Remote sensing of surface meltwater routing in the Denmark Basin of the Northern Greenland Ice Sheet

被引：0
|
作者：
Li Y. [1 ,2 ]
Yang K. [1 ,3 ,4 ]
Liu J. [1 ]
Zhang W. [1 ]
Wang Y. [1 ]
机构：
[1] School of Geography and Ocean Science, Nanjing University, Nanjing
[2] The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang
[3] Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing
[4] Collaborative Innovation Center of South China Sea Studies, Nanjing
来源：
National Remote Sensing Bulletin | 2024年 / 28卷 / 06期
基金：
中国国家自然科学基金;
关键词：
Greenland ice sheet; ice melting; polar remote sensing; proglacial river; river remote sensing; supraglacial river;
D O I：
10.11834/jrs.20242431
中图分类号：
学科分类号：
摘要：
Mass loss from the Greenland Ice Sheet (GrIS) has accelerated in recent decades, with profound effects on global sea-level rise. During each summer, the meltwater forms supraglacial rivers and then is transported to the proglacial zone, eventually flowing into the ocean and forming a continuous supraglacial-proglacial river system. This continuous supraglacial-proglacial drainage system directly results in the mass loss of the GrIS and has an important impact on the changes in the marine environment. Satellite images can directly observe the temporal and spatial distribution of supraglacial and proglacial rivers and have been widely used in the study of the GrIS. The satellite-derived observation can provide key information, such as the location, morphology, and dynamic changes of rivers. It has become an important way to analyze meltwater routing. In this study, 361 scenes of Sentinel-2 and Landsat 8 satellite images are used to extract the supraglacial and proglacial rivers in the Denmark supraglacial-proglacial basin of the northeastern GrIS during the melt seasons (from July to August) and monitor their spatial distribution and dynamic changes. Furthermore, satellite-derived observation and meltwater runoff simulated by regional climate models (MARv3.12 and RACMO2.3p2) are compared and analyzed, and then the lag time of the supraglacial-proglacial drainage system is estimated. The main contents and conclusions of this study include the following three aspects: (1) The proglacial river width is in the range of 100—2000 m and experiences a seasonal trend. The ice surface meltwater shows similar variation characteristics, advancing to the high-altitude areas of the ice surface (up to ~1400 m) at the initial stage of ablation, and then gradually receding to the edge of the ice sheet (up to ~500 m). (2) A significant positive correlation is found between satellite-derived proglacial river width and meltwater on the ice surface (R=0.87, P<0.01), forming a continuous supraglacial-proglacial drainage system that can effectively transport the meltwater each summer. (3) MARv3.12 and RACMO2.3p2 models can accurately simulate the meltwater runoff in the supraglacial-proglacial drainage system on a large area and long-term scale, and the simulated meltwater runoff and satellite-derived ice surface meltwater (MAR: R=0.87; RACMO: R=0.84, P<0.01) and proglacial river width (MAR: R=0.89; RACMO:R=0.88, P<0.01) have strong correlations. (4) The r between the simulated lagged meltwater runoff and satellite-derived proglacial river width (MAR: R=0.93; RACMO: R=0.92, P<0.01) increased, which is significantly higher than that of the instantaneous meltwater runoff. The optimal lag time of the supraglacial-proglacial drainage system in the Denmark Basin is approximately 2 days. This lag time quantitatively represents the efficiency of meltwater routing in the supraglacial-proglacial drainage system. © 2024 Science Press. All rights reserved.
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页码：1433 / 1452
页数：19
相关论文
共 88 条
  • [1] Agosta C, Amory C, Kittel C, Orsi A, Favier V, Gallee H, Van Den Broeke M R, Lenaerts J T M, Van Wessem J M, Van De Berg W J, Fettweis X., Estimation of the Antarctic surface mass balance using the regional climate model MAR (1979-2015) and identification of dominant processes, The Cryosphere, 13, 1, pp. 281-296, (2019)
  • [2] Andrews L C, Catania G A, Hoffman M J, Gulley J D, Luthi M P, Ryser C, Hawley R L, Neumann T A., Direct observations of evolving subglacial drainage beneath the Greenland Ice Sheet, Nature, 514, 7520, pp. 80-83, (2014)
  • [3] Arekhi M, Goksel C, Balik Sanli F, Senel G., Comparative evaluation of the spectral and spatial consistency of Sentinel-2 and Landsat-8 OLI Data for Igneada Longos Forest, ISPRS International Journal of Geo-Information, 8, 2, (2019)
  • [4] Ashmore P, Sauks E., Prediction of discharge from water surface width in a braided river with implications for at-a-station hydraulic geometry, Water Resources Research, 42, 3, (2006)
  • [5] Bajjali W., Watershed delineation, ArcGIS for Environmental and Water Issues, pp. 235-245, (2018)
  • [6] Bjerklie D M, Moller D, Smith L C, Dingman S L., Estimating discharge in rivers using remotely sensed hydraulic information, Journal of Hydrology, 309, 1, pp. 191-209, (2005)
  • [7] Chen Z Q, Liu J P, Fan G Z, Hu Y Y., Comparison of 2013 and 2012 Greenland Ice Sheet surface melt and associated mechanisms, Chinese Journal of Atmospheric Sciences, 39, 4, pp. 757-766, (2015)
  • [8] Chu V W., Greenland ice sheet hydrology: a review, Progress in Physical Geography: Earth and Environment, 38, 1, pp. 19-54, (2014)
  • [9] Claverie M, Ju J C, Masek J G, Dungan J L, Vermote E F, Roger J C, Skakun S V, Justice C., The Harmonized Landsat and Sentinel-2 surface reflectance data set, Remote Sensing of Environment, 219, pp. 145-161, (2018)
  • [10] Colucci R R., The Greenland Ice sheet lost 18 billion tons of water in 3 days after unusually warm temperatures between July 15 and 17. Arctic Sea Ice extent is declining on its way to the September annual minimum, (2022)
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