leaving the Virtual System it will probably not of concern; however their water consumption and waste products often are. In addition, their behavioural interaction with the Virtual System is often also of concern. If you need to consider their mass Input to the Virtual System, you need to convert the number of tourists into organic matter input using the available sewage system. If the tourists do not enter into the Social or the Economic Components and if they contribute to a significant portion of the population or to its variability, then you should treat the contribution as an external Input. If they do enter dynamically into your simulation analysis, then they become an internal Input to the Virtual System and the Input representing their contribution must be more carefully calculated. In this case, their relevance to the loading will be a function of their contribution, i.e. whether it is significant to the background loading or not.

Information:

For the Interaction of Level 1 information Inputs:

Nutrient Variability: If the variability of an Input is significant, then the information about the variability is necessary as part of the Input. This is because using mean values over long periods obscure the information about variability of the loading. Pulses of nitrogen loading due to sudden, intense rainfall events can produce a significantly different response in an estuarine system, as compared to the same estuary exposed to the same mean value but with little or no variability. Often the response of the system to the variation of the input is more important to the simulation of the Impact, than the response to the mean value of the Input.

Nutrient Ratio: Likewise, the ratios between nutrients constitute an information Input, which can be an important control, e.g. on phytoplankton speciation. Additionally, these ratios can be used to identify and label different nutrient sources (as can N-isotope information).

For the Interaction of Level 2 information Inputs:

Market Price: The price of a harvested commodity is an information Input. If the price of a harvested commodity is determined at a larger scale than the Virtual System, it is an external Input. However, if a scenario wants to investigate the effect of the illegal harvest of this commodity, which is determined at scale of the Virtual System, then the illegal price becomes an internal Input and must be simulated as a function of internal parameters.

Governance Restriction: Policy directives made outside of the Virtual System jurisdiction (e.g. the local policy makers) would constitute an external information input parameter. If policy directives can be changed because of the internal function of the system i.e. because of decisions made inside the Virtual System, it would be considered an internal Input function.

For the Interaction of Level 3 information Inputs:

Public Awareness: If the public acceptance of some aspect of the virtual system is held constant (e.g. FAO exposure level), it would enter as an external control parameter in the simulation analysis. However, in some cases a scenario may require the public reaction to increased or decreased toxic levels, then the toxic level and the public acceptance would vary in the simulation. The acceptable exposure level would then become an internal Input function.


Converting Data
Commonly, the Input variables need some data processing to make them useable for the simulation analysis. The reasons are several; data omission, dimensional differences, necessary transformations, etc. After any conversions you may need to do it will useful to add an extra column to your Input Table, noting the conversion.

Examples:

Omission: An omitted parameter often occurs because it was not included in the original data monitoring plan for the human activities. In this case, you can choose a data substitution (see below) or you can leave it out, depending on its relevance.

Dimensions: A variable may have inconvenient dimensions or units. This can happen in several aspects: deltaT, spatial, geochemical, biological, partial representation, etc. More specifically:

• If the time-scale of the variable is compatible with the simulation, usually the units can be converted rather simply.

• If the time-scale of the variable is different than that desired, you can apply an interpolation scheme thus conserving the basic statistics of the series. The same applies when you need to fill minor gaps in the time series. Larger gaps should be specially noted, as they will possibly increase the calibration error.

Transformations: Non-conservative substances are changed between the location of the Input source (where data are available) and the boundary of the Virtual System. In this case, you can decide either to estimate the transformation empirically or to include the transformation process as a component in the simulation model. More specifically:

• The meteorological data may be scarce relative to a watershed. If your needs are more specific, you can use spatial combinations of meteorological stations or you can get spatially gridded data.

• The point source data is located upriver from the estuary. You can use estimations of the river uptake and denitrification or you can include them in the simulation model.

• Non-point nitrogen data for a river outflow is available only in annual averages. If an important scenario involves, for example, land-use or agricultural practices then the relevant land-use should be included in the Virtual System. If the loading is necessary only as part of the river input to the estuary, without specifically entering into a scenario, then the input can be simulated empirically at the needed time step and matching the known long term mean. The short-term variability can be correlated with the drainage forcing, i.e. as with the rainfall or irrigation patterns.


