A physics-informed, machine learning emulator of a 2d surface water model: What temporal networks and simulation-based inference can help us learn about hydrologic processes

Reed M. Maxwell, Laura E. Condon, Peter Melchior

Research output: Contribution to journalArticlepeer-review

Abstract

While machine learning approaches are rapidly being applied to hydrologic problems, physics-informed approaches are still relatively rare. Many successful deep-learning applications have focused on point estimates of streamflow trained on stream gauge observations over time. While these approaches show promise for some applications, there is a need for distributed approaches that can produce accurate two-dimensional results of model states, such as ponded water depth. Here, we demonstrate a 2D emulator of the Tilted V catchment benchmark problem with solutions provided by the integrated hydrology model ParFlow. This emulator model can use 2D Convolution Neural Network (CNN), 3D CNN, and U-Net machine learning architectures and produces time-dependent spatial maps of ponded water depth from which hydrographs and other hydrologic quantities of interest may be derived. A comparison of different deep learning architectures and hyperparameters is presented with particular focus on approaches such as 3D CNN (that have a time-dependent learning component) and 2D CNN and U-Net approaches (that use only the current model state to predict the next state in time). In addition to testing model performance, we also use a simplified simulation based inference approach to evaluate the ability to calibrate the emulator to randomly selected simulations and the match between ML calibrated input parameters and underlying physics-based simulation.

Original languageEnglish (US)
Article number3633
JournalWater (Switzerland)
Volume13
Issue number24
DOIs
StatePublished - Dec 1 2021
Externally publishedYes

Keywords

  • Hydrologic modeling
  • Hydrologic runoff processes
  • Machine learning
  • Model emulation
  • Simulation-based inference

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Biochemistry
  • Aquatic Science
  • Water Science and Technology

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