Leveraging ICESat, ICESat-2, and Landsat for Global-Scale, Multi-Decadal Reconstruction of Lake Water Levels

Lakes provide important water resources and many essential ecosystem services. Some of Earth's largest lakes recently reached record-low levels, suggesting increasing threats from climate change and anthropogenic activities. Yet, continuous monitoring of lake levels is challenging at a global scale due to the sparse in situ gauging network and the limited spatial or temporal coverage of satellite altimeters. A few pioneering studies used water areas and hypsometric curves to reconstruct water levels but suffered from large uncertainties due to the lack of high-quality hypsometry data. Here, we propose a novel proxy-based method to reconstruct multi-decadal water levels from 1992 to 2018 for both large and small lakes using Landsat images and ICESat (2003-2009) and recently launched ICESat-2 (2018+) laser altimeters. Using the new method, we evaluate reconstructed levels of 342 lakes worldwide, with sizes ranging from 1 to 81,844 km2. Reconstructed water levels have a median root-mean-square error (RMSE) of 0.66 m, equivalent to 57% of the standard deviation of monthly level variability. Compared with two recently reconstructed water level data sets, the proposed method reduces the median RMSE by 27%-32%. The improvement is attributable to the new method's robust construction of high-quality hypsometry, with a median R2 value of 0.92. Most reconstructed water level time series have a bi-monthly or higher frequency. Given that ICESat-2 and Landsat can observe hundreds of thousands of water bodies, this method can be applied to conduct an improved global inventory of time-varying lake levels and thus inform water resource management more broadly than existing methods. Landsat images and laser altimeters were leveraged to reconstruct multi-decadal lake levels of both large and small lakes Reconstructed water levels were validated against observed levels on 342 global lakes with a median error of 0.66 m Most of the reconstructed lake level time series have a bi-monthly or higher frequency