Li et al. (2026) Constrained differentiable hybrid hydrologic modeling across multiple process backbones: accuracy gains and risks to physical consistency

Identification

• Journal: Journal of Hydrology
• Year: 2026
• Date: 2026-10-01
• Authors: Jiaoyang Li, Guoqiang Tang, Jun Xia
• DOI: 10.1016/j.jhydrol.2026.136486

Research Groups

Not specified in the provided text.

Short Summary

This paper explores constrained differentiable hybrid hydrologic modeling across multiple process backbones, assessing its potential for accuracy improvements and the associated risks to physical consistency.

Objective

• To develop and evaluate constrained differentiable hybrid hydrologic models built upon multiple process backbones, analyzing their impact on modeling accuracy and potential compromises to physical consistency.

Study Configuration

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

Methodology and Data

• Models used: Constrained differentiable hybrid hydrologic modeling across multiple process backbones.
• Data sources: Not specified in the provided text.

Main Results

The provided text is a journal pre-proofs header and does not contain the main results of the study.

Contributions

Introduces and evaluates constrained differentiable hybrid hydrologic models built upon multiple process backbones, providing insights into their potential for enhanced accuracy and the critical assessment of their physical consistency.

Funding

Not specified in the provided text.

Citation

@article{Li2026Constrained,
  author = {Li, Jiaoyang and Tang, Guoqiang and Xia, Jun},
  title = {Constrained differentiable hybrid hydrologic modeling across multiple process backbones: accuracy gains and risks to physical consistency},
  journal = {Journal of Hydrology},
  year = {2026},
  doi = {10.1016/j.jhydrol.2026.136486},
  url = {https://doi.org/10.1016/j.jhydrol.2026.136486}
}