Preprints
https://doi.org/10.5194/tc-2022-132
https://doi.org/10.5194/tc-2022-132
 
26 Jul 2022
26 Jul 2022
Status: this preprint is currently under review for the journal TC.

Ice thickness and water level estimation for ice-covered lakes with satellite altimetry waveforms and backscattering coefficients

Xingdong Li1,3, Di Long1,3, Yanhong Cui1,3, Tingxi Liu2,3, Jing Lu4, Mohamed A. Hamouda5,6, and Mohamed M. Mohamed5,6 Xingdong Li et al.
  • 1State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
  • 2Water Conservancy and Civil Engineering College, Inner Mongolia Key Laboratory of Water Resource Protection and Utilization, Inner Mongolia Agricultural University, Hohhot, 010018, China
  • 3Collaborative Innovation Center for Integrated Management of Water Resources and Water Environment in the Inner Mongolia Reaches of the Yellow River, Hohhot, 010018, China
  • 4State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
  • 5Department of Civil and Environmental Engineering, United Arab Emirates University, Al Ain, 15551, United Arab Emirates
  • 6National Water and Energy Center, United Arab Emirates University, Al Ain, 15551, United Arab Emirates

Abstract. Lake ice, serving as a sensitive indicator of climate change, is an important regulator of regional hydroclimate and lake ecosystems. For ice-covered lakes, traditional satellite altimetry-based water level estimation is often subject to winter anomalies that are closely related to the thickening of lake ice. Despite recent efforts made in exploiting altimetry data to resolve the two interrelated variables, i.e., lake ice thickness (LIT) and water level of ice-covered lakes, several important issues remain unsolved, including the inability of estimating LIT with altimetric backscattering coefficients in ungauged lakes due to the dependence on in situ LIT data. It is still unclear what role lake surface snow plays in the retrieval of LIT and water levels in ice-covered lakes with altimetry data. Here we developed a novel method to estimate lake ice thickness by combining altimetric waveforms and backscattering coefficients without using in situ LIT data. To overcome complicated initial LIT conditions and better represent thick ice conditions, a logarithmic regression model was developed to transform backscattering coefficients into LIT. We investigated differential impact of lake surface snow on estimating water levels for ice-covered lakes when different threshold retracking methods are used. The developed LIT estimation method, validated against in situ data and cross-validated against modelled LIT shows an accuracy of ~0.2 m and is effective in detecting thin ice that cannot be retrieved by altimetric waveforms. We also improved estimation of water levels for ice-covered lakes with a strategy of merging lake water levels derived from different threshold methods. This study facilitates a better interpretation of satellite altimetry signals from ice-covered lakes and provides opportunities for a wider application of altimetry data to the cryosphere.

Xingdong Li et al.

Status: open (until 20 Sep 2022)

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Xingdong Li et al.

Xingdong Li et al.

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Short summary
This study blends advantages of altimetry backscattering coefficients and waveforms to estimate ice thickness for lakes without in situ data and provides an improved water level estimation for ice-covered lakes by jointly using different threshold retracking methods. Our results show that a logarithmic regression model is more adaptive in converting altimetry backscattering coefficients into ice thickness and lake surface snow has differential impacts on different threshold retracking methods.