2019
DOI: 10.3390/rs11121467
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Assessment of Water Storage Change in China’s Lakes and Reservoirs over the Last Three Decades

Abstract: Lakes and reservoirs are essential elements of the hydrological and biochemical cycles, considered sentinels of global climate change. However, comprehensive quantifications of their water storage changes (∆V) at a large spatiotemporal scale are still rare. Here, we integrated a global surface water dataset and SRTM digital elevation models, both available from Google Earth Engine platform at a spatial resolution of 30 m, to evaluate ∆V for a total of 760 lakes and reservoirs across China at an annual timescal… Show more

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Cited by 31 publications
(22 citation statements)
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“…Therefore, using the surface water occurrence of GSW to extract lake area changes at different stages can better represent the variations of lake water bodies over the years. Previous studies have also demonstrated that surface water occurrence can be used to characterize changes in water area (Pekel et al, 2016;Fang et al, 2019;Luo et al, 2019). According to the variation of water area with WF fluctuation of 10-90% and 25-75%, the seasonal amplitude of water storage variations of 27 large lakes (except Poyang and Dongting Lakes) is 14.10 ± 4.74 Gt and 15.42 ± 5.12 Gt, respectively.…”
Section: Discussionmentioning
confidence: 99%
“…Therefore, using the surface water occurrence of GSW to extract lake area changes at different stages can better represent the variations of lake water bodies over the years. Previous studies have also demonstrated that surface water occurrence can be used to characterize changes in water area (Pekel et al, 2016;Fang et al, 2019;Luo et al, 2019). According to the variation of water area with WF fluctuation of 10-90% and 25-75%, the seasonal amplitude of water storage variations of 27 large lakes (except Poyang and Dongting Lakes) is 14.10 ± 4.74 Gt and 15.42 ± 5.12 Gt, respectively.…”
Section: Discussionmentioning
confidence: 99%
“…Good correlations between remote sensing reflectivity and water quality parameters have been confirmed across a wide range of oceanic and inland waters by different research groups [64][65][66]. However, the inversion models applicable to different water quality parameters can vary due to regional and band sensitivity differences [67][68][69]. Landsat blue-green band and near-infrared band have strong water penetration ability and the ability to distinguish water clarity [70], [71].…”
Section: A Model Assessmentmentioning
confidence: 93%
“…The main processing steps in the creation of the HydroLAKES dataset included vectorization of raster data, manual identification and removal of river and wetland polygons, removal of duplicates and overlapping polygons, dissolving of segmented polygons into individual lake entities, correction of corrupt or incorrect polygon geometry, removal of small islands within lakes, smoothing of water body shorelines to reduce inconsistencies between data sets of different initial resolutions, and establishing a 0.03-km 2 cut-off based on lake surface area. Numerous studies had utilized this dataset to select lakes and reservoirs of analysis and to determine their geographical extent [39][40][41][42], but the evaluation of this dataset and its uncertainties is rare. However, the developer of this dataset compared this dataset with four global estimates of lakes and lake area from Verpoorter et al [43], Downing et al [44], Lehner and Döll [45] and Lerman [46].…”
Section: Hydrolakes Datasetmentioning
confidence: 99%