Ari Jolma

Modeling the effectiveness of oil combating from an ecological perspective--a Bayesian network for the Gulf of Finland; the Baltic Sea.

Maritime traffic poses a major threat to marine ecosystems in the form of oil spills. The Gulf of Finland, the easternmost part of the Baltic Sea, has witnessed a rapid increase in oil transportation during the last 15 years. Should a spill occur, the negative ecological impacts may be reduced by oil combating, the effectiveness of which is, however, strongly dependent on prevailing environmental conditions and available technical resources. This poses increased uncertainty related to ecological consequences of future spills. We developed a probabilistic Bayesian network model that can be used to assess the effectiveness of different oil combating strategies in minimizing the negative effects of oil on six species living in the Gulf of Finland. The model can be used for creating different accident scenarios and assessing the performance of various oil combating actions under uncertainty, which enables its use as a supportive tool in decision-making. While the model is confined to the western Gulf of Finland, the methodology is adaptable to other marine areas facing similar risks and challenges related to oil spills.

Interfacing environmental simulation models and databases using XML

Abstract Typically in environmental management tasks one needs to examine and explore data from several sources, use simulation models, develop scenarios, assess impacts, and provide support for decision makers. Here we consider the eXtensible Markup Language (XML) standard in developing information transfer techniques between databases and simulation models. Suitability of XML as the agreed data transfer format is studied in a sample application, where two snow models of different complexity are linked with input data extracted from a relational database. The simple case study demonstrated that with free and easily accessible tools it was relatively straightforward to develop an XML interface between a meteorological data set and simulation models. Based on the case study, a structure for a more comprehensive system comprising model and data resources, and a broker application that acts as an intermediate between the user and those resources, is presented. We believe that such an XML-based structure is worth exploring on the track towards an open modelling framework. Such a framework would allow models developed by various expert groups to connect easily to a common information system.

Dynamic mapping of nature values to support prioritization of coastal oil combating

Due to the continuing demand of oil for energy production and to manufacture various consumables the threat to coastal ecosystems by maritime oil transport remains at a high level. Clearly there is a need for tools that can be used in mitigating the environmental damage incurred by oil accidents. This paper presents a dynamic mapping application for comparing the ecological value of the shoreline of the Gulf of Finland to guide decisions on where to lay oil booms after an oil spill accident. The maps were required to combine conservational value of different species occurrences, the estimated level of oil exposure, the estimated recovery potential of the occurrences following the accident, and the efficiency of oil booms to safeguard the species inhabiting the occurrences. The mapping was tested in representative cases. The results indicate that the ranking of neighbouring shoreline streches changes depending on weather conditions and on choices made in developing the significance criteria. To the authors knowledge this work represents a unique effort in combining biological and geospatial expertise to provide decision support for oil combating. Such support is essential should ecological values truly be included in oil combating planning and management.

Combining Conservation Value, Vulnerability, and Effectiveness of Mitigation Actions in Spatial Conservation Decisions: An Application to Coastal Oil Spill Combating

Increasing oil transportation and severe oil accidents in the past have led to the development of various sensitivity maps in different countries all over the world. Often, however, the areas presented on the maps are far too large to be safeguarded with the available oil combating equipment and prioritization is required to decide which areas must be safeguarded. While oil booms can be applied to safeguard populations from a drifting oil slick, decision making on the spatial allocation of oil combating capacity is extremely difficult due to the lack of time, resources and knowledge. Since the operational decision makers usually are not ecologists, a useful decision support tool including ecological knowledge must be readily comprehensible and easy to use. We present an index-based method that can be used to make decisions concerning which populations of natural organisms should primarily be safeguarded from a floating oil slick with oil booms. The indices take into account the relative exposure, mortality and recovery potential of populations, the conservation value of species and populations, and the effectiveness of oil booms to safeguard different species. The method has been implemented in a mapping software that can be used in the Gulf of Finland (Baltic Sea) for operational oil combating. It could also be utilized in other similar conservation decisions where species with varying vulnerability, conservational value, and benefits received from the management actions need to be prioritized.

A software system for assessing the spatially distributed ecological risk posed by oil shipping

A maritime accident involving an oil tanker may lead to large scale mortality or reductions in populations of coastal species due to oil. The ecological value at stake is the biota on the coast, which are neither uniformly nor randomly distributed. We used an existing oil spill simulation model, an observation database of threatened species, and a valuation method and developed a software system for assessing the spatially distributed ecological risk posed by oil shipping. The approach links a tanker accident model to a set of oil spill simulations and further to a spatial ecological value data set. The tanker accident model is a Bayesian network and thus we present a case of using a Bayesian network in geographic analysis. A case in the Gulf of Finland is used for illustration of the methodology. The method requires and builds on an extensive data collection and generation effort and modeling. The main difference of our work to earlier works on using a Bayesian network in geospatial setting is that in our case the Bayesian network was used to compute the probabilities of spatial scenarios directly in a global sense while in earlier works Bayesian networks have been used for each location separately to obtain global results. The result was a software system that was used by a distributed research team. We develop a system for assessing spatially distributed ecological risk.We link geospatial scenarios with a Bayesian network to obtain an expected scenario.Assessment of ecological risk of oil spill requires an extensive data collection.Foreign function interfaces are effective in linking heterogeneous software.

StreamPlan : A Support System for Water-Quality Management on a River Basin Scale

Abstract StreamPlan was developed as an integrated, easy-to-use software system for analysing alternative water quality management policies on a river basin level. These include uniform emission reduction and effluent standard based strategies, ambient water quality criteria and least-cost strategies, total emission reduction under minimized costs, mixed strategies, local and regional policies, and strategies with economic instruments. A distinctive feature of StreamPlan is the integration of a detailed model of wastewater generation on the municipal level with water quality modelling and policy analysis tools on a river basin scale. StreamPlan allows the modelling of cost and abatement efficiency of different wastewater treatment plant upgrading schemes based on projected wasteloads and a description of existing facilities. StreamPlan is composed of six spreadsheet workbooks and an external linear programming package. The power and familiarity of spreadsheet operations enhance ease of use. The spreadsheets own programming language seamlessly integrates databases and models in workbooks and worksheets into a complete system. Moreover, the total development time for the system was just over one person year.

