Anthony J Jakeman
email: tony.jakeman@anu.edu.au

Sondoss El Sawah
email: sondoss.elsawah@anu.edu.au

Joseph H A Guillaume

Suzanne A Pierce

Making Progress in Integrated Modelling and Environmental Decision Support

Keywords: Integrated modelling, environmental decision support, modelling tools

Integrated modelling and environmental decision support are increasingly important as society tackles some of the most complex challenges of our generation, with impacts on future generations. When integrated modelling is successful, the results can be transformational yet the core elements for generating that success are not always clear. There is an elusive element to finding the best mix of methods, models and approaches for any given problem. This raises issues for repeatability and questions regarding how the emerging metadiscipline will converge in order to consistently achieve quality results or increased understanding of the processes that lead to success. Key challenges include the need to diagnose elements that lead to successful process, training for professional and technical competencies, and increased access to stable platforms and interchangeable models and modelling tools. This paper aims to summarize some of the key process and product related challenges of integrated modelling and environmental decision support.

Introduction

Most environmental and sustainability issues we face today are known to be wicked or messy problems. Wicked problems arise in contexts where uncertainty and conflicts are rife (Rittel and Webber [START_REF] Rittel | Dilemmas in a General Theory of Planning[END_REF]). To manage these complex issues, we need to take a precautionary, adaptive and evidence-based approach that seeks to balance the needs of current and future generations. To help inform decision making and adaptive planning, there are several requirements:

• Engaging relevant interest groups in participatory processes to collectively frame the issues to be addressed, share knowledge and engender trust • Modelling activities (both qualitative and quantitative) that acquire, systematize and integrate the knowledge about: the problem under scrutiny, data, information and perspectives in order to improve system understanding and clarify trade-off options • Managing the major sources of uncertainty in the decision making process by identifying, ranking, communicating and purposefully reducing the crucial components • Creating stable and accessible information and knowledge-based systems that support efficient storage, processing and (re)-use of available data, knowledge and models • Committing sufficient resources to the overall decision making process or, at least, using available resources efficiently be they financial, technical or facilitative.

This paper focuses on the role that integrated modelling can play in effectively and efficiently meeting decision support requirements. A worthwhile question then is: how far are we as an environmental modelling and software community along this path? To help address this question, the paper has three aims:

(1) Give a brief snapshot of the integrated modelling field and some of its key achievements and weaknesses that we can improve and build upon (Sections 2 and 3) (2) Proffer some suggestions as to where we, as a community of modellers and software developers, would like to be in a decade or so in terms of providing major progress to support the environmental decision making process (Section 4) (3) Discuss some of the opportunities that the modelling community can embrace to achieve such progress (Section 5).

Integrated Modelling

Undoubtedly there has been a lot of activity in the environmental modelling area since the use of computers has become routine. Indeed that activity has been growing and models are increasingly used to support environmental decisions. There is really no alternative to the use of modelling, a term and activity used here in the broadest sense. Indeed modelling is unavoidable and more necessary the messier the problem. Integrated environmental modelling can be viewed as a "metadiscipline" which integrates knowledge and practices across multiple scientific fields (e.g. hydrology, ecology, economics, various social sciences) to improve understanding of the ecological, social and economic outcomes of management decisions. The challenge is to treat integrated modelling as a process that strives for credibility and accessibility. Ravetz [START_REF] Ravetz | Integrated Environmental Assessment Forum: Developing Guidelines for "Good Practice[END_REF] goes so far as arguing for evaluation of the process of integrated model development rather than the product, stating that in such circumstances ''the inherently more difficult path of testing of the process may actually be more practical.'' We would argue the need for evaluation of both process and product and recognize the mutual support lent by each objective.

3 Where are we now?

The development and application of integrated modelling stand on a number of pillars or building blocks which constitute both the modelling process and content development (see Figure 1). These include: (1) models, (2) modelling guidelines, (3) participatory modelling, (4) modelling paradigms, (5) methods and tools, and (6) software/hardware technologies. The following sub-sections address these pillars.

The legacy of models

First of all we now have a legacy of disciplinary models for a wide range of biophysical and socio-economic processes that is relevant for incorporating in integrated models and/or modelling. In each of hydrology, ecology, economics, and other enabling fields there is a very rich literature proposing, applying and (to a lesser extent) evaluating models of different types for different purposes utilizing a varying quantity and quality of data. In that literature there is much that can be learnt about the values of these models as component candidates for an integrated model. In the integrated modelling literature, Oxley et al. [START_REF] Oxley | Integrated Modelling and Decision-Support Tools: a Mediterranean Example[END_REF] discuss the difference between domain-specific research models and integrated policy-oriented models. They identify several criteria when considering a model for including as a component in an integrated model or integrated modelling exercise, such as: purpose/use of the integrated model, adequacy of represented processes, accuracy and resolution of data. Integrating, adapting and rebuilding are three strategies for incorporating existing models into an integrated platform. Each strategy has its own advantages and disadvantages in terms of development costs, flexibility of the end-product, and the degree to which user requirements are met. Advances in software development have provided technologies and tools that can effectively support the technical integration of models. This will be further discussed in Sub-section 3.6.

Protocols and Guidelines

Secondly, in line with the dual but mutually supportive premises of accentuating the process and evaluating the products of modelling, some accepted protocols and guidelines exist about how one develops models and software. Adopting good modelling practices improves the chances for the reusability and extendibility of models. The Position Paper [START_REF] Jakeman | Ten Iterative Steps in Development and Evaluation of Environmental Models[END_REF] on the development and evaluation of environmental models is just one example of a host of literature and recommended practice that includes Refsgaard [START_REF] Refsgaard | Quality Assurance in Model Based Water Management -Review of Existing Practice and Outline of New Approaches[END_REF], Van der Sluijs et al. [START_REF] Van Der Sluijs | Combining Quantitative and Qualitative Measures of Uncertainty in Model Based Environmental Assessment: the NUSAP System[END_REF] and Lee [START_REF] Lee | Bridging the Gap Between Theory and Practice[END_REF].

A weakness to date that could easily be obliterated is the need for heavier concentration on the evaluation of models as products, most of which being reported remains pathetically thin. Bennett et al. [START_REF] Bennett | Performance Evaluation of Environmental Models[END_REF] summarize quantitative and qualitative methods of model evaluation, which will hopefully help place the practice on a trajectory that clarifies the level of a model's credibility and, as a necessary reporting condition, explicitly characterizes its limitations.

Participatory Modelling

Thirdly, we now have a maturing literature on the role of modelling and participatory processes for decision support and a rapidly increasing pool of researchers dedicated to participatory modelling. This research line is informed by a wealth of knowledge and practices developed in psychological and social inquiry fields including: action research, soft systems thinking and cognitive/behavioural decision making. Becker et al. [START_REF] Becker | How can Models Help Implementing the Water Framework Directive?[END_REF] provide a good summary of where and how participation enters the various stages of integrated modelling. Bousquet and Voinov [START_REF] Bousquet | Modelling with Stakeholders[END_REF] is a good introduction to "Modelling with stakeholders" and is evidence of the strong trend towards participatory modelling. A deficiency at present, however, seems to be that there is an imbalance in the research between there being too much science push and not enough stakeholder pull in general. There are two interrelated causes for this perceived gap. First, there has been little attention paid to systematically evaluating and demonstrating the practical impacts of the process and its products on decision making. Second, participatory modelling remains more of an art than a science. Whereas it is recognized that the research design highly depends on the project's context and purpose, there is still a lack of understanding of how/why a process works in practice to achieve a desired outcome. Recent work has started to recognise these drawbacks, such as Mathews et al. [START_REF] Matthews | Raising the Bar? The Challenges of Evaluating the Outcomes of Environmental Modelling and Software[END_REF]. Perhaps it is just a matter of time until the value of the science is more recognised and this perceived imbalance reverses.

Paradigms for Integrated Modelling

Fourthly, we have some powerful paradigms or families for integrated modelling, each with their own suitability to different situations. These include The strengths and weaknesses of these paradigms in different contexts are characterized in [START_REF] Jakeman | Integrated Assessment of Impacts of Policy and Water Allocation Change Across Social[END_REF], according to modelling purpose, nature of the data at one's disposal, the breadth of issues being addressed, capability of the paradigm for handling uncertainty, and disciplinary model components to be incorporated. As applications of these paradigms mount, the literature here is becoming increasingly helpful in illustrating what sort of utility they possess.

