2021
DOI: 10.1029/2020wr027666
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Tracking lake surface elevations with proportional hypsometric relationships, Landsat imagery, and multiple DEMs

Abstract: Multidecadal inland surface water dynamics are of increasing interest due to their universal importance and widespread impact on climate, industry, agriculture, ecology, and society (Prigent et al., 2012). At the most basic level, water surface dynamics represent the spatiotemporal distribution of water on the landscape and describe when, where, and how much water is present. For lakes and reservoirs in particular, many studies have focused on four key metrics:

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Cited by 14 publications
(12 citation statements)
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“…Since the SRTM overpassed the region in February of 2000, the lowest reservoir elevations, which often occur in May or June for some reservoirs, might not be fully captured. Small reservoirs with substantial changes within 1 day and large reservoirs with steep bathymetry would thus be more subject to SRTM DEM errors, as similarly found in previous studies (Weekley & Li, 2021 ; Zhang & Gao, 2020 ). Nevertheless, as the storage volumes corresponding to each elevation were estimated using the difference from the reservoir storage volume at capacity as calculated from Equation 1 , employing the public reservoir data at capacity improved the accuracy of the E‐V estimates.…”
Section: Resultssupporting
confidence: 59%
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“…Since the SRTM overpassed the region in February of 2000, the lowest reservoir elevations, which often occur in May or June for some reservoirs, might not be fully captured. Small reservoirs with substantial changes within 1 day and large reservoirs with steep bathymetry would thus be more subject to SRTM DEM errors, as similarly found in previous studies (Weekley & Li, 2021 ; Zhang & Gao, 2020 ). Nevertheless, as the storage volumes corresponding to each elevation were estimated using the difference from the reservoir storage volume at capacity as calculated from Equation 1 , employing the public reservoir data at capacity improved the accuracy of the E‐V estimates.…”
Section: Resultssupporting
confidence: 59%
“…The first section is to extract reservoir boundaries using the Joint Research Centre (JRC) Global Surface Water Mapping Layers v1.3, of which errors of commission and omission are <5% (Pekel et al., 2016 ). The second section is to derive the AEV relationships using the SRTM DEM, following well‐established methods from previous studies (Biswas et al., 2021 ; Bonnema et al., 2016 ; Gao et al., 2012 ; Weekley & Li, 2021 ). The third section is to generate time series of reservoir surface areas using the Edge Otsu threshold method (Donchyts et al., 2016 ) for Landsat‐8 and Sentinel‐1 imagery.…”
Section: Methodsmentioning
confidence: 99%
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“…While the Pixel QA band for Landsat 8 does include a bit for terrain occlusion, the earlier missions lack this critical information (USGS, 2018(USGS, , 2019a. Despite that limitation, terrain shadow could be calculated from the merged topography/ bathymetry datasets and used to eliminate terrain shadow areas from the analysis (Weekley & Li, 2021).…”
Section: Terrain Slope and Terrain Shadowmentioning
confidence: 99%
“…However, the uncertainties of their reconstructed levels appear to be large probably due to the lack of high‐quality hypsometry and the validation was limited to 14 lakes. This is particularly concerning given the large influence of hypsometry on the accuracy of estimated levels (Crétaux et al., 2016; Weekley & Li, 2021).…”
Section: Introductionmentioning
confidence: 99%