Nela Bala Raju, Singh Gulab, Kulkarni Anil V
Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, 400076, Maharashtra, India.
Divecha Centre for Climate Change, Indian Institute of Sciences, Bengaluru, 560012, Karnataka, India.
Environ Monit Assess. 2022 Oct 22;195(1):15. doi: 10.1007/s10661-022-10658-9.
Retrieval of glacier ice thickness is extremely important for monitoring water resources and predicting glacier dynamics and changes. The inter-annual glacier ice thickness observations (more than 5 years) exploit the glacier mass changes. Ice thickness is one of the important parameters to predict the future sea-level rise. Without adequate knowledge and precise information of glacier ice thickness distribution, future sea-level changes cannot be accurately assessed. In this study, we use an existing flow model to estimate the ice thickness of the High Mountain Asia (HMA) glaciers, using remote sensing techniques. The glacier ice velocity is one of the significant parameters in the Laminar flow model to retrieve the ice thickness. The glacier ice velocity is derived by utilizing the Differential SAR Interferometry (DInSAR) technique. The most optimum DInSAR data (ALOS-2/PALSAR-2) is used for estimating the ice velocity of the HMA glaciers. The ice thickness is mainly estimated for five different states in the HMA region, namely Himachal Pradesh, Uttarakhand, Sikkim, Bhutan, and Arunachal Pradesh. Most of the states are observed with a mean ice thickness of 100 m. Five benchmark glaciers (Samudra Tapu, Bara Shigri, Chhota Shigri, Sakchum, and Gangotri glaciers) are also selected for validating our results with the existing thickness information. The issues related to velocity-based ice thickness inversion are also emphasized in this study. The high-velocity rate due to the influx of melting water from adjacent glaciers causes an increment in the flow rate. This abnormal velocity derives erroneous ice thickness measurements. This is one of the major problems to be considered in the velocity-based thickness-derived procedures. Finally, the investigation suggests the inclusion of the velocity influencing parameters in the physical-based models for an accurate ice thickness inversion.
获取冰川冰厚度对于监测水资源以及预测冰川动态和变化极为重要。多年(超过5年)的冰川冰厚度观测可利用冰川质量变化情况。冰厚度是预测未来海平面上升的重要参数之一。如果没有关于冰川冰厚度分布的充分知识和精确信息,就无法准确评估未来海平面变化。在本研究中,我们利用现有流动模型,采用遥感技术来估算亚洲高山(HMA)地区冰川的冰厚度。冰川冰流速是层流模型中用于反演冰厚度的重要参数之一。冰川冰流速通过利用差分合成孔径雷达干涉测量(DInSAR)技术得出。使用最优化的DInSAR数据(ALOS - 2/PALSAR - 2)来估算HMA地区冰川的流速。主要针对HMA地区的五个不同邦,即喜马偕尔邦、北阿坎德邦、锡金邦、不丹和阿鲁纳恰尔邦估算冰厚度。大多数邦观测到的平均冰厚度为100米。还选取了五条基准冰川(萨穆德拉塔普冰川、巴拉希格里冰川、乔塔希格里冰川、萨克楚姆冰川和甘戈特里冰川),以便用现有的厚度信息验证我们的结果。本研究还强调了与基于流速的冰厚度反演相关的问题。来自相邻冰川的融水涌入导致的高流速会使流速增加。这种异常流速会得出错误的冰厚度测量结果。这是基于流速的厚度推导过程中要考虑的主要问题之一。最后,调查表明在基于物理的模型中纳入流速影响参数,以便进行准确的冰厚度反演。