Reconstructing Reservoir Water Level-Area-Storage Volume Curve Using Multi-source Satellite Imagery and Intelligent Classification Algorithms

Summary, in English

The water level-area-storage volume (Z-A-V) relationship serves as the cornerstone of reservoir operations, governing water allocation, flood mitigation, and power generation. Sedimentation-induced capacity alterations can progressively degrade Z-A-V accuracy, yet systematic curve updates remain inadequately implemented across developing nations. Traditional reconstruction approaches face inherent limitations due to resource-intensive requirements including costly field surveys and data scarcity. Emerging satellite remote sensing technologies show transformative potential for dynamic reservoir monitoring, though their application in Z-A-V curve updating still requires substantive exploration. This study evaluates the capability of multi-source satellite imagery, including optical data from Landsat 8 and Sentinel-2, and synthetic aperture radar (SAR) data from Sentinel-1, for accurately reconstructing the Z-A-V curve. To extract high-accuracy reservoir surface extents, two advanced algorithms–Random Forest Classification (RFC) and Otsu thresholding–are applied to the optimal and SAR imagery, respectively, to delineate water and non-water pixels. The suitability of the satellite-derived Z-A-V curve is further assessed by estimating the storage capacity loss due to sedimentation accumulation and comparing these estimates with that derived from design curve. Using the Hongjiadu Reservoir in the upper reach of the Wujiang River, China, as a case study, the results show that: (1) all three satellite datasets accurately extract the reservoir surface areas, achieving average accuracies of 96%, 89%, and 88% for Sentinel-1, Landsat 8, and Sentinel-2, respectively; (2) the Z-A curve reconstructed from Sentinel-1 SAR imagery achieves the highest accuracy with and the coefficient of determination (R²) > 0.99 and root mean square error (RMSE) < 1; (3) the storage capacity loss estimated from Sentinel-1-derived Z-V curve (0.065 billion m³) closely aligns with the sedimentation-based estimate (0.077 billion m³), outperforming other imagery sources. This study demonstrates the significant potential of integrating high-quality satellite imagery and intelligent algorithms to enable frequent, cost-effective, and large-scale updates to reservoir Z-A-V curves, bridging a crucial gap in adaptability evaluation of multi-source remote sensing-based Z-A-V characteristic reconstructions.

• Author: Xu Gui; Qiumei Ma; Jiqing Li; Zheng Duan; Lihua Xiong; Chong Yu Xu
• Department/s: Dept of Physical Geography and Ecosystem Science; BECC: Biodiversity and Ecosystem services in a Changing Climate; MERGE: ModElling the Regional and Global Earth system
• Publishing year: 2025
• Language: English
• Publication/Series: Water Resources Management
• Links: http://dx.doi.org/10.1007/s11269-025-04205-7
• Document type: Journal article
• Publisher: Springer Science and Business Media B.V.
• Topic: Earth Observation
• Keywords: Machine learning; Multi-source satellite imagery; Remote sensing; Reservoir; Storage; Surface area
• Status: Epub
• ISBN/ISSN/Other: ISSN: 0920-4741