Snow depth and ice thickness derived from SIMBA ice mass balance buoy data using an automated algorithm

• Published in: International Journal of Digital Earth
• Main Authors: Zeliang Liao, Bin Cheng, JieChen Zhao, Timo Vihma, Keith Jackson, Qinghua Yang, Yu Yang, Lin Zhang, Zhijun Li, Yubao Qiu, Xiao Cheng
• Format: Article in Journal/Newspaper
• Language: English
• Published: Taylor & Francis Group 2019
• Subjects: snow; sea ice; ice thickness; ice mass balance buoy; polar oceans; Mathematical geography. Cartography; GA1-1776; Sea ice
• Online Access: https://doi.org/10.1080/17538947.2018.1545877; https://doaj.org/article/394f3585c2f341b1bd2c8dde116352b4

An ice mass balance buoy (IMB) monitors the evolution of snow and ice cover on seas, ice caps and lakes through the measurement of various variables. The crucial measurement of snow and ice thickness has been achieved using acoustic sounders in early devices but a more recently developed IMB called the Snow and Ice Mass Balance Array (SIMBA) measures vertical temperature profiles through the air-snow-ice-water column using a thermistor string. The determination of snow depth and ice thickness from SIMBA temperature profiles is presently a manual process. We present an automated algorithm to perform this task. The algorithm is based on heat flux continuation, limit ratio between thermal heat conductivity of snow and ice, and minimum resolution (±0.0625°C) of the temperature sensors. The algorithm results are compared with manual analyses, in situ borehole measurements and numerical model simulation. The bias and root mean square error between algorithm and other methods ranged from 1 to 9 cm for ice thickness counting 2%–7% of the mean observed values. The algorithm works well in cold condition but becomes less reliable in warmer conditions where the vertical temperature gradient is reduced.