Methods and Tools

Fifthly, we have a myriad of methods and tools to assist with integrated modelling and the development of decision support systems [START_REF] De Ridder | A Framework for Tool Selection and Use in Integrated Assessment for Sustainable Development[END_REF]. These include:

• Data acquisition and analysis tools (e.g. GIS, data mining)

The Software and Hardware Technology

Sixthly, the available software technology is advancing rapidly, perhaps much faster than the science of integrated participatory modelling. Many technologies have been, and can be further, leveraged to support integrated modelling. These include:

• Code coupling technologies such as APIs (e.g. interface wrappers), Open MI interfaces, and component modelling technologies (e.g. Active X)

• Digital communication and learning technologies, such as 3-D visualization and interactive gaming technologies [START_REF] Hartig | EcoTRADE -A Multi-Player Network Game of a Tradable Permit Market for Biodiversity Credits[END_REF] • Distributed and parallel computing technologies, such as cloud computing.

4 Where would we like to be?

Uncertainty Management

In keeping with the emphasis on process and product, a primary aim should be to identify and communicate the sources of uncertainty at all stages of the decision making process. And, as far as is necessary to meet end user needs, one should for example be clarifying trade-offs, and reducing and characterizing uncertainty, such that its effect on objectives can be described by propagating each uncertainty through all stages of that process. Importantly, the existence of residual uncertainty must be recognised and measures planned to address that uncertainty as it arises.

The stages of the decision making process include project scoping, problem framing, searching for options, analysis of candidates, deliberation, implementation and monitoring and evaluation. Reducing uncertainty in problem framing and in the model used for analysis is a key outcome for the more rigorous methods recommended in the next sections. However, it is also necessary to describe and communicate the underlying, often implicitly considered, uncertainties. Scenarios and multi-model techniques [START_REF] Rojas | Application of a Multimodel Approach to Account for Conceptual Model and Scenario Uncertainties in Groundwater Modelling[END_REF] can be used to capture multiple frames of a system and its drivers. There are numerous techniques for addressing uncertainty in the model structure and parameters [START_REF] Guillaume | Implementing a Framework for Managing Uncertainty Holistically[END_REF], the merits of which are discussed in more detail in Section 3.3. In deliberation, implementation, and monitoring and evaluation, we must be clear about uncertainties accumulated from the rest of the process and their effect on the confidence that we can have in predicted outcomes. There is some belief that discussing the uncertainty in predictions weakens the credibility and authority of recommendations. It must however be acknowledged that uncertainty exists, whether it is explicitly discussed or not, and the risk of failure of decision support will be best minimised by consciously planning how that uncertainty is communicated. By explicitly considering the sources of uncertainty throughout the process, it becomes clear that much can be done to improve the way it is addressed; and indeed many methods already exist, for example to account for and quantify uncertainties in search through optimisation and planning data acquisition for monitoring .

Note that we do have frameworks and methods to better manage uncertainty more holisically [e.g. 17,18] but we require studies that apply these frameworks with appropriate tools so as to show the way the process can be enhanced and the confidence between outcomes predicted.

Problem Framing

Our observation is that in general much less time is given to framing the problem under scrutiny than is desirable. Although problem framing and structuring can be more critical than the computational techniques we use to generate solutions, there is a perceived temptation to rush to "writing equations and computer code" before developing a sufficient understanding of the problem at hand. A profound investigation of the situation context is the foundation for a rigorous modelling effort to achieve higher impact results. Otherwise we take the high risk of wasting resources on developing super-elegant but less relevant and less useful models. While the upfront cost of exploring the ambiguous elements of a modelling context may seem expensive, it is often worth the extra effort because it can yield results that a community or interest groups will value.

There is a wide range of problem structuring methodologies and techniques for exploring and framing issues in ambiguous and ill-defined decision making contexts including: soft systems methodologies [START_REF] Rosenhead | What's the Problem: An Introduction to Problem Structuring Methods[END_REF], narrative analysis and value-focused thinking. Outputs from these inquiries (mainly qualitative insights) help modellers "get a sense" of how the system works, stakeholder values and issues of concern. This knowledge provides the basis for defining modelling purpose, use, design and functional requirements.

A key challenge facing modellers is how to translate qualitative stakeholder views and values into formal model inputs (i.e. scenarios) and outputs (i.e. indicators). It is observed that in many cases the link between stakeholder frames and models are weak, and the logic information flow is obscure, and sometimes, disconnected. This weakness may defeat the modelling purpose and undermine a model's credibility and relevance. Whereas this problem is inevitable given the different philosophical stances and natures underlying interpretive/soft framing and formal/hard modelling, there is still potential for bridging this gap, or at least transparently reporting inconsistencies. Promising steps in this direction include [START_REF] Kok | The potential of Fuzzy Cognitive Maps for Semi-Quantitative Scenario Development, with an Example from Brazil[END_REF][START_REF] Kok | Combining Two Approaches of Integrated Scenario Development to Combat Desertification in the Guadalentin Watershed, Spain[END_REF].

These concerns must be balanced with a determination to keep the model as simple as possible to meet the objectives. In some cases this will mean simplifying the objectives to obtain usefully accurate answers.

Model and Data Assurance, Uncertainty and Sensitivity

Much improved modelling practice is warranted and has several requirements. Placing more emphasis on specifying and assessing basic model limitations should be within easy reach. This is mostly a matter of the peer review community insisting more vigorously on researchers being more comprehensive in this regard. Some items in a list of limitations are easily specified and relate to the model assumptions, while others can be revealed by sensibility and sensitivity testing. Do the parameter values make sense? Are some parameters redundant? Is the fit of predictions to observations unacceptably poor under important conditions? Model performance evaluation, beyond perfunctory sensitivity analysis and basic calibration checks, are needed to advance the usability for real world problems.

Sensitivity analysis (SA) needs to be a common step in any complex model evaluation, and indeed could be applied much more in the model construction stage where alternative hypotheses are being entertained. But SA is typically applied in a perfunctory manner, for example merely changing parameters one at a time by some proportional amount after acceptance of the model. Saltelli and Annoni [START_REF] Saltelli | How to Avoid a Perfunctory Sensitivity Analysis[END_REF] illustrate the dangers of this simplistic practice. While methods of SA need to be better developed for complex models, including those with a strong spatial and multidisciplinary component, use of first cut methods like that of Morris and frequency domain analysis can be more widely applied as very useful screening tools.

Uncertainty analysis (UA) of a model, as opposed to the modelling process, is starting to receive increased attention. But it also needs to be more pervasive, and indeed eclectic. Much of the current focus on UA is either on sampling techniques like pseudo Monte Carlo or on sophisticated and computationally demanding Bayesian techniques where obtaining convergence seems to be an art. At best these methods seem most suited to single discipline models that are not overly complex. In many cases, simpler methods may suffice. Norton [START_REF] Norton | Algebraic Sensitivity Analysis of Environmental Models[END_REF] for example argues and demonstrates the ease of algebraic SA, where operations in an equation can be combined to find the sensitivities of its output to variations in contributing factors. The challenge for UA is to find ways of propagating uncertainties between model components of an integrated model that have been analyzed individually in an manner that is appropriate for that model component. Another focus in UA should be augmented consideration of errors in input data that drives a model, on output data that is used to calibrate and/or assess performance, and on alternative model structure hypotheses. In the end what would be very advantageous is a catalogue of uncertainties and a ranking of them, preferably informed by their effect on the decision options to understand which uncertainties have crucial consequences.

Long term modelling projects

Most integrated modelling and DSS development efforts are short term projects which are deemed completed upon the delivery of an end product in the form of a tool and/or final results report. Therefore, there is little attention paid to the postimplementation phases in the model's lifecycle, including: tools/results use, maintenance and summative evaluation. This impairs our understanding of how models are actually used in the policy domain and reduces chances for improvement. In addition, most projects are single shot event whereas increasing returns can be achieved through cycles of implementation. Whereas this observation has been made frequently in the integrated modelling literature [e.g. [START_REF] Toth | State of the Art and Future Challenges for Integrated Environmental Assessment[END_REF][START_REF] Letcher | Application of an Adaptive Method for Integrated Assessment of Water Allocation Issues in the Namoi River Catchment[END_REF] we cannot perceive much improvement in this direction. Mysiak et al. [START_REF] Mysiak | Review of Model Based Tools with Regard to the Interaction of Water Management and Agriculture[END_REF] argue that the existing research funding mechanisms impede long term projects and widen the perceived gap between science and policy making.

