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 <16.5 m, demonstrating the potential of ICESat-2/ATLAS for improved reconstruction of the bathymetry of clear water inland lakes. Our study reveals for the first time, that the Tibetan Plateau has an estimated total lake water volume of 1043.69 ± 341.31 km3 for 33,477 lakes (>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.

中文翻译：

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利用 ICESat-2 光子计数激光测高重建青藏高原湖泊测深


湖泊测深对于量化和表征水下形态及其地球物理状态非常重要，这对于水文和生态研究至关重要。主要是由于青藏高原的恶劣环境，严重缺乏湖泊测深测量，限制了对湖泊总体积及其演变的准确估计。在这里，我们通过将 ICESat-2/ATLAS（高级地形激光测高系统）数据与数值模型相结合，提出了一种新的湖泊测深重建。使用一种改进的基于网格的光子噪声去除方法来解决本地白天隐藏在背景噪声中的光子信号。开发的模型在青藏高原的 7 个湖泊中进行了验证，并显示出模拟和测量的湖泊体积之间的良好一致性，最大水深的平均绝对百分比误差为 8.0%，湖泊体积模拟的平均绝对百分比误差为 19.7%。然后，该模型与自仿射理论相结合，用于估计其他湖泊的水量。从 ICESat-2/ATLAS 获得的湖泊深度与深度为 <16.5 m 的湖泊的可用原位测量显示出良好的一致性（RMSE = 0.69 m;rRMSE = 10.3 %），证明了 ICESat-2/ATLAS 在改进清水内陆湖泊测深重建方面的潜力。我们的研究首次揭示了 2022 年青藏高原的 33,477 个湖泊（x3E0.01 km2）估计湖泊总水量为 1043.69 ± 341.31 km3。超过 70% （∼734.8 km3） 的湖水储存集中在内高原，其中黄河流域占 10.9 % （∼113.9 km3），其次是印度河流域，占 7.2 % （∼75.1 km3）。 我们的研究提供了一种强大的方法来估计原位测量稀缺的湖泊总体积，并且可以扩展到其他清水湖泊，从而有助于更准确的全球评估和地球地表水资源分布的全面量化。