Beyond GIS: Integrating dynamic simulation models and GIS for natural resources and environmental management Dr. Kurt Fedra Environmental Software & Services Gumpoldskirchen, AUSTRIA Email: kurt@ess.co.at www.ess.co.at ABSTRACT Natural resources and environmental management are inherently spatially distributed problems. Thematic maps and GIS therefore provide a powerful paradigm for data management, analysis and communication. Environmental and resource management problems however also exhibit functional and dynamic complexity that goes well beyond the basic analytical capabilities of traditional GIS systems. Complex dynamics of non-linear systems, stochastic behavior, optimization and decision support require additional analytical capabilities that are usually found in the domain of simulation and optimization models. This paper argues for the integration of GIS and dynamic simulation and optimization modeling to better support natural resources and environmental management tasks with integrated spatial decision support systems. Examples of application that go beyond classical GIS include land use change, water resources, integrated coastal zone management, urban and industrial air quality assessment and management and transportation, as well as technological risk that all affect, and are largely determined by, natural resources and techno-economic systems. Georeferenced data and thus GIS and RS layers provide input to dynamic analytical tools, which in turn produce spatially distributed, but also dynamic results, that can be displayed and efficiently communicated as map layers or topical maps. INTRODUCTION Natural resource and environmental management problems always have a spatial component. But their most important characteristics include complex processes and relationships, involving numerous interacting elements with multiple attributes, and a dynamic behavior that goes well beyond the analytical capabilities of most commercial GIS software. The tools required for analysis include dynamic and spatially distributed simulation models, optimization models, expert systems, and decision support tools based on concepts of systems analysis and operations research more than geostatistics. The primary paradigm of a GIS is the map, an inherently static concept of limited attributes. While modern GIS extend the scope of what can be done within this paradigm towards digital cartography considerably, and elaborate applications can be built within existing GIS systems and tool kits (see, for example, http://isworld.org/dss/sdss.htm) there still is a broad class of problem situations that do involve spatial elements, but where the GIS components are only auxiliary to another problem solving approach. The main requirements for a spatial information system that go beyond classical GIS capabilities and may require an alternative approach of embedded GIS are
The obvious answer is to link and integrate GIS functionality with these specialized tools for complex and dynamic analysis. The practical question is often how to couple the different tools smoothly, which approach and paradigm drives the overall application. Many GIS systems are open to link external components that read and generate georeferenced data, using the GIS, its data structures, user interface and display capabilities as the overall framework. Native analytical functions, however, are usually limited to Boolean overlay analysis, buffers (or more general, neighborhood analysis), network and routing tasks, and spatially distributed processes that can be expressed in terms of cellular automata in some cases, often applied in the context of urban development or forest fire studies (Unger, 2000; Clarke and Gaydos, 1998;), or hydrology (Maidment and Djokic, 2000). An alternative approach is to build information systems that are designed and customized for their specific purpose and audience or user group, but include or embed GIS functionality. However, rather than accessible to a more or less general purpose GIS interface, GIS functionality here is smoothly integrated in support of the primary function, if and where needed rather than an analytical component in its own right. While this may limit the scope and range of generic GIS functions and operations, it greatly simplifies use as any and all GIS functions are directly related to the basic systems operations, available only where needed, context sensitive and smoothly integrated. STRATEGIES OF INTEGRATION A range of strategies exist for coupling dynamic models and complex analytical tools with GIS, and these span a range from low to high integration: isolated with data exchange, loose coupling, tight coupling, and full integration (Fedra 1993, Raper and Livingstone 1993, Nyerges 1993, Peuquet 1999). The integration strategy is considered as isolated if the analytical tools are run independently of a GIS, which, if at all, is used in parallel, and integrated if it is run using the GIS through data exchange. Models may be coupled using various strategies which may include the exchange of transfer files between the model and the GIS and/or embedding model commands within a GIS toolkit which may offer various scripting languages. Finally, the integration may be achieved by embedding GIS functionality within the primary application, using the GIS as tool for spatial data