Recent significant subseasonal fluctuations of supraglacial lakes on Greenland monitored by passive optical satellites

IF 11.4 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES
Jiahui Qiu , Jiangjun Ran , Natthachet Tangdamrongsub , Xavier Fettweis , Shoaib Ali , Wei Feng , Xiaoyun Wan
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引用次数: 0

Abstract

Temporarily impounded liquid water on the surface of the Greenland Ice Sheet (GrIS), prominently represented as supraglacial lakes (SGLs), may enhance ice flow and modulate surface meltwater runoff, serving as a dynamic indicator of the cryohydrologic cycle. Despite their importance in understanding glacier mass balance and regional climate change, a detailed description of SGLs and their intra-annual fluctuations across the entire GrIS remains understudied. Here, we present a deep learning-based approach to automatically map SGLs from passive optical satellite imagery across the entire GrIS during the melt seasons of 2017–2022. Approximately 150,000 Sentinel-2 and Landsat 8/9 images were utilized, each representing a 5-day average composite at a 10 km × 10 km grid resolution, with the Landsat images used as possible supplements. SGL predictions by the proposed method demonstrate high performance, achieving an F1-score of up to 0.959 compared to the independent test dataset. This high accuracy enables a detailed analysis of the key role SGLs play in enhancing surface ablation by absorbing solar radiation and delivering meltwater. The SGL-driven ablation effect was most pronounced in the South-West basin of the GrIS, where the peak lake area in July accounted for 44.9 % of the total GrIS-wide lake area. In contrast, the lowest magnitude (4.2 %) was observed in the South-East basin, despite similarly strong ablation in this region. Among all the generated SGL occurrence grids, peak SGL areas in certain grids (∼14 % of the total) were observed in May or September, rather than exclusively during the typical high-ablation months of June to August, reflecting regional and elevation-dependent variations. Grids further from the ice sheet margin generally showed peak SGL areas later in the melt season, which is evident in the western part of the GrIS. Monthly SGL peak areas shift dramatically from 253.18 ± 123.94 km2 to 5084.90 ± 1043.26 km2, with the lowest in May 2018 and the highest in August 2021. An extraordinary area spike occurred in September 2022 and was particularly monitored in the South-West basin, where abnormally intense rainfall and runoff simulated by the Modèle Atmosphérique Régional (MAR) model were recorded. Our study highlights the significance of examining SGL area changes at short temporal intervals to understand the dynamics of cryospheric hydrology under future climate scenarios.

Abstract Image

被动光学卫星监测的格陵兰冰川上湖泊近期显著的次季节波动
暂时滞留在格陵兰冰盖(GrIS)表面的液态水,主要表现为冰上湖(SGLs),可以增强冰流并调节地表融水径流,作为冰冻水文循环的动态指标。尽管它们在了解冰川质量平衡和区域气候变化方面具有重要意义,但对整个GrIS的SGLs及其年内波动的详细描述仍未得到充分研究。在这里,我们提出了一种基于深度学习的方法,在2017-2022年融化季节期间,从整个GrIS的无源光学卫星图像中自动绘制SGLs。使用了大约15万张Sentinel-2和Landsat 8/9图像,每张图像代表10公里× 10公里网格分辨率的5天平均合成图像,并使用Landsat图像作为可能的补充。与独立测试数据集相比,该方法的SGL预测性能优异,f1得分高达0.959。这种高精度使我们能够详细分析SGLs通过吸收太阳辐射和输送融水来增强表面消融的关键作用。sgl驱动的消融效应在西南盆地最为明显,7月湖泊峰值面积占GrIS全湖面积的44.9%。相比之下,东南盆地观测到的震级最低(4.2%),尽管该地区同样强烈的消融。在所有生成的SGL发生栅格中,某些栅格中的SGL峰值区域(约占总数的14%)在5月或9月观测到,而不是只在6月至8月的典型高消融月份观测到,这反映了区域和海拔相关的变化。离冰盖边缘较远的格网通常在融化季节较晚的时候显示出sigl的峰值,这在GrIS的西部很明显。月SGL峰值面积从253.18±123.94 km2急剧变化至5084.90±1043.26 km2,其中2018年5月最低,2021年8月最高。2022年9月,西南盆地出现了一个异常的峰值,并受到了特别的监测,在那里,mod atmospheremacrique r区域(MAR)模型模拟了异常的强降雨和径流。我们的研究强调了在短时间间隔内研究SGL面积变化对了解未来气候情景下冰冻圈水文动态的重要性。
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来源期刊
Remote Sensing of Environment
Remote Sensing of Environment 环境科学-成像科学与照相技术
CiteScore
25.10
自引率
8.90%
发文量
455
审稿时长
53 days
期刊介绍: Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing. The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques. RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.
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