Chapter Ten Free and Open Source Geospatial Tools for Environmental Modelling and Management

Abstract Geospatial (geographical) software systems (GIS) are used for creating, viewing, managing, analysing and utilising geospatial data. Geospatial data can include socioeconomic, environmental, geophysical, and technical data about the Earth and societal infrastructure and it is pivotal in environmental modelling and management (EMM). Desktop, web-based, and embedded geospatial systems have become an essential part of EMM, providing pre- or post-processing of geospatial data, analysis and visualisation of results or a graphical user interface (GUI). Many local, regional, national, and international efforts are underway to create geospatial data infrastructures and tools for viewing and using geospatial data. When environmental attribute data is linked to these infrastructures, powerful tools for environmental management are instantly created. The growing culture of free/libre and open source software (FOSS) provides an alternative approach to software development for the field of GIS (FOSS4G). To provide an overview of FOSS4G for EMM, we analyse platforms, software stacks, and EMM workflows. In the FOSS world the barriers to interoperability are low and thus the software stack tends to be thicker than in the proprietary platform. The FOSS4G world thrives on the evolution of software stacks and platforms. We provide examples of software stacks built from current FOSS4G that support EMM workflows and highlight the advantages of FOSS4G solutions including opportunities to redistribute resulting modelling tools freely to end-users and to support general goals of openness and transparency with respect to modelling tools.

Innovative Tools for Water Quality Management and Policy Analysis: Desert and Streamplan

This paper presents the major features of two decision support systems (DSS) for river water quality modeling and policy analysis recently developed at the International Institute of Applied Systems Analysis (IIASA), DESERT and STREAMPLAN. DESERT integrates in a single package data management, model calibration, simulation, optimization and presentation of results. DESERT has the flexibility to allow the specification of both alternative water quality models and flow hydraulics for different branches of the same river basin. Specification of these models can be done interactively through Microsoft® Windows commands and menus and an easy to use interpreted language. Detailed analysis of the effects of parameter uncertainty on water quality results is integrated into DESERT. STREAMPLAN, on the other hand, is an integrated, easy-to-use software system for analyzing alternative water quality management policies on a river basin level. These policies include uniform emission reduction and effluent standard based strategies, ambient water quality and least-cost strategies, total emission reduction under minimized costs, mixed strategies, local and regional policies, and strategies with economic instruments. A distinctive feature of STREAMPLAN is the integration of a detailed model of municipal wastewater generation with a water quality model and policy analysis tools on a river basin scale.

Modeling the role of the close-range effect and environmental variables in the occurrence and spread of Phragmites australis in four sites on the Finnish coast of the Gulf of Finland and the Archipelago Sea.

Phragmites australis, a native helophyte in coastal areas of the Baltic Sea, has significantly spread on the Finnish coast in the last decades raising ecological questions and social interest and concern due to the important role it plays in the ecosystem dynamics of shallow coastal areas. Despite its important implications on the planning and management of the area, predictive modeling of Phragmites distribution is not well studied. We examined the prevalence and progression of Phragmites in four sites along the Southern Finnish coast in multiple time frames in relation to a number of predictors. We also analyzed patterns of neighborhood effect on the expansion and disappearance of Phragmites in a cellular data model. We developed boosted regression trees models to predict Phragmites occurrences and produce maps of habitat suitability. Various Phragmites spread figures were observed in different areas and time periods, with a minimum annual expansion rate of 1% and a maximum of 8%. The water depth, shore openness, and proximity to river mouths were found influential in Phragmites distribution. The neighborhood configuration partially explained the dynamics of Phragmites colonies. The boosted regression trees method was successfully used to interpolate and extrapolate Phragmites distributions in the study sites highlighting its potential for assessing habitat suitability for Phragmites along the Finnish coast. Our findings are useful for a number of applications. With variables easily available, delineation of areas susceptible for Phragmites colonization allows early management plans to be made. Given the influence of reed beds on the littoral species and ecosystem, these results can be useful for the ecological studies of coastal areas. We provide estimates of habitat suitability and quantification of Phragmites expansion in a form suitable for dynamic modeling, which would be useful for predicting future Phragmites distribution under different scenarios of land cover change and Phragmites spatial configuration.

Species and habitats in danger: estimating the relative risk posed by oil spills in the northern Baltic Sea

Largescale oil spills can have adverse eff ects on biodiversity in coastal areas where maritime oil transportation is intense. In this article we conducted a spatial risk assessment to study the risk that potential tanker accidents pose to threatened habitat types and species living in the northern Baltic Sea, which has witnessed a rapid increase in maritime oil transportation within the past two decades. We applied a probabilistic method, which combines three components: a Bayesian network describing tanker accidents and uncertainties related to them, probabilistic maps showing the movement of oil, and a database of threatened species and habitats in the area. The results suggest that spatial risk posed by oil spills varies across the area, and does not correspond, for example, to the frequency of accidents in a given area. The relative risk is highest for seashore meadows, which is important to take into account when managing these habitats. Our analysis underlines the importance of a thorough risk assessment, which is not only based solely on one or two specifi c factors such as accident probabilities or the trajectories of spilled oil but also contains as broad a view of the consequences as possible. We believe that the probabilistic methodology applied in the study will be of high interest to people who have to cope with uncertainties typical for environmental risk assessment and management.