HESSD Hydrology and Earth System Sciences Discussions HESSD Hydrol. Earth Syst. Sci. Discuss. 1812-2116 Göttingen, Germany 10.5194/hess-2022-345 Using simulation-based inference to determine the parameters of an integrated hydrologic model: a case study from the upper Colorado River basin Hull Robert https://orcid.org/0000-0002-0216-5250 1 Leonarduzzi Elena https://orcid.org/0000-0002-6811-9118 2 De La Fuente Luis 1 Tran Hoang Viet 3 4 Bennett Andrew https://orcid.org/0000-0002-7742-3138 1 Melchior Peter 5 6 Maxwell Reed M. https://orcid.org/0000-0002-1364-4441 2 3 7 Condon Laura E. https://orcid.org/0000-0003-3639-8076 1 Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA High Meadows Environmental Institute, Princeton University, Princeton, NJ, USA Civil & Environmental Engineering, Princeton University, Princeton, NJ, USA Atmospheric Sciences & Global Change Division, Pacific Northwest National Laboratory, Richland, WA, USA Center for Statistics and Machine Learning, Princeton University, Princeton, NJ, USA Department of Astrophysical Sciences, Princeton University, Princeton, NJ, USA Integrated Ground Water Modeling Center, Princeton University, Princeton, NJ, USA National Science Foundation 1835794 2040542 07 11 2022 2022 1 38 Copyright: © 2022 Robert Hull et al. 2022 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://hess.copernicus.org/preprints/hess-2022-345/ The full text article is available as a PDF file from https://hess.copernicus.org/preprints/hess-2022-345/hess-2022-345.pdf

<p>High-resolution, spatially-distributed process-based models are a well-established tool to explore complex watershed processes and how they may evolve under a changing climate. While these models are powerful, calibrating them can be difficult because they are costly to run and have many unknown parameters. To solve this problem, we need a state-of-the-art, data- driven approach to model calibration that can scale to the high-compute, high-dimensional hydrologic simulators that drive innovation in our field today. Simulation- Based Inference (SBI) uses deep learning methods to learn a probability distribution of simulation parameters by comparing simulator outputs to observed data. The inferred parameters can then be used to run calibrated model simulations. This approach has pushed boundaries in simulator-intensive research from cosmology, particle physics, and neuroscience, but is less familiar to hydrology. The goal of this paper is to introduce SBI to the field of watershed modeling by benchmarking and exploring its performance in a set of synthetic experiments. We use SBI to infer two common physical parameters of hydrologic process-based models, Manning&rsquo;s Coefficient and Hydraulic Conductivity, in a snowmelt-dominated catchment in Colorado, USA. We employ a process-based simulator (ParFlow), streamflow observations, and several deep learning components to confront two recalcitrant issues related to calibrating watershed models: 1) the high cost of running enough simulations to do a calibration; 2) finding &lsquo;correct&rsquo; parameters when our understanding of the system is uncertain or incomplete. In a series of experiments, we demonstrate the power of SBI to conduct rapid and precise parameter inference for model calibration. The workflow we present is general-purpose, and we discuss how this can be adapted to other hydrology-related problems.</p>