Abstract. Lakes and reservoirs are crucial elements of the
hydrological and biochemical cycle and are a valuable resource for
hydropower, domestic and industrial water use, and irrigation. Although their
monitoring is crucial in times of increased pressure on water resources by
both climate change and human interventions, publically available datasets of
lake and reservoir levels and volumes are scarce. Within this study, a time
series of variation in lake and reservoir volume between 1984 and 2015 were
analysed for 137 lakes over all continents by combining the JRC Global
Surface Water (GSW) dataset and the satellite altimetry database DAHITI. The
GSW dataset is a highly accurate surface water dataset at 30 m resolution
compromising the whole L1T Landsat 5, 7 and 8 archive, which allowed for
detailed lake area calculations globally over a very long time period using
Google Earth Engine. Therefore, the estimates in water volume fluctuations
using the GSW dataset are expected to improve compared to current techniques
as they are not constrained by complex and computationally intensive
classification procedures. Lake areas and water levels were combined in a
regression to derive the hypsometry relationship (dh ∕ dA) for all
lakes. Nearly all lakes showed a linear regression, and 42 % of the lakes
showed a strong linear relationship with a R2 > 0.8, an
average R2 of 0.91 and a standard deviation of 0.05. For these lakes and
for lakes with a nearly constant lake area (coefficient of variation
< 0.008), volume variations were calculated. Lakes with a poor
linear relationship were not considered. Reasons for low R2 values were
found to be (1) a nearly constant lake area, (2) winter ice coverage and
(3) a predominant lack of data within the GSW dataset for those lakes. Lake
volume estimates were validated for 18 lakes in the US, Spain, Australia and
Africa using in situ volume time series, and gave an excellent Pearson
correlation coefficient of on average 0.97 with a standard deviation of
0.041, and a normalized RMSE of 7.42 %. These results show a high
potential for measuring lake volume dynamics using a pre-classified GSW
dataset, which easily allows the method to be scaled up to an extensive
global volumetric dataset. This dataset will not only provide a historical
lake and reservoir volume variation record, but will also help to improve our
understanding of the behaviour of lakes and reservoirs and their
representation in (large-scale) hydrological models.
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