Water Storage Volume Monitoring Using SWOT-Derived Water Level and Surface Slope Data

On the Korean Peninsula, where precipitation is highly concentrated during the summer monsoon season, reservoirs play a critical role as primary sources of available water. Effective monitoring of reservoir storage is therefore essential for water resource management and drought–flood mitigation. However, a large proportion of reservoirs in Korea are ungauged, making continuous in situ monitoring of water storage difficult. In this context, satellite remote sensing, which enables systematic observation over large and inaccessible areas, provides an effective alternative for reservoir monitoring. Conventional satellite-based water body monitoring has primarily relied on optical and SAR imagery, such as Sentinel-1 and Sentinel-2 data. While these datasets are effective for estimating reservoir surface area, they have inherent limitations in retrieving water surface elevation. To overcome these limitations, recent studies have increasingly utilized Surface Water and Ocean Topography (SWOT) data, which enable direct observation of water surface elevation and water surface slope. These capabilities provide essential hydrodynamic information that cannot be derived from conventional optical or SAR imagery. In this study aims to estimate reservoir water storage volume by integrating reservoir surface area derived from SAR and optical satellite imagery with water surface elevation and slope information obtained from SWOT observations. The SWOT-based reservoir storage volume estimates are validated by comparison with in situ water storage measurements. This approach has the potential to support reservoir storage estimation in ungauged regions and contribute to regional-scale water resource monitoring.

 

Keywords: Water storage volume, Reservoir, SWOT

 

Acknowledgment

This research was supported by the BK21 FOUR (Fostering Outstanding Universities for Research) funded by the Ministry of Education (MOE, Korea) and National Research Foundation of Korea (NRF). This work is financially supported by Korea Ministry of Land, Infrastructure and Transport (MOLIT) as 「Innovative Talent Education Program for Smart City」. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2022-NR070339). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2024-00416443). This work was supported by Korea Environment Industry & Technology Institute(KEITI) through Technology development project to optimize planning, operation, and maintenance of urban flood control facilities, funded by Korea Ministry of Climate, Energy and Environment(MCEE)(RS-2024-00398012).