Formulating qualitative features using interactive visualization for analysis of multivariate spatiotemporal data

DiscoverWater is a web-based visualization tool developed to enable the visual representation of data, and thus, aid scientific and societal understanding of hydrologic systems. Open data sources are coalesced to, for example, illustrate the impacts on streamflow of irrigation withdrawals. Scientists and stakeholders are informed through synchronized time-series data plots that correlate multiple spatiotemporal datasets and an interactive time-evolving map that provides a spatial analytical context. Together, these components elucidate trends so that the user can try to envision the relations between groundwater-surface water interactions, the impacts of pumping on these interactions, and the interplay of climate. Aligning data in this manner has the capacity for interdisciplinary knowledge discovery and motivates dialogue about system processes that we seek to enhance through qualitative features informed through quantitative models. DiscoverWater and its connection is demonstrated using two field cases. First, it is used to visualize data sets from the High Plains aquifer, where reservoir- and groundwater-supported irrigation has affected the Arkansas River in western Kansas. Second, data and model results from Barton Springs segment of the Edwards aquifer in Texas reveal the effects of regional pumping on this important urbanizing aquifer system. Identifying what is interesting about the data and the modeled system in the two different case studies is a step towards moving typically static visualization capabilities to an adaptive framework. Additionally, the dashboard interface incorporates both quantitative and qualitative information about distinctive case studies in a machine-readable form, such that a catalog of qualitative models can capture subject matter expertise alongside associated datasets. As the catalog is expanded to include other case studies, the collection has potential to establish a standard framework able to inform intelligent system reasoning.