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

Abstract

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.

GRDL: A New Global Reservoir Area‐Storage‐Depth Data Set Derived Through Deep Learning‐Based Bathymetry Reconstruction

“…This fitting allowed us to retrieve corresponding water levels or storage using the Landsat-based water area as the predictor. The continuous reservoir water area time series was also supplemented by and Yao, Minear, et al (2023). It's essential to recognize that Yigzaw et al ( 2018) is the only data set that offers area-storage-depth profiles for global reservoirs, independent of specific data types.…”

“…It's essential to recognize that Yigzaw et al ( 2018) is the only data set that offers area-storage-depth profiles for global reservoirs, independent of specific data types. Subsequent to this fitting, we compared our estimated water levels and storage with reference data obtained from SRTM, in situ surveys, and and Yao, Minear, et al (2023), who organized all LiDAR and satellite altimetry references.…”

“…We have utilized the Global database of lake water storage (GLWS) data set from and Yao, Minear, et al (2023) for validation. and Yao, Minear, et al (2023) constructed a time series detailing the water level and area for 921 global reservoirs using satellite altimetry, LiDAR measurements, and Landsat imagery. These reservoirs are grouped into four categories based on their data sources and derivation methodologies.…”

“…Survey validation samples: reservoirs with available in situ survey data used for model validation. GLWS-Poly validation samples: belong to the "Poly" category as defined by Yao, Livneh, et al (2023) andYao, Minear, et al (2023), derived from data sources including Hydroweb, G-REALM, and DAHITI, and were used for validation within this research. GLWS-LinearLi validation samples: the "LinearLi" category in andYao, Minear, et al (2023), grounded on ICESat-2 (Y Li et al, 2020)…”

“…The first category integrates optical imagery, such as data from MODIS (Gao et al, 2012), Landsat (Y. Li et al, 2023;, and Sentinel-2 (Yao, Minear, et al, 2023), for tracking water area variations with radar altimetry or in situ data to monitor water level changes. This method is highly accurate but limited in its applicability due to the restricted temporal and spatial coverage of radar altimetry and in situ data.…”