Zan et al. (2025) Parameter Uncertainty in Water–Salt Balance Modeling of Arid Irrigation Districts

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Agronomy
• Year: 2025
• Date: 2025-12-07
• Authors: Ziyi Zan, Zhiming Ru, Changming Cao, Kun Wang, Guangyu Chen, Hangzheng Zhao, Xinli Hu, Su Li, Weifeng Yue
• DOI: 10.3390/agronomy15122814

Research Groups

Implied to be a research institution in China focused on water resource management and agricultural sustainability, given the study location in the Hetao Irrigation District.

Short Summary

This study developed a lumped water–salt balance model for arid irrigated regions, integrating farmland and non-farmland areas with a vertical structure, and introduced a novel calibration approach combining random sampling with Kernel Density Estimation (KDE) to address parameter uncertainty. The model satisfactorily simulated groundwater depth and general soil salinity trends, providing a robust tool for water and salt management in data-scarce environments.

Objective

• To develop a robust lumped water–salt balance model for the Hetao Irrigation District (HID) in China, capable of precise water and salt management by integrating farmland and non-farmland areas and a vertical structure.
• To introduce and validate a novel calibration approach, combining random sampling with Kernel Density Estimation (KDE), to identify optimal parameter ranges and enhance model reliability and robustness in the face of parameter uncertainty.

Study Configuration

• Spatial Scale: Hetao Irrigation District (HID) in China, with specific calibration and validation performed in the Yichang sub-district. The model integrates farmland and non-farmland areas and is vertically structured into a root zone, transition layer, and aquifer.
• Temporal Scale: Focused on simulating the dynamics of water and salt balance over time, implying continuous or time-series simulation, though specific duration (e.g., years) is not mentioned.

Methodology and Data

• Models used: Lumped water–salt balance model, vertically structured into root zone, transition layer, and aquifer.
• Data sources: Observational data from the Yichang sub-district for model calibration and validation.

Main Results

• The water balance module performed satisfactorily in simulating groundwater depth, achieving an R² of 0.79 for calibration and 0.65 for validation.
• The salt balance module effectively replicated the general trends of soil salinity dynamics, although with lower R² values, attributed to high spatial variability and data scarcity.
• The novel calibration approach, combining random sampling with Kernel Density Estimation (KDE), successfully identified optimal parameter ranges, narrowed parameter value ranges, and enhanced model reliability and robustness by addressing parameter uncertainty.
• Sensitivity analysis (SA) was incorporated to identify key parameters influencing the water–salt model.

Contributions

• Developed a practical and robust lumped water–salt balance model specifically tailored for arid irrigated regions like the Hetao Irrigation District.
• Introduced an innovative calibration methodology (random sampling with KDE) that identifies optimal parameter ranges rather than single values, significantly enhancing model robustness and reliability in hydrological modeling.
• Provides a methodological reference for addressing common challenges of parameter uncertainty in hydrological modeling, particularly valuable for data-scarce regions.
• The model integrates farmland and non-farmland areas and incorporates a vertical structure (root zone, transition layer, aquifer), offering a more comprehensive representation of the water and salt balance.

Funding

Not specified in the provided text.

Citation

@article{Zan2025Parameter,
  author = {Zan, Ziyi and Ru, Zhiming and Cao, Changming and Wang, Kun and Chen, Guangyu and Zhao, Hangzheng and Hu, Xinli and Li, Su and Yue, Weifeng},
  title = {Parameter Uncertainty in Water–Salt Balance Modeling of Arid Irrigation Districts},
  journal = {Agronomy},
  year = {2025},
  doi = {10.3390/agronomy15122814},
  url = {https://doi.org/10.3390/agronomy15122814}
}