Andrew Bennett

Andrew Bennett is an Assistant Professor in the Department of Hydrology and Atmospheric Sciences at the University of Arizona. He holds a Ph.D. in Civil & Environmental Engineering from the University of Washington (2021). Research focuses on hydrologic modeling and machine learning, emphasizing large-scale terrestrial hydrology and model-data integration.

Current work develops deep learning frameworks for groundwater parameter inversion, HydroLSTM-based catchment modeling, and simulation-based inference for complex watershed simulators. Research addresses spatiotemporal machine learning applications from regional to continental scales, coupled land-atmosphere modeling for Arctic regions, and reproducibility in hydrologic modeling. Bennett creates computational tools including SubsetTools for ParFlow model data processing and advances cyberinfrastructure for reproducible hydrologic research.

Methodological innovations include physics-inspired AI approaches, explainable AI for neural network interpretation in flux simulations, and process-conditioned bias correction for streamflow models. Research examines fundamental challenges in model evaluation, data partitioning strategies, and temporal aggregation effects on hydrologic predictions.

Educational Background: Ph.D. research developed at University of Washington established foundations in model evaluation, information theory applications, and ice sheet model validation techniques.