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Artificial neural network-assisted glacier forefield soil temperature retrieval from temperature measurements

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Abstract

Soil temperature is one of the most important glacio-meteorological parameters that play a critical role in glacier energy and mass balance dynamics, surface hydrological processes, and glacier-atmosphere interaction. However, the availability of the data is acutely scarce in the Himalayan glaciated region. In this study, we applied artificial neural network (ANN) models for the prediction of soil temperature of glacial forefield region of the Pindari Glacier (Central Himalaya). Three-layer feed-forward ANN models were developed and tested for estimating multi-depth soil temperatures using concurrent and antecedent air-soil temperature data for one complete annual cycle as inputs for the models. Models with different combinations of input variables were tested, and best sets of variables were selected based on the prediction accuracy. Rigorous statistics were further employed to compare the performances of different models. High concurrence was obtained between ANN-estimated and measured soil and air temperatures as evident by various correlation coefficients and error ranges. In a boarder perspective, our results point toward the applicability of developed ANN models to provide robust soil temperature prediction for the glacial forefield regions of the Central Himalaya.

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Data availability

The data used in this research is included in the manuscript. Any further data requirement from this central Himalayan glacier site will be available upon reasonable request.

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Acknowledgments

The Department of Science and Technology (DST) is thankfully acknowledged for the Fast-track fellowship to the corresponding author. N.S. sincerely acknowledges DST for the financial support to set up meteorological stations at the Pindari Glacier (File No. SR/FTP/ES-166/2014). Authors are thankful to the Centre for Glaciology (CFG) and Director, Wadia Institute of Himalayan Geology (WIHG) for all logistical support.

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Authors and Affiliations

  1. Centre for Glaciology, Wadia Institute of Himalayan Geology, Dehradun, 248001, India

    Mohit Singhal, Akhilesh Chandra Gairola & Nilendu Singh

Authors
  1. Mohit Singhal
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  2. Akhilesh Chandra Gairola
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  3. Nilendu Singh
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Contributions

M.S. and N.S. conceived, designed, and performed this study. Authors contributed equally to analyses and interpretation of results and drafting of the manuscript.

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Correspondence to Nilendu Singh.

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Singhal, M., Gairola, A.C. & Singh, N. Artificial neural network-assisted glacier forefield soil temperature retrieval from temperature measurements. Theor Appl Climatol 143, 1157–1166 (2021). https://doi.org/10.1007/s00704-020-03498-5

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