Ioannis N Athanasiadis

Challenges in Modelling of Environmental Semantics

Keywords:

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Environmental modeling, almost since its infancy, was challenged with issues of integration and reuse. Today it has become natural to conduct integrated studies by putting together data and models originating from diverse sources. The process starts with the selection of suitable models, i.e. capable of producing the desired outputs directly, or outputs that can be easily transformed to the desired ones. Then, model input requirements needs to be matched with data, so that the models can be executed.

While this simplification makes it sound as an easy task, in the contrary the reality is very challenging. This process is never a two-step action, rather an on-going, iterative process: data limitations have an impact on the models chosen, and model performance drives the needs for additional data sources. At the same time, questions to be answered change with the better understanding of the system, so that more aspects are covered: the better we understand the system behavior via simulations the more we change it. In this respect, scientists performing integrated modeling are challenged to develop skills that span from tedious data reformatting to advancing science, by creating new models. Integrated modeling is challenged with developing methodologies that manage with the inherit properties of environmental data and models.

Environmental data are spatiotemporally referenced, but (more importantly) uncertain to some degree, as they inherit the measurement instruments' failures, biases and noise [START_REF] Rizzoli | Delivering environmental knowledge: a semantic approach[END_REF]. At the same time, environmental data is a resource in scarcity. Already in Agenda 21, it was highlighted that "the gap in the availability, quality, coherence, standardization and accessibility of data between the developed and the developing world has been increasing, seriously impairing the capacities of countries to make informed decisions concerning environment and development." [START_REF]UN Earth Summit: Agenda 21[END_REF] Today, we experience the lack of information not only in the developing countries, where limited data records are available, but also in the developed ones, as we are flooded with data, which are not universally accessible. Environmental data are often hidden in silos, encoded with poor standards, in legacy systems, and some times are not available digitally, or human intervention is needed to access them. Issues of copyright and licensing, though changing fast, still limit open access to environmental data. Despite the abundance of data available still we need scientists to scout for data that are needed for integrated studies.

Environmental models inherit the complexity, uncertainty, scaling, and integration qualities from the physical world [START_REF] Athanasiadis | A roadmap to domain specific programming languages for environmental modeling: key requirements and concepts[END_REF], which are observed as characteristic properties of the environmental systems. Rizzoli and Young in [START_REF] Rizzoli | Delivering environmental decision support systems: Software tools and techniques[END_REF] summarized environmental systems as heterogeneous, spatiotemporal dynamic systems, with stochastic and periodic components. [START_REF] Denzer | Generic integration of environmental decision support systems -state-of-theart[END_REF] [START_REF] Denzer | Generic integration of environmental decision support systems -state-of-theart[END_REF] to overcome the problems in environmental model integration insisted on model abstraction, communication and generality as three essential tests for model integration. Undoubtedly, most models today wouldn't pass those tests.

Model implementations today are poorly designed and documented, as they have been originally developed for single, or limited use. Model reuse, composition and chaining via workflows are characteristics that we have never designed for. Furthermore, one needs to consider that when an environmental model is encoded in a programming language, new limitations are introduced compared to the original modeling assumptions. Hardly ever can these assumptions be represented directly in the implementation language of choice; on the contrary, this knowledge resides with the modelers [START_REF] Athanasiadis | A roadmap to domain specific programming languages for environmental modeling: key requirements and concepts[END_REF].

Both data and models encode domain knowledge that resides with the specialists. However this knowledge often is not accessible, and integrated modeling teams need to establish contact with original data and model producers to be trained to use them properly. Undoubtedly, we have not reached a level where data and models come with such a detailed documentation so that third-party scientists can reuse them soundly, or detailed meta-information so that machines can invoke them directly. We are still far away from the vision of a common environmental information space (Figure 1), where agencies, organizations and the public will have unhampered, universal access to environmental data and models.

Semantics for an environmental information space

Common information spaces have been realized in other application areas (ie retail, banking, entertainment, travel, etc), so one could argue that it is a matter of time or resources to happen for environmental information. However, this is not the case due to the subjective nature of environmental information. In contrast to other areas, both data and models in the environmental sector are subject to interpretation. In the case of data collection, attributes measured, instruments used, sampling methods and quality check procedures depend on the particular goal of the specific study. Have the goals been different; one may have selected different equipment or applied different methodologies, which will have led to other results. The same holds for modeling, as theory, scale and boundaries depend on the problem definition. For such reasons, model integration and reuse in a common environmental information space needs to allow for interpretation. There is no universally agreed view of environmental information, which means that we need interpretations relevant for an individual, a project or a community.

Semantic modeling has been proposed as a remedy for overcoming longstanding issues of model integration. In our previous work with Villa and Rizzoli [START_REF] Villa | Modelling with knowledge: a review of emerging semantic approaches to environmental modelling[END_REF] we identified two approaches to semantic modeling. In the mediation approach, formal knowledge is the key to automatic integration of datasets, models and analytical pipelines. The next step, applied experimentally at this stage, is the knowledge-driven approach, where the knowledge is the key not only to integration, but also for overcoming scale and paradigm differences, and automated knowledge discovery. Today, more than ever we are in need for developing common environmental information spaces that enable integrated modeling, following a sharing resource model (Figure 1). Each peer offers data or models, and others are able to discover and reuse them. The prime requirement of such an information space is the need for subjective interpretations: The same data or models can be interpreted differently for different studies. In the mediating approach, the challenge is for semantic annotations that allow for subjectivity. While there have been significant efforts to build domain ontologies by several projects, there was limited take up by broader communities. Apart from a few very basic nomenclatures, the rest of the domain ontologies I have used (or developed) were in one or another way biased by the problem at hand.

The second key requirement for such a common environmental information space is the transferability of scientific workflows. We have experienced times and again how difficult it is to perform the same study even in a nearby location: Data sources are missing, models do not converge, and corrective actions or new assumptions are needed. The major problem here is that the expert knowledge is hidden in model implementation or data archives, and our tools are not capable for manipulating our sources. Expert intervention is needed to "adjust data" and "turn model knobs". A semantically-aware common environmental information space needs to make such dependencies explicit and offer tools to match data offerings with model requirements.

Additionally, a common environmental information space for integrated modeling needs to: a. Overcome obstacles of syntactic interoperability, by offering plug and play services for transforming data sources b. Allow for data and model substitution, to enable model comparison in scientific workflows c. Offer uniform services for output visualization, to allow for less engagement in producing visualizations d. Document results provenance, ensuring the transparency of results e. Allow for uncertainty quantification and error propagation f. Allow for sensitivity analysis Today, a common environmental information space is further challenged by the Internet of the Things: In the years to come we expect an abundance of sensory data to become available at very low cost, at real near time, over the Internet. This has already started to transform our view on performing local studies, engaging with communities and employing participatory methods for data collection. This will change integrated modeling methodologies, as more data will be around, but at the same time it will raise the bar for discovering such information, annotating them and evaluating their added value. A common environmental information space needs to hook up to sensor networks and allow models not only to run again as new data arrive, but to adapt as conditions change.

Another important factor that challenges our view on integrated modeling is the raise of high performance computing and the technologies for manipulating big data.

Hardware acceleration and virtual computing infrastructures already allow massive simulations at a very large scale. However, still there is an entry barrier for making such computing infrastructure available. A common environmental information space needs to provide with seamless access to virtual computing infrastructures.

3

Case study

In the following, I present a case study where we try to meet some of the challenges of integrated modelling with sensor data using semantic technologies. Based on a Greek NGO experience in large carnivores conservation in the mountain ecosystems of northern Greece, we built a generic architecture for wildlife information fusion, sharing and reuse. The ALPINE wildlife modeling system (hereafter, ALPINE for short) is a semantic modelling system for wildlife monitoring, management and conservation. ALPINE aims to demonstrate how live streaming data from animal tracking sensors can be effectively combined with geo-statistical analysis models, in order to assess habitat suitability, and to quantify the risks of wildlife interaction with manmade infrastructures. The overall system architecture (Figure 2) involves three layers of services, and is currently under development using Thinklab [START_REF] Villa | Modelling with knowledge: a review of emerging semantic approaches to environmental modelling[END_REF], the semantic modelling infrastructure of ARIES [START_REF] Villa | Thinklab software repository[END_REF]. The first service layer deals with making available environmental data, originating either directly from sensors, or public and private archives. Animal tracking data from collars and eco-geographical field data and infrastructure networks are made available to a common environmental space. The semantic modelling system smooths out technical details for retrieving and transforming data, and also allows for interpretations tailored to the specific modelling exercise. The ALPINE system simplifies access via using open data protocols and making data discoverable through rich annotations. Open Geospatial Consortium standards have been employed to offer syntactic in-teroperability: Sensor Observation Service [START_REF] Athanasiadis | Towards a semantic framework for wildlife modeling[END_REF] for sharing sensor data, and Web Coverage Service [START_REF]OGC: Sensor Observation Service, Open Geospatial Consortium Standard[END_REF] for datasets of geographical nature. Both field data, collected by ALPINE sensors (i.e. from GPS/GSM collars), and background information are annotated with problem-specific semantics, which offer interpretations for the particular problem and allows data to be matched to models.

