High-Resolution Hydrogeologic Predictions through Advanced Spatial, Spatiotemporal, and Visual Analytics
Groundwater recharge and shallow groundwater flow are two components of the hydrologic cycle that are affected by and directly influence many natural and societally important activities.
Understanding and predicting recharge and shallow groundwater flow has historically involved data and models with spatial and temporal resolutions that perform poorly in predicting both extreme events and high-resolution behavior. Increases in measurement technology over the past two decades have resulted in data with sub-meter spatial and sub-hour temporal resolutions. Although these data offer potential for significant advances in hydrogeologic prediction, the resulting file size and computational complexity have effectively limited their use.
The aim of this research is to develop new applications of statistical- and machine-learning algorithms to improve our characterization and prediction capabilities of shallow hydrogeologic properties and processes, and to develop novel applications for utilizing visual analytic methods to enhance the description, analysis, and understanding of this complex, correlated spatiotemporal system. These goals will be achieved through the development of new programs that utilize GPU processing capabilities to achieve improvements in computational performance, making it possible to apply these computationally expensive algorithms and applications to modern high-resolution spatial and spatiotemporal data sets.
Contact: Don Keefer (firstname.lastname@example.org)