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全文摘要次数: 112 全文下载次数: 46
引用本文:

DOI:

10.11834/jrs.20243435

收稿日期:

2023-10-19

修改日期:

2024-03-16

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基于SWOT卫星模拟数据的湖泊水储量观测能力分析:以青藏高原湖泊为例
梁炜轩, 冯伟, 孙明智, 钟敏
中山大学
摘要:

湖泊作为地表水的重要组分,在全球水循环中发挥着关键作用,同时也是全球气候变化的“哨兵”。于2022年12月发射的地表水和海洋地形(SWOT)卫星将首次对全球湖泊进行二维宽刈幅观测。为了在今后更好地应用SWOT卫星数据,本文将先行对SWOT卫星的应用潜力进行综合模拟和评估。本文使用法国国家空间研究中心(CNES)水文模拟工具,对位于青藏地区的拉昂错、玛旁雍错、昂拉仁错和仁青休布错四个湖泊进行了SWOT卫星观测数据的模拟研究,评估SWOT卫星观测湖泊水面高、湖泊面积以及湖泊水储量的能力。模拟研究表明,针对青藏地区的四个典型湖泊,SWOT卫星观测湖泊平均水面高的精度优于0.02 m,模拟水面高序列与真实序列相关系数超过0.9,能够较好地反映湖泊水面高随季节的变化;SWOT观测湖泊面积的误差大部分在10%以内。SWOT卫星在湖泊水量反演中有很大的应用前景。SWOT估计水面高和面积所产生的误差对湖泊静态水量估计的影响较小,而湖底地形的估计误差对湖泊静态水量估计的影响较大,所以获取更高精度的湖泊深度先验数据是未来获得更高湖泊水储量的关键。

Analysis of the potential of SWOT mission to estimate lake water storage using simulated data: A case study in Qinghai-Tibet Plateau
Abstract:

Lakes, known as "sentinels" of global climate change, are significant contributors to the worldwide water cycle. Accurately estimating lake water storage and its fluctuations is crucial for forecasting global climate change. The Surface Water and Ocean Topography (SWOT) mission, launched in December 2022, offers comprehensive observations of the world"s lakes, providing a major advancement in our understanding of lakes on a global scale. In order to better utilize SWOT data in the future, this article will conduct simulations and evaluate the application potential in lake storage estimation of SWOT mission. We will generate SWOT lake data, estimate the simulated water storage of four lakes located on the Qinghai-Tibet Plateau, analyze the errors of lake water storage derived from SWOT simulation data, and suggest necessary considerations for future use of SWOT mission in estimating lake water storage. By addressing these issues, we hope to offer valuable insights for more accurate utilization of SWOT data in estimating lake water storage in the future. In this experiment, we employed the CNES SWOT hydrology toolbox to generate simulated data of lakes. The toolbox contains three primary components: the Large Scale Simulator, RiverObs, and LOCNES. The tool computes the lake extent data intersected by the SWOT wide swath. Subsequently, the tool will generate point cloud data within the overlapping area. Each point cloud dataset contains detailed information, including the water surface height. Next, the RiverObs tool is employed to create SWOT river data in shapefile format. Finally, the LOCNES tool is used to generate lake data from the data that is not categorized as part of the river system. The National Tibetan Plateau Scientific Data Center (TPDC) provides bathymetry point data for four lakes. This study uses Topo to Raster in ArcGIS to generate the lake bathymetry. Besides, this article also uses the global lake bathymetry GloBathy. we use the true maximum depth data of four lakes provided by the TPDC to regenerate modified GloBathy data. In summary, this experiment gets three sets of lake bathymetry data of each four lakes, including true bathymetry(tru), the initial GloBathy(ori), and the modified GloBathy(mod). At last, this paper uses the SWOT simulated data to estimate the lake water storage. we analyzed the impact of errors in SWOT mission on lake storage estimation. In the SWOT PIXC data, the errors of majority of water surface elevation measurements are less than 1 m. After averaging at the scale of the lakes, the errors were mostly within 0.02 m. The error of the water surface height measured by most SWOT point clouds is less than 1 m. The correlation coefficient between the simulated water surface height sequence of the SWOT mission and the real sequence exceeds 0.9. It shows that SWOT mission can well reflect the seasonal changes in the lake water surface height. The relative error in estimating lake area from SWOT observations are less than 10 % due to the dark water effect. In this study, SWOT simulated data were used to estimate lake water storage using three types of lake bathymetry. It showed that the errors in water surface elevation had a relatively small impact on the accuracy of lake water volume estimation, while the errors in estimating the lake topography had a more significant impact on the accuracy of lake water volume estimation. The research indicates that SWOT mission have significant prospects for lake water volume estimation. Obtaining higher-precision prior data on lake depths is critical for improving accuracy in lake water storage estimation in the future. In the future, we can combine SWOT mission data with other hydrological satellite data and surface measurement data to get more accurate water storage changes in surface water.

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