Second, the integrated modeling layer employs statistical, geospatial and Bayesian models for ecological niche factor analysis. Models are annotated with problemspecific semantics and made available to a common environmental space. Thinklab semantic modeling engine allows for chaining models in scientific workflows, substituting models with alternatives, and feeding models with data to produce results. Specifically, three kinds of models are made available through the ALPINE system: Geospatial models allow operational interpretations of spatial sources and are typically used for creating derived information from original data, as buffering functions and density analysis. Bayesian models are employed for building probabilistic models in order to incorporate causal associations from evidence. For a more detailed discussion on Bayesian modeling for ecological risk assessments see [START_REF]OGC: Web Coverage Service, Open Geospatial Consortium Standard[END_REF]. Last, Ecological Niche Factor Analysis (ENFA) is a statistical procedure that uses only presence data, suitable to compare distributions among spaces that a population has a reasonable probability to occur using eco-geographical variables and the global space [START_REF] Pollino | Parameterisation and evaluation of a bayesian network for use in an ecological risk assessment[END_REF]. The ALPINE integrates seamlessly these three kinds of models in a platform in order to enable scientists to perform their assessments.

Third, the presentation layer generates maps and reports with the system results. Typically scientists spend adequate amount of time in order to analyse their results and post-process them. The ALPINE system will incorporate such aspects in the workflow, so that maps and reports are generated, as new data arrive in the system and assessments are updated. For this we employ reusable templates that will incorporate model results.

The ALPINE system is intended for scientists who aim to answer questions related to habitat suitability and wildlife-human interactions. It enables scientists to hook up sensor data streams coming live from sensors with geographical information and build scientific workflows to support integrated modeling studies. The ALPINE system tackles some of the semantic challenges for incorporating sensors in integrated modelling studies: The Thinklab modelling engine of ALPINE (a) minimizes human involvement in data preprocessing and manipulation, especially as new data arrive from sensors; (b) makes easier to re-run models, as new data arrive from sensors; and (c) provides tools for exporting results in different formats.

Epilogue

This paper aimed to preview some challenges for integrated modeling through a common information space of semantically shared environmental data and models. I believe we are close in realizing such a vision. Many of the building blocks are already in place. We have several success stories for standardizing nomenclatures, offering data as services through long-term archives, making model available as ser-vices and enabling model composition and execution in local or remote infrastructures. At the same time we are trapped with legacy software and institutional problems that do not allow such a vision to come true. Another significant part I didn't touch in this paper is the human side of the problem. In the current academic and scientific system there are very little incentives for building a sharing culture, which is a prerequisite for a common information space for integrated modeling.

Figure 1 :

 1 Figure 1: A vision of a common environmental information space (Figure from [6])

Figure 2 :

 2 Figure 2: The ALPINE architecture for wildlife monitoring (Figure from [9])

adfa, p. 1, 2011. © Springer-Verlag Berlin Heidelberg 2011

Acknowledgements

Research in this study has received funding by European and national funds from NSRF 2007-2013, OP Competitiveness and Entrepreneurship, Cooperation 2011, in the context of the ALPINE project (grant 11SYN-6-411). I am grateful to Prof Ferdinando Villa for the insightful discussions that led me to the views presented here.


Ralf Denzer
email: ralf.denzer@enviromatics.org

Topics in Environmental Software Systems

Keywords: environmental software systems, ESS, environmental information systems, EIS, environmental decision support systems, EDSS, ISESS, environmental informatics, enviromatics

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

It is probably impossible to define a clear "problem space" or "discipline/domain boundary" for the ICT application domain named "environmental informatics" or "environmental software systems" (ESS). The more concrete one tries to capture these boundaries the larger the problem space seems to get and the boundaries become fuzzy and difficult to grasp. There have been discussions whether this is a "discipline" (a pure computer scientist will say: no), an "applied discipline", a "domain" and so forth. For the remainder of this article I shall call it "field of work" or just "the field" because this sounds neutral enough. Starting early on, there have been attempts to classify what environmental software systems are, to provide an overview of research and application, like for instance in the first paper [START_REF] Page | Environmental informatics -towards a new discipline in applied computer science for environmental protection and research[END_REF] of the first ISESS book [START_REF]Environmental Software Systems[END_REF]. An early book [START_REF]Environmental informatics -methodology and applications of environmental information processing[END_REF] is structuring the field according to the dividing line between technology and application [START_REF]Environmental informatics -methodology and applications of environmental information processing[END_REF]. Reading some of the material today reveals interesting aspects.

Seeing it today, some material looks naïve, but I have always been surprised how many of the hard problems which our field is dealing with today have already been identified 25 years ago, and on what impressive level, considering that informatics as a discipline was in its infancies. My own entry into the field was on September 30 and October 1, 1986, when my boss at the time, Dr. Andreas Jaeschke of KfK (today Karlsruhe Institute of Technology, KIT) organised the first recognised scientific event regarding environmental software systems, figure 1. It was my first day at work after university. The proceedings were published as a KfK report [START_REF] Jaesche | Informatikanwendungen im Umweltbereich[END_REF] and are probably the earliest proceedings published in the field. I remember a talk in which a representative of a German federal state ministry reported about an analysis being carried out towards an integrated state-wide information system. That analysis had revealed that the state operated several hundred information systems relevant for environmental applications (unfortunately that statement was not published in the paper, so you just have to believe me). Needless to say that most of them were incompatible with each other -this just seems to be the way how ICT's grow in large organisations in an uncontrollable fashion -a situation which many readers today may be able to relate to.

Every book or proceedings on ESS has to find a way to structure the content, which is nothing else than trying to structure the current status of the field. Some books try to apply the dividing line: methodology / application while others don't, and clearly each volume is influenced by actual research and policy trends. For instance the 2013 and 2011 proceedings [START_REF]Environmental Software Systems[END_REF][START_REF]Fostering Information Sharing, IFIP AICT[END_REF] have a mixed approach (technology chapters like future internet or semantics vs. application chapters like risk management or climate change) while the 2015 book [START_REF]-Infrastructures, Services and Applications[END_REF] applies a pure ICT structuring approach as follows:

• context articles, including keynotes and discussion papers • information systems, information modelling and semantics • decision support tools and systems • modelling and simulation systems • architectures, infrastructures, platforms and services • requirements, software engineering and software tools • analytics and visualization • high performance computing and BigData During the late 1990's there have been on-going discussions whether an environmental information system (EIS) is something different than an environmental decision support system (EDSS). A workshop report [START_REF] Swayne | Environmental Decision Support Systems -Exactly what are they?[END_REF] of ISESS 1999 [START_REF]Environmental Software Systems Vol. 3 -Environmental Information and Environmental Decision Support[END_REF] is trying to answer that question. In order to avoid exclusions I have used myself the term "EIDSS" for environmental information and decision support system, a terrible acronym. I have personally gone back to the term environmental software system, because it is neutral enough and relatively clear: a software system supporting environmental applications.

Another important topic has been the "meta thing". In the early days, particularly in the German scene which was heavily data centric, there was a never ending discussion about meta-data or meta-information, for instance as far back as [START_REF] Schütz | Bericht der Arbeitsgruppe Metainformation[END_REF]. Sometimes the "meta things" are hyphened, sometimes written together, sometimes written apart, but the never ending mystery of what "the meta things" are has not ceased to exist, although some so-called "meta-data standards" are applied in everyday life today. Still, many colleagues have come to the conclusion that the distinction is artificial because whether something is "meta" or not depends on the use, as has been stated in [START_REF] Schimak | Meta-Information -A basic instrument in developing an Open Architecture and Spatial Data Infrastructure for Risk Management[END_REF]. Actual real world meta-data standards and some service interface designs probably do not hold for long under this viewpoint.

Then it has been obvious for a long time that there is a very strong connection to environmental modelling and simulation, as many applications, particularly those for decision support apply models to investigate alternatives, specifically policy alternatives and planning alternatives. Looking into existing conferences and journals one must acknowledge that there are many more articles about modelling than (at least high quality) articles about ESS, and the only relevant journal, Environmental Modelling & Software, which has first been driven from the modelling side, still finds it a challenge to attract high quality innovative software papers.

This paper is trying to put ISESS into an international context of several related activities, and also aims at identifying typical patterns appearing over and over again in ESS. Starting with the international landscape and a complete record of ISESS events, I will make an attempt to analyse typical ISESS topics, based on a rough review of all ISESS proceedings [START_REF]Environmental Software Systems[END_REF][START_REF]Environmental Software Systems[END_REF][START_REF]Fostering Information Sharing, IFIP AICT[END_REF][START_REF]-Infrastructures, Services and Applications[END_REF][START_REF]Environmental Software Systems Vol. 3 -Environmental Information and Environmental Decision Support[END_REF][START_REF]Environmental Software Systems[END_REF][START_REF]Environmental Software Systems Vol. 4 -Environmental Information and Indicators[END_REF][START_REF]-Environmental Knowledge and Information Systems[END_REF][START_REF]Environmental Risk Assessment Systems, International Federation for Information Processing[END_REF][START_REF]Dimensions of Environmental Informatics, International Federation for Information Processing[END_REF][START_REF]Environmental Software Systems[END_REF].

2

The Conference Landscape

EnviroInfo

The first symposium [START_REF] Jaesche | Informatikanwendungen im Umweltbereich[END_REF] developed from 1987 on into a conference name "Symposium Informatik im Umweltschutz" which later became the "EnviroInfo" conference series. In parallel a working group named "Informatik im Umweltschutz" (computer science for environmental protection) 1 was installed as a committee of the German Informatics Society 2 . That working group can be considered as the nucleus of the field in the early days. EnviroInfo has been held in Germany and neighbouring countries since 1997. For an overview dated in 2011 see [START_REF] Pillmann | EnviroInfo Conferences: Knowledge Exchange Platform for Information Technology in Environmental Sustainability Research, State 2011 and Outlook[END_REF].

