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Geographical Information Systems and Dynamic Models Geographical Information Systems and Dynamic Models Development and application of a prototype spatial modelling language WPA van Deursen PhD-thesis, may 19, 1995 Faculty of Spatial Sciences University of Utrecht The Netherlands @1995,2000, WPA van Deursen Oostzeedijk Beneden 23a 3062 VK Rotterdam, The Netherlands [email protected] This publication is also available in paper form. This publication is part of NGS, Netherlands Geographical Studies. NGS 190 W P A VAN DEURSEN Geographical Information Systems and Dynamic Models; development and application of a prototype spatial modelling language -- Utrecht 1995: Knag/Faculteit Ruimtelijke Wetenschappen Universiteit Utrecht. 206 pp, 44 figs, 8 tabs. ISBN 90-6809-206-5 Dfl 38,00 Publications of this series can be ordered from KNAG / NETHERLANDS GEOGRAPHICAL STUDIES, P.O.Box 80123, 3508 TC Utrecht, The Netherlands (Fax +31 30 253 5523; E-mail [email protected]). Please refer to this publication as: Van Deursen W.P.A. 1995. Geographical Information Systems and Dynamic Models. Ph.D. thesis, Utrecht University, NGS Publication 190, 198 pp. Electronically available through www.carthago.nl Please feel free to copy and redistribute this PDF document, provided that no changes are made to the document. The PCRaster software, which implements the techniques described in this document (although not exactly) is available through www.pcraster.nl Rotterdam, November 2000 Willem van Deursen, [email protected] 1 INTRODUCTION 1.1 The use of Geographical Information Systems and dynamic models in physical geography, ecology and environmental studies A convenient way to define a Geographical Information System (GIS) is to say that it is a set of computer tools capable of storing, manipulating, analysing and retrieving geographic information [Burrough, 1986; Maguire, 1991]. Several questions arise from this definition. What exactly is geographic information; and what is meant by the sentence 'storing, manipulating and analysing and retrieving geographic information'? Both these questions are the cause of much discussion and research in the GIS society. This thesis will touch upon the first question, and will discuss thoroughly an approach for dynamic modelling with GIS. In this context dynamic modelling can be regarded as a subset of 'manipulating and analysing geographic data'. Geographic information is information that has a geographic attribute, that is, it is linked to some location. We can recognise two main types of geographic information [Goodchild 1992]. The first type relates to the concept of objects, features that can have a certain set of attributes. It is quite convenient to think of objects as being geographically limited in size, and objects are quite often man-made or stem from classification of the world. In contrast to the object-related information, there is the concept of fields. Fields are not geographically limited, but may have different attribute values at different locations. Elevation is an example of a field. The attribute 'metres above sea level' is defined over the total area, but may take different values at different locations. The major discussion in the GIS world about the vector and raster approaches is mainly along the lines of the concept of objects and fields. The second important aspect of the definition of GIS is the capability for manipulating and analysing the geographic information. Manipulating and analysing refer to retrieving data from the geographical database and creating new information by combining this data. The analysis and manipulation are done through commands and functions. A major milestone in the development of GIS capabilities for manipulating and analysing geographic information has been the formulation of Map Algebra and Cartographic Modelling [Tomlin and Berry, 1979; Tomlin, 1983; Berry 1987]. Map Algebra is an integrated set of functions and commands for analysing and manipulating geographic information. The major significance of Map Algebra is that it does not provide ad hoc operations and functions for all possible geographic analyses that could be conceived, but instead provides limited generic functionality, which can be used as primitives for the analysis. By combining operations, Cartographic Modelling allows for more complex analysis. Cartographic Modelling has been successfully applied in fields such as environmental planning, land evaluation, forest management and so on. As the understanding of the processes that govern the development and degradation of landscapes and our environment evolves, so does our need for simulation models which describe these processes. Simulation models are simplifications of the real world, sets of rules that describe our perspective on the processes. The use of simulation models serves several goals. One of the most important goals is that the models can be used to assess the development or the reaction of our environment in response to human interactions. By using simulation models we can assess land degradation due to watershed management [De Roo, 1993; De Jong, 1994], increased river discharge as a result of climate change [Kwadijk, 1993], or changes in ecosystems due to increased atmospheric deposition of nitrogen [Van Deursen and Heil, 1993]. Another major goal for using simulation models is that they increase the insight and knowledge that we have on the system. Applying various sets of input parameters when using the simulation model may provide insight in the reaction and sensitivity of the processes that are modelled. Simulation models can be used to assess the effects of management options before the options are tried out in the real landscape. This increases understanding of the real world without causing irreversible damage to the landscape. Recently, researchers in numerous sciences are developing or using environmental process models which use spatial distributed data, or geographic information. This information is quite often available in GIS. The natural evolution for both GIS and simulation models is a tighter integration between GIS and these models, thus aiming for the best of both worlds. Cartographic Modelling covers quite a long stretch of the road towards a tighter integration of GIS and (static) models. However, it falls short on the aspects where dynamic models and more complex algorithms are involved. 1.2 Objectives of the thesis This research deals with approaches and concepts for a fruitful integration of dynamic models and GIS. The first chapters give an overview of the current state of GIS, the characteristics of dynamic models and the research in the area of linking dynamic models and GIS. Next, the thesis describes the concepts behind an integration of dynamic models and GIS. This section continues with an overview of the generic functionality required for an integrated GIS-dynamic model environment and an overview of current state-of-the-art GIS-programs in relation to this required functionality. A prototype GIS for dynamic modelling, called PCRaster, is presented, which has been developed as part of this thesis: it is an operational implementation of the ideas and concepts discussed. The GIS consists of a general purpose GIS and a specific toolbox with modules for dynamic modelling. The third section provides a detailed description of the modules of the new GIS. This section discusses the application of the provided modelling techniques in several problems in physical geography and ecology. The fourth section of the thesis gives an overview of several different case studies. These studies serve as an example of the concepts and techniques described in the thesis. Case studies implementing existing (assumed validated and calibrated) models in the GIS are discussed. This section gives particular emphasis to the capabilities of the system for different applications. The final chapter examines the degree to which this study has provided and tested a prototype integrated GIS for dynamic modelling in environmental studies. The question to be answered in this thesis is · Can we build a general purpose GIS with intrinsic dynamic modelling functionality, which can be used to develop, apply and evaluate models for a large number of environmental processes? This question leads to several research questions to be answered in this study: · What are the advantages and disadvantages of the different techniques available for linking GIS and dynamic models? · Can we classify the dynamic models currently used for physical geography, ecology and environmental issues into useful sets of approaches and processes? · Can we build general purpose tools to match these approaches and processes and are they capable of giving reasonable results for a variety of problems over a wide range of spatial scales? · Are these tools sufficiently robust to be given to GIS users who a) are not expert modellers, and/or b) have access to large amounts of quite general data of mixed quality? · For what kinds of dynamic modelling do they present a reasonable solution - where do they provide a useful supplement to existing methods? · What should be the level of abstraction of these modules? · Can we successfully model real-world problems in this GIS environment? 1.3 Inspiration for this research One major source of inspiration for this research stems from a publication describing a hydrological study carried out in the 1930's. Merill Bernard took the task of using the results of runoff plot experiments to predict the effect of land use on runoff and erosion on a 3 km2 plot [Bernard, 1937; Hjemfelt
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