Substituting Data
It is possible that you will have to deal with situations where important data are missing. The simplest solution is to omit the data from the simulation analysis, but only if you can do that without seriously compromising the accuracy of the output or its relevance to most of the scenarios. If you cannot omit the data easily, you can use a substitution of data through various means, providing it is justified against the objectives and it is subjected to sensitivity tests and error evaluations. Several of the main options for data substitution exist and can be used singly or in combination.

Examples :

Substituting for gaps: Observational data often have gaps. There are numerous options for filling in data gaps. Regardless of the method, some scientific judgment should be exercised here: the bigger the gap, the more judgment. The following are just suggestion and are not intended to be comprehensive. More specifically:

• in some simulation software you can find features that perform interpolation and data fitting (e.g. EXTENDSim).

• small gaps in time series: if the data can be copied into statistical software, you can make a simple fit and recalculation.

• larger gaps in time series: if the original series is long enough in order for you to determine the spectrum, then you can compute large gaps using Fourier, or similar, analysis.

• vertical space gaps: often the nutrient values are sampled at a vertical resolution different from the electronic sensor sampling (CTD). These should be vertically averaged relatively to the depth of the water column that you need to represent in the model.

• horizontal space gap: You will often need horizontal integrations in order to represent the area modelled. You can use objective analysis, or similar, interpolation schemes to make these integrations in an efficient way.

• if hydrodynamic models already exist for the area of interest, you can use them to acquire vertical and horizontal integrations.

• you can also use GIS data, when available, to generate horizontal integrations on land.

Adapting similar data : Processes are less unique than systems, and therefore, you can more easily justify the use of similar data deriving from alternative sources. When calibration data are totally unavailable in the process level, you should consider using data coming from a different area, but it would be more hazardous to do this in the functional component level and not acceptable at the ESE or system model level. In any case, when you use data deriving from a different area you must make sure that the functional range of validity is similar and that the controlling constants and parameters are not case dependent. More specifically:

• a model for the resuspension of sediments, might use the same data for calibration when used for two sea-areas, assuming that the roughness-length and drag coefficients are similar and the kinetic energy is of the same order of magnitude.

• tourist preferences might be considered similar within the same region and same tourist resources, as bathing beach, sailing conditions, recreational fishing, etc. in the Mediterranean, North Sea, Baltic, Atlantic areas.

Using a proxy variable : The raw Input information may not always match the Input needs for the simulation analysis, but most of the times this is what you need. You should take care so that you will do the conversion in a defendable mode and also include any additional information when needed. More specifically:

• commonly, the chlorophyll concentrations are used as a proxy for phytoplankton biomass. Sometimes even double proxies are used in order to convert satellite data at the chlorophyll wavelength and then to estimates of plankton biomass.

• the freshwater balance and external salinity data can be used to calculate the estuarine circulation and flushing.

Constructing empirical data : When the observational data you have are insufficient, particularly on the process level, empirical data for calibration can be generated from the experimental data already existing or from the literature. More specifically:

• often the results of lab experiments can contribute to points on the calibration curve. Calibrations against literature values can be used in the same way. These actions support the credibility of the process representation.

• usually, illegal or recreational fish catch are not available. You could use estimations from other, similar costal zones or put it into the model as a parameter that can take values in a pre-known, determined range.

• sometimes sunlight data are not available. You could use an empirical relationship depended on a solar constant and the latitude, adding the cloudiness determined from meteorological data.

• often, discharge estimates of toxic chemicals from industrial human activities are not known. You could use average values from the literature.

Simulating Input Data: As stated earlier, you have two main options regarding simulation. You can either simulate an input function or variable (external simulation), or you can change the boundaries and include it to the Virtual System (internal simulation). More specifically:

External Simulation

• If a scenario requires evaluating how touristic preferences would change concerning a given Impact, e.g. removal of a seafood species from the market, increased turbidity in bathing beaches, adding day-care facilities, etc. you can simulate the original level of preference and simulate the change based on a statistically reasonable assessment of preference change for the scenario. This is a good example of using ‘change’ as an indicator of policy direction.

• If a scenario requires the response to a large event, as a probable risk factor, which is possible but is not in the data, you can do that by adding a simulated event to the existing data stream.

