Remote Sensing‐Based Modeling of the Bathymetry and Water Storage for Channel‐Type Reservoirs Worldwide

Abstract

Estimations of reservoir bathymetry and storage are of great significance due to their substantial impacts on hydrological processes and water resource management. However, existing approaches for reservoir bathymetry construction often rely on field measurements, which restricts their application at regional and global scales. This study proposes a novel Approach for Determining the BAthymetry and water storage of channel-type Reservoirs, hereafter referred to as ADBAR, for which only open-access digital elevation model (DEM) and satellite images are required. The basic idea of ADBAR is to utilize the geomorphological similarity and topographical continuity of the reservoir inundation area with its lateral valleys and upstream/downstream regions to predict underwater bathymetry. Forty-eight reservoirs with different topographic and geometric characteristics were selected for method validation. The selected reservoirs were all impounded after the year 2000, so the modeled reservoir bathymetry can be validated by the "reference" reservoir storage calculated using the exposed topography in SRTM DEM and the mapped water extents from spectral images. The difference between the estimated and reference storages is about 13% on average. Furthermore, the modeled results in two selected basins with dense reservoir distributions, the Upper Yellow River Basin in China and the Tocantins River Basin in Brazil, are comparable with the documented effective storage capacities. The validations for both individual reservoirs and the two large basins demonstrate that ADBAR is a robust tool for estimating reservoir bathymetries and storage capacities and thus facilitates the modeling of reservoir impacts on water budgets at large and global scales.

Development of a Reservoir Flood Control Scheme for Global Flood Models

“…Note that while DEMs representing reservoir extent and bathymetry is necessary for the procedure mentioned above, such a data set does not exist currently. Although new estimation methods for reservoir bathymetry and storage‐depth‐area relationships have recently been developed (Li et al., 2020; Liu et al., 2020), global data sets have not been realized yet and are therefore, desired…”

Estimating Reservoir Release Using Multi-Source Satellite Datasets and Hydrological Modeling Techniques

“…The second aligns with advances in constructing reservoir geometry using digital elevation model. Liu et al [9] modeled the reservoir bathymetry with SRTM DEM and satellite imagery. The principle is to utilize geomorphological similarity and topographical continuity of reservoirs to model bathymetry.…”

“…Reservoir release is an essential variable as it affects hydrological processes and water availability downstream [9,10]. Estimation of reservoir release is of great significance for downstream hindered by upstream reservoir behaviors.…”

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

“…This discrepancy allows us to generate numerous simulated reservoirs to train a deep learning model specifically for reservoir bathymetry reconstruction. This approach is supported by observations that key characteristics such as the cross-sectional shape of river valleys and the longitudinal profile of channel-type reservoirs can be inferred from surrounding DEMs (Fang et al, 2023;K. Liu et al, 2020).…”

“…Despite having lower accuracy relative to techniques such as satellite altimetry, DEMs are increasingly recognized in research as valuable tools for underwater bathymetry projection, especially in contexts where deploying traditional methods is problematic (Fang et al., 2023; K. Liu & Song, 2022; K. Liu et al., 2020; Tseng et al., 2016; Weekley & Li, 2019; Yao et al., 2018; S. Zhang & Gao, 2020; Zhu et al., 2019). Tseng et al.…”