Software development methodologies

A software development methodology is a prescriptive one that structures and coordinates the technical process of designing, implementing, and testing a software product. Existing software development methodologies provide a complementary framework of concepts, practices, methods and tools that can improve the effectiveness and efficiency of the modelling process and products. Using one of these methodologies enables modellers to apply mechanisms that explicitly incorporate usability aspects into the modelling process, such as: user-centred design and prototyping. To increase the awareness of the integrated modelling and software community about software development concepts, Verweij et al [START_REF] Verweija | An IT Perspective on Integrated Environmental Modelling: The SIAT Case[END_REF] present an IT perspective on the integrated modelling process.

How do we get there and what are the opportunities?

There is an enormous opportunity for members of the integrated modelling and environmental decision support community not only to contribute to the decision making process technically, but also to play a role as neutral broker. Teams are starting to emerge which express the combined overall scientific and participatory strengths -through engaging the interest groups, framing the problem with them and applying scientifically reputable tools.

In order to advance our metadiscipline, several strategies suggest themselves. One is to share the accumulated learnings obtained by those teams from studying messy problems. This will require mechanisms that encourage exchanges across research community boundaries to share problems and solutions.

Another strategy is to develop awareness in teams to select an integrated modelling and software approach that fits the problem well and to comprehensively apply tools that satisfactorily enhance the quality of the process and characterise the credibility of the product. Such an open mindset and capacity necessitates changes to the way that most modellers are educated. Researchers in the main tend to become modellers either from being motivated by the need to solve a problem that is in their sphere of interest or by coming through specific disciplines. In the former case many do not have modelling training. In the latter they may have more training in but tend to have preferred ways of treating problems. What is required is a move towards an education and training focus on the discipline of modelling itself that emphasizes the problem context, while also providing exposure to a broad range of problem contexts, disciplines and methods. A modeller's satisfaction would then derive from the greater insights generated from looking for, finding and applying the most relevant tools to the problem -in stark contrast, say, to the rewards of applying approaches that fit within one's comfort zone. Better modelling training is essential. To some extent we have been going backwards with so few modellers able to write serious code. This is epitomized by the advent of the interface which has prevented many from seeing what's inside the models they use. This education of modellers can take many shapes. One is to develop the relevant majors in undergraduate degrees or create professional training programs that advance modelling capacity for mid-career levels. Another is to insist on accreditation of modellers. A community of integrated modellers exists with too few points of contact. Developing a community of practice for integrated modelling with recognized access points, such as cyberinfrastructure hubs and workshops, makes sense to us.

Participatory modelling and greater consideration and documentation of the modelling process can also support ongoing improvement of modelling practice. In some cases, modelling in a participatory setting could be considered a service rather than a product (though it is very much a research service requiring much acumen). The focus is then on the model enabling social learning and building capacity, with both the model's development and use being facilitated by its creators. This improves the modeller's capacity to evaluate the model, and helps ensure it has made an impact regardless of the fate of the product. Documentation of the modelling process to the extent that it is recoverable [START_REF] Checkland | Model Validation in Soft Systems Practice[END_REF], and consideration of future re-use of the model, can also facilitate adaptive modelling, such that new work is aware of and builds on past experiences. A database of documented recoverable experiences and their evaluation can provide inspiration for better methods to tackle overlooked but often essential tasks, as well as provide evidence for revision and evolution of best practice guidelines.

What such an agenda implies about research infrastructure has been discussed in [START_REF] Jakeman | Outstanding Research Issues in Integration and Participation for Water Resources Planning and Management[END_REF] and bears repeating. "Plainly a new research and education effort as broad and as discipline-spanning as the one outlined above poses some questions about the adequacy of present research, education and training arrangements. It implies time scales of the order of a decade, funding on a scale allowing continuity for a community large enough to make significant progress on broad and demanding issues, and a means of getting researchers and research users from very different backgrounds to talk to each other and learn each others' perspectives and priorities. It also poses hard practical problems in sourcing, testing, describing, providing access to and archiving data."

Fig. 1 .

 1 Fig. 1. Pillars in the field of integrated modelling and environmental decision support


Albrecht Gnauck

Bernhard Luther
email: luther@tu-cottbus.de

Wilfried Krug
email: wkrug@dualis-it.de

Using a Commercial Optimisation Tool for Fine Tuning of Parameters of an Eutrophication Model

Keywords: Optimisation, water quality modelling, eutrophication, parameter estimation

Simulation models are well-known informatic tools to manage environmental knowledge. Current approaches to ecosystems modelling are theoretically based on information theory, thermodynamics, topology, or systems theory. Water quality models are used for managing eutrophication problems. In the past, the Cottbus Eutrophication Simulator (CEUS) has been designed on the base of MATLAB and SIMULINK, which enables an user to couple the simulator with different software tools. To quantify these relations differential equations, some site constants and model specific parameters have to be specified. The fine tuning of parameters of an ecological model can be considered from the perspective of a suitable optimization procedure. Especially the commercial optimization software ISSOP realised different optimization algorithms which are traced back to one standard form of discrete optimization. In the paper, results of parameter optimisation will be presented for important water quality indicators. The results of parameter fine tuning and possibilities of parameter optimisation are discussed.

Introduction

Modern environmental management decisions are based on mathematical models of ecosystems or ecological processes. To get suitable simulation results for management purposes the process of setting up of parameter values and initial conditions is of high importance. Management options for eutrophication control will be obtained by scenario analyses with changing parameter values [START_REF] Straškraba | Freshwater Ecosystems -Modelling and Simulation[END_REF].

The problem of parameter optimisation can be described related to a models quality and accuracy. Under the prerequisite of well-founded initial parameter settings a higher accuracy can be reached with the help of iterative optimisation. The optimisation procedures refer to goal functions including the output variables of the model. The term of parametrisation in ecological modelling denotes the determination of quantitative values of parameters. Three different approaches can be taken:  A preliminary estimate is obtainable from laboratory and field observations of processes and effects by means of correlation analysis or by parameter estimation techniques.  Combinations of parameters in keeping with a modelled situation may be obtained by means of estimation from parameter optimisation techniques.  Estimates of parameter importance of a simulation model may be obtained by sensitivity analysis. The goal of sensitivity analysis is to determine how sensitive the model is to changes of parameter values, which is the basis for model validation and parameter estimation. Investigations of parameter changes are carried out for an eutrophication model of a lowland river basin.

2 The Optimisation Tool ISSOP Krug [3] The dialogue between external and internal models and optimisation methods is realised by a universal parameter interface. Following optimisation methods are included: CENUM -component wise enumeration, DISOPT -a quasi-gradient method, EVOL -an evolutionary optimisation strategy, SIMCARLO -optimisation by Monte Carlo method, SIMGEN -optimisation by a genetic algorithm. Other optimisation procedures can be added. Before starting an optimisation run each simulation problem is automatically transformed into the standard problem of optimisation. On the lowest level of this architecture simulation models, goal functions and internal process models are given explicitly. External static and dynamic simulation models can be implemented without any restriction. Convexity of goal functions is not necessary.

Parameter optimisation and sensitivity

The parameter sensitivity according to the output variables gives information how to select and weight the parameters and on which range and accuracy they have to be treated (fig. 2).

Output variables Goal functions Parameter Sensitivity

Initial parameters

Optimisation procedures

Optimal parameters

Fig. 2. From initial parameter settings to optimal parameter settings.

