Reconstructing Lake Storage for the Major Water Bodies in the Aral Sea Basin Using Multi-DEM Hypsometry

In arid-zone water resource management and water-security assessment, changes in water-body volume are key indicators of water availability and regulation performance. However, arid-zone lakes often lack sufficient bathymetric information to constrain geometry under low lake-level conditions. Shrinkage-driven hydrological disconnection can destabilize extrapolation of water level–storage relationships. This increases uncertainty in quantifying long-term storage changes. Here, we develop a multi-digital elevation model (DEM) hypsometry framework to reconstruct near-monthly lake storage for 1993–2024, recovering storage during low-level periods without bathymetric surveys. Reconstructed changes agree with independent satellite altimetry (r = 0.93 for level and 0.90 for storage), outperforming above-water-only (r ≈ 0.637 for water level) and conventional model-selection base-lines (r ≈ 0.753 for water level). The framework was quantified across three scenarios: expanding lakes, lake systems and reservoirs, and terminally shrinking lakes. For the persistently shrinking Big Aral Sea, under the whole-lake modeling assumption, the Copernicus-based reconstruction provides a cumulative storage change of −214.3 km3, closest to the satellite altimetry estimate of −210.68 km3 among the tested DEMs. In contrast, other DEMs overestimate the 1993–2024 cumulative loss by 66.15–141.01 km3. Sub-lake modeling further adjusts the Shuttle Radar Topography Mission (SRTM)-based cumulative change to −248.38 km3, substantially reducing structural bias caused by lake disconnection. This study provides a transferable technical framework for lake storage reconstruction in arid regions under degraded low lake-level conditions and hydrological disconnection.

Authors

Institutions

Publication Details

Journal
Remote Sensing
Published
2026-03-03
DOI
https://doi.org/10.3390/rs18050763
Primary Topic
Flood Risk Assessment and Management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Reconstructing Lake Storage for the Major Water Bodies in the Aral Sea Basin Using Multi-DEM Hypsometry

Xiaoting Ma, Shan Huang, Longhui Li, Xi Chen et al.
Remote Sensing
Flood Risk Assessment and Management
article

Reconstructing Lake Storage for the Major Water Bodies in the Aral Sea Basin Using Multi-DEM Hypsometry

Xiaoting Ma, Shan Huang, Longhui Li, Xi Chen, Liao Yang, Liu Tie, Ma Xuexi
article en

Abstract

In arid-zone water resource management and water-security assessment, changes in water-body volume are key indicators of water availability and regulation performance. However, arid-zone lakes often lack sufficient bathymetric information to constrain geometry under low lake-level conditions. Shrinkage-driven hydrological disconnection can destabilize extrapolation of water level–storage relationships. This increases uncertainty in quantifying long-term storage changes. Here, we develop a multi-digital elevation model (DEM) hypsometry framework to reconstruct near-monthly lake storage for 1993–2024, recovering storage during low-level periods without bathymetric surveys. Reconstructed changes agree with independent satellite altimetry (r = 0.93 for level and 0.90 for storage), outperforming above-water-only (r ≈ 0.637 for water level) and conventional model-selection base-lines (r ≈ 0.753 for water level). The framework was quantified across three scenarios: expanding lakes, lake systems and reservoirs, and terminally shrinking lakes. For the persistently shrinking Big Aral Sea, under the whole-lake modeling assumption, the Copernicus-based reconstruction provides a cumulative storage change of −214.3 km3, closest to the satellite altimetry estimate of −210.68 km3 among the tested DEMs. In contrast, other DEMs overestimate the 1993–2024 cumulative loss by 66.15–141.01 km3. Sub-lake modeling further adjusts the Shuttle Radar Topography Mission (SRTM)-based cumulative change to −248.38 km3, substantially reducing structural bias caused by lake disconnection. This study provides a transferable technical framework for lake storage reconstruction in arid regions under degraded low lake-level conditions and hydrological disconnection.

Remote SensingVol. 18(5)
Nanjing Normal University (CN), Chinese Academy of Sciences (CN), Ghent University (BE), Research Center for Ecology and Environment of Central Asia (CN), Xinjiang Institute of Ecology and Geography (CN), National Academy of Sciences of the Republic of Kazakhstan (KZ), Geospatial Research (United Kingdom) (GB), Kazakh National Agrarian Research University (KZ), Institute of Tibetan Plateau Research (CN), Zhejiang University of Technology (CN)
Clean water and sanitation
Openalex Percentile: Top 48%
Flood Risk Assessment and Management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.