Zhao et al. (2026) Low-Elevation DEM Sensitivity in Landsat-Based Reconstruction of Long-Term Relative Lake Volume Anomalies

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

Identification

• Journal: Water
• Year: 2026
• Date: 2026-09-15
• Authors: Yidan Zhao, Weidong Tao, Xiwei Qin, Yanting Li
• DOI: 10.3390/w18182303

Research Groups

• Institute of Remote Sensing, Key Laboratory of Digital Earth Science, Chinese Academy of Sciences
• College of Resources Science and Technology, Beijing Normal University

Short Summary

This study presents a framework for monitoring relative water-level and volume anomalies in data-scarce closed basins using DEM-aware approaches. The method combines Landsat-derived lake area with DEM-based H-A-V relationships to reconstruct relative water-level and volume anomalies.

Objective

• Investigate the feasibility of using DEM-aware approaches for long-term lake storage monitoring in high-elevation regions with limited data availability.

Study Configuration

• Spatial Scale: High-elevation region, Xiao Qaidam Lake (China)
• Temporal Scale: 1996–2025

Methodology and Data

• Models used: Landsat-derived lake area, DEM-based H-A-V relationships
• Data sources: Sentinel-2, ICESat-2 ATL13, three DEMs (FABDEM, SRTM, ASTER GDEM)

Main Results

• Long-term increases in lake area and relative water-level anomalies were observed across all three DEM scenarios.
• The 2025 DEM-referenced relative water-level anomalies ranged from 3.29 to 3.99 m.
• The 2025 DEM scenario-based relative volume anomalies ranged from 3.46 to 4.14 × 10^8 m^3.

Contributions

• This study provides a novel framework for monitoring relative water-level and volume anomalies in data-scarce closed basins using DEM-aware approaches, addressing the limitations of traditional methods.
• The results demonstrate the potential of this approach for long-term lake storage monitoring in high-elevation regions with limited data availability.

Funding

• National Natural Science Foundation of China (Grant No. 41971013)
• Key Research Program of Frontier Sciences, Chinese Academy of Sciences (Grant No. QYZDY-SSW-DQC011)

Citation

@article{Zhao2026LowElevation,
  author = {Zhao, Yidan and Tao, Weidong and Qin, Xiwei and Li, Yanting},
  title = {Low-Elevation DEM Sensitivity in Landsat-Based Reconstruction of Long-Term Relative Lake Volume Anomalies},
  journal = {Water},
  year = {2026},
  doi = {10.3390/w18182303},
  url = {https://doi.org/10.3390/w18182303}
}