His research is focused on regional-scale landscape hydrology, examining the terrestrial hydrologic cycle and its relationship to climate, vegetation and biogeochemical cycles. He co-developed the Landscape Hydrology Model (LHM), an integrated modeling tool to study large-scale, fine-resolution hydrologic processes using modest computational tools. Partly due to the challenge of providing fine-resolution inputs at regional scales, and because of the importance of the questions at those scales he has become involved in all aspects of “big data” discovery, processing, and analysis. This includes using machine learning algorithms to yield insights into environmental phenomena and to better prepare inputs for process-based models. He is also actively involved in field data collection, and view this as a critical and foundational aspect of hydrologic sciences.