ISESS

The IFIP working group WG 5.11 "Computers and Environment" was founded in 1992, and was the first attempt to make the field, which was very much dominated by German speaking countries, more international. ISESS 1995 was the first event organised by the WG and it has been organised since on a biannual schedule. ISESS attempts to provide a world-wide coverage of ESS topics and has been held in North America, Europe and Australia / New Zealand (see sections 3 ff.)

MODSIM

The Modelling and Simulation Society of Australia and New Zealand (MSSANZ3 ) has been organising the MODSIM conference since the 1970's. MODSIM has a thematic relationship to our field in the sense that there are always sessions related to environmental modelling and for about 10 years there have some been sessions related to environmental software which were co-organised by individuals of WG 5.11 in order to bridge the gap between the modelling and the software community. MODSIM is held in Australia and New Zealand, the last one being held in 2013 [START_REF]MODSIM2013, 20th International Congress on Modelling and Simulation. Modelling and Simulation Society[END_REF].

IEMSS

The International Environmental Modelling & Software Society4 has been organising the IEMSS conference since 2002, and ISESS respectively WG5.11 have often coorganised sessions about environmental software at IEMSS. IEMSS has a strong focus on modelling, yet at the same time a considerable thematic overlap with ISESS in the field of software. IEMSS has a world-wide coverage of topics and has been held in Europe and North America. The last IEMSS conference was held in 2014 [START_REF]Proceedings of the 7th International Congress on Environmental Modelling and Software[END_REF].

The White Spots on the Map

None of the above conferences has ever been held in South America, Africa or Asia. These parts of the world are to be covered yet.

The ISESS Conference Series

As illustrated above, the International Symposium on Environmental Software Systems (ISESS) is one of several overlapping forums discussing issues of environmental information systems, environmental decision support systems, environmental software systems, environmental informatics, eco-informatics or enviromatics. ISESS was founded by Ralf Denzer and Gerald Schimak in 1995, with support from the German Informatics Society Working Group 4.6 "Computer Science for Environmental Protection", the International Federation for Information Processing (IFIP) Working Group 5.11 "Computers and Environment" and our friend David Russell at the Pennsylvania State University (PSU) campus in Malvern, PA. The first symposium received great support from PSU and turned out to be one of many success stories.

Since then the symposium has been held in the following countries: the United States, Canada, Austria, New Zealand, Portugal, the Czech Republic, Italy; in several years, joint sessions were held in conjunction with the biennial meeting of iEMSs (International Environmental Modelling and Software Society). ISESS has been an IFIP event since 1995 and is organized by WG 5.11.

Since its establishment WG 5.11 has been led by Giorgio Guariso (1991-1999), Ralf Denzer (1999Denzer ( -2005) ) and Dave Swayne (2005Swayne ( -2011)). Since 2011 it is in the hands of Gerald Schimak. Several individuals have served as vice-chairs and secretaries of the WG and many members of the WG have been active supporters for a long time without holding an official position.

For more than two decades ISESS has brought together researchers dealing with environmental information challenges trying to provide solutions using forwardlooking and leading-edge IT technology.

4

A Complete History of Events

ISESS Conferences

During the past 20 years we have seen the publisher of IFIP change several times and electronic publishing has become the most important medium for scientific publications. ISESS has followed this transition and therefore there is not a single place where all ISESS proceedings can be accessed. The first and second proceedings (ISESS 1995, ISESS 1997) were published by IFIP publisher Chapman & Hall (C&H):

• R. Denzer, D. Russell, G. Schimak (eds.), Environmental Software Systems [START_REF]Environmental Software Systems[END_REF] • R. Denzer, D. A. Swayne, G. Schimak (eds.), Environmental Software Systems Vol. 2 [START_REF]Environmental Software Systems[END_REF] Then IFIP changed publisher and the third proceedings (ISESS 1999) were published by IFIP publisher Kluwer Academic Press:

• R. Denzer, D. A. Swayne, M. Purvis, G. Schimak (eds.), Environmental Software Systems Vol. 3 -Environmental Information and Environmental Decision Support, [START_REF]Environmental Software Systems Vol. 3 -Environmental Information and Environmental Decision Support[END_REF] As print publications were becoming increasingly difficult (particularly their cost for smaller conferences), the fourth and fifth proceedings (ISESS 2001, ISESS 2003) were published by the organizers of the symposium under IFIP ISBN:

• D. A.

ISESS Workshops and Co-Organised Events

In addition to the bi-annual conference in odd-numbered years, the ISESS community has organized workshops and co-organised events in most even-numbered years. These co-organized events were either published in with IEMSS, or as best paper peer-reviewed journal issues.

From the events in 1998 and 2000, the best papers were invited to be extended and improved in a peer review process, and were published as special issues of Environmental Modelling and Software (EM&S) and Advances In Environmental Research (AIER): Along with the first IEMSS conference in Lugano, WG 5.11 co-organised a session which is available through IEMSS:

•
• ISESS Session at IEMSS 2002, Tool integration in environmental decision support systems, in [START_REF]iEMSs 2002 International Congress: "Integrated Assessment and Decision Support[END_REF], see www.iemss.org/iemss2002/special_sessions.phtml#isess, pp.

271-389

The 2004 workshop published a special issue of EM&S, again based on the best contributions at that workshop: [START_REF] Denzer | Environmental Software Systems[END_REF] From March 2015 on, the website www.enviromatics.org will maintain a complete repository of these volumes and will point readers to all other ISESS related publication information.

A Review of ISESS Topics

A rough review of topics of all ISESS books presents the reader with a large variety of topics, software tools and applications. Table 3 shows the tables of content of each ISESS conference publication. In the sequel I would like to point the reader to a selection of papers from each of the proceedings, which I think were typical for the developments at the time:

• 1995 ─ [START_REF] Lenz | Requirements of integrated information systems from an ecological point of view[END_REF]: gives an introduction into requirements of integrated information systems from an ecological point of view ─ [START_REF] Zingler | Complex metadata management in earth observation for environmental research[END_REF]: identifies early on the problems of processing large amounts of earth observation data ─ [34]: based on the example of ecological monitoring, the paper shows how hard it can be to analyse environmental data, and that it may be difficult to grasp; the paper identifies methods and tools from artificial intelligence ─ [START_REF] Mayer | Visualizing the spatial and temporary dynamics of ozone concentration data[END_REF]: shows the use evolving methodologies of scientific data visualization • 1997 ─ [START_REF] Booty | Great lakes toxic chemical decision support system[END_REF]: shows the needs and system design challenges for integrated decision support based on GIS, visualisation, models and data management components ─ [START_REF] Yow | Inside an environmental data archive WWW site[END_REF]: demonstrates an early application of web technologies ─ [START_REF] Burgard | Environmental problems -a transnational solution supporting cooperation of all relevant social forces[END_REF]: is one of the first environmental planning applications demonstrating the integration across national borders ─ [START_REF] Clemen | Integrating simulation models into environmental information systems -model analysis[END_REF]: shows the need to integrate models into information systems for environmental research • 1999

─ [START_REF] Quinn | A decision support system for real time management of water quality in the San Joaquin River, California[END_REF]: identifies real time processing needs for water quality management in a catchment involving many stakeholders with conflicting interests ─ [START_REF] Güttler | An EIS called WunDa[END_REF]: is one of the early papers discussing distributed design of information systems, integrating spatial and fact information coming from different data sources over networks ─ [START_REF] Leon | Water quality model integration in a decision support system[END_REF]: discusses the integration of simulation models for flood management into decision support ─ [START_REF] Argent | Environmental software systems in water resources: problems and approaches[END_REF]: is a workshop report discussing problems and solutions for water-related software systems ─ [START_REF] Swayne | Environmental Decision Support Systems -Exactly what are they?[END_REF]: is a workshop report discussing the boundaries between environmental information systems and environmental decision support systems • 2001

─ [START_REF] Braaten | A framework for using indicators and environmental software systems for analysis of catchment condition[END_REF]: discusses the fundamental role of indicators in the generation of higher level information suitable for decision support ─ [START_REF] Martin | A river biology monitoring system (RBMS) for English and Welsh rives[END_REF][START_REF] Schimak | Environmental monitoring system UWEDAT -a flexible computerized system for monitoring of environmental issues[END_REF]: show typical system designs for environmental monitoring systems ─ [START_REF] Swayne | Putting the decision in DSS[END_REF]: identifies decision theory as baseline for decision support systems ─ [START_REF] Argent | A modelling shell for participatory assessment and management of natural resources[END_REF]: presents requirements and use cases for participatory processes involving a wide stakeholder audience • 2003

