Liu et al. (2026) A hybrid machine learning and optimal stomatal behavior model to reveal the role of vegetation dynamics in potential evapotranspiration and drought

Identification

• Journal: Journal of Hydrology
• Year: 2026
• Date: 2026-04-01
• Authors: Weiqi Liu, Shaoxiu Ma, Haiyang Xi, Bingyao Wang, Kun Feng, Atsushi Tsunekawa
• DOI: 10.1016/j.jhydrol.2026.135446

Research Groups

Not available from the provided text.

Short Summary

This study develops a hybrid machine learning and optimal stomatal behavior model to investigate the influence of vegetation dynamics on potential evapotranspiration and drought conditions.

Objective

• To reveal the role of vegetation dynamics in potential evapotranspiration and drought using a novel hybrid modeling approach.

Study Configuration

• Spatial Scale: Not available from the provided text.
• Temporal Scale: Not available from the provided text.

Methodology and Data

• Models used: A hybrid machine learning and optimal stomatal behavior model.
• Data sources: Not available from the provided text.

Main Results

Not available from the provided text.

Contributions

• Introduction of a novel hybrid modeling framework combining machine learning and optimal stomatal behavior for hydrological studies.
• Enhanced understanding of the complex interplay between vegetation dynamics, potential evapotranspiration, and drought.

Funding

Not available from the provided text.

Citation

@article{Liu2026hybrid,
  author = {Liu, Weiqi and Ma, Shaoxiu and Xi, Haiyang and Wang, Bingyao and Feng, Kun and Tsunekawa, Atsushi},
  title = {A hybrid machine learning and optimal stomatal behavior model to reveal the role of vegetation dynamics in potential evapotranspiration and drought},
  journal = {Journal of Hydrology},
  year = {2026},
  doi = {10.1016/j.jhydrol.2026.135446},
  url = {https://doi.org/10.1016/j.jhydrol.2026.135446}
}