Three classical approaches to sensitivity analysis can be distinguished: 1. Sensitivity to major parameter changes (The values of some parameters are arbitrarily changed within the expected limits of their validity, and the response of system investigated). 2. Experimental sensitivity analysis (The parameter values p i are changed by a finite amount Δp, the model rerun and model outputs for nominal and changed parameter vectors abstracted. The result depends on Δp). 3. Analytical sensitivity analysis (Sensitivity functions are calculated representing partial derivatives of state variables u j to parameters: S(p i ) = ∂u j /∂p i . This differential method of sensitivity analysis is based on linearisation around the nominal solution by numerical or graphical procedures). This procedure is not covered within this paper. Estimating sensitivity to external parameters, those connected with driving variables or site constants, a picture is getting how a given freshwater ecosystem would behave under different conditions. One particular problem is the sensitivity to sampling intervals or kinds of approximation of driving variables. For internal parameters, those characterising state variables, the goal is to determine the importance of parameters for the approximation of the model to reality. Attention has to be devoted mainly to those parameters, to which the model is most sensitive [START_REF] Luther | Parameter Sensitivity of an Eutrophication Model[END_REF].

Results and discussion

To investigate parameter optimisation and sensitivity a MATLAB based stationary 1D eutrophication model for shallow water bodies [START_REF] Gnauck | Water Quality Management of a Sub-Watershed of the Elbe River[END_REF] was coupled with the commercial optimisation tool ISSOP [START_REF] Krug | Modelling, Simulation and Optimisation for Manufacturing, Organisational and Logistical Processes[END_REF]. Concerning the eutrophication process in figure 3 an overview is presented on direct relationships of important model parameters to model variables. The parameters themselves can be assigned to essential processes in the water body and in the sediment.

The overall growth rate of phytoplankton mainly is determined by the production of algae biomass and the half saturation constants KS P (of phosphorus) and KS NO3 (of nitrate). It is restricted by an upper bound GROW MAX for the nutrient uptake of the phytoplankton. So the constants GROW MAX , KS P and KS NO3 directly affect the phytoplankton concentration and the concentration of the nutrients nitrogen and phosphorus. The nitrogen fraction of algae biomass FA1 and the ammonification rate B 3 directly affect the nitrogen compounds ammonium and nitrate. The half saturation constant KS A in the balance equation of zooplankton affect zooplankton and phosphorus. The filtration rate FRZ of zooplankton has a direct influence on zooplankton only, and the respiration rate RESP of phytoplankton connects phytoplankton and phosphorus.

For the eight parameters GROW MAX , KS P , KS NO3 , KS A , FRZ, FA1, B 3 and RESP initial values (p*) are given (table 1). On the base of these values any parameter p has been varied in the interval p. According to these parameters a ranking of the variables x = {CHA, o-PO 4 -P, NH 4 -N, NO 3 -N} is presented by means of a Hasse diagram [START_REF] Brüggemann | Partial Order in Environmental Sciences and Chemistry[END_REF] in fig. 4 regarding the global differences  ~x = max p,t |x| = max p,t |x p (t)| = max p,t |x p (t)-x p* (t)| . It is noticeable that the four parameters GROW MAX , FA1, KS P , and KS NO3 that directly affect the algae growth form the upper half of all rankings. The lower half is formed by the filtration rate of zooplankton, the respiration rate of phytoplankton and the parameters B 3 (ammonification rate) and KS A (half saturation constant in the balance equation of zooplankton). The two latter mentioned can be seen as parameters of sea-internal driving forces. More precisely all of the four variables show a partition of the parameter set into three groups:

 Group G 1 = { GROW MAX , FA1, KS P } highest sensitivity  Group G 2 = { KS NO3 , B 3 }
mean sensitivity  Group G 3 = { KS A , FRZ, RESP } lowest sensitivity Figure 5 shows the sensitivity of the variables CHA, o-PO 4 -P, NH 4 -N and NO 3 -N according to parameter KS NO3 , figure 6 shows the results for parameter B 3 .

Investigations of parameter sensitivity are essential tasks of the modelling procedure. Especially for complex systems like freshwater ecosystems these investigations are necessary to mark the parameter range of validity for environmental management decisions. The output of the eutrophication simulator investigated is most sensitive for parameters characterising phytoplankton growth and dynamics. A second level of sensitivity is given by parameters describing nutrient cycles within the water body. In opposite of that, the influence of zooplankton to phytoplankton was found very stiff.

Fig. 1 .

 1 Fig. 1. The ISSOP optimisation architecture.

Fig. 3 .

 3 Fig. 3. Direct and indirect relations between model parameters and model variables.

Fig. 4 .

 4 Fig. 4. Hasse diagram of the variables CHA, o-PO 4 -P, NH 4 -N, NO 3 -N.

Fig. 5 .Fig. 6 .

 56 Fig. 5. Sensitivity of the variables CHA, o-PO 4 -P, NH 4 -N and NO 3 -N to KS NO3 . 0 0.02 0.04 -2 -1 0 1 2  CHA ( g/l) 0 0.02 0.04 -2 -1 0 1 2  o-PO4-P ( g/l)

  developed a software tool ISSOP to support manufacturing, organisational and logistic processes. It includes an optimisation interface of MATLAB models. The ISSOP architecture of discrete optimisation methods used is shown in fig. 1.

		Optimisation Methods
	CENUM	DISOPT	EVOL	SIMCARLO	SIMGEN	...
		Universal Parameter Interface
	dynamic		static	internal
	Variables, States,		Amount of Purchase,	...
	Goal Functions	Cost of Setup
	Simulation Models	Explicit Goal	Internal Process	...
		Functions	Models

Table 1 .

 1 Reference values and ranges of the eight parameters.

	Parameter p	Reference value p*	Lower bound p lower = 10%•p*	Upper bound p upper = 200%•p*
	GROWMAX	5	0.5	10
	KSP	30	3	60
	KSNO3	0.1	0.01	0.2
	KSA	0.06	0.006	0.12
	FRZ	0.1	0.01	0.2
	FA1	50	5	100
	B3	0.02	0.002	0.04
	RESP	1.4•10 -5	1.4•10 -6	2.8•10 -5

Group G 1 Group G 2 Group G 3


Ivan Holoubek

Ladislav Dušek

Jana Klánová

Miroslav Kubásek

Jiří Jarkovský

Roman Baroš

Klára Komprdová

Zdeňka Bednářová

Richard Hůlek

Jiří Hřebíček

GENASIS Information System: a Global Environmental Assessment of Persistent Organic Pollutants

Global ENvironmental ASsessment and Information System (GENASIS) is a tool developed by expert teams of the Research Centre for Toxic Compounds in the Environment (RECETOX) and the Institute for Biostatistics and Analyses (IBA) of the Masaryk University in Brno. The aim of GENASIS is to compile validated data on persistent organic pollutants, including their properties, sources, long-term levels, life times, transport mechanisms, effects and risks, scattered throughout various institutions and ministries, and to provide tools for their visualization, analyses, interpretation, assessment of environmental and human risks or modelling of fate. Such a tool should significantly enhance comprehensive understanding of the fate of POPs in the environment, their impacts on ecosystem and the human population.

Introduction

Supporting the sustainable development and management of natural environment means among others prevention of risks and stresses connected to the global changes of climate and pollution. Protection of the environment and biodiversity, as well as prevention of environmentally induced diseases requires implementation of the international measures based on the comprehensive analysis of global trends in environmental as well as epidemiological data.

Background

Although protection of the environment and human health is an objective of several international conventions on toxic chemicals and wastes (namely Stockholm, Basel, and Rotterdam Conventions), the concept of evaluation of effectiveness of their measures have been developed only recently. Global monitoring plan (GMP) as one of the effectiveness evaluation mechanisms under the Stockholm Convention (SC) was designed to addresses the trends in ambient air and human milk levels of persistent organic pollutants and invited the countries to establish the long-term monitoring programmes. Mechanism of evaluation of data collected under the GMP, however, has not been developed yet.

Lack of widely applicable and publicly available environmental databases was identified as a problem also in the SWOT analyses on the national (strategic documents as Long-term directions in research and development in the Czech Republic) as well as European (reports of thematic working group on Environmental sciences of the European Strategic Forum on Research Infrastructures) levels.

Initiation of the GENASIS project

The decisions adopted by the Conferences of the Parties to the Basel, Rotterdam and Stockholm Conventions on enhancing cooperation and coordination among the three conventions (the so-called synergies decisions) invited Parties to coordinate their efforts when implementing the three conventions to ensure close cooperation among relevant ministries and programmes at the national levels. The Research centre for toxic compounds in the environment (RECETOX) together with the Institute for biostatistics and analyses (IBA) answered this call by activating the GENASIS project (www.genasis.cz).