─ [START_REF] Pillmann | Integrating environmental knowledge with information and communication technologies[END_REF]: discusses integration efforts on the way from data to knowledge ─ [START_REF] Smiatek | Web service-based mapping tool for meteorology and chemistry transport models[END_REF]: shows the use of services to wrap simulation models ─ [START_REF] Steffens Th | Integration of heterogeneous system components to realize an operational tracking service[END_REF]: presents GPS based tracking for the optimisation of logistics processes ─ [START_REF] Fiala | Workflow model of environmental municipal administration in the Czech Republic[END_REF]: is a discussion of workflow systems for environmental administrations ─ [START_REF] Frysinger | Human factors of environmental decision support systems[END_REF]: presents the reader with a discussion of complexity issues related to human factors of ESS ─ [START_REF] Th | Motivation for an IT framework for the implementation of the European Water Framework Directive[END_REF]: is one of the early papers describing multi governmental reporting, in this case for the European Water Framework Directive; this paper was one of several papers related to projects of the European research framework program, which became an important research driver at the time [START_REF] Denzer | Enviromatics[END_REF] • 2005 ─ [START_REF] Hrebicek | Analysis of environmental information management in the Czech Republic[END_REF][START_REF] Geiger | Examples of public environmental information systems and portals in Baden-Wuerttemberg[END_REF]: give a good perspective on typical government information systems at state and national level ─ [START_REF] Fleming | Sensor web enabling the advanced fire information system[END_REF]: is one of the first papers discussing sensor web enablement ─ [START_REF] Argent | A new approach to integrated catchment modelling: the catchment modelling toolkit[END_REF]: discusses integrated modelling toolkits and their frameworks ─ [START_REF] Andrienko | Interactive aggregation methods for analysing results of environmental modelling[END_REF]: shows how the combination of analysis and visualisation methods are embraced towards interactive environmental data analytics ─ [START_REF] Denzer | ORCHESTRA -development of an open architecture for risk management in Europe[END_REF]: is the first publication of a series of research papers towards environmental service infrastructures -research which has dominated the ESS part of the European 6 th framework research program, see also [START_REF] Th | Service-oriented design of environmental information systems[END_REF] • 2007 ─ the first papers on semantics and ontologies appear, both for use in the data and in the modelling sphere [START_REF] Gendarmi | Water protection information management by syntactic and semantic interoperability of heterogeneous repositories[END_REF][START_REF] Huber | Development and application of component-based generic farm system simulator implementing a semantically enriched integrated modelling framework[END_REF][START_REF] Bügel | Application of semantic services in ORCHESTRA[END_REF][START_REF] Dokas | Ontology to support knowledge representation and risk analysis for the development of early warning system in solid waste management operations[END_REF]] ─ [START_REF] Havlik | [END_REF] is a large project to progress sensor web enablement and its standards ─ [START_REF] Stewart | SADA: a freeware decision support tool integrating GIS, sample design, spatial modelling and risk assessment[END_REF] is one example out of a class of applications devoted to risk management, a DSS integrating various tools, based on free software ─ [START_REF] Quinn | An environmental monitoring data management system for providing stakeholder assurances toward meeting contaminant mass loading policy objectives[END_REF][START_REF] Leger | Communicating air quqlity[END_REF] are two of several articles discussing the communication of information to the public ─ [START_REF] Neteler | Free and open source GIS applications in environmental modelling[END_REF][START_REF] Jolma | Geoinformatica: a modelling platform built on FOSS[END_REF] ─ in Europe, the landscape is progressively understood as part of the Digital Agenda for Europe and the so-called Single Information Space for the Environment [START_REF] Hrebicek | eEnvironment: reality and challenges for eEnvionment implementation in Europe[END_REF]; many papers in the 2001 proceedings [START_REF]Environmental Software Systems[END_REF] are in relationship to this theme ─ along this line, there is a number of articles related to platforms and standards allowing the composition of services, e.g. [START_REF] Gadica | Using negotiation for dynamic composition of services in multiorganisational environmental management[END_REF][START_REF] Jolma | Software architectures for distributed environmental modelling[END_REF][START_REF] Roman | Open environmental platforms: top-level components and relevant standards[END_REF]] ─ this includes infrastructures for semantics [START_REF] Nesic | Towards a semantically unified environmental information space[END_REF] ─ climate change applications are progressing more and more, see e.g. [START_REF] Sander | Integrating climate change in the urban planning process -a case study[END_REF] • 2013 ─ ESS pick up developments of the Future Internet initiative, see e.g. [START_REF] Th | The future internet enablement of the environmental information space[END_REF] ─ mobile applications on smart mobile devices play an increasingly important role; in [START_REF] Kubasek | Mapping of illegal dumps in the Czech Republic -using a crowd-sourcing approach[END_REF] an example is given to use crowd sourcing to cope with illegal waste dumping ─ again many papers are in relationship to pan national information infrastructures, e.g. for reporting [START_REF] Schleidt | INSPIREd air quality reporting: European air quality e-reporting based on INSPIRE[END_REF] ─ [START_REF] Komenda | Visual analytics in environmental research: a survey on challenges, methods and available tools[END_REF] is giving an introduction into visual analytics methods and tools

The most obvious new theme in the 2015 edition of ISESS is High Performance Computing. A workshop is organised in collaboration with the Australian NCI (National Computing Initiative) 8 .

Patterns in Environmental Software Systems?

It is difficult to draw more fundamental conclusions about the nature of the field without a more in-depth analysis. ESS are often defined as software systems, a) which contain heterogeneous space-time information, often geospatial multi-dimensional scientific information with some uncertainty, b) which often support complex decision making processes, c) which often use different software tools and methodologies to solve a problem, d) which often have to integrate data across boundaries of various sorts, e) where often information is re-used beyond its original intention, f) where it is necessary to bridge the gap between science and practical application.

Although there are several overview papers of the field, I have not found a real classification or taxonomy in the literature.

One approach to look at it which I would like to repeat here is one which was developed in a project to define a curriculum on environmental informatics, as part of an EU-Canadian education project [START_REF] Swayne | Teaching EIS Development -The EU Canada Curriculum on Environmental Informatics[END_REF]. While this curriculum was never implemented in terms of course modules, the structure defined by the project is probably still somewhat useful, though some terms may be outdated and it is probably not complete. The idea is to structure the information processing in terms of a level-of-complexity approach, which clearly distinguishes between situations in which information is produced, processed, integrated and re-used. Table 4 shows this proposal.

Whether there are typical patterns in ESS or not, one point is becoming very clear when going through the history of ISESS from back to front, namely that:

The field which we call Environmental Software Systems is progressively defined as a synthesis of infrastructures, platforms, re-usable services and tools which provide end users with local-to-global transparent access to information and services; the challenge remains to supply software developers with improved means to build reliable, integrated, multi-tool systems at reasonable cost.

Fig. 1 .

 1 Fig. 1. First ESS proceedings[START_REF] Jaesche | Informatikanwendungen im Umweltbereich[END_REF]

  Journal Environmental Modelling & Software (EM&S), Volume 16 No. 5 (2001) • Journal Advances in Environmental Research, Vol. 5 No. 4, November 2001

Table 1 .

 1 Swayne, R. Denzer, G. Schimak (eds.), Environmental Software Systems Vol. 4 -Environmental Information and Indicators, International Federation for Information Processing [13] • G. Schimak, D. A. Swayne, N.T. Quinn, R. Denzer (eds.), Environmental Software Systems Vol. 5 -Environmental Knowledge and Information Systems [14] ISESS Conferences Today Springer also has the rights of the early Kluwer and Chapman & Hall books (ISESS 1995, ISESS 1997, ISESS 1999), as Kluwer was merged with Springer in 2004 and Chapman & Hall had been bought by Kluwer in 1997. Therefore all proceedings which were published by a publishing house are in one place today and can be accessed through Springer and Springer Link. They are available as hard cover and/or soft cover, as eBooks and (most of them) as individual articles.

	The sixth and seventh proceedings (ISESS 2005, ISESS 2007) were published, but
	this time as electronic versions under IFIP ISBN:

• D. A. Swayne, T. Jakeman (eds.), Environmental Software Systems, Vol. 6 -Environmental Risk Assessment Systems

[START_REF]Environmental Risk Assessment Systems, International Federation for Information Processing[END_REF]

• D. A. Swayne, J. Hřebíček (eds.), Environmental Software Systems, Vol. 7 -Dimensions of Environmental Informatics

[START_REF]Dimensions of Environmental Informatics, International Federation for Information Processing[END_REF]

The eight proceedings (ISESS 2009) were again published electronically, but not under IFIP ISBN:

• D. A. Swayne, R. Soncini-Sessa (eds.), Environmental Software Systems, Vol. 8,

[START_REF]Environmental Software Systems[END_REF]

Today, Springer is the official IFIP publisher and ISESS is published by Springer in IFIP Advances in Information and Communication Technology (AICT)

5

. Proceedings number nine and ten were published as follows:

• J. Hřebíček, G. Schimak, R. Denzer (eds.), Environmental Software Systems, Vol. 9 -Frameworks of eEnvrionment, IFIP AICT 359 [5] • J. Hřebíček, G. Schimak, M. Kubásek, A.E. Rizzoli (eds.), Environmental Software Systems, Vol. 10 -Fostering Information Sharing, IFIP AICT 413

[START_REF]Fostering Information Sharing, IFIP AICT[END_REF]

and the current conference, ISESS 2015 in Melbourne is again a Springer book in the IFIP AICT series

• R. Denzer, R. M. Argent, G. Schimak, J. Hřebíček, (eds.), Environmental Software Systems, Vol. 11 -Infrastructures, Services and Applications, IFIP AICT 448

[START_REF]-Infrastructures, Services and Applications[END_REF]

Table 2 .

 2 ISESS workshops and co-organised events at IEMSS

	• ISESS Special Session at IEMSS 2006, Integrated software solutions for environ-
	mental problems -architecture, frameworks and data structures, in [23], see,
	www.iemss.org/iemss2006/sessions/s5.html
	• ISESS Special Session and Workshop at IEMSS 2008, Session S1: Data and sensor
	networks and environmental modelling and Workshop W1: Creating robust sensor
	networks architecture and infrastructure, in [24], see
	www.iemss.org/iemss2008/index.php?n=Main.S1
	• ISESS Special Session at IEMSS 2010, Interaction Design for Environmental In-
	formation Systems, in [25], see www.iemss.org/iemss2010/Volume3.pdf
	• ISESS Special Sessions at IEMSS 2014, Session A4: Smart and Mobile Devices
	for Environmental Applications, Session A5: Parallel Simulation of Environmental
	Phenomena, Session A6: Semantics, Metadata and Ontologies of Natural Systems,
	in [26], volume 1, see:
	www.iemss.org/sites/iemss2014/papers/Volume_1_iEMSs2014_pp_1-602.pdf

• Journal Environmental Modelling & Software (EM&S),

Volume 22 No. 4 (2007)

, pp.415-448, Special Section : Environmental Risk and Emergency Management, for an introduction see

[START_REF] Quinn | Environmental Risk and Emergency Management[END_REF]

In 2006, 2008, 2010 and 2014, WG 5.11 was again co-organizer of specific sessions at IEMSS conferences.

