Liu et al. (2026) Mapping drawdown-zone bathymetry using SWOT observations: implications for global monitoring of lake inundation and storage changes

Identification

• Journal: Journal of Hydrology
• Year: 2026
• Date: 2026-02-14
• Authors: Lingyang Liu, Kai Liu, Pengfei Zhan, Chunqiao Song
• DOI: 10.1016/j.jhydrol.2026.135130

Research Groups

• State Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 211135, China
• University of Chinese Academy of Sciences, Nanjing, 211135, China
• University of Chinese Academy of Sciences, Beijing, 100049, China
• Poyang Lake Wetland Research Station, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Jiujiang 332899, China

Short Summary

This study develops a novel Spatial Iterative Filtering and Weighted Average Fusion (SIF-WAF) method to integrate multi-temporal SWOT observations for high-precision mapping of lake drawdown zone bathymetry, demonstrating its potential for global monitoring of lake inundation and storage changes.

Objective

• To develop and validate an effective method for integrating multi-temporal Surface Water and Ocean Topography (SWOT) observations to reconstruct dynamic lake drawdown topography with high completeness and accuracy.

Study Configuration

• Spatial Scale: Ten representative lakes worldwide, spanning diverse climatic, hydrologic, and geomorphologic conditions; global analysis revealing potential for reconstruction in approximately 17% of global lake areas, particularly in the Amazon Basin, West Africa, East Asia, and South Asia.
• Temporal Scale: SWOT data from July 2023 to July 2024; reconstruction of dense time-series of water levels.

Methodology and Data

• Models used: Spatial Iterative Filtering and Weighted Average Fusion (SIF-WAF) method.
• Data sources: Surface Water and Ocean Topography (SWOT) satellite (Ka-band Radar Interferometer), ICESat-2 (for validation), Landsat/Sentinel Virtual Constellation (imagery for water level reconstruction).

Main Results

• The SIF-WAF method reconstructs over 95% of drawdown topography in most cases.
• Validation against ICESat-2 data indicates a mean absolute error (MAE) below 1 meter (m) for the reconstructed topography.
• Integration of derived topography with Landsat/Sentinel imagery successfully reconstructed dense time-series of water levels, achieving a high validation accuracy (R² > 0.85) in capturing seasonal and long-term changes.
• Approximately 17% of global lake areas are seasonally exposed and can potentially be reconstructed using SWOT and SIF-WAF.

Contributions

• Development of the novel Spatial Iterative Filtering and Weighted Average Fusion (SIF-WAF) method for robust and high-precision reconstruction of lake drawdown topography using multi-temporal SWOT observations.
• Demonstration of SWOT's unprecedented capability for dynamic mapping of lake drawdown bathymetry.
• Highlighting the broader implications of SWOT and the SIF-WAF method for global monitoring of lake inundation and storage changes under intensifying climate variability.

Funding

• Not specified in the provided text.

Citation

@article{Liu2026Mapping,
  author = {Liu, Lingyang and Liu, Kai and Zhan, Pengfei and Song, Chunqiao},
  title = {Mapping drawdown-zone bathymetry using SWOT observations: implications for global monitoring of lake inundation and storage changes},
  journal = {Journal of Hydrology},
  year = {2026},
  doi = {10.1016/j.jhydrol.2026.135130},
  url = {https://doi.org/10.1016/j.jhydrol.2026.135130}
}