As recommended by the synergies decisions, the GENASIS system provides a national information database available to experts as well as to the general public, useful for preparation of national positions for meetings of the conferences of the Parties and other bodies of the Basel, Rotterdam and Stockholm Conventions, but also for the development of regional policies and measures protecting human health and the environment from the harmful impacts or adverse effects of hazardous chemicals and wastes.

Information database

The GENASIS system is built as a modular structure providing complex services to a wide range of potential users. The initial version of the database launched in 2010 contains data from the long-term integrated monitoring at the Košetice observatory which is a part of the European Monitoring and Evaluation Programme (EMEP) [START_REF] Holoubek | Trends in background levels of persistent organic pollutants at Kosetice observatory, Czech Republic. Part I. Ambient air and wet deposition 1988-2005[END_REF][START_REF] Dvorska | Košetice, Czech Republic -ten years of air pollution monitoring and four years of evaluating the origin of persistent organic pollutants[END_REF], and long-term data from the ambient air MOnitoring NETworks (MONET) in the Czech Republic, Central and Eastern Europe, Africa, Central Asia and Pacific Islands [START_REF] Harner | Passive air sampling for persistent organic pollutants: introductory remarks to the Special Issue[END_REF][START_REF] Klánová | Passive air sampler as a tool for long-term air pollution monitoring: Part 1. Performance assessment for seasonal and spatial variations[END_REF][START_REF] Čupr | Passive air sampler as a tool for long-term air pollution monitoring: Part 2. Air genotoxic potency screening assessment[END_REF][START_REF] Klánová | Monitoring of persistent organic pollutants in Africa. Part 1: Passive air sampling across the continent in 2008[END_REF][START_REF] Klánová | Assessing meteorological parameters on the performance of PUF disks passive air samplers for POPs[END_REF]. All available data are stored in the intranet database linked to an ArcGIS server enabling spatial interpretation of all results. The user has an access to the maps of the sampling sites, their description, as well as to the POPs concentration values at all sites. Descriptive summary statistics and basic analyses of time series (seasonal and long-term patterns, robust trend estimates) is also available for selected data.

Data from the large-scale monitoring of soil, sediment, and surface water [START_REF] Holoubek | Trends in background levels of persistent organic pollutants at Kosetice observatory, Czech Republic. Part II. Aquatic and terrestric environments 1988-2005[END_REF][START_REF] Růžičková | An assessment of air-soil exchange of polychlorinated biphenyls and organochlorine pesticides across Central and Southern Europe[END_REF] will be included in a new version the GENASIS system, which is currently under development and will be launched in 2011. Enhanced analytical tools allowing implementation of algorithms for spatial analyses, and modules enabling comparative analyses of multiple substances or matrices will be also introduced [START_REF] Komprda | Application of an unsteady state environmental distribution model to a decadal time series of PAH concentrations in the Central Europe[END_REF][START_REF] Kubošová | Spatially resolved distribution models of POP concentrations in soil: A stochastic approach using regression trees[END_REF]. Based on the contract signed between RECETOX and the Czech Ministry of Environment, an import of data from external sources will also be initiated in 2011. Compatibility of the GENASIS system with existing databases is crucial in order to assess the environmental patterns, calibrate the indicator systems, and implement the legislation requirements. The fully developed system will serve as an interactive, on-line national POPs inventory of the Czech Republic.

Analytical and interpretation tools

Development of the information system includes accumulation of environmental data from various sources, their validation, followed by the advanced data analyses, interpretation, visualization, and spatial presentation supported by the geographic information systems (GIS).

Available interpretation tools include the POPspedia database providing information on chemicals of interest, detailed description of the sampling sites and sampling techniques, summary description of contamination, analyses of the time series and seasonal variability, comparison of sites, regions, time periods, and analytes, correlation analysis, and model calculators.

E-learning modules focused on the assessment of ecological risks provide the additional support to variety of users. A methodology of the ecological risk assessment starts with the problem formulation, continues with the assessments of exposure, effects, and risks, and is completed by the risk communication and management Fig. 2. GENASIS system architecture 6 Deliverables and impacts of the GENASIS project RECETOX acting as the National POPs Centre of the Czech Republic became a documentation centre gathering available national information on environmental risks and impacts on human health relevant to the three conventions. However, in the role of the SC Regional Centre, RECETOX also offers GENASIS as a global system for information exchange and a clearing-house mechanism for all three conventions. Such a system is very valuable for the evaluation of the effectiveness of the Stockholm Convention measures. Currently, GENASIS hosts all available data from the international MONET project monitoring POPs in ambient air of Europe, Africa, Asia and Pacific.

The international dimension of both, the MONET and GENASIS programmes, provides an opportunity for strengthening technical assistance and capacity building in the field of data collection, interpretation and visualization on the regional and global levels. Special attention is being paid to the needs of developing countries and countries with economies in transition.

By providing access to necessary information, the GENASIS system supports decision-making on the sound management of chemicals throughout their life cycles as well as the sound management of hazardous wastes. It is also an important tool for the development of policies and measures protecting the environment and human health, and supporting sustainable development.

Last but not least, it is a tool for the joint outreach and enhancement of public awareness as an important means of prevention and environmental protection. As such, the GENASIS provided a model of coordination mechanism and example of good coordination practice, and it was selected for publication in the book of Synergies success stories jointly prepared by the Division for Sustainable Development, United Nations Department of Economic and Social Affairs (UNDESA), and Secretariats of the Stockholm, Basel, and Rotterdam Conventions in 2011.

Fig. 1 .

 1 Fig. 1. Temporal trend in PCBs concentration in active air sampling at Košetice station (median and 5-95th percentile; 1996-2008).

Fig. 3 .

 3 Fig. 3. A scheme of the e-learning module focused on ecological risk assessment

Acknowledgments. Development of the GENASIS system is supported by the CETOCOEN project (CZ.1.05/2.1.00/01.0001) of the European Structural Funds, and the INCHEMBIOL project (MSM 0021622412) of the Ministry of Education of the Czech Republic.


Miroslav Kubasek
email: kubasek@iba.muni.cz

Jiri Hrebicek
email: hrebicek@iba.muni.cz

Jiri Kalina

Ladislav Dusek
email: dusek@iba.muni.cz

Ivan Holoubek
email: holoubek@recetox.muni.cz

Semantics Annotations of Ontology for Scenario: Anthropogenic Impact and Climate Change Issues

Keywords: TaToo, semantic web, POPs, Stockholm Convention, GENASIS, SVOD, anthropogenic impact, global climate change

The synthesis of existing Persistent Organic Pollutants (POPs) pollution monitoring databases with epidemiological data is considered for identifying some impacts of POPs on human health. This task requires new, rich, data, services and models discovery capabilities from a multitude of monitoring networks and web resources. The FP7 project TaToo (Tagging Tool based on a Semantic Discovery Framework) is setting up a semantic web solution to close the discovery gap that prevents a full and easy access to web resources. The use of TaToo tools together with software GENASIS and SVOD is discussed as TaToo validation scenario for anthropogenic impact and global climate change influence on POPs trajectory. This paper contains the first propose of POPs and cancer domain ontology intended for TaToo framework.

Introduction

The FP7 project TaToo (Tagging Tool based on a Semantic Discovery Framework) aims to set up a semantic web solution to close the discovery gap that prevents a full and easy access to environmental resources on the web [START_REF] Rizzoli | TaToo: tagging environmental resources on the web by semantic annotations[END_REF]. The core of the project will focus on the development of tools allowing third parties to easily discover environmental resources (data and/or services residing on different information nodes) on the web and to add valuable information in the form of semantic annotations to these resources, thus facilitating future usage and discovery, and kicking off a beneficial cycle of information enrichment.

TaToo validates the usability of its developments through the implementation of three different validation scenarios. All three scenarios are embedded in highly complex environmental domains and are therefore mainly addressed to domain expert groups and communities as well as to technically skilled users.

This paper contains the detailed framework of the Masaryk university 1 validation scenario (MU scenario), which is the anthropogenic impact and the influence of global climate change on this impact [START_REF] Hřebíček | Anthropogenic Impact and Global Climate Change. Description of Validation Scenario in TaToo Project[END_REF], [START_REF] Hřebíček | Validation Scenario for Anthropogenic Impact and Global Climate Change for Tatoo[END_REF]. The purpose of the paper is to give the overview of the MU scenario (background, objectives, and available tools), the definition of users who will use the TaToo tools, describe the possible use cases, and to provide with the mock ups.

