HydroGIS 96: Application of Geographic Information Systems in Hydrology and Water Resources Management (Proceedings of the Vienna Conference, April 1996). IAHS Publ. no. 235, 1996. 613 GIS and scientific visualization for hydrological simulation SALLY KLEINFELDT, JONATHAN DECKMYN, CLAUDIO PANICONI Centre for Advanced Studies, Research and Development in Sardinia (CRS4), Via Nazario Sauro 10, 1-09123 Cagliari, Italy BART COSYN Laboratory of Hydrology and Water Management, University of Ghent, Coupure Links 653, B-9000 Gent, Belgium Abstract Understanding the spatio-temporal characteristics of soil moisture and subsurface flow processes at the catchment-scale involves the use of many different types of data, such as field measurements, remote sensing images, digital elevation models, and results from hydrological simulation. All of these data types have a strong geographic component, so it is natural to consider geographic information systems (GIS) as a primary tool for data organization and analysis. However, current GIS have numerous limitations which make them incapable of completely supporting the needs of this type of hydrological research. In this paper we argue that some of the important weaknesses of GIS are the very strengths of scientific visualization, and that a natural solution is to combine or integrate these two types of systems. The strengths and weaknesses of GIS and scientific visualization systems will be discussed, and a case study of integration, using tools that are available today, is presented for an application involving hydrological modelling. INTRODUCTION Geographic information systems incorporate data models and functionality specially tuned to map making and geographic analysis. Although traditional GIS have excellent georeferencing and image processing capabilities, they cannot handle data in more than two dimensions. In applications to catchment-scale hydrology, for instance, subsurface (3D) processes and multi-temporal (4D) satellite images and simulations cannot be effectively visualized. Scientific visualization - the transformation of large data sets into colour images and animations that human pattern recognition capabilities can process — is a relatively new technology that can address the visualization shortcomings of GIS. Because of their complementary nature, there has been discussion about how to bring visualization concepts and techniques into GIS, and vice versa (Rhyne et al, 1994). The reality today, however, is that the systems are separate. This is not necessarily a disadvantage, as one can capitalize on the unique strengths of each as building blocks for the desired research system. We will present a case study involving a hydrological modelling 614 Sally Kleinfeldt et al. application, where we use separate GIS and scientific visualization systems, with a well- defined interface between the two. The implementation is based as much as possible on off-the-shelf components, with a minimum of custom software development. GEOGRAPHIC INFORMATION SYSTEMS In general, GIS provide both database management (creation, update, query, control) and graphical display (essentially mapping) of spatially distributed data. Robust systems also provide processing and analytical functions such as change of projection, resampling, neighbourhood analysis, etc. A variety of data models has been created to support GIS functions. These include cell and point grids (implemented as raster structures), and polygons, triangular networks (TINs), contours, and irregular points (implemented as vector structures). The origins of GIS in applications related to cartography can account for many of the strengths as well as some of the weaknesses of these systems. Current GIS offer a broad range of functionality to produce high quality maps. Georeferencing, coordinate transformation, image processing, and manipulation and query of maps are all strengths. Like paper maps however, GIS are limited to static 2D representations, and none of the existing data models handle more than two dimensions. As a result, visualization of 3D phenomena, 3D spatial data analysis, and time dependent processes cannot be handled properly within GIS (Maidment, 1993). The integration of relational database technology within GIS is also generally strong because the biggest commercial demands for GIS are for simple record-keeping and complex queries (Goodchild et al., 1992). Data storage is generally efficient, and very large data sets can be handled, including satellite images. However, data management (in the sense of organizing large numbers of data sets, as opposed to large numbers of relational records), is weak. GIS impose rigid requirements for storage locations and have poor facilities for capturing meta-data (data about the data) of a non-geographic nature. They do not support arbitrary, user-defined scenarios for organizing data. They do not have any notion of change through time, or of relationships among data sets (such as one image being derived from another through a sequence of processing steps, or several alternative data sets which were produced with different parameter settings of a simulation). Although GIS user interfaces are generally graphical today, taking advantage of modern windowing technology, they are still often overly complex and hard to use (Davies & Medyckyj-Scott, 1994). They take a significant amount of time to learn, and thus are often only available to experts within an organization. SCIENTIFIC VISUALIZATION SYSTEMS Scientific visualization is a method of comprehending large, often complex and multi­ dimensional data sets generated by simulation or physical measurement, by converting them into colour images. A wealth of visualization techniques are available today, covering both surface rendering and volume rendering. Some visualization systems offer the user the capability to explore and create new kinds of visualizations. There are GIS and scientific visualization for hydrological simulation 615 several such systems (sometimes called application builders or dataflow systems), all architecturally based on seminal work by Upson et al. (1989) and Haber & McNabb (1990). In dataflow systems, visualization is broken down into discrete modules, each of which performs a specialized task. The modules are assembled together into a dataflow network, the output of which is an image plus widgets to control the processing parameters of the modules. Scientific visualization systems provide data models that are suitable for either regular arrays or irregular data consisting of nodes and connections (for example, suitable for finite element analysis). Nodes can usually contain any number of data values, and arrays can be any number of dimensions. They also provide data models for geometric objects composed of primitives such as polygons and spheres which are used for rendering. Some of the weaknesses that are inherent to a traditional GIS are the very strengths of scientific visualization systems. Foremost among these is their ability to support n- dimensional data representation and visualization. Scientific visualization systems are extremely interactive, with control widgets that allow real-time adjustments to colours, scaling, position, and other nuances that greatly affect a researcher's ability to perceive important data patterns. In addition, time series animation capabilities have been built into most scientific visualization systems. While the data models for scientific visualization systems are powerful in their generality and flexibility, they contain no specifically geographic information, and have no knowledge of geographic coordinate systems. Geographic transformation and analysis functions are not built into these systems. The user interfaces of dataflow visualization systems are organized around "boxes and arrows" dataflow diagrams, which is a very intuitive concept. In spite of this, there are a great many sophisticated data transformation operations that can be performed, and like GIS, they are most effectively used by an expert. INTEGRATION OF GIS AND SCIENTIFIC VISUALIZATION SYSTEMS Hydrological applications (and environmental research in general) need the strengths of both GIS and scientific visualization systems. Geographic operations are essential, such as rectifying images and digital elevation models that may have different resolutions and orientations. So are visualizations of three-dimensional, time series data, using both surface and volume visualization techniques. Both map-based and non-map-based visualization metaphors must be available. The question then is, how can we integrate GIS and scientific visualization systems? At present the two fields are separate, with little exchange of ideas (Robertson & Abel, 1993). There are two basic ways in which GIS and scientific visualization systems could be integrated. Tight integration - the merging of GIS and scientific visualization capabili­ ties into a single, powerful system - holds the most promise for interactive analysis and visualization. This could be accomplished either by bringing scientific visualization functionality into a GIS, or GIS functionality into a visualization system. In each of these scenarios the changes required are so profound that it might be best to think of the result as a new, hybrid system. Regardless of the specific implementation scenario, a common data model which supports 3D and 4D functions in both GIS and visualization compo- 616 Sally Kleinfeldt et al. nents is a conditio sine qua non, and today's GIS are far