Towards calibration of physics-based integrated surface-groundwater models using deep-learning based surrogates and simulation based inference
Andrew Bennett1, Benjamin Horowitz2, Elena Leonarduzzi3, Yueling Ma4, Amanda Triplett5, Robert Hull6, Laura E Condon5, Reed M Maxwell7 and Peter Melchior8, (1)University of Arizona, Department of Hydrology & Atmospheric Sciences, Tucson, AZ, United States, (2)Princeton University, Department of Astronomy, Princeton, United States, (3)WSL Swiss Federal Institute for Forest, Snow and Landscape Research, Birmensdorf, Switzerland, (4)Princeton University, Princeton, United States, (5)University of Arizona, Hydrology and Atmospheric Sciences, Tucson, AZ, United States, (6)University of Arizona, Hydrology and Atmospheric Sciences, Tucson, United States, (7)Princeton University, Civil & Environmental Engineering, High Meadows Environmental Institute, Princeton, United States, (8)Princeton University, Astrophysical Sciences and the Center for Statistics and Machine Learning, Princeton, NJ, United States