Description of the scenario

The MU scenario of TaToo project named Anthropogenic impact and global climate change is managed by Masaryk University (MU). This scenario is dealing with the correlation of environmental pollutants and their health impact on the population and the correlation of transport of environmental pollutants with global climate change [START_REF] Hřebíček | Anthropogenic Impact and Global Climate Change. Description of Validation Scenario in TaToo Project[END_REF], [START_REF] Hřebíček | Validation Scenario for Anthropogenic Impact and Global Climate Change for Tatoo[END_REF]. The aim is to create a central place for researchers, domain experts and decision makers to discover and access interdisciplinary knowledge in more efficient and usable way that is the currently state of the art. Due to the fact that there is an enormous amount of information resources in scientific fields, which is steadily growing, available search mechanisms like search engines, scientific networks and similar technologies are not sufficient to meet the complex requirements of today's researchers and scientists. The result of conventional discovery processes are often not matching the domain context of the users and obligate them the tedious task of filtering large result sets to obtain the original object of the interest of the researcher intended to find with the search. Therefore the need arises for an improving discovery method, which will incorporate the domain knowledge and additional semantic information into the search in order to obtain a more fitting result for the specific context of the user.

The MU scenario not only aims to improve the discovery of scientific resources for one particular domain, but also tries to discover and create new relationships among different domains. The correlation of environmental pollutants including their transport due to global climate change and their health impact on the population is only one significant example of creating new relationships among different domains. These dependencies could represent new scientific insights for already available resources and connect the knowledge of the single domains. These relationships should facilitate further discovery process to deliver matching resources of multiple domains.

The MU scenario represents the close cooperation and joint venture of two university institutes: the Research Centre for Toxic Compounds in the Environment2 (RECETOX) and Institute of Biostatistics and Analyses3 (IBA).

RECETOX is an independent institute of the MU. RECETOX performs research, development, education and expertise in the field of environmental contamination by toxic compounds with specific focus on persistent organic pollutants (POPs), polar organic compounds, toxic metals and their species and natural toxins -cyanotoxins. It is also Stockholm Convention Regional centre 4 for capacity building and transfer of technology in Central and Eastern European countries. The Stockholm Convention on Persistent Organic Pollutants5 (Stockholm convention) is a global treaty to protect human health and the environment from chemicals that remain intact in the environment for long periods, become widely distributed geographically and accumulate in the fatty tissue of humans and wildlife. RECETOX is formed by several research divisions, service laboratories and technology-transfer centres: Environmental chemistry and modelling, Ecotoxicology and risk assessment, Trace laboratory, and Laboratory of data analyses. Research and development of the centre include monitoring of environmental matrices, studies of environmental fate and effects (ecotoxicology) of toxic compounds, ecological and human risk assessment as well as the development of informational and expert systems.

RECETOX launched the first version of the Global Environmental Assessment and Information System (GENASIS) 6 in January 2010 [START_REF] Brabec | GENASIS: System for the Assessment of Environmental Contamination by Persistent Organic Pollutants[END_REF]. GENASIS provides information support for implementation of the Stockholm convention at an international level [START_REF] Klánová | Towards the Global Monitoring of POPs -Contribution of the MONET Networks[END_REF]. The initial phase of the GENASIS project is focused on data from regular monitoring programmes of POPs, providing a general overview of spatial patterns and temporal trends of pollutants concentrations [START_REF] Urbánek | Monitoring and Assessment of Environmental Impact by Persistent Organic Pollutants[END_REF].

IBA is a research institute of the MU, which is focused on delivering solutions to research problems arising in the environment and human health and it is providing related services, especially in the field of biological and clinical data analysis, organization and management of clinical trials, software development and Information and Communication Technology (ICT) applications. IBA research activities are primarily focused on organizational and expert services for large scientific projects. IBA is formed by four divisions: Division of Data Analysis, Division of Clinical Trials, Division of Information and Communication Technologies, and Division of Environmental Informatics and Modelling. For example, IBA created the first web portal for epidemiology of malignant tumours in the Czech Republic, the System for Visualizing of Oncological Data (SVOD) 7 , based on the data from the Czech National Oncology Registry [START_REF] Dušek | Czech Cancer Care in Numbers 2008-2009[END_REF].

Dušek [START_REF] Dušek | The national web portal for cancer epidemiology in the Czech Republic[END_REF] pointed in 2009:"A full-area monitoring of the environmental risk factors in all main environmental components is performed in the Czech Republic. The main objective of this functional monitoring network is the estimation of exposure to xenobiotic substances, and the evaluation of subsequent risks to human health. The system provides information for health risks management and also serves for public education, which is a prerequisite for active care of one's own health. The outputs from monitoring systems may also be used for assessing human risks associated with cancer epidemiology. Data about POPs are of key importance, since these compounds are known to have a wide spectrum of carcinogenic effects, a tendency to bioaccumulation, and are subject to long-distance transport."

The objective of the MU scenario is to use and validate the resulting tagging and discovery framework of the TaToo project. Since the primary scope of the TaToo project is to facilitate the discovery of environmental resources, this scenario delivers the perfect opportunity to validate the resulting solution against challenging real word problems. There are numerous scientific domains available and actively researched at the MU, but two important domains have been carefully chosen to demonstrate and validate the envisioned functionality of the TaToo project. The vision of the MU scenario is that other scientific domains could follow the initial institutes to further spin a new kind of knowledge network to deliver a new generation of tools and methods to effectively and conveniently support the scientific user in their daily work.

Align ontology for POP's and cancer domain

The MU case study ontology is aligned to the TaToo ontology framework through the TaToo Bridge ontology. Importing the bridge ontology enable to have all of the class, property and individual definitions from the Bridge ontology available for use in our proposed ontology. Figure 1 shows classes from the first version of the bridge ontology which are important in proposed align ontology.

Fig 1. TaToo Bridge ontology

Analyze of POP's and cancer interests

By analyzing of both domains, i.e. environmental and human risk assessment areas we identify four levels of proposed ontology system.

The first one named nomenclature contains a set of nominal descriptors identifying key objects (variables) which should be mostly identified in investigation or in research searching for information (POPs -chemical compounds and diseasescancer diagnoses). This level is highly standardized adopting internationally unified, extensively translated nomenclature systems. The proposed framework works with key (major) nomenclature system and additional (supporting) sub-systems. Proposed ontology architecture is relatively rigid in this dimension as the used nomenclature systems are based of widely accepted international consensus. In proposed version of ontology we used ICD-10 class hierarchy (International Classification of Diseases) and recommended POPs and Matrix taxonomy based on Stockholm Convention.

The second level is classifiers which present a attributes determining some key properties of the examined objects (chemical compounds, diseases). Only classifiers highly relevant for exposure environmental studies and risk assessment studies are adopted. In result, the classifiers represent binary codes or multiple categories, typically derived on the basis of some external information reachable from standardized database, evidence-based literature, thesaurus or encyclopedia (properties of given chemical compounds, properties of disease at the time of diagnosis, etc.). The set of attributes is flexible according of used classifiers; e.g. In case of studies focusing on some special topics etc. This level is included in the proposed ontology.

Next level is information source identifiers -necessary descriptors of the source of information which is processed or needed. These attributes also refer to some type of validity scoring because they describe type of studies and other information sources which can be regarded as relevant. Furthermore, this set of attributes allows the users to specify studied problem or scientific field to be inspected. Representative from this level in proposed ontology is ProjectType showed in Fig. 2.

Fig 2. Example of information source identifiers -ProjectType

Last level contains obligatory descriptors which represent family of variables describing the lowest level of the information processed or searched for. It means set of variables which are obligatory to understand numerical values reached in searching engines (units, sampled, matrices, epidemiological measures, time and site specification, etc.). These descriptors are strictly obligatory and represent valid information, i.e. only information, numerical value supplied with these descriptors can be regarded as valid and trustworthy.

Identification of basic concepts

By analyzing of our two domains (POPs contamination and cancer epidemiology) we identified number of concepts. Below we describe several of them, which are necessary for understanding of proposed concept and proposed connections with MERM (Minimal Environmental Resource Model) concept [START_REF] Pariente | A Model for Semantic Annotation of Environmental Resources: The TaToo Semantic Framework[END_REF]. The MERM concept has been developed following the NeOn methodology [START_REF] Gómez-Pérez | NeOn Methodology in a Nutshell[END_REF]. The MERM describes the structure of resources and annotations in TaToo, being a reference data model for both services and user interfaces.

