Reconstructing Tibetan Plateau lake bathymetry using ICESat-2 photon-counting laser altimetry

IF 3.5 2区 生物学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY
Xiaoran Han , Guoqing Zhang , Jida Wang , Kuo-Hsin Tseng , Jiaqi Li , R. Iestyn Woolway , C.K. Shum , Fenglin Xu
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引用次数: 0

Abstract

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.
利用 ICESat-2 光子计数激光测高法重建青藏高原湖泊水深图
湖泊水深测量对于量化和描述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。主要由于青藏高原环境恶劣,湖泊水深测量数据严重缺乏,限制了对湖泊总量及其演变的准确估算。在此,我们结合 ICESat-2/ATLAS(高级地形激光测高系统)数据和数值模型,提出了一种新的湖泊水深重建方法。我们采用了一种改进的基于网格的光子噪声去除方法,以解决当地白天被背景噪声掩盖的光子信号。对青藏高原上的七个湖泊进行了验证,结果表明模拟湖泊体积与测量湖泊体积之间具有良好的一致性,最大水深的平均绝对百分比误差为 8.0%,湖泊体积模拟误差为 19.7%。随后,将该模型与自扇理论相结合,估算了其他湖泊的水量。从ICESat-2/ATLAS获得的湖泊水深与现有水深为16.5米的湖泊的现场测量结果显示出良好的一致性(RMSE = 0.69米;rRMSE = 10.3%),这表明ICESat-2/ATLAS具有改进内陆清水湖泊水深重建的潜力。我们的研究首次揭示了青藏高原在 2022 年 33,477 个湖泊(>0.01 平方公里)的湖泊总水量估计为 1043.69 ± 341.31 立方公里。超过 70% 的湖泊蓄水量(∼734.8 km3)集中在内蒙古高原,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稳健的方法,可用于估算缺乏原位测量的湖泊总量,并可推广到其他清水湖泊,从而有助于更准确地进行全球评估和全面量化地球地表水资源的分布。
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来源期刊
ACS Chemical Biology
ACS Chemical Biology 生物-生化与分子生物学
CiteScore
7.50
自引率
5.00%
发文量
353
审稿时长
3.3 months
期刊介绍: ACS Chemical Biology provides an international forum for the rapid communication of research that broadly embraces the interface between chemistry and biology. The journal also serves as a forum to facilitate the communication between biologists and chemists that will translate into new research opportunities and discoveries. Results will be published in which molecular reasoning has been used to probe questions through in vitro investigations, cell biological methods, or organismic studies. We welcome mechanistic studies on proteins, nucleic acids, sugars, lipids, and nonbiological polymers. The journal serves a large scientific community, exploring cellular function from both chemical and biological perspectives. It is understood that submitted work is based upon original results and has not been published previously.
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