Preprints
https://doi.org/10.5194/hess-2022-345
https://doi.org/10.5194/hess-2022-345
 
07 Nov 2022
07 Nov 2022
Status: this preprint is currently under review for the journal HESS.

Using simulation-based inference to determine the parameters of an integrated hydrologic model: a case study from the upper Colorado River basin

Robert Hull1, Elena Leonarduzzi2, Luis De La Fuente1, Hoang Viet Tran3,4, Andrew Bennett1, Peter Melchior5,6, Reed M. Maxwell2,3,7, and Laura E. Condon1 Robert Hull et al.
  • 1Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA
  • 2High Meadows Environmental Institute, Princeton University, Princeton, NJ, USA
  • 3Civil & Environmental Engineering, Princeton University, Princeton, NJ, USA
  • 4Atmospheric Sciences & Global Change Division, Pacific Northwest National Laboratory, Richland, WA, USA
  • 5Center for Statistics and Machine Learning, Princeton University, Princeton, NJ, USA
  • 6Department of Astrophysical Sciences, Princeton University, Princeton, NJ, USA
  • 7Integrated Ground Water Modeling Center, Princeton University, Princeton, NJ, USA

Abstract. 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’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 ‘correct’ 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.

Robert Hull et al.

Status: open (until 08 Jan 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2022-345', Keith Beven, 07 Nov 2022 reply
    • AC1: 'Reply on RC1', Robert Hull, 22 Nov 2022 reply
  • RC2: 'Comment on hess-2022-345', Keith Beven, 08 Nov 2022 reply

Robert Hull et al.

Model code and software

This is a repository for conducting simulation-based inference in the Taylor Basin Robert Hull https://github.com/rhull21/sbi_taylor

Robert Hull et al.

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Short summary
As the stress on water resources from climate change grows, we need models that represent water processes at the scale of counties, states, and even countries in order to make viable predictions about things will change. While such models are powerful, they can be cumbersome to deal with because they are so large. This research explores a novel way of increasing the efficiency of large-scale hydrologic models using an approach called Simulation-Based Inference.