仰角(弹道)
水深测量
高原(数学)
环境科学
水文学(农业)
地质学
体积热力学
水下
水位
地形地貌
海洋学
地貌学
地图学
量子力学
地理
数学分析
物理
几何学
数学
岩土工程
作者
Kai Liu,Chunqiao Song,Pengfei Zhan,Shuangxiao Luo,Chenyu Fan
标识
DOI:10.3389/feart.2022.925944
摘要
The widespread lakes on the Tibetan Plateau (TP) are key components of the water cycle, thus the knowledge of their spatial distribution and volume is crucial for understanding the hydrological processes under ongoing climate change. Many previous studies focus on investigating surface elevation, inundation area variations and water volume changes for these lakes. However, how much water is stored in lakes across the TP remains relatively unexplored. It is because of the incapacity of satellite remote sensing methods in lake depth measurements and the high cost of field bathymetric measurement. This study developed a low-cost approach by integrating remote sensing data and limited underwater surveys. The observed lake areas and surface elevations generated the elevation-area relationship. Underwater surveys were conducted to detect the potentially “maximum” lake depths using three optimized survey routes. With the constraint of lake-bottom elevation, the lake-bottom zone area could be estimated for calculating the lake volume. Experiments on nine TP lakes with different size and geometric characteristics demonstrate that the optimized survey line along the lake short axis is well balanced in efficiency and accuracy, with an overall volume bias of 15% approximately. The proposed hypsometric curve method coupled with the bottom elevation measurement is expected to provide a simplified but efficient solution for estimating the lake water volume on the TP, which could be applicable to ungauged lakes in other harsh environments.
科研通智能强力驱动
Strongly Powered by AbleSci AI