Lake bathymetry is important for quantifying and characterizing underwater morphology and its geophysical state, which is critical for hydrological and ecological studies. Due primarily to the harsh environment of the Tibetan Plateau, there is a severe lack of lake bathymetry measurements, limiting the accurate estimation of total lake volumes and their evolutions. Here, we propose a novel lake bathymetry reconstruction by combining ICESat-2/ATLAS (Advanced Topography Laser Altimetry System) data with a numerical model. An improved grid-based photon noise removal method is used to address the photon signal buried in the background noise during the local daytime. The developed model was validated for seven lakes on the Tibetan Plateau and showed good agreement between simulated and measured lake volumes, with an average absolute percentage error of 8.0 % for maximum water depth and 19.7 % for lake volume simulations. The model was then utilized to estimate the water volume of other lakes by combining it with the self-affine theory. The lake depths obtained from ICESat-2/ATLAS show good agreement (RMSE = 0.69 m; rRMSE = 10.3 %) with available in-situ measurements for lakes with depths 0.01 km2) in 2022. Over 70 % (∼734.8 km3) of the lake water storage is concentrated in the Inner Plateau, with the Yellow River basin accounting for 10.9 % (∼113.9 km3), followed by the Indus River basin with 7.2 % (∼75.1 km3). Our study provides a robust method for estimating total lake volumes where in-situ measurements are scarce and can be extended to other clear water lakes, thus contributing to more accurate global assessments and towards comprehensive quantification of Earth's surface water resources distribution.