• At the beginning of the Formulation Step, much of the input data may not yet be available. In this case, you can simulate an input function directly within your selected simulation software.

• Many times one finds that an input parameter or constant obtained from the literature is not functioning properly in one’s formulation. This is common with dynamic parameters, which are considered as constant for a particular application but actually vary dependent on the case-study. In such cases, you should consider changing the value of the parameternoting this in the model documentation.

Internal Simulation

• If as a result of the scenario discussions with the Reference Group of your area, it becomes desirable to include in the simulation model another portion of the coastal zone, the best solution would be to include this within the Virtual System, if feasible. Land-use considerations are a good example where the scenario would like to include other human activities that were not formally treated. However, in many cases the additional scenario requirement can be approximated and treated as an external Input to the system (i.e. one that changes the simulation output but the human activity Input is not changed by the simulation output).

• Something that can also occur is that the original boundaries selected were not sufficient to achieve the accuracy needed for the simulation, so that what was formerly an Input function becomes an additional component to the simulation.

Combining tricks: Often it is necessary to combine one or more of the above substitution tricks. This is fine if you can reasonably defend it. More specifically:

• the commercial fish catch may be erratically sampled and/or not represent the total fishing mortality in the Ecological Component model. In this case, the economic assessment could be restricted to only the market portion, or it could be extended to include the total catch. In either case, you will still need to estimate the non-market portions by defendable means.

• tourist data might only be available seasonally, from airport arrivals or hotel registrations. If you consider that these data are incomplete, then you might make some estimation to ‘fill in’ the data set and to make it representative and suitable for the model time-step, by using empirical correlations with published data from other similar locations or situations.

• a small watershed may be important for your simulation but there may not be any measured runoff data available. You can add a land drainage component in your simulation model or you can approximate water yield (specific to land-use/soil type) as a fraction of the local rainfall.


Get data for ESE assessment

Scenario Clarification
Soon after starting the Formulation Step, you must come to communication with your Reference Group in order to clarify the main scenarios and their indicators. Without this clarification, the Formulation Step activities will be too open-ended, because the scope of the Virtual System and the specific output products will remain unresolved. During this process, please keep in mind not to agree to those scenarios that would require more resources or data than you have to complete the implementation of the SAF. If you find out that you do not have the data and information to assess a scenario that is important for your Reference Group, explain to them the limitations and explore possible alternatives.

The clarification should be based on the difficulty of executing a scenario. The level of difficulty should be weighed against the necessity or relevance to the chosen Policy Issue. The following comments should help:

Level of difficulty - listed by the level of increasing difficulty:

• scenarios involving merely changing the input values or testing the output sensitivity to possible change.
• scenarios requiring the modification of an internal component – like inserting an alternative technology, making another type of economic or social analysis, or exploring another scale of policy options.
• scenarios requiring the addition of an internal component – like a different land-use problem.
• scenarios relating to changes towards an unrelated Impact, such that a different cause-&-effect chain or assessment would be required or such that a change to the economic method or social assessment would be necessary.

Any further changes proposed during the Appraisal Step, should be at one of the lower levels of difficulty.

Scenario Wording: The wording of the scenario should be summarized in a simple statement that refers to an option concerning the Policy Issue. This statement should be then further elaborated in the form of a question, to make its formulation and analysis clear. If your References Group requires any specific indicators or your team needs them you should include them in the text of the ESE Component Assessment Plans.

Example (from the implementation of the SAF at Mar Piccolo, Taranto, Italy ):

Impact: The reduction of the productivity and the quality of the mussel culture.

Policy Issue: Including Mussel Culture in a Management Plan for Sustainable Use of the Mar Piccolo Resources.

Thematic Scenarios with operational ‘ Baby Scenarios’:

A. Evaluating the environmental conditions controlling Mussels growth.
A1. To what extent would optimal, environmental conditions reduce the costs of mussel culture and increase socio-economic benefits?
A2. What kind of indicators can we use to estimate the mussel growth based on different types of food?
A3. What would be nutrients target ratio BE in order to optimize MP productivity?
A4. To what degree are contaminant substances or organisms inhibiting or endangering the mussel’s growth?

B. Evaluating the measures and costs needed to sustainable Mussels growth
B1. Are there other uses preventing better environmental conditions for mussel culture?
B2. What technological options or policy strategies are available to mitigate these damaging effects?
B3. What are the socio-economic consequences of these options or strategies?