CancerTopic -defined as sub-concept of the MERM Topic concept and represent the possible keywords belonging into cancer risk domain.

POPsTopic -defined as sub-concept of the MERM Topic concept and represent the possible keywords belonging into POPs domain.

CancerAnnotation -defined as sub-concept of the MERM Annotation concept and represent the group of annotations connected with cancer risk domain.

POPsAnnotation -defined as sub-concept of the MERM Annotation concept and represent the group of annotations connected with POPs domain.

Matrix -define the taxonomy of possible matrixes and this concept is connected with POPsTopic sub-concept.

Disease -define the taxonomy of possible diseases. Because our domain is restricted to cancer epidemiology, we also define sub-concept Cancer and example class BreastCancer. These sub-concepts are also connected with ICD10 taxonomy of diseases. This taxonomy is used through connection with Diagnose class (sub class of CancerTopic).

In proposed ontology are also others concepts, but they have similar structure as classes mentioned above. We plan later extend these concepts with all aspects mentioned in chapter 2.1.

Alignment domain ontology to the TaToo ontology framework

The TaToo Ontology framework comprises the bridge ontology, a number of domain ontologies, and a number of alignment ontologies. The framework is not limited by a number of different domain ontologies, that is, by a number of different environmental sub-domains whose resources are managed by the TaToo system. However, in order to be able to plug-in a domain ontology in the TaToo ontology framework, the domain ontology needs to be accompanied by an appropriate mapping interface. This mapping interface is identified in the framework as an alignment ontology.

There are two possible alignment strategies for mapping these ontologies. The first mapping strategy, uses the rdfs:subClassOf construct defined in the RDFS to map concepts of the domain ontology to the concepts of the MERM and bridge ontology. The second mapping strategy, assumes the usage of the owl:equivalentClass and owl:sameAs constructs defined in OWL.

For mapping of our domains to the TaToo ontology framework we used the first strategy. Mapping is based on four ontology concepts: CancerAnnotat, POPsAnnotat, CancerTopic and POPsTopic. Fig. 3 illustrates these mappings and connections between them.

Description of basic concepts

We describe in this section some of identified concepts in more detail. These examples are not trivial and show an interesting perspective of described domains from the ontology design point of view.

First one is illustrated at Fig. 4, where is presented POPsTopic class, which is the super class of Topic from the MERM ontology. It has sub classes Matrix, and Compound which are connected with POPs domain and enable to classify annotated resource. First contain information about used matrix and the second information about monitored compound.

Conclusions

This paper contains the first initial proposal of align domain ontology for POPs contamination and cancer epidemiology domains. The development of domain ontologies in the TaToo Ontology framework is an evolutionary process, so we plan to extend them by adding new specific concepts and relationships that are currently not provided.

Fig 3 .

 3 Fig 3. Mappings domain concepts to the TaToo ontology framework

Fig 4 .

 4 Fig 4. Definition of POPsTopic class The next one describes the definition of CancerTopic class. We can see on Fig. 5 that this class has a super class Diagnose and a sub class EpidemiologicalMeasures. The class Diagnose is equal with the Cancer class which is sub class of Diseases. The

Fig 5 .

 5 Fig 5. Definition of CancerTopic class

http://www.muni.cz

http://www.recetox.muni.cz

http://www.iba.muni.cz

http://www.recetox.muni.cz/index-en.php?pg=regional-pops-center

http://www.pops.int

http://www.genasis.cz

http://www.svod.cz

Acknowledgments

The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement nr. 247893.


Jiří Kadlec

Daniel P Ames
email: dan.ames@isu.edu

Design and Development of Web Services for Accessing Free Hydrological Data from the Czech Republic

Keywords: Hydrology, web services, interoperability, information system

The target of the open source hydrodata.cz web application and web services is to bridge the gap between modelers and field hydrologists by providing free, publicly accessible precipitation, snow, temperature, and discharge data series from multiple organizations in the Czech Republic using the standardized the CUAHSI WaterML data format and search services. Data providers contributing to this effort include the CUAHSI, NOAA, Charles University experimental watershed network, Czech watershed authorities, and networks of volunteer observers. Time period covered includes the floods of 2006, 2009, 2010 and 2011 as well as the 2007 drought. Adopting the WaterML standard also allows the use of other free third party client side web based and desktop data discovery software toolswhich has the added benefit of making the Czech Republic hydrological data more readily accessible to the international community.

Introduction

Hydrologic simulation models are used extensively by scientists and water management authorities for evaluating the response of water ecosystems to land use and climate changes. A large number of open source simulation models are available, including SWAT [START_REF] Arnold | Large Area Hydrologic Modelling and Assessment Part I: Model Development[END_REF],WASIM-ETH [START_REF] Schulla | Model Description WaSiM-ETH[END_REF], HSPF [START_REF] Donigian | Hydrological Simulation Program -Fortran (HSPF)[END_REF] and AGNPS [START_REF] Young | AGNPS: A nonpointsource pollution model for evaluating agricultural watersheds[END_REF]. Using open source tools for rainfall-runoff simulation (as opposed to proprietary software) can give the researcher better understanding of the approximations used in the model because of access to source code and hence inherent model assumptions and implementation. It also encourages improvement of the model by the user community [START_REF] Gregersen | OpenMI: open modelling interface[END_REF]. The obstacle in implementing either open source or proprietary models by researchers in the Czech Republic is limited availability of precipitation, discharge and soil data for model parameter setup, calibration and validation. Similar to other European countries, the Czech Republic has a long record of systematic meteorological observation and soil mapping. These observations are rarely accessible to the general public. Fig. 1 shows an example of obtaining hourly precipitation data for a recent flash flood event for a small catchment in south-west Bohemia. The process includes contacting several organizations, finding the responsible data managers, signing the data agreement, and transferring the data fee. In each organization contacted by the user, the data manager must connect to an internal database, find any available observation sites, export database query results to text file and deliver the text file to the user.

Fig. 1. Obtaining Precipitation data from Blšanka watershed, Czech Republic

Some free data sources already exist in the Czech Republic. Indeed, as required by the information access law of the Czech Republic and emergency early warning system, many government organizations are required by law to publish real-time records of streamflow on the voda.gov.cz [START_REF]Water Management Information Portal[END_REF] internet server. A user with programming experience may set up a method of automated, scheduled data retrieval from this server (Fig. 2). This approach has already been used by the raft.cz white water rafting information web site [START_REF]Raft.cz Water Sport Portal[END_REF] for the Czech rivers and streams to determine the best time of the year for river navigation. A problem with this approach is large amount of HTML parsing code that requires ongoing maintenance when the structure of the original HTML page changes. Fig. 2. Getting stream stage dataautomated method used by raft.cz

CUAHSI Hydrologic Information System

The Consortium of Universities for Advanced Hydrologic Science Hydrologic Information System (CUAHSI HIS) is a distributed internet system for sharing hydrologic time series field and derived observations from international and U.S agencies, universities, experimental watersheds and other organizations [START_REF] Maidment | Bringing water data together[END_REF]. The system consists of three main components: HydroServer, HydroCatalog and HydroDesktop (Fig. 3). This configuration follows the well-established "publish-findbind" model used throughout the Internet for web services [START_REF]Open Geospatial Consortium[END_REF].

