Huang et al. (2026) How does gross primary production uncertainty impact evapotranspiration prediction within the carbon–water coupled model?

Identification

• Journal: Journal of Hydrology
• Year: 2026
• Date: 2026-02-14
• Authors: Lingxiao Huang, Yizhe Wang, Meng Liu, Yue Sun, Rong Fan, Zhao-Liang Li
• DOI: 10.1016/j.jhydrol.2026.135131

Research Groups

• State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
• University of Chinese Academy of Sciences, Beijing 100101, China
• State Key Laboratory of Efficient Utilization of Arable Land in China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
• Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China

Short Summary

This study quantitatively assessed the impact of remote sensing-based Gross Primary Production (GPP) uncertainty on Evapotranspiration (ET) predictions within carbon-water coupled models, revealing significant negative effects on both accuracy and spatiotemporal patterns of ET estimates.

Objective

• To quantitatively assess the impact of GPP uncertainty on ET estimation accuracy within carbon-water coupled models and to investigate how GPP uncertainty shapes the spatiotemporal patterns of ET simulations.

Study Configuration

• Spatial Scale: Global and regional scales.
• Temporal Scale: 8-day for GPP observations and ET simulations; 2001 to 2015 for global ET estimates and interannual trends.

Methodology and Data

• Models used: Classical carbon–water coupled modeling framework.
• Data sources: 11 state-of-the-art remote sensing (RS)-based GPP products, 20,943 8-day GPP observations.

Main Results

• GPP uncertainty significantly affected both the accuracy and spatiotemporal patterns of ET estimation.
• Using 8-day ET simulations driven by observed GPP as a benchmark, the 11 GPP products increased the root mean square error (RMSE) by an average of 26.94% (ranging from 16.50% to 33.25%) and decreased the coefficient of determination (R²) by an average of 13.58% (ranging from 8.43% to 18.07%).
• Different GPP inputs led to annual ET differences of up to ±200 mm per year in some regions.
• Produced highly divergent, and sometimes opposing, interannual ET trends at global and regional scales from 2001 to 2015.

Contributions

• Provided a quantitative assessment of the impact of remote sensing-based GPP uncertainty on ET predictions within carbon-water coupled models.
• Demonstrated the significant influence of GPP uncertainty on both the accuracy and spatiotemporal patterns of ET simulations.
• Highlighted the critical need to improve GPP accuracy to enhance the reliability of carbon–water coupled modeling, particularly for large-scale hydrological and Earth system applications.

Funding

Not specified in the provided text.

Citation

@article{Huang2026How,
  author = {Huang, Lingxiao and Wang, Yizhe and Liu, Meng and Sun, Yue and Fan, Rong and Li, Zhao-Liang},
  title = {How does gross primary production uncertainty impact evapotranspiration prediction within the carbon–water coupled model?},
  journal = {Journal of Hydrology},
  year = {2026},
  doi = {10.1016/j.jhydrol.2026.135131},
  url = {https://doi.org/10.1016/j.jhydrol.2026.135131}
}