C. Evaluating human health effects from exposure to hazardous levels of contaminants or microorganisms.
C1. What are the implications to human health due to mussel’s uptake of hazardous substances or microorganisms?
C2. What are the health costs projected from exposure to these contaminants?


Revise System Definition
In the Design Step you have constructed diagrams that assist you in identifying the boundaries and contents of the Virtual System, describe the Impacts and the Responses from the human activities, define the initial scenarios and identify the ecological, economic and social data necessary for your ESE analysis and simulation. During the Formulation Step you will need to revise these diagrams (if needed), that will know be archived as descriptive of the simulation analysis (e.g. cause-&-effect, conceptual models, component processes, etc.). As you will now know the data and information availability and you will have decided on the methods that will be used for the analysis, your diagrams should be revised in order to match the final formulation structure for the simulation. Along with these diagrams you may find useful to also others, which you will judge necessary to explain the simulation or which will serve later for the end-user and scientific presentations.

Scientific Justifications
Just as you have to make approximations concerning the Input data, you must also make approximations about the structure and dynamics of the Virtual System functionality, which you are trying to simulate. This is where your understanding of the ecological system plays a critically important role. It is this material that will make your scientific outcome interesting and the results of the simulation more credible.

Example:

Approximations: In order to capture the needed functionality with your models, you can devise mechanisms to represent space for the purpose of the time-simulation. The need must be weighed against the output required by the scenarios and against the need - in terms of resources available - to resolution required. More specifically:

Spatial Resolution
• The need to calculate or visualize changes relative to a geographic grid. You could do that using a GIS adaptation appropriate for the simulation software you use (for ExtendSim, a PCRaster adaption has been developed through the project SPICOSA).
• The need to integrate quantities or compute advective fluxes in aquatic systems with hydrodynamic numerical models. If it is available it could be useful for providing data distributions or calibration checks, as stated in former subtasks.

Parameter Space
• Vertical resolution is often necessary and you can assess it by adding components that represent a depth based on the value of a parameter e.g., ‘light depth’ or mixed layer depth, nephaloid thickness, etc., calculated in your simulation software and used to define a internal functional component.
• Horizontal resolution in the aqueous regime may be necessary in order to distinguish different spatial components again based on a parameter calculated by your model, e.g., effluent plume, tidally mixed volume, etc.

Virtual Space
In the case of land-use or benthic regimes (fixed in space) a virtual space can be defined having the mean characteristics of the selected land-use, e.g. soil type, vegetation, inclination, distance to stream, benthic vegetation, area fished, etc.


Accompanying analysis
Frequently you will need to use various data analysis methods in order to approximate variables and relationships.

Statistical.
• You may need to establish a correlation between a model parameter and an observed parameter (proxy).
• You may need to determine the best fit to empirical data to facilitate its smooth representation in the simulation software.

Dynamical.
• You may need to estimate the circulation at an area within the estuary by determining a dynamical relation between the wind-direction with occasional flow observations at that area. In addition, you could use numerical model results, if available, to do the same thing.
• You may need to estimate the PAR (photosynthetically active radiation) in the euphotic layer using the local solar elevation and cloudiness from meteorological data.

Functional Estimates .
• You will have to estimate and make defendable approximations for many characteristics, inputs, or initial conditions.
• You may need to estimate the most appropriate phytoplankton species (or size groups) to represent the primary productivity.
• You may need to estimate the extent of benthic damage by bottom trawling.
• You may need to estimate the amount of local atmospheric deposition.
• You may need to estimate the soil and vegetation characteristics of local land-use using GIS.
• You may need to estimate the loading from non-point sources (if not calculated in Extend).

In many cases, mainly connected to the Economic and Social Component you may find out that there are several aspects of your Virtual System that are rather difficult to model properly, but that are very important for your Policy Issue and scenarios. These are important copmpnents of the interpretative analysis, because information about them often helps in translating the model’s results into “real world” conclusions. For example, you may need to incorporate the illegal aspects of a human activity in your analysis.