handling, display of georeferenced data and results as topical maps, and selected GIS functionality such as overlay of neighborhood analysis. There are tradeoffs unique to each of the coupling strategies (e.g.,Van Deursen 1995), but in general a high level of integration is flexible and adaptable to changes in model inputs and parameters, but also more expensive as it will not be based on ready made tools. Also, this strategy involves no computational overhead and sources of error in file conversion, which can increase usability for complex operations through dedicated implementation and user interfaces. For spatial DSS, similar to complex dynamic models and real-time information system applications, there are numerous elements that need to be considered beyond georeferenced data handling, display and GIS functionality. However, the choice of approach and tools in all cases depends on the problems to be solved, information and legacy data available, and also the time and resources available in each case. Whether we embed GIS functionality in an information and decision support system, or complex dynamic modeling and DSS functionality within a GIS framework, is to some degree a matter of choice and preference of the development (to a man with a hammer, the world is full of nails), but should depend on where the main emphasis of a given problem situation and thus tool needs to be. In any case, the primary questions to be addressed before designing and implementing any information system should be WHY, WHAT FOR, FOR WHOM, from which, usually, the HOW will follow. Application domains and examples Any or all environmental domains provide examples for the combined use of GIS and environmental models and spatially explicit analytical tools. Common applications relate to air quality management, water resources, coastal zone management, urban and regional development, and the more methodologically defined class of environmental decision support systems (Fedra, 2000). Air quality assessment and management: A prototypical specific application domain is urban and industrial air quality assessment and management, where usually traffic generated emission play the dominant role (Fedra, 2004; Fedra, 2002a). The primary concerns are compliance with regulatory standards for emissions and ambient air quality, and the relation of emission patterns to ambient air quality, as the obvious control is by reducing emissions. This requires monitoring of both emissions and ambient quality, the observations and forecasting of regional and local meteorological conditions which drive the atmospheric dispersion and conversion processes, and simulation and optimization models that provide detailed status report with much higher resolution than a few monitoring stations can provide. Simulation models also provide the possibility to generate short-term forecasts that may be used to trigger control measures such as use of alternative fuels in large power plants of limitation to urban traffic, scenario analysis for planning and impact assessment, and optimization tools to design least costs strategies to meet air quality targets.  Figure 1: thermal power plant object, georeferenced emission source  Figure 2: dispersion model and impact assessment for a single stack  Figure 3: Real-time now-casting of air quality for an urban domain. The spatial components are obvious: emission sources are distributed in space, and so are monitoring stations. Model results are computed for specific receptor areas or on a regular grid, using spatially distributed input data such as orography, land use derived roughness, and population distribution for exposure analysis. However, the primary tasks of an air quality assessment and management system will relate to the analysis of monitoring data time series (compliance is usually formulated as the occurrence and frequency of violations), the management of emission data including multi-criteria ranking and benchmarking and a large amount of administrative information associated to industrial plants, boilers, and stacks. Central components are the different simulation tools from now-casting in real-time including data assimilation, scenario analysis, and forecasting, as well as optimization. The role of embedded GIS components is in the management of the georeferenced data such as point, line, and area sources of emission, the monitoring stations, distributed model input data, spatially explicit model results, and post-processing e.g., for population exposure which is a classical overlay analysis. Integrated Coastal Zone Management To study the sustainable management of scarce resources in the coastal zone, and water in particular, requires a complex and interdisciplinary methodology. The approach described here is based on the integration of socio-economic analysis and indicators, defining scenarios, and a set of quantitative simulation models to explore the impacts and outcomes of these possible development scenarios. The main components are object data bases of georeferenced data and a GIS to compile consistent data for the case studies, including the socio-economic data that are summarized in a set of indicators, a set of models that describe the coastal zones including: a dynamic land use change model, a water resources model with embedded estimation tools for rainfall-runoff