Estimating Reservoir Sedimentation Rates and Storage Capacity Losses Using High‐Resolution Sentinel‐2 Satellite and Water Level Data

• Published in: Geophysical research letters Vol. 50; no. 16
• Main Authors: Yao, Fangfang, Minear, J. Toby, Rajagopalan, Balaji, Wang, Chao, Yang, Kehan, Livneh, Ben
• Format: Journal Article
• Language: English
• Published: Washington John Wiley & Sons, Inc 28.08.2023; Wiley
• Subjects: Altimeters; Bathymeters; Bathymetry; Freshwater; Inland water environment; Remote sensing; Remote sensing techniques; Reservoir sedimentation; Reservoir storage; Reservoirs; Satellite altimetry; Satellites; Sediment; Sedimentation; Sedimentation & deposition; Sedimentation rates; Sediments; Sensing techniques; Sentinel‐2; Storage capacity; storage capacity loss; Storage conditions; Surveys; Water levels; Water storage; United States > US
• ISSN: 0094-8276; 1944-8007
• DOI: 10.1029/2023GL103524

In nearly all reservoirs, storage capacity is steadily lost due to trapping and accumulation of sediment. Despite critical importance to freshwater supplies, reservoir sedimentation rates are poorly understood due to sparse bathymetry survey data and challenges in modeling sedimentation sequestration. Here, we proposed a novel approach to estimate reservoir sedimentation rates and storage capacity losses using high‐resolution Sentinel‐2 satellites and daily in situ water levels. Validated on eight reservoirs across the central and western United States, the estimated reservoir bathymetry and sedimentation rates have a mean error of 4.08% and 0.05% yr−1, respectively. Estimated storage capacity losses to sediment vary among reservoirs, which overall agrees with the pattern from survey data. We also demonstrated the potential applications of the proposed approach to ungauged reservoirs by combining Sentinel‐2 with sub‐monthly water levels from recent satellite altimeters. Plain Language Summary Reservoir storage capacity is steadily lost due to sediment filling, which threatens freshwater supplies both now and in the future. Yet, lost reservoir storage capacities to sediment are largely unknown. Here, we develop a generic method to estimate capacity losses and reservoir sedimentation rates by leveraging remote sensing techniques. We tested on eight reservoirs across the central and western United States and found capacity losses and sedimentation rates vary across reservoirs. The proposed method offers a promising alternative to evaluate and predict capacity losses in reservoirs nationwide and globally, and thus supports effective water managements and planning for sustainable freshwater supplies in the future. Key Points High‐resolution Sentinel‐2 images and daily in situ water levels were used to estimate reservoir sedimentation rates and capacity losses Estimated reservoir sedimentation rates and storage capacity losses have a mean error of 0.05% yr−1 of full storage capacity Potential applications of this method to ungauged reservoirs are feasible with sub‐monthly level data from recent satellite altimeters