2009 Second Editions and Enviromatics.org

	• R. Denzer (ed.), Environmental Software Systems Vol. 4 -Environmental Infor-
	mation and Indicators, Second Edition [27]
	• R. Denzer (ed.), Environmental Software Systems Vol. 5 -Environmental
	Knowledge and Information Systems, Second Edition [28]
	• R. Denzer (ed.), Environmental Software Systems, Vol. 6 -Environmental Risk
	Assessment Systems, Second Edition [29]
	• R. Denzer (ed.), Environmental Software Systems, Vol. 7 -Dimensions of Envi-
	ronmental Informatics, Second Edition [30]
	• R. Denzer (ed.), Environmental Software Systems, Vol. 8, Second Edition,

The proceedings of

2001, 2003, 2005, 2007 and 2009

were published under IFIP copyright in different formats (print, CD, USB) and were neither available from Springer nor on-line at one central place. Following an official request in November 2014, IFIP granted me the right to re-publish these proceedings. They are now available as second-editions, for now in a relatively simple form, as one downloadable PDF. They will be available permanently as follows:

Table 3 .

 3 ISESS tables of content (omitting keynotes, workshops and tutorials)

	Event Topics
		Environmental information systems
		Modelling and simulation
		Environmental management
	1995	Decision support
		Distributed environmental information
		Artificial intelligence applications
		Environmental data visualization
		Ecological and agricultural applications
		Decision support
		Environmental information systems and meta information
	1997	Industrial applications
		GIS applications
		Modelling and simulation
		Object orientation
		Environmental information systems tools and techniques
	1999	Environmental information systems implementations
		Environmental decision support systems
		Environmental indicators
	2001	Environmental modelling Environmental information systems
		Environmental decision support systems
		Environmental information systems
		Environmental information services
	2003	Environmental assessment, modelling and simulation Integration
		Environmental knowledge and decision support systems EC 5 th framework applications
		Special session on successes and failures
		Corporate and public environment information systems
		Tools and techniques
	2005	Wide scale monitoring Risk management
		State information systems
		Applied decision support systems
		Integrative tools

  are papers on evolving open source software in the area of geographical information systems • 2009 ─ [73] describes the ICT-Ensure project, which has amongst others established a literature repository around the EnviroInfo community 6 ─ [74] discusses coupling of models using OpenMI ─ [75] is one of the first papers on crisis management; ISESS here and there overlaps with the community on ICT for crisis management 7 ; see also [76] ─ [77] is one of many papers to come (and yet to come) on climate change • 2011

Table 4 .

 4 Proposed enviromatics curriculum, taken from[START_REF] Denzer | A Computing Program for Scientists and Engineers -What is the Core of Computing?[END_REF]

	I: WHY ENVIROMATICS ?	II.E Decision Support
	1. History of Enviromatics Developments	19. Target Groups for Decision Support
	2. Application Areas	20. IT Techniques and Systems for Decision Sup-
	3. Introduction into the ECCEI Course	port
	4. Introduction into the Common Example	21. Scenarios
		22. Presentation in DSS
	II: ENVIROMATICS BASE METHODS
	II.A Problem Definition and System Analysis	III: ENVIROMATICS INTEGRATION
	5. Problem Definition	METHODS
	6. System Analysis	III.F EIS Interoperability
	II.B Data Management / Information Modelling	23. Integration Problems
	7. Environmental Data, Data Preparation and	24. Environmental Data Standards
	Acquisition	25. Building and Managing Environmental Data
	8. Monitoring	Networks
	9. Environmental Databases and Environmental	III.G Meta Information Systems
	Information Systems (EIS)	26. Properties of Meta Information
	10. Information Modelling	27. Environmental Data Catalogs
	11. Meta Information in Environmental Databases	28. Environmental Catalogs on the
	II.C Data Analysis	World Wide Web
	12. Environmental Statistics	29. Multilingual Information Systems
	13. Geographical Information Systems	III.H Open EIS Architectures
	14. Visualization	30. Properties of Open EIS Architectures
	II.D Diagnosis and Interpretation	31. Review of Architectures
	15. Environmental Risk and Impact Assessment	32. Generic EIS Infrastructures
	16. Environmental Models
	17. Environmental Indicators
	18. Diagnosis and Artificial Intelligence

adfa, p. 1, 2011. © Springer-Verlag Berlin Heidelberg 2011

adfa, p. 2, 2011. © Springer-Verlag Berlin Heidelberg 2011

http://enviroinfo.eu

http://www.gi.de

http://www.mssanz.org.au

http://www.iemss.org

http://www.springer.com/series/6102

http://www.iai.kit.edu/ictensure

http://www.iscramlive.org

http://nci.org.au


Katharina Schleidt
email: katharina.schleidt@umweltbundesamt.at

Evolution of Environmental Information Models

Keywords: Environmental data and service modelling, extension of standardized data models, collaborative data modelling, semantic alignment approaches, processes and governance structures for collaborative data modelling

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Access to environmental data is necessary for environmental research and control; often, this requires the use of data from different sources. This in turn entails a tedious process of accessing data in various formats, identifying the relevant concepts in the various data, then aligning and merging this data before the necessary analyses can be performed.

To ease this process, various initiatives have been launched around the world with the aim of providing standardized access to environmental data. While these initiatives differ in thematic focus, spatial extent or governance, they all strive to provide standardized data models and service specifications for the access to and use of environmental data.

Based on these developments, easy access to standardized and harmonized environmental data should be a simple process. Unfortunately, this initial impression is deceptive. Many of the existing standards only cover the core concepts of a domain; extensions are left to the thematic communities using these standards. As these thematic extensions grow and develop, they rapidly introduce new concepts not aligned with similar concepts stemming from a different thematic area [START_REF] Tóth | A Conceptual Model for Developing Interoperability Specifications in Spatial Data Infrastructures[END_REF], we find ourselves dangerously close to the starting point.

Thus, an ongoing revision process becomes necessary, with many similar characteristics as the initial standardization process, but with additional challenges such as:

• A wider participant scope, as the adoption of the existing standardized data models leads to wider uptake; • More complex concepts, as the scope widens through extensions; • Less governance, as officially the problem is considered to be solved. This paper sketches the requirements for such ongoing collaboration on a standardized data model for environmental data. It gives an overview of the types of problems already identified as well as those likely to happen in the foreseeable future. Based on these findings, it describes possible mechanisms to support this process.

Background

Various initiatives, stemming from various environmental sub-domains, have been launched in the last years with the goal of allowing for easy access to relevant data through the standardization of data models and service specifications. These include thematically narrow initiatives such as the Long Term Ecological Research or Biodiversity communities (see the ILTER 1 , GBIF2 and TDWG3 sites) as well as thematically broad initiatives such as Research Data Alliance4 or the European INSPIRE Initiative [2]. While data standards are often in place for core concepts, there is always a need to extend these in order to support new or alternative requirements.

In this paper, we use the European INSPIRE Initiative [2] as an illustrative example of both the challenges faced in such an undertaking as well as to provide examples of ways to manage these problems. However, the challenges described are the same for all initiatives attempting this task, and thus the conclusions reached have a wider validity.

The INSPIRE Directive

The Due the wide diversity of formats and structures in which spatial data are organized and accessed in the Community, data specifications have been provided to facilitate the use of spatial data from different sources across the MS's. Network services for sharing spatial data between the various levels of public authority in the Community make it possible to discover, transform, view and download spatial data and to invoke spatial data and e-commerce services.

Process and stakeholders

Before we go into technical details, we must first consider the processes required for the creation, and subsequent extension of a harmonized data model, as well as the various stakeholders involved in this process. According to Craglia [START_REF] Craglia | Building INSPIRE: The Spatial Data Infrastructure for Europe[END_REF], INSPIRE has some characteristics that make it particularly challenging [START_REF] Tóth | A Conceptual Model for Developing Interoperability Specifications in Spatial Data Infrastructures[END_REF]:

1. The infrastructure is built on those of 27 Member States of the European Union in more than 23 languages. This requires the coexistence and collaboration of very different information systems, professional and cultural practices, 2. Given this complexity, it was necessary to adopt a consensus-building process, involving hundreds of national experts, to develop the technical specifications for INSPIRE, 3. Existing standards must be tested in real distributed and multilingual settings, 4. Standards that are not mature enough, or leave too much room for different interpretation (because of the legally mandated implementation) have to be refined, 5. Standards which do not yet exist must be developed, 6. Inconsistency and incompatibility of data and metadata must be addressed for the 34 themes that fall within the scope of the Directive [START_REF] Tóth | A Conceptual Model for Developing Interoperability Specifications in Spatial Data Infrastructures[END_REF] In order to counter these challenges, the following development process was defined: Once the data specification development was advanced to the point where the TWGs were satisfied with their thematic data models, these data models together with their specification were made available to interested parties from the European Member States (MS). The MS were encouraged to test these preliminary data models both through manual scrutiny of the data specifications provided as well as by filling these data models with data stemming from their national data holdings. The feedback from this testing and validation process was in turn sent to the responsible TWGs, for a second round of data specification development (with all ensuing feedback loops illustrated above). More information on the INSPIRE data specification development process can be found in the JRC deliverable D2.6 Methodology for the development of data specifications [START_REF]D2.6 Methodology for the development of data specifications Retrieved from[END_REF] Unfortunately, while the implementation, testing and validation step performed by the MSs did give much valuable feedback on thematic requirements, it provided little support for the task of harmonizing and aligning the data model itself. This is due to the fact that this thematic feedback usually came from dedicated thematic departments within the national environmental agencies as well as other stakeholders. Thus, while let's say the air quality department provided thorough feedback on the requirements stemming from air quality monitoring and the water management group provided feedback on requirements stemming from the water domain, there was little consideration on the fact that similar concepts from these two domains could be encoded in the same manner; this task was left to the TWG members as well as the coordinators from the JRC. Thus, despite best effort from all parties involved, inconsistencies in the INSPIRE data models were not avoidable.

