Based on ICESat-2 LiDAR altimeter data and empirical equations derived from self affine theory, the volume of lakes with an area greater than 0.01 km ² on the Qinghai Tibet Plateau in 2022 was estimated. The study found that the simulated lake volume matched well with the measured values on site, with an average absolute percentage error of 8.0% for the maximum lake depth and 19.7% for the lake volume. It is estimated that the total water storage capacity of lakes with an area greater than 0.01 km ² on the Qinghai Tibet Plateau in 2022 will be 1043.69 ± 341.31 km ³, of which about 70% (~734.8 km ³) of the lake water storage capacity is concentrated in the inner plateau (Qiangtang Plateau). This data, based on remote sensing methods, has preliminarily clarified the water storage capacity of lakes on the Qinghai Tibet Plateau, providing important data support for predicting future changes in lake water volume, analyzing hydrological balance, and managing water resources.
1. ICESat-2 ATL03 altimetry data: from Earthdata platform（ https://search.earthdata.nasa.gov ）Obtained ICESat-2 ATL03 (v006) data available from 2019 to 2023 (Figure 1b). Compared to processed high-end products, ATL03 retains more original photons. The ATL03 dataset applies atmospheric delay, geolocation, solid earth tides, and other geophysical and medium corrections. 2. In situ depth measurement data: The in-situ depth measurement data was compiled from a total of 35 lakes in TP. 3. Additional data: The dynamic lake boundaries delineated by Landsat TM/ETM+/OLI time series from 2000 to 2022 are used to calculate the changes in lake area over time and filter the ICESat-2 ATL03 data of the study area.
In this dataset, we have developed a new method for reconstructing the water depth of clear lakes using ICESat-2 penetrating photons. This method relies on a three-stage mathematical function (arctangent linear arctangent) to characterize the lake water depth profile. Subsequently, a stratified sampling scheme was used to select 60 lakes within the Qinghai Tibet Plateau (TP) and generate their water depth data using this method. Based on the self affine theory, the volumes of other lakes on the Qinghai Tibet Plateau were estimated, and the total water storage capacity of the lakes in 2022 was obtained. The method proposed in this study can generate lake water depth information at low cost. The Qinghai Tibet Plateau lake water storage dataset not only helps to deeply understand the spatial distribution characteristics of lake water bodies, but also provides basic data support for lake morphology and ecology research.
The model proposed in this study effectively utilizes the shoreline terrain above the lake surface and combines the penetration characteristics of ICESat-2/ATLAS with underwater terrain reference, reducing the average APE to 19.7%. Compared with using simple linear or quadratic functions, or directly extending the digital elevation model downwards using spline functions, three-stage mathematical functions make the convergence effect closer to the natural terrain transition of the lake bed. Although the complexity increases, it may improve the accuracy of reconstructing water depth measurements. The accuracy of simulating water volume in this study is lower than that obtained by skeleton interpolation method, but higher than that obtained by numerical simulation method.
This work is licensed under
CC BY 4.0 (Creative Commons Attribution 4.0 International License).
CSTR:11738.11.NCDC.ZENODO.DB6986.2025
10.5281/zenodo.13864780
