Integrating SWOT With Multi‐Source Satellite Observations for Near‐Daily Reservoir Water Level Monitoring

Abstract Reservoirs play a crucial role in global water resource management. Monitoring reservoir hydrologic dynamics is critical for assessing climate variability and anthropogenic regulation. However, traditional satellite altimetry faces multiple challenges hindering high frequency and accuracy water level monitoring. This study develops a proof‐of‐concept framework that integrates multi‐source satellite data, with the Surface Water and Ocean Topography (SWOT) mission as the primary data source, to generate high‐resolution reservoir water level time series. The SWOT‐anchored integration framework establishes a unified two‐dimensional height reference by rule‐based virtual station selection and monthly water surface elevation difference fields. On this reference frame, heterogeneous nadir/laser altimetry from multiple missions are cross‐calibrated, while multi‐source area series are converted to dense levels via reservoir‐specific hypsometry model and then fused. The framework's robustness and re‐applicability were confirmed using eight representative Chinese reservoirs. Results demonstrate that the integrated multi‐source water level time series significantly enhanced observation frequency, achieving near‐daily temporal resolution and capturing detailed non‐linear and short‐term water level dynamics. The water level observation frequency for all reservoirs based on SWOT exceeds 20 times per yr, with the highest reaching 38 times. After multi‐sensor synthesis, the water level observation frequency increased by 3.2–8.1 times, yielding an average of 121 observations annually. Validation results showed strong correlations ( R 2 > 0.90) and low errors (0.46 m ≥ MAE ≥ 0.11 m), confirming the method's robustness and effectiveness. Instead of treating SWOT as another input, this framework standardizes water levels across sensors and tracks, enabling a scalable and transferable multi‐mission synthesis for global reservoir monitoring under changing climatic and anthropogenic pressures.

Authors

Institutions

Publication Details

Journal
Water Resources Research
Published
2026-02-01
DOI
https://doi.org/10.1029/2024wr039711
Citations
1
Primary Topic
Flood Risk Assessment and Management
Type
article
Field-Weighted Citation Impact
36.62

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Integrating SWOT With Multi‐Source Satellite Observations for Near‐Daily Reservoir Water Level Monitoring

Shuangxiao Luo, Chenyu Fan, Chen Tan, Pengfei Zhan et al.
1 citations
Water Resources Research
Flood Risk Assessment and Management
36.62
article

Integrating SWOT With Multi‐Source Satellite Observations for Near‐Daily Reservoir Water Level Monitoring

Shuangxiao Luo, Chenyu Fan, Chen Tan, Pengfei Zhan, Jida Wang, Linghong KE, Chunqiao Song, Yaling Lin, Kai Liu
article en
1 citations

Abstract

Abstract Reservoirs play a crucial role in global water resource management. Monitoring reservoir hydrologic dynamics is critical for assessing climate variability and anthropogenic regulation. However, traditional satellite altimetry faces multiple challenges hindering high frequency and accuracy water level monitoring. This study develops a proof‐of‐concept framework that integrates multi‐source satellite data, with the Surface Water and Ocean Topography (SWOT) mission as the primary data source, to generate high‐resolution reservoir water level time series. The SWOT‐anchored integration framework establishes a unified two‐dimensional height reference by rule‐based virtual station selection and monthly water surface elevation difference fields. On this reference frame, heterogeneous nadir/laser altimetry from multiple missions are cross‐calibrated, while multi‐source area series are converted to dense levels via reservoir‐specific hypsometry model and then fused. The framework's robustness and re‐applicability were confirmed using eight representative Chinese reservoirs. Results demonstrate that the integrated multi‐source water level time series significantly enhanced observation frequency, achieving near‐daily temporal resolution and capturing detailed non‐linear and short‐term water level dynamics. The water level observation frequency for all reservoirs based on SWOT exceeds 20 times per yr, with the highest reaching 38 times. After multi‐sensor synthesis, the water level observation frequency increased by 3.2–8.1 times, yielding an average of 121 observations annually. Validation results showed strong correlations ( R 2 > 0.90) and low errors (0.46 m ≥ MAE ≥ 0.11 m), confirming the method's robustness and effectiveness. Instead of treating SWOT as another input, this framework standardizes water levels across sensors and tracks, enabling a scalable and transferable multi‐mission synthesis for global reservoir monitoring under changing climatic and anthropogenic pressures.

Water Resources ResearchVol. 62(2)
Nanjing University of Chinese Medicine (CN), University of Illinois Urbana-Champaign (US), Hohai University (CN), Nanjing Institute of Geography and Limnology (CN), University of Chinese Academy of Sciences (CN), Sichuan Normal University (CN)
National Natural Science Foundation of China, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences
Clean water and sanitation
Openalex Percentile: Top 1%
Flood Risk Assessment and Management
36.62
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.