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 for hydrological

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 , 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 making and geographic analysis. Although traditional GIS have excellent georeferencing and 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 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 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 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 . 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 . 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 , 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 from supporting such models. Moreover, current GIS and scientific visualization software is largely proprietary and dominated by a few major vendors, adding a further impediment to tight integration. Not surprisingly, there are no examples as yet of attempts at tight integration of GIS and scientific visualization systems. Loose integration — treating GIS and scientific visualization systems as components of a larger system - does not offer the possibility of easy interplay between the two components. They do not share the same data model and their interactions must be through an interface layer involving a translation step. Although ready availability of all needed functionality within a single system is an obvious goal, the complexity of a system offering all the functionality of both GIS and dataflow visualization would be overwhelming. For any given application, only a subset of functionality is needed, with different applications requiring different subsets. Loose integration excels at mixing and matching the right pieces to form a final system. What is needed to accomplish this is an integration framework and modular components to plug into it. The advantages of modu­ larity are widely recognized today in software engineering and -aided design. We describe elsewhere in more detail how systems integration in the environmental sciences could benefit from adopting an analogous approach (Kleinfeldt et al., 1996).

CASE STUDY

We will present as a case study the loose integration of GIS and scientific visualization systems on an application involving hydrological simulation. The application is part of a larger study of techniques to estimate surface soil moisture from active microwave radar measurements, and the validation of these techniques using ground truth measurement and hydrological simulation over a range of scales (field, subcatchment, and catchment) (De Troch et al, 1994). Accomplishing these objectives demands coordination of both data (from different field sites and of diverse types) and techniques (models, tools, and systems). Our integration effort was focused on addressing these coordination needs.

Framework

To reduce the complexity associated with the distributed nature of the project, we decided to manage all data for the project in a central repository. Because of the data management shortcomings of GIS, we chose to create an independent repository, with data organized by site and data type under directories in the file system. Meta-data about each site, type, and data object is captured in parseable ASCII files. To reduce the complexity associated with the heterogeneous nature of the data, the repository data is stored in standard formats, chosen for their generality and ability to store meta-data in addition to data. For tabular data we use ASCII files with Freeform (Habermann & Miller, 1993) format descriptions in companion text files, while for array data we use the Hierarchical Data Format (NCSA, 1993). Georeferencing information can be stored as meta-data inHDF files, which thus become our interface between GIS, visualization, and modelling systems. GIS and scientific visualization for hydrological simulation 617

To make the repository accessible to users, we created a graphical user interface (HYBROW, for Hydrologie Browser) (Kleinfeldt, 1995), shown in Fig. 1. The top half of the HYBROW panel provides facilities for browsing sites and data types (through pop up menus), while the bottom half provides a canvas for placing and manipulating icons that represent data objects. HYBROW was implemented in C and Tcl/Tk (Ousterhout, 1994), a simple, public domain scripting language with built-in facilities for creating X Windows "widgets". HYBROW and its supporting standard formats, repository, and meta-data files give us a framework for the management of our data and loose integration of our tools. However, we have described how both GIS and scientific visualization systems are most often used by experts. How can we put individual analysis functions from these large, complex systems into the hands of novices? A useful method of making a complex tool

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Fig. 1 HYBROW, repository browser and integration framework. 618 Sally Kleinfeldt et al. tractable is to put a shell around it that hides the complexity and makes only the desired functionality accessible. The shell takes care of details like the inputs and outputs, the invocation string, and tool commands. This is called encapsulation, a term and concept borrowed from computer-aided design. We have encapsulated functions from GRASS, AVS, Iris Explorer, Gnuplot, and our hydrological model. Encapsulated tools are access­ ible in HYBROW as functions that can be invoked by clicking on data object icons.

Model, GIS, and visualization functions

The model considered in this study is used to simulate the partitioning of precipitation into évapotranspiration and runoff. This partitioning is an essential component of the water balance, and its simulation requires detailed knowledge of atmosphere, land surface, and soil properties. These properties form the inputs to the model, and their interplay determines the hydrological response which is conveyed as model output (Paniconi & Wood, 1993). Our examples show the application of the model to the 114 km2 Zwalm catchment south of Ghent (Belgium). The modelling process involves the following steps: (1) Processing digital elevation data in a GIS to extract basin characteristics such as ridges, stream channels, overland flow paths, and subcatchments. The files resulting from topographic analysis are a primary input to the numerical model.

Fig. 2 AVS visualization of subsurface soil water velocities. The represents the water , and the discharge of water across the seepage face at the left boundary is clearly seen. GIS and scientific visualization for hydrological simulation 619

(2) Calculating and assembling additional information required for the simulation, including: soil characteristics, precipitation and evaporation rates, the pressure head conditions at the start of the simulation, and various parameters that control time stepping, numerical iteration, and model output. (3) Running the hydrological model. (4) Analysing and visualizing simulation results. The model outputs detailed information on pressure heads, soil moisture content, and water fluxes, calculated at the land surface and in the soil zone and underlying aquifer. Step 1 is a manual process performed by GIS experts outside of HYBROW, and the results are then imported into the repository. Steps 2, 3, and 4 may all be performed from HYBROW. Analysis and visualization functions from GIS and visualization systems are created by expert users and encapsulated. Interesting functions that would normally require familiarity with GIS or visualization systems can in this way be easily run by a novice from the context of browsing data files and model outputs in HYBROW. To give an example, Fig. 2 shows a combination of AVS visualization techniques (hedgehog and isosurface) that help us visualize and interpret vector fields of soil water fluxes. The arrows represent the soil water velocity direction (one arrow per node in the 3D array), while the length of the arrows represents magnitude. The position of the water table is shown by the isosurface drawn through the nodes where pressure head equals zero. The volume can be moved with the mouse and examined from any direction. To reduce clutter, the arrows can be displayed only on a planar surface, and the plane dragged through the volume to see how the vectors change through the space. The arrows can also be coloured to represent the pressure head values at each node. Animations of this type of visualization for successive time steps of a simulation give the user a tangible picture of the subsurface dynamics of a catchment or aquifer.

CONCLUSIONS

Loose integration of GIS and scientific visualization systems is a feasible alternative to tight integration, and offers significant advantages. We have adopted this approach in a hydrological modelling application, using off-the-shelf GIS and visualization tools within a management-focused framework. By using a modern graphical user interface development tool (Tcl/Tk), and focusing on the encapsulation and integration of already available components, an extensible, easy to use system was developed in under 1 person year that can be readily expanded and reconfigured as project needs change. We urge developers to consider seriously the need for more modularity in GIS, so that individual functions could be plugged as needed into an integration framework. Dataflow visualization systems are designed to support encapsulation, but GIS (especially proprietary GIS) are not. Perhaps the monolithic supertool approach will eventually prove unsupportable, and research into new GIS architectures will explore more modular approaches. When easy integration of modular components containing both GIS and scientific visualization functionality becomes possible, environmental researchers will have a truly powerful assortment of tools at their disposal. 620 Sally Kleinfeldt et al.

Acknowledgements This research has been carried out with the financial support of the European Environment Research Programme (contract EV5V-CT94-0446) and the Sardinian Regional Authorities.

REFERENCES

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