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Satellite altimetry has been effectively used for monitoring lake level changes in recent years. This work focused on the integration of multiple satellite altimetry datasets from ICESat-1, Envisat and Cryosat-2 for the long-term (2002–2017) observation of lake level changes in the middle and lower Yangtze River Basin (MLYB). Inter-altimeter biases were estimated by using the gauged daily water level data. It showed that the average biases of ICESat-1 and Cryosat-2 with respect to Envisat were 6.7 cm and 3.1 cm, respectively. The satellite-derived water levels were evaluated against the gauged data. It indicated significantly high correlations between the two datasets, and the combination of three altimetry data produced precise water level time series with high temporal and spatial resolutions. A liner regression model was used to estimate the rates of lake level changes over the study period after the inter-altimeter bias adjustment was performed. The results indicated that ~79% of observed lakes (41/52) showed increasing trends in water levels with rates up to 0.203 m/y during 2002–2017. The temporal analysis of lake level variations suggested that ~60% of measured lakes (32/53) showed decreasing trends during 2002–2009 while ~66% of measured lakes (79/119) exhibited increasing trends during 2010–2017. Most of measured reservoirs displayed rapidly rising trends during the study period. The driving force analysis indicated that the temporal heterogeneity of precipitation can be mainly used to explain the observed pattern of lake level changes. The operation of reservoirs and human water consumption were also responsible for the lake level variations. This work demonstrated the potential of integrating multiple satellite altimeters for the long-term monitoring of lake levels, which can help to evaluate the impact of climate change and anthropogenic activities on regional water resources.

remote sensing

Monitoring Long-Term Lake Level Variations in Middle and Lower Yangtze Basin over 2002–2017 through Integration of Multiple Satellite Altimetry Datasets