Economic Component
If you neglected to choose the methods for the economic assessment during the Design Step, you should address that task now. It is advisable to prefer a collective-based rather than an individual-based approach because one of the SAF’s motivating arguments is that sustainability is better acheived by collective rationalit, rather than by the aggregate results of individually rational choices which may lead to a disastrous outcome. Review the appropriateness of the methods you have chosen with respect to the scale and the focus of the Policy Issue and its scenarios. You can find more analytical information about those aspects to the supporting documents b and c.

Scale:
As a method, the SAF is independent of spatial and socio-economic scale. It is your Policy Issue and its scenarios that will determine the scale. However, for obvious management restriction you may consider limiting the application to “local-to-regional” scales. Of course, exceptions apply if the Policy Issue is already based on a larger scale. In any case, limiting does not mean restriction. That means that the scale limits the dynamics of the Virtual System (where the assessment occurs), but it does not limit the scale of the inputs. Another reason for limiting the scale is that obtaining the necessary information on the market at national or international scales could be resource limiting e.g. the export price of a commodity might be determined externally to the Virtual System, in which case it would be specified as an external input. The accuracy of a cost benefit analysis (CBA) can vary or reverse dependent on the time scale and the extent of linked costs and benefits, which might reach beyond your Virtual System.

Focus:
The focus of the economic analysis that you will select should be limited in demonstrating how a market assessme simulation involving both the environmental andnerally willd to severaldiscussed inach of the pnarios. The o the Impact under studythe simulati in this impact which then will caus the market with respec indirect, or non market values, see Fig. 4.

Regarding the Economic Component of your analysis you must in mind that some of the interactions will be quantitative (monetary, or numeric) and they can be incorporated into a simulation model formulation. Other interactions or functionalities of the Economic Component will not be able to be simulated and must then be assessed qualitatively. One alternative for that can be the supply chain analysis (SCA) that lends itself well to tracing the market flows as a function of an environmental change, e.g. WadBos model. More detailed guidelines regarding the simulation and the interpretive analysis of the economic component can be found in <placeholder citation: WP2 2008 D2.2 TO BE UPDATED>

Influence Category

HA examples

Market Value

Natural System

Social Acceptance

Harvesting

 

 

 

 

Withdrawing resources

Fishing, aquaculture, mining, withdrawing

Direct value= f(effort, price)

Loss= f(degree of sustainable yield)

Balance= f(sustainable market benefits)

Occupying

 

 

 

 

Displaying or modifying resources

Urbanization, cultivation, shore development

Indirect value= f(long-term economic benefit)

Damage= f(degree of sustainable development)

Balance= f(sustainable non-market benefits)

Wasting

 

 

 

 

Adding substances to resources

Nutrient loading,

TPM &DOM loading, toxins, heavy metals, pathogens, synthetics

Direct benefit= f(effort saved)

Damage= f(dispersive/biological carrying capacity, waste management)

Balance= f(human & ecosystem health)

 

 

 

 

 

Protecting

 

 

 

 

Conserving or reducing impact on resources

Tourism, parks, preserves, zoning laws

Loss of benefit= f(extent of resources removed from market)

Benefit= f(size, initial condition, ambient, controls)

Balance =f(public use &ambient ecosystem stabilization)


Fig.a. An example of economic and social responses to the effects of Human Activities on Ecological systems. This illustrates how the ESE response varies within each Influence categories, and how spreading the Policy Issue over several Influence categories can complicate the ESE assessments needed.

Social Component
Through your Virtual System there will be several good choices concerning the response to the Impact, directly, or indirectly through the market economy or through non-market values. This is a decision that should be made after discussions with your Reference group but also after considerations inside your team. The social scientists engaged to the SAF implementation should judge the most appropriate linkages between the Social Component and the Economic or Ecological one and also decide on the analysis that will be implemented. The analysis must be feasable with available resources. An easy and interesting area to pursue that concerns the public perception. It is usually easy to understand qualitatively and often linked to the demand side of the market economy. Other aspects that would be relatively easily evaluated and of high importance for the Reference Group would be changes in the employment, in health costs, in the public perceptions of the ecological capital, etc. The objectives of your social analysis must concern a direct link with the Impact and one or more of the scenarios. You need to state these objectives clearly in the text description. The Interpretive Analysis, which complements the simulation portion, can and should have a larger scope but it should still be linked to the Policy Issue.

Next step