Water quality managers analyze data collected in the field to assess environmental conditions and enact policy based on water quality impairments identified in this analysis. Water quality is often based on measures of water chemistry and the health of biological communities. There are many factors spread across the landscape that contribute to water quality. This adds a spatial dimension to the problem. Furthermore, data are often analyzed based on aggregations of site level data to multiple hierarchies of watersheds. This paper presents a multidimensional data model which incorporates hydrological spatial hierarchies for the purpose of analyzing water quality data at multiple resolutions. The data model was implemented in a relational database management system and linked with a geographic information system to provide visual exploration of data across multiple levels within the spatial hierarchy. Data mining techniques such as classification and association rule generation were applied to data at multiple levels of the hydrologic spatial hierarchy. Classification was applied to predict the health of fish communities based on site habitat characteristics and measures of water chemistry. Association rules were developed to determine relationships between site characteristic and water quality variables and fish community health. The results of the classification and association rules were then compared across two levels of the hydrographic spatial hierarchy.
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