Already within the harmonized INSPIRE data specifications, despite the constraints set out by the INSPIRE Generic Conceptual Model [5], equivalent concepts from individual thematic data specifications vary due to difficulties in alignment across thematic domains. Now that the INSPIRE data and service specifications have been finalized and are being implemented on both the national as well as the European levels, further extensions are being created of these base INSPIRE data models. For example, an extension of the INSPIRE data models for European Air Quality Reporting has been finalized and is now operational5 ; the same approach is currently being followed for various other environmental reporting obligations. While these extended data models will be tailored to the requirements of a specific reporting obligation, they will undoubtedly be adding certain similar concepts such as an European station code to the base INSPIRE models. At present there is no mechanism in place to enable alignment of equivalent concepts across the various pending extensions of the core data models.

Examples

In the following section, we shall provide some examples from the INSPIRE domain.

In the first example, we shall show how inconsistencies crept into the tightly governed INSPIRE data specification process. In the second example, we shall show how this problem will certainly be exacerbated now that the INSPIRE data models are available and being extended for thematic purposes, with little or no governance to assure alignment.

INSPIRE Data Specifications -Tight Governance

In theory, the INSPIRE Generic Conceptual Model (GCM) as well as the Methodology for the development of data specifications govern the data specification process. However, despite vigilance by all parties concerned, discrepancies appeared such as follows:

• In the INSPIRE data specification for Area management/restriction/regulation zones and reporting units, the name of the zone is defined as the INSPIRE base type GeographicalName. • In the INSPIRE data specification for Environmental Monitoring Facilities, the name of the facility is defined as the ISO 19103 type CharacterString.

Fig. 2. Example -Name in INSPIRE

The rationale behind this difference is that within the TWG responsible for the Environmental Monitoring Facilities theme the decision was reached that the complexity of the Geographical name type was far greater than the requirements for Environmental Monitoring Facilities (see cost/benefit considerations in the INSPIRE data specification cycle shown above). However, this is sure to cause confusion in developers creating applications for multiple INSPIRE Themes.

INSPIRE Extensions -Loose Governance

Now that the INSPIRE data and service specifications have been finalized and are being implemented on both the national as well as the European levels, further extensions are being created of these base INSPIRE data models. An extension of the INSPIRE data models for European Air Quality e-Reporting has been finalized and is now operational 6 ; the same approach is now being followed for various other environmental reporting obligations [START_REF] Schleidt | INSPIREd Air Quality Reporting -European Air Quality e-Reporting Based on INSPIRE[END_REF]. While these extended data models will be tailored to the requirements of a specific reporting obligation, they will undoubtedly be adding some similar concepts such as an European station code to the base INSPIRE models. At present there is no mechanism in place to enable alignment of equivalent concepts across the various pending extensions of the existing data models.

In the example below we show the AQD_Station class developed for the European Air Quality e-Reporting and derived from the INSPIRE EnvironmentalMonitoring-Facility Class. Based on the requirements of the underlying air quality directive 2008/50/EC [7] and Commission Implementing Decision 2011/850/EU [START_REF]COMMISSION IMPLEMENTING DECISION of 12[END_REF], in addition to the basic name provided by the INSPIRE EnvironmentalMonitoringFacility Class, a European Station Code must be provided. This attribute has been added to the AQD_Station definition as shown below.

Fig. 3. Example -INSPIRE Extension

The process performed for European Air Quality e-Reporting shall now be repeated for various environmental reporting obligations, in each case the concepts ensuing from the legal requirements stemming from the relevant legislation shall be identified and the base INSPIRE data models will be extended accordingly. The likelihood that a concept as a European Station Code will be required in other thematic domains is high; however, there is no mechanism in place to assure that this will be done in the same manner in each case, leading to subtle differences in these data specifications, and hindering reuse of mechanisms and code when working across thematic areas.

While this divergence could conceivably be managed by the European Environment Agency (EEA) when it pertains to the data specifications required in the environmental reporting domain, there is currently no mechanism in place for such coordination on extensions stemming from other areas or actors (i.e. national thematic extensions).

The INSPIRE Maintenance and Implementation Framework (MIF) is currently being set up between the European Commission and the Member States to guide further development of the INSPIRE process. An expert group called INSPIRE Maintenance and Implementation Group (MIG) with representatives of the INSPIRE national contact points has been established. Of the tasks identified under the Maintenance and Implementation Work Programme (MIWP), the problems discussed here will be addressed by MIWP-14: Theme specific issues of data specifications & exchange of implementation experiences in thematic domains [START_REF]MIWP-14: Theme specific issues of data specifications & exchange of implementation experiences in thematic domains Retrieved from[END_REF].

Lessons Learned

The examples above illustrate how despite best effort by all parties involved there is an inherent creep towards diversification in a data specification process. This can be seen as evidence that despite all the work to date on creating a process for the development of harmonized data specifications, there is still a necessity for further work in this area. In addition, this task becomes more challenging once the governance becomes looser as seen in the INSPIRE extension example. Thus, further mechanisms must be put in place in order to avoid a new level of chaos to creep over the aligned core. Such mechanisms must be defined and put into place in the following areas:

• Governance: the INSPIRE data specification development is governed by the JRC, with increasing support from the EEA. However, this does not cover extensions ensuing from drivers not coming from the European administrative domain. Thus, mechanisms are required enabling a higher level of inclusion among a wider range of stakeholders. • Processes: currently the INSPIRE maintenance process is being defined and various working groups are being created for this work. However, the same problem as pertains to governance is also active here, with little support for Non-European extensions. In addition to inclusion of a wider base of stakeholders, mechanisms will be necessary that facilitate the access of data stemming from outside parties. Also, as illustrated above, even with the rigid processes set up by the JRC for the base INSPIRE data specification work, discrepancies still occurred. • Tools: Various web sites as well as a common repository for the UML data models provide support in finding agreement on necessary extensions to the INSPIRE data specifications. However, the alignment of concepts remains a manual process, depending on the meticulous perusal of the various specifications by humans. If tools were available that support the alignment between different concepts, this would make the alignment process much easier and less prone to human error.

Coming back from the more specific INSPIRE example detailed above to the general question of collaborative extension of environmental data models, it becomes clear that the challenges described in the INSPIRE process apply to other similar initiatives. Similar difficulties encountered in maintaining compatibility among extensions have been seen in the various other thematic data sharing initiatives mentioned earlier. The problems described become ever more difficult to handle the larger and more disparate the user community becomes. A great deal of effort has been put into the development of processes and supporting tools for collaborative data model development by various stakeholders in different thematic and geographic areas, but there is still a long way to go until this fully supports the requirements of the communities involved.

These must also be adapted to allow for thematic extension of existing standardized data models by diverse stakeholders without this leading to a new level of diversification.

Based on examples from the INSPIRE data specification process, we have seen how easily such discrepancies arise, both with tight governance and even more so with loose governance. Only if the problems identified are properly addressed, and the necessary steps taken will it be possible to provide a truly harmonized and usable data system across thematic domains both for now as well as for future requirements.

Fig. 1 .

 1 Fig. 1. Steps in the INSPIRE data specification cycle[START_REF] Tóth | A Conceptual Model for Developing Interoperability Specifications in Spatial Data Infrastructures[END_REF] All the tasks leading to the Data specification development were undertaken by the members of the Thematic Working Group (TWG) constituted for developing the data model for a specific theme. Based on the use cases provided, and utilizing the ISO 191XX suite of standards, data models have been developed for the representation of the data themes listed above. The TWG members were supported by JRC staff in finding points for alignment between themes, as well as making the best use of existing data types stemming from both the underlying ISO standards as well as the INSPIRE base models.Once the data specification development was advanced to the point where the TWGs were satisfied with their thematic data models, these data models together with their specification were made available to interested parties from the European Member States (MS). The MS were encouraged to test these preliminary data models both through manual scrutiny of the data specifications provided as well as by filling these data models with data stemming from their national data holdings. The feedback from this testing and validation process was in turn sent to the responsible TWGs, for a second round of data specification development (with all ensuing feedback loops illustrated above). More information on the INSPIRE data specification development process can be found in the JRC deliverable D2.6 Methodology for the development of data specifications[START_REF]D2.6 Methodology for the development of data specifications Retrieved from[END_REF] Unfortunately, while the implementation, testing and validation step performed by the MSs did give much valuable feedback on thematic requirements, it provided little support for the task of harmonizing and aligning the data model itself. This is due to the fact that this thematic feedback usually came from dedicated thematic departments within the national environmental agencies as well as other stakeholders. Thus, while

