Development of a deep learning emulator for a distributed groundwater–surface water model: Parflow-ml

Hoang Tran, Elena Leonarduzzi, Luis De la Fuente, Robert Bruce Hull, Vineet Bansal, Calla Chennault, Pierre Gentine, Peter Melchior, Laura E. Condon, Reed M. Maxwell

Research output: Contribution to journalArticlepeer-review

Abstract

Integrated hydrologic models solve coupled mathematical equations that represent natural processes, including groundwater, unsaturated, and overland flow. However, these models are computationally expensive. It has been recently shown that machine leaning (ML) and deep learning (DL) in particular could be used to emulate complex physical processes in the earth system. In this study, we demonstrate how a DL model can emulate transient, three-dimensional integrated hydrologic model simulations at a fraction of the computational expense. This emulator is based on a DL model previously used for modeling video dynamics, PredRNN. The emulator is trained based on physical parameters used in the original model, inputs such as hydraulic conductivity and topography, and produces spatially distributed outputs (e.g., pressure head) from which quantities such as streamflow and water table depth can be calculated. Simulation results from the emulator and ParFlow agree well with average relative biases of 0.070, 0.092, and 0.032 for streamflow, water table depth, and total water storage, respectively. Moreover, the emulator is up to 42 times faster than ParFlow. Given this promising proof of concept, our results open the door to future applications of full hydrologic model emulation, particularly at larger scales.

Original languageEnglish (US)
Article number3393
JournalWater (Switzerland)
Volume13
Issue number23
DOIs
StatePublished - Dec 1 2021

All Science Journal Classification (ASJC) codes

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

Keywords

  • Complex system
  • Deep learning
  • Groundwater
  • Hydrology
  • LSTM
  • Machine learning
  • Modeling
  • Upper Colorado
  • Water storage

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