Integrating a geographic information system, a scientific visualization system, and a precipitation model

Abstract

Investigating natural, potential, and human-induced impacts on hydrologic systems commonly requires complex modeling with overlapping data requirements, plus massive amounts of one- to four-dimensional data at multiple scales and formats. Given the complexity of most hydrologic studies, the requisite software infrastructure must incorporate many components including simulation modeling and spatial analysis with a flexible, intuitive display. Integrating geographic information systems (GIS) and scientific visualization systems (SVS) provides such an infrastructure. This paper describes an integrated system consisting of an orographic precipitation model, a GIS, and an SVS. The results of this study provide a basis for improving the understanding of hydro-climatic processes in mountainous regions. An additional benefit of the integrated system, the value of which is often underestimated, is the improved ability to communicate model results, leading to a broader understanding of the model assumptions, sensitivities, and conclusions at a management level.Investigating natural, potential, and human-induced impacts on hydrologic systems commonly requires complex modeling with overlapping data requirements, plus massive amounts of one- to four-dimensional data at multiple scales and formats. Given the complexity of most hydrologic studies, the requisite software infrastructure must incorporate many components including simulation modeling and spatial analysis with a flexible, intuitive display. Integrating geographic information systems (GIS) and scientific visualization systems (SVS) provides such an infrastructure. This paper describes an integrated system consisting of an orographic precipitation model, a GIS, and an SVS. The results of this study provide a basis for improving the understanding of hydro-climatic processes in mountainous regions. An additional benefit of the integrated system, the value of which is often underestimated, is the improved ability to communicate model results, leading to a broader understanding of the model assumptions, sensitivities, and conclusions at a management level.