  INSPIRE Directive entered into force on the 15th of May 2007. It recognizes that definition and enforcement of European Community policy on the environment requires the easy availability of high quality spatial data. The Infrastructure for Spatial

	sure that the infrastructures for spatial information created by the MS are compatible
	ANNEX I and usable in across administrative levels and national boundaries.
	1. Coordinate reference systems
	2. Geographical grid systems
	3. Geographical names
	4. Administrative units
	5. Addresses
	6. Cadastral parcels
	7. Transport networks
	8. Hydrography
	9. Protected sites
	ANNEX II
	1. Elevation
	2. Land cover
	3. Orthoimagery
	4. Geology
	ANNEX III
	1. Statistical units
	2. Buildings
	3. Soil
	4. Land use
	5. Human health and safety
	6. Utility and governmental services
	7. Environmental monitoring facilities
	8. Production and industrial facilities
	9. Agricultural and aquaculture facilities
	10. Population distribution -demography
	11. Area management/restriction/regulation zones and reporting units
	12. Natural risk zones
	13. Atmospheric conditions
	14. Meteorological geographical features
	15. Oceanographic geographical features
	16. Sea regions
	Information in the European Community (INSPIRE) should assist policy-making in 17. Bio-geographical regions
	relation to policies and activities that may have a direct or indirect impact on the envi-18. Habitats and biotopes
	ronment. The 34 spatial data themes covered by INSPIRE have been structured in the 19. Species distribution
	three annexes to the directive, the themes are as follows: 20. Energy resources
	21. Mineral resources
	INSPIRE is based on the European Member States (MS) spatial information infra-
	structures, that are made compatible with common implementing rules and are sup-
	plemented with measures at Community level. These rules and measures should en-

adfa, p. 1, 2011. © Springer-Verlag Berlin Heidelberg 2011

http://www.ilternet.edu/

http://www.gbif.org/

http://www.tdwg.org/

https://rd-alliance.org/

http://www.eionet.europa.eu/aqportal


Ari Jolma
email: ari.jolma@pji.fi

Anne-Mari Ventelä
email: anne-mari.ventela@pji.fi

Marjo Tarvainen
email: marjo.tarvainen@pji.fi

Teija Kirkkala
email: teija.kirkkala@pji.fi

An Interactive Website for the River Eurajoki

Keywords: Environmental management, Web, Web services, Geospatial, Sensor observation

de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

River Eurajoki in South West Finland has for decades been heavily impacted by human activities. For example the waste water treatment plant (WWTP) of the Eura municipality and two companies discharged raw sewage into the river for a long time due to insufficient capacity of the plant. The plant management kept the bypass secret for many years. Also, for example in May 2011 a paper mill leaked 50 kg of 2,2'diallyl-4,4'-sulfonyldifenol (a chemical used in coating paper and on the Nordic Council of Ministers' list of chemicals that are dangerous for the environment) into its sewer and most of that probably went trough the WWTP into the river. Similar and even worse leaks have happened before. Microbiological studies made this year (2014) from the river water have often revealed concentrations of enterococci and E. coli that are over the limits for water used, e.g., for vegetable irrigation. The use of the river water was forbidden for swimming and irrigation from September 2012 to the summer of 2013 and again in spring 2014. The poor condition has led to citizen activism and much concern among municipalities and environmental authorities [START_REF] Saavalainen | Eurajoki became an open sewer -City of Rauma still using it as a water supply (in Finnish)[END_REF]. "Our Common River Eurajoki" 1 is a joint project carried out by the Pyhäjärvi Institute, University of Turku, and JVP-Eura (the company running the WWTP in Eura municipality). It is coordinated by the Pyhäjärvi Institute and runs from March 2013 to April 2015. The project aims for a long-term sustainable and collaborative platform for improving and managing the river and its catchment by linking citizen activism and environmental monitoring to water quality planning and management. The goal is to increase awareness, facts, and knowledge about the environmental situation while at the same time acknowledging the existing data sources and social media. The work plan of the project comprises development of a website, real-time water quality monitoring, and a sociological study about the relationship between the river and the public. The website is planned to support and link existing activities and to provide access to existing and new data about the river. In this paper we focus on the website the project is developing. The website is online as eurajoki.info.

Using the web for collaboration rather than just as a media for publishing content is a change that has happened in the last ten years [START_REF] O'reilly | What Is Web 2.0: Design Patterns and Business Models for the Next Generation of Software[END_REF]. The term "Web 2.0" has been used to describe the development. According to Wikipedia, a Web 2.0 site may allow users to interact and collaborate with each other in social media dialogue as creators of user-generated content in a virtual community. Collaborative websites can be used for environmental management. Geospatial information and mapping is often a core element of the reported attempts. For example [START_REF] Boulos | Crowdsourcing, citizen sensing and sensor web technologies for public and environmental health surveillance and crisis management: trends, OGC standards and application examples[END_REF] discusses large-scale citizengenerated content in the web and its role in environmental monitoring and crisis management. A collaborative environmental decision support system that uses standards based web services is demonstrated in [START_REF] Sikder | Interoperability in Web-Based Geospatial Applications[END_REF].

2

Materials and Methods

River Eurajoki and its Observation

Eurajoki is a 52.9 km long river in Southwestern Finland with a catchment area of 1336 km 2 . It originates at Lake Säkylän Pyhäjärvi, whose ecological status is good, but there are several point sources polluting the river, and many smaller streams with possibly poorer water quality enter the main branch. Also non-point source pollution from agriculture, forestry, peat production, and rural areas may be significant at times. The average discharge of the river at its mouth is 9.6 m 3 /s. Eurajoki flows into a bay in the Bothnian Sea, which is a part of the Baltic Sea. There are 18 observation and measurement sites along the river and in the database that have been used for hydrological (water quantity and quality) measurements. Some sites have been used for continuous measurements; some have been sampled at regular or irregular intervals, some both. Observations and measurements have been done and/or commissioned by private organizations (Pyhäjärvi Institute is one of them) or companies, and by municipal or governmental authorities. Private citizens also sometimes share observations in social media. The observation/measurement workflow depends on who makes the measurement but it is often rather complex involving more than one party and many types of data cleansing and processing especially in the case of continuous measurements and averaged and derived values.

2.2

Requirements

Requirements engineering is according to [START_REF] Nuseibeh | Requirements Engineering: A Roadmap[END_REF] the process of discovering the purpose of the software by identifying the stakeholders and their needs, and documenting these for analysis, communication, and implementation.

The eurajoki.info website was initiated with several goals in mind: 1) to create a hub for existing sites and social media, 2) to aid in formulating the future water quality management work, 3) to display the existing and new water quality data, 4) to aid in collecting and displaying cultural and experiential data and stories about the river, 5) to display map datasets of the river and its catchment, and 6) to allow access to existing and new reports about the river and its environmental state. It was understood that a website is only one product for water quality data and that there is a need for improved information management in general. Especially support for collaboration and data exchange among individuals and organizations, and publication of data and results was seen important. This strengthened the interest in modern web technologies and distributed systems that are based in standard protocols.

Web mapping and presentation of water quality data were core requirements. It was seen important to be able to visualize the data and spatial information together. The measurement and observation locations, measured quantities (variables), and data itself and their attributes should be browsable. Annotating data was seen important. Public, after adequate identification, should be allowed to add stories to the map and participate in discussions. "A story" was defined as text and pictures that are attached to a point location.

New requirements appear as experience is obtained. For example the state of a web page should be attainable simply with a single URL. The reason for this is how information is often shared in social media with links.

Architecture

Five-tiered software architecture, such as the one presented by [START_REF] Tafti | Development of a Framework for Applying ASYCUDA System with N-Tier Application Architecture[END_REF], was selected as the basis for the development. The tiers are visualization, presentation, business, data access, and data. We define the visualization tier as the website as presented by the web browser. A website consists of web pages, which may be static or dynamic. Web pages are written in HTML (or one of its versions), which allows embedding of objects such as images, interactive forms, program code for interactive and dynamic content, etc.

We define the presentation tier as the data formats and the communication between the client (user) application and the servers. For a website http and HTML are naturally the most important communication standards. When it comes to geospatial and observation data, the OGC (Open Geospatial Consortium) offers at least two standards, namely WFS (Web Feature Service) and SOS (Sensor Observation Service) for publishing data [START_REF] Bermudez | Web Feature Service (WFS) and Sensor Observation Service (SOS) Comparison to Publish Time Series Data[END_REF]. SOS is a part of the SWE (Sensor Web Enablement) framework of OGC; the other parts are standards for observation and measurement data, sensor and observation processing descriptions, and sensor planning service. The OGC discussion paper 'CUAHSI WaterML' defines another family of web services, called WaterOneFlow, that was developed for communicating hydrologic data. WaterOne-Flow was created by the CUAHSI (Consortium of Universities for the Advancement of Hydrologic Science, Inc.) community of US universities.

We define the business tier as the code which implements and enables the workflows that the website supports. The business tier may be implemented on the server side or on the client side. Server side code is executed on demand (when a client requests a web page), possibly taking parameters or other input from the client, and it may use any resources it has available to produce a web page or other content for the client.

The data access tier is the code which connects to the database and prepares data for the presentation or business tier, or stores data coming from those tiers to the database.

The data tier is the database and database management system, which accepts connections to the database, and typically interprets the data access and management commands. The database schema is an important element of the database.

Software

There are several web mapping and other interactive data visualization technologies for the web. Common ones include Adobe Flash, Microsoft Silverlight, Apache Flex, HTML5, and JavaScript. Each alternative has its pros and cons. We chose JavaScript because it is well supported by web browsers and operating systems, and because of the availability of good free and open source libraries that we could use. There are several options for developing web pages using JavaScript. These include JavaScript libraries and server side APIs, especially for web mapping, which to use. Our first try was to use the Ext JS library for time series visualization. Ext JS is a large framework for rich interactive and visual web content. GeoExt project develops a library for integrating the mapping library OpenLayers with Ext JS. However, we later found out about the Flot library for time series visualization, which in our opinion supports better time series visualization. For example it can handle missing data, which is common in environmental data sets. Flot is based on jQuery, which was thus selected as the core library instead of Ext JS.