Fig. 3. Main components of the CUAHSI hydrologic information system

HydroServer

A "HydroServer" is any server connected to the Internet which exposes a standard web service defined by CUAHSI [START_REF] Tarboton | The HydroServer Platform for Sharing Hydrologic Data[END_REF]. Primary web methods of the web service include GetSites, GetSiteInfo and GetValues. The GetSites method returns all sites with available data. GetSiteInfo returns the list of variables available for a specific site. GetValues returns the time series for the specified site, variable and time interval. The results of the web methods are returned in the standardized WaterML format [START_REF] Zaslavsky | CUAHSI WaterML[END_REF]. WaterML is an extended variant of the XML language and includes specific elements for defining attributes of hydrologic time series. WaterML requires the data publisher to include complete metadata about the time series. Each HydroServer web service method response is a fully self-describing WaterML document which contains metadata for units, data source, method, quality control level, site location and variable data type. CUAHSI is working together with the Open GIS Consortium (OGC) and World Meteorological Organization (WMO) to estabilish WaterML 2.0 which will be fully compliant with the widely used WFS (Web Feature Service) and SOS (Sensor Observation Service) standards. The adoption of services oriented architecture exposes a platformindependent interface which may be implemented by any server based web application technology. A default implementation (HydroServer) is provided by CUAHSI as open source software [START_REF] Horsburgh | An integrated system for publishing environmental observations data[END_REF]. The core of the HydroServer is a Microsoft SQL Server database using the Observations Data Model (ODM) schema [START_REF] Horsburg | A relational model for environmental and water resources data[END_REF]. The comprehensive schema of the ODM database is designed for efficiently storing climate, hydrology and water quality time series observations. The official release of CUAHSI HydroServer requires a virtual private or dedicated server with the Microsoft Windows Server operating system. A simplified implementation of HydroServer on a commercial web hosting provider is described later in this paper.

HydroCatalog

HydroCatalog provides a centralized registry of publicly accessible HydroServers. It exposes a catalog web service with a set of web methods that allow searching for sites and time series across multiple servers. The main web method of the web service is GetSeriesCatalogForBox. Given a latitude and longitude bounding box, keyword, start time and end time, the metadata of all time series which match the search criteria are returned in XML format. In order to be searchable by HydroCatalog, the HydroServer must be registered with a central catalog. A key step in the registration process is the assignment of keywords. For each variable measured by the HydroServer, the matching keyword is chosen from the CUAHSI Ontology Keyword list [START_REF] Maidment | Bringing water data together[END_REF]. After registration, HydroCatalog runs a periodic harvesting operation which ensures that any additional sites or variables may be searched. The CUAHSI catalog, HIS Central, is operated by the San Diego Supercomputer Center (SDSC).

Client Applications

Numerous desktop and web based client applications have been to support the discovery, download and analysis of hydrological time series data made available by the CUAHSI HIS. The desktop applications include HydroExcel and HydroDesktop. HydroExcel [START_REF] Maidment | Bringing water data together[END_REF] supports the download and descriptive statistical analysis of time series from a user specified HydroServer web service. HydroDesktop is an open source geographic information system (GIS)based tool with special features for hydrological data visualization and analysis [START_REF] Ames | Introducing the Open Source CUAHSI Hydrologic Information System DesktopApplication[END_REF]. The map component of HydroDesktop enables detailed spatial analysis of the observation sites. Recently a web based client similar to HydroDesktop has been developed [START_REF]HydroDesktop Web Map Application[END_REF] with the same core functionality as HydroDesktop (search, map, graph and data export). It is expected that additional client applications will be developed in the near future, customized for specific regions or research domains.

Software Design and Implementation

Analysis of Requirements

For a more detailed analysis of data and software requirements we have selected precipitation, air temperature, water stage, discharge and snow cover data requirements. As a first step, the potential data providers with existing meteorological and hydrological observations covering the Czech Republic were examined.

Evaluation of Data Sources

Hourly precipitation time series are published by the Czech Hydrometeorological Institute (CHMI) flood warning service (HPPS) for previous 7 days from automated rain gauge stations. The CHMI radar network provides images of radar reflectivity for previous day in 2 km, 15 minute resolution. A series of combined 24-hour radar / ground station precipitation maps is also available starting January 2007. In these maps, precipitation for each pixel is represented showing discrete color interval. Some parts of the Czech Republic have coverage of radar networks in Germany, Poland and Slovakia. For the southern part of the country, the global satellite derived precipitation data products (PERSIANN) are available [START_REF] Hsu | Precipitation estimation from remotely sensed information using artificial neural networks[END_REF]. Precipitation is also published from automated sites of the Czech watershed authorities on the government water portal [START_REF]Water Management Information Portal[END_REF] and by university experimental watershed networks. Precipitation data are susceptible to large measurement errors. By combining multiple systems, automated quality control can be enhanced. Air temperature hourly time series are available from the Czech watershed authorities and from university experimental watersheds. The existing NCDC Hourly data web service in the CUAHSI HIS system also provides long term temperature time series from several locations in the Czech Republic. Water stage and discharge observations are publicly available on the government water portal [START_REF]Water Management Information Portal[END_REF]. Additional observations are published by municipality owned early warning systems [START_REF] Hladiny | cz Early Warning System[END_REF] and by university experimental watershed networks. Snow cover is an important input parameter for rainfall-runoff modeling during the winter period. Daily observations of snow depth are publicly accessible on the web sites of CHMI and watershed authorities. Several volunteer groups also measure snow depth. This includes HS Brdy (Brdy mountain guard) whose cross country skier observers provide detailed coverage of snow conditions in Brdy, the largest forested mountain are in inland Bohemia [START_REF] Hs Brdy | Brdy mountain guard[END_REF]. Table 1 shows the list of data providers contributing to this system, including the current status.

Implementation of HydroServer for Small Organizations

For smaller data providers, including volunteer observer groups, the main obstacle for providing data in the form of HydroServer web services are associated with hardware and software requirements of the default CUAHSI HydroServer software. These organizations frequently use third-party webhosting services. However, the original HydroServer software was designed to work on large dedicated university servers. Initial testing has shown that third party webhosting providers do not allow running applications under full trust and restrict the usage of some software libraries which were required by the original HydroServer. To address such limitations, the source code of HydroServer was modified, creating a customized product: HydroServer Lite specifically to meet the needs of a Czech HIS implementation. HydroServer Lite may be deployed on any ASP.NET webhosting service, reducing maintenance costs for the organization.

Overcoming Data Security Concerns

The large Czech government supported organizations (CHMI, watershed authorities) were initially reluctant to make any changes on their servers in support of the hydrodata.cz web services and the CUAHSI HIS system. As a result, an alternative strategy has been implemented (Fig. 4). The hydrodata.cz web server has been set up on a large third party webhosting service. Each day, an automatic scheduled task is launched. This program sends a request to the CHMI web pages, parses the HTML code using regular expression template, extracts time series observations and stores these to the hydrodata.cz database. The standard HydroServer web service is built on top of this database. In case the formatting of the CHMI web page changes significantly, a message is sent by the system, notifying the hydrodata.cz manager that a change in the html parser template is required. With increasing future awareness of the CUAHSI HIS system and more emerging applications, it is expected that the CHMI and other organizations will gradually implement the HydroServer web services directly on their own servers as HIS becomes more widely adopted.

Fig. 4 .

 4 Fig. 4. Adaptation of HydroServer for Czech Republic Snow HydroServer web service

Table 1 .

 1 Organizations from the Czech Republic contributing to CUAHSI web services

	Organization	Type of observations
	CHMI	Snow depth, precipitation, stage, discharge
	Povodí Ohře	Snow depth, precipitation, air temperature, stage,
		discharge
	Povodí Vltavy	Precipitation, stage, discharge
	Charles University Prague Water stage, precipitation, temperature, solar
		radiation, conductivity, dissolved oxygen
	HS Brdy	Snow depth

Conclusion

The Czech Republic has become one of first regions in the world outside North America full, countrywide coverage of snow and precipitation data made publicly accessible through the CUAHSI HIS (Fig. 5). After fully integrating discharge records, this will allow to re-calculate the surface water balance of any watershed in the country. Other potential applications include hydrology education and agricultural management and planning. The hydrodata.cz web services and application are in the prototype testing stage. By developing the services as an open source system, the authors are convinced that contributions from new users will lead to improvement in system usability. Major developments planned in near future include: (1) Integration of satellite and radar estimates of precipitation, which requires more efficient handling of gridded datasets, (2) Integration of downscaled global climate model outputs, (3) Addition of publicly accessible services providing soil and land use maps using the WMS / WCS standards, (3) Inclusion of more volunteer meteorological observer networks in the system, and (4) Implementation of pay-per-access method to enable access to data sets from institutions which require the purchase fee. By implementing an automated internet payment system, more users will be able to promptly access data sets which require additional financial support. Adopting the WaterML standard also allows the use of other free third party client side web based and desktop data discovery software toolswhich has the added benefit of making the Czech Republic hydrological data more readily accessible to the international community.