Meng et al. (2025) Application of the InVEST model to quantify the annual water yield of Yellow River Basin

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

Identification

• Journal: Springer Link (Chiba Institute of Technology)
• Year: 2025
• Date: 2025-12-01
• Authors: Tianxin Meng, Irina Maltseva, Shuoting Xiao
• DOI: 10.1051/e3sconf/202567002005/pdf

Research Groups

Not explicitly mentioned in the provided text.

Short Summary

This study evaluates the annual water yield in the Yellow River Basin for 2020 using the InVEST model, finding a total yield of 74.35 billion cubic meters influenced by precipitation and evapotranspiration.

Objective

• To evaluate the annual water yield in the Yellow River Basin for the year 2020 and analyze how precipitation, land use, and evapotranspiration influence water resource distribution.

Study Configuration

• Spatial Scale: Yellow River Basin
• Temporal Scale: Annual (for the year 2020)

Methodology and Data

• Models used: InVEST model
• Data sources: Precipitation, land use, and evapotranspiration data (specific sources not detailed in the provided text).

Main Results

• The annual water yield in the Yellow River Basin for 2020 was 74.35 billion cubic meters.
• Water yield was highest in the southwest and southeast regions and lowest in the northwest region.
• Spatially, water yield positively correlates with precipitation and negatively correlates with evapotranspiration.
• The impact of land cover types on water yield requires further investigation.

Contributions

• Provides an effective methodology for assessing the water resource status of the Yellow River Basin.
• Offers a scientific foundation for basin water resource management.

Funding

Not explicitly mentioned in the provided text.

Citation

@article{Meng2025Application,
  author = {Meng, Tianxin and Maltseva, Irina and Xiao, Shuoting},
  title = {Application of the InVEST model to quantify the annual water yield of Yellow River Basin},
  journal = {Springer Link (Chiba Institute of Technology)},
  year = {2025},
  doi = {10.1051/e3sconf/202567002005/pdf},
  url = {https://doi.org/10.1051/e3sconf/202567002005/pdf}
}