modeling, irrigation water demand estimation, water allocation, surface and groundwater quality, and coastal water quality; the model results are in turn summarized as indicators and derived indices, and used for a multi-criteria comparative assessment of alternative scenarios, and a discrete optimization multi-objective multi-criteria methodology that is designed for participation of a wide range of actors and stake holders. As a final element, the data and tools are accessible over the Internet, including basic embedded GIS components, adding a public information component to the basic analytical approach. v The international RTD projects SMART and OPTIMA address several case studies around the Mediterranean, including Cyprus, Turkey, Lebanon, Jordan, Palestine, Egypt, Tunisia and Morocco. These cases provide a broad range of examples ranging in size from 120 to 18,000 km2 to test a common methodology under a range of physiographic settings. The main emphasis of SMART is on integrated coastal zone development (Fedra and Feoli, 1998, Post and Lundin, 1996) and water resources in particular, using a scenario analysis approach (http://www.ess.co.at/SMART/). Sharing the same models and concepts, OPTIMA goes one step further towards decision support by including explicit optimisation with participatory elements. OPTIMA concentrates on water resources at the river basin scale, going beyond scenario analysis by a multi-criteria optimization approach (http://www.ess.co.at/OPTIMA/). Based on concepts of the EU Water Framework Directive (2000/60/EC) this combines economic efficiency with meeting environmental targets and constraints under the overall umbrella of sustainable development.  Figure 4: a dynamic land use change model for the coastal zone (Lebanon) Decision Support Systems: A generic application domain for embedded GIS are (spatial) decision support systems. There is no single accepted definition of what constitutes a Decision Support System (DSS) in the technical literature. There are, however, a few common traits in most definitions: A decision support system (DSS) is both a process and a tool for solving problems that are too complex for humans alone, but usually too qualitative for only computers. Multiple objectives can complicate the task of decision-making, especially when the objectives conflict. As a process, a DSS is a systematic method of leading decision-makers and other stakeholders through the task of considering all objectives and then evaluating options to identify a solution that best solves an explicit problem while satisfying as many objectives as possible to as high a degree as possible. A DSS is computer based, including hardware, software, and data; it must assist in making non-trivial decisions, but beyond that, there is little agreement. Analyzing the literature, the overwhelming number of cases that claim DSS status refer to relatively simple information and model systems that focus on problem representation and in most cases, WHAT-IF type scenario analysis. A considerably smaller group addresses optimization tools with usually a strong Operations Research and mathematical programming focus. Spatial DSS include spatially distributed criteria, or use the location and spatial relationship of objects in their analysis. A good starting point for information on spatial DSS can be found at http://isworld.org/dss/sdss.htm. "Decision support system" is defined as an approach or methodology for supporting decision-making. It uses an interactive, flexible, and adaptable computer-based information systems especially developed for supporting the solution for a specific non-structured management problem. It uses data, provides an easy user interface, and can incorporate the decision maker's own insights. In addition, a DSS usually uses models and is built (often by end users) by an interactive and iterative process (evolutionary prototyping process). It supports all phases of decision-making and may include a knowledge component. The basic functions of a DSS include:
Integrating or embedding various components into a seamless application is becoming much easier in modular client-server systems, with the Internet (or Intranet) the basic overall architecture. Web-based access to tools and services offered by distributed servers within a TCP/IP based network, with the client based on standard browser web technology including Java. Standards like the OpenGIS and Open Web Specifications of the Open Geospatial Consortium, Inc. (OGC, http://www.opengeospatial.org/), greatly improve interoperability and thus the potential for integration and embedded structures. Internet Map or GIS servers like MapServer (http://mapserver.gis.umn.edu/) not only offer open source tools (http://www.opensource.org/), but also provide a standardized data interface that makes reuse and sharing of components and data sets easier, and thus development of generic components in modular systems more realistic if not feasible in the first place. Thus, together with ever more powerful and affordable computing technology and high resolution multi-attribute spatial data from ever more and better but also more accessible and affordable space-based Geo-Observation Systems, the future of complex applications with embedded GIS looks bright. REFERENCES
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