Monitoring Global Lake Dynamics Based On Multi-source Satellite Altimetry Data(1992-2020)

Lake is an important part of surface water,and it plays an important role in human life and production.Lakes are also often regarded as the sentinel of Earth,because lake dynamics are closely related to climate change and human activities.Traditional lake monitoring relies on water-level observations of hydrological stations,but hydrological stations are unevenly distributed and the measurements are hard to access.Therefore,it is of great importance to monitor global lake water-level dynamics by satellite altimetry data.Currently,although there are several open-access datasets providing global lake water-level dataset based on satellite altimetry data,these datasets cover a small number of lakes.The main object of this study is to produce a global long-term lake water-level dataset which covers more lakes by integrating multi-source satellite altimetry data,providing basic data support for water resource management and climate change studies.This study compared the error introduced by topographic data and satellite altimetry data in lake volume estimation,compared commonly applied lake water-level extraction methods for radar altimetry data,developed a new waveform retracking algorithm,produced long-term water-level time series of 2170 worldwide lakes,and revealed global lake dynamics from 1992 to 2020.The main conclusions of this study are as below:(1)The water-volume estimation error based on altimetry data is only 10-18%of that of topographic data.Especially for lakes greater than 100 km~2,the volume error based on altimetry data is only 8%-16%of that of topographic data.(2)ICE-1 method performs the best among traditional waveform retracking methods,but the water level tends to be underestimated when the lake is frozen.To overcome this problem,this study designed a new method MSIE(Maximum Sub-waveform Integrated Energy).Experimental results show that the new method can significantly improve the performance of Sentinel-3,and partially improve the performance of ERS-1,ERS-2,and Envisat over frozen lakes.Compared with the ICE-1method,the correlation coefficient between MSIE results and gauge water level increased by 0-22%,and the height measurement error RMSE(Root Mean Square Errors)decreased by 7-40%.(3)The produced global lake water-level dataset covers 2170 lakes greater than 50km~2,and the number of observed lakes is 4 to 9 times that of similar altimetry datasets.Validation results on 34 lakes show that the dataset has an average RMSE of 0.36 m.(4)From 1992 to 2020,54%of global lakes have a rising trend,and the overall change rate tends to zero.In terms of intra-annual water-level change rate,the amplitude of reservoirs is about four times that of natural lakes.Basin-scale statistical results show that lakes in the Qiangtang Basin have the greatest water-level rising rate with an average of 0.18 m/yr,while the lakes in the Tigris-Euphrates Basin have the greatest water-level declining rate with an average of-0.13 m/yr.