OpenLayers was chosen over other alternatives because of its good functionality, and support for standard and common geospatial data services 2 .

The 52°North Sensor Web Community has developed a SOS server, for which various clients exist, including Javascript ones 3 . For WaterOneFlow there is server software but currently no JavaScript client library exists to our knowledge. Despite of 2 JavaScript web mapping libraries are compared for example on page http://tinyurl.com/o8k3b4k (URL made tiny and tested 2014-10-04) 3 A demonstration website which uses 52°North SOS and a JavaScript client has been developed by British Antarctic Survey (http://tinyurl.com/my2erpv). Their JavaScript client uses the Flot library, which was the inspiration for us to use Flot. CSIRO has implemented a Google Maps based client for 52°North SOS (http://tinyurl.com/nxgae7k). (URLs made tiny and tested 2014-10-04) these existing solutions it was decided to develop a lightweight ad hoc solution as a simple initial attempt and as a learning experience. Both the server and the client side of the ad hoc solution are described below.

On the server side we used Perl CGI programs for WFS, for story editor/server, and the ad hoc server. The overlay raster maps were developed into map tiles and set up as tile service. The WFS we used is a part of the Geoinformatica suite and built on top of GDAL and its Perl API.

PostgreSQL relational database management system (RDBMS) with PostGIS extension was used as the main data storage (image and pdf files are stored in file system). phpPgAdmin and Perl CGI programs were used for administrative data management.

Results and Discussion

The architecture of the developed system is shown in figure 1. The system is implemented on four subsystems: client web browser, website (eurajoki.info), dedicated services (ajolma.net), and common services (Google map tiles, OpenStreetMap map tiles, and National Land Survey of Finland map tiles). The five tiers of the conceptual architecture are divided between these subsystems. Visualization is defined mostly by the content downloaded from the website and tiled maps downloaded from services. Presentation is based on standards except for time series. Business logic is implemented partly in JavaScript that runs on the client and partly in dedicated services. Data access is based in SQL within dedicated services. Data is in ten tables in the RDBMS.

The web pages of eurajoki.info comprise several types of files. PHP is used to compose the HTML pages and to create certain pieces of JavaScript into the HTML (see below). The website architecture is flat and consists of seven web pages with uniform style. The pages are front page, management plan, water quality mapping, story map, atlas, reports, and about page. Each page has its own Facebook discussion thread. The front page has also the Facebook feed from the citizen activists' Facebook account. The management plan page is currently a stub and it will be developed more at a later stage. The water quality mapping page (figure 2.) has an OpenLayers map panel and a Flot time series panel. The story map and the atlas have a map panel for browsing and editing (story map). The overlays (historical maps and aerial photos) are tiled maps so they load fast and are easy to pan. The reports page contains PDF reports about the river environment. The about page contains information about the site, its developers, data, etc.

The JavaScript code developed for the website is divided into ten files and totals 973 lines 4 .

Water Quality Mapping

The water quality mapping page (figure 2) contains panels for a map, site information, control form, and time series graph. The JavaScript code associated with the page obtains data from services, and displays it on the panels. The measurement and observation sites are obtained as a WFS layer object. The time series data is obtained from the service as JSON (JavaScript Object Notation) and handed over to the Flot library for visualization. Both map and time series panels have interactive functionality that is mostly managed by the two libraries. The control panel contain standard HTML form widgets for the locations and variables (select multiple list boxes), begin and end dates (text entries with associated jQuery calendar widgets), and a button for updating the time series graph. The loca-tions and variables lists are updated with data obtained from the time series data server (retrieved also as JSON). The locations are linked to WFS features in JavaScript through a key attribute. Thus the selection in the list widget can be synchronized with selected features on the map. The locations are also linked to variables from which there is data in this location. Thus it is possible to synchronize the selected locations with the selected variables.

Fig. 2.

A screenshot of the water quality mapping page. A is the map panel, five features/locations are selected and one that is not selected has a popup information box. B is general information to the user. C is information about the selected locations. D is the list of locations. E is the list of variables. F is the time period. G is the button to update the graph. H is the time series graph. I is links to raw data and URL of the visualization.

A solution to the state-in-a-URL problem (section 2.2), was to use PHP to create JavaScript that sets selected locations, variables and time period in initialization code. The current implementation of the solution is not complete because it does not set the selected locations in the map panel and it does not restore the possible zoom and pan of the time series graph.

Ad Hoc Observation Service

The requirements for the time series services were the following. The server must be able to list at least the locations, the measured variables for each location, and the period from which there is data for each measured variable; the server must be able to list the measured variables; and the server must be able to provide time series from requested locations, variables, and period. The service must also provide notes and comments linked to the locations, variables, and data points. The server should return the data as JSON for easy consumption by the client JavaScript code.

The requirements were met with a small Perl CGI program (409 lines of code) that supports three commands: GetDatasets, GetVariables, and GetDataset. The program uses SQL to retrieve metadata and data from the RDBMS and then sends it out as JSON. The observation and measurement database consists mainly of three tables: locations, variables, and data. Complexity is added to the solution by characteristics of data, in particular data being either daily data or data with arbitrary measurement time, and data being either "checked" or "not checked". The former is further complicated by the fact that some data sets may have data points that have only an arbitrary date while some other data points have an arbitrary time stamp (i.e., also hour and minute). The checkedness of data sets is not a simple concept; some datasets can go through several checks and may change more than once due to new calibration data etc. The current solution has four data tables: two for checked data (checked data is shown by default) and two for daily data and two for data with time stamps. The fact that some data sets may have data points in two tables complicates the service code.

Story Map Editor

The story map was required to have user authentication for editing stories. This requirement was filled by adding an identification dialog before the user enters the story editor. The identification dialog asks for an email address (or name) and a password. These are later used as keys to the data the user has submitted. By default data that a user has submitted is not public. It has to be explicitly set as public by somebody having appropriate rights to the database. The story editor (its mapping part) is based on transactional WFS (WFS-T). In order to keep things simple on the client side, the stories are always served through the same WFS layer. However, for this to work, the service had to be changed to require the email and password fields and serve features according to those fields: no values: serve public data, values given: serve private data specified by those fields. This feature is not a part of WFS specification.

Overall Assessment

The result fulfills the baseline requirements. The website uses Facebook social plugins and allows public to create content, however the content has to be published by admins. The solution uses two servers on the Internet, one for the website and one for the dedicated services. The latter has a database and custom CGI programs. The solution we developed in this study did not require extensive development as the program code line counts show.

One or Two Standards for Geospatial Features and Observation Data?

Several middleware programs were developed for the services. Some of them are standard, some are standard but extend the standard somehow, some could be standard, and some are specific to this purpose. According to the CUAHSI WaterML Web Services page 5 WaterOneFlow has eight functions (requests), which are very close to the ad hoc time series server we implemented in this study. The OGC SWE, due to its generic nature, is rather complex and thus it may be difficult to implement considering varying database schemas, and server and client software. Noting this, [START_REF] Jirka | A Lightweight Approach for the Sensor Observation Service to Share Environmental Data across Europe[END_REF] defined a lightweight SOS profile for facilitating its practical application. The lightweight profile is very similar to the ad hoc profile of this study but it goes beyond ours by including a transactional part for inserting observations and sensors, which in our case can be done but only via separate tools.

The application scenario of [START_REF] Jirka | A Lightweight Approach for the Sensor Observation Service to Share Environmental Data across Europe[END_REF] is European-wide sharing of environmental data. Our case study focuses on a single river, which is a part of a river basin. The river basin falls mostly within one administrative region, but constitutes only a fraction of it. It is worth considering what types of data services are needed and useful at each level. It could be argued that at higher level the data and information needs about lower level parts would be simpler or less dimensioned (averages, loads at system boundaries, etc.) and when the focus is on a specific system, there data needs are more detailed and also of different kind.

Our system uses two services, WFS and sensor data service, at the same time and in coordination. The coordination is to a large degree ensured by the use of a single database. Although the features (observation and measurement sites) appear in different services and thus as separate instances, they are in reality the same entities in the database. The reason for two services is also practical: WFS layers are well supported by common web mapping clients while SOS is not so well supported.

From a user point of view the technology behind a website has little importance if they do not affect the user experience. However, interoperability becomes very apparent when for example one measurement data stream cannot be added to the same graph as the others.

Conclusion

The website has been online since December 2013. The project ends in April 2015 but the website will be kept online after that. Currently the website has a rather steady usage with 150 or more unique visitors per month. Statistics show that water quality mapping and reports are the most popular pages on the site and only a small fraction view the stories or the tiled overlay maps. Only the project team has so far used the story editor. The discussion forum on the front pages has attracted some use but forums on other pages only little. However, this was expected since the activist Facebook forum (which is shown on the front page) is the main forum for discussions. Future work will focus on making the provided data and information more meaningful for the public and developing the page that is dedicated to supporting the development of the quality management plan.

Fig. 1 .

 1 Fig. 1. Architecture of the developed system

http://www.pyhajarvi-instituutti.fi/english/image/pdftiedostot/yhtejoki_leaflet_brief.pdf

The code is available at http://tinyurl.com/leg4yp9 (URL made tiny and tested 2014-10-04)

http://his.cuahsi.org/wofws.html

Acknowledgements

The "Our Common River Eurajoki" project is funded by the Regional Development Fund of the European Union (project code A32601).