Wang et al. (2026) A DeepONet surrogate for accelerating distributed hydrological model simulations

Identification

• Journal: Journal of Hydrology
• Year: 2026
• Date: 2026-03-01
• Authors: Tao Wang, Jiaxin Shi, Zhongjing Wang, Jingjing Duan, Zuowen Tan, Yongnan Zhu, Jiaqi Zhai
• DOI: 10.1016/j.jhydrol.2026.135311

Research Groups

The specific research groups, labs, or departments are not specified in the provided text. The authors are Tao Wang, Jiaxin Shi, Zhongjing Wang, Jingjing Duan, Zuowen Tan, Yongnan Zhu, and Jiaqi Zhai.

Short Summary

This paper introduces a DeepONet surrogate model designed to significantly accelerate the simulation speed of distributed hydrological models.

Objective

• To investigate the effectiveness and efficiency of a DeepONet surrogate in accelerating distributed hydrological model simulations.

Study Configuration

• Spatial Scale: Not specified in the provided text.
• Temporal Scale: Not specified in the provided text.

Methodology and Data

• Models used: DeepONet (as a surrogate model). The specific distributed hydrological model being surrogated is not named.
• Data sources: Not specified in the provided text.

Main Results

• The main results are not detailed in the provided text.

Contributions

• Proposing and applying a DeepONet surrogate as a novel approach to enhance the computational efficiency and accelerate simulations of distributed hydrological models.

Funding

• Funding projects, programs, and reference codes are not specified in the provided text.

Citation

@article{Wang2026DeepONet,
  author = {Wang, Tao and Shi, Jiaxin and Wang, Zhongjing and Duan, Jingjing and Tan, Zuowen and Zhu, Yongnan and Zhai, Jiaqi},
  title = {A DeepONet surrogate for accelerating distributed hydrological model simulations},
  journal = {Journal of Hydrology},
  year = {2026},
  doi = {10.1016/j.jhydrol.2026.135311},
  url = {https://doi.org/10.1016/j.jhydrol.2026.135311}
}