Huang et al. (2026) Reconstructing Lake Storage for the Major Water Bodies in the Aral Sea Basin Using Multi-DEM Hypsometry

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Identification

• Journal: Remote Sensing
• Year: 2026
• Date: 2026-03-03
• Authors: Shan Huang, Xi Chen, Liao Yang, Liu Tie, Longhui Li, Ma Xuexi, Bing Yue, Nannan Wu, Akhylbek K. Kurishbayev, Imanmadi Duman, Hossein Azadi, Xiaoting Ma
• DOI: 10.3390/rs18050763

Research Groups

[Not specified in the provided text.]

Short Summary

This study developed a multi-digital elevation model (DEM) hypsometry framework to reconstruct near-monthly lake storage in arid zones, demonstrating its superior accuracy in recovering storage during low-level periods and hydrological disconnection compared to conventional methods.

Objective

• To develop a multi-digital elevation model (DEM) hypsometry framework to reconstruct near-monthly lake storage for 1993–2024, specifically recovering storage during low-level periods without bathymetric surveys.

Study Configuration

• Spatial Scale: Arid-zone lakes, including the Big Aral Sea, and generalized across expanding lakes, lake systems and reservoirs, and terminally shrinking lakes.
• Temporal Scale: Near-monthly reconstruction for the period 1993–2024.

Methodology and Data

• Models used: Multi-digital elevation model (DEM) hypsometry framework, whole-lake modeling assumption, sub-lake modeling. Specific DEMs mentioned include Copernicus-based and Shuttle Radar Topography Mission (SRTM)-based.
• Data sources: Multi-digital elevation models (DEMs) for reconstruction; independent satellite altimetry for validation.

Main Results

• The reconstructed changes in lake storage and level show strong agreement with independent satellite altimetry (Pearson correlation coefficient, r = 0.93 for water level and r = 0.90 for storage).
• The framework significantly outperforms above-water-only methods (r ≈ 0.637 for water level) and conventional model-selection baselines (r ≈ 0.753 for water level).
• For the persistently shrinking Big Aral Sea, the Copernicus-based reconstruction, under the whole-lake modeling assumption, yielded a cumulative storage change of −214.3 km³, which is closest to the satellite altimetry estimate of −210.68 km³.
• Other tested DEMs overestimated the 1993–2024 cumulative storage loss by 66.15 km³ to 141.01 km³.
• Sub-lake modeling further adjusted the SRTM-based cumulative change to −248.38 km³, substantially reducing structural bias caused by hydrological disconnection.

Contributions

• Provides a transferable technical framework for lake storage reconstruction in arid regions, particularly under degraded low lake-level conditions and hydrological disconnection, where conventional methods struggle due to lack of bathymetric information.
• Enhances the accuracy of quantifying long-term storage changes in arid-zone lakes by addressing uncertainties arising from hydrological disconnection and insufficient bathymetric data.

Funding

[Not specified in the provided text.]

Citation

@article{Huang2026Reconstructing,
  author = {Huang, Shan and Chen, Xi and Yang, Liao and Tie, Liu and Li, Longhui and Xuexi, Ma and Yue, Bing and Wu, Nannan and Kurishbayev, Akhylbek K. and Duman, Imanmadi and Azadi, Hossein and Ma, Xiaoting and Ma, Xiaoting and Ma, Xiaoting},
  title = {Reconstructing Lake Storage for the Major Water Bodies in the Aral Sea Basin Using Multi-DEM Hypsometry},
  journal = {Remote Sensing},
  year = {2026},
  doi = {10.3390/rs18050763},
  url = {https://doi.org/10.3390/rs18050763}
}