Efficiency of artificial neural networks for glacier ice-thickness estimation: a case study in western Himalaya, India

Mohd Anul Haq,Mohd Farooq Azam,Christian Vincent

doi:10.1017/jog.2021.19

Abstract

Abstract Knowledge of glacier volume is crucial for ice flow modelling and predicting the impacts of climate change on glaciers. Rugged terrain, harsh weather conditions and logistic costs limit field-based ice thickness observations in the Himalaya. Remote-sensing applications, together with mathematical models, provide alternative techniques for glacier ice thickness and volume estimation. The objective of the present research is to assess the application of artificial neural network (ANN) modelling coupled with remote-sensing techniques to estimate ice thickness on individual glaciers with direct field measurements. We have developed two ANN models and estimated the ice thickness of Chhota Shigri Glacier (western Himalaya) on ten transverse cross sections and two longitudinal sections. The ANN model estimates agree well with ice thickness measurements from a ground-penetrating radar, available for five transverse cross sections on Chhota Shigri Glacier. The overall root mean square errors of the two ANN models are 24 and 13 m and the mean bias errors are ±13 and ±6 m, respectively, which are significantly lower than for other available models. The estimated mean ice thickness and volume for Chhota Shigri Glacier are 109 ± 17 m and 1.69 ± 0.26 km3, respectively.

Highlights

Himalayan glaciers have been showing wastage over the last few decades (Brun and others, 2017; Azam and others, 2018; Bolch and others, 2019); their dynamics are gradually adjusting (Azam and others, 2012; Dehecq and others, 2019) to the current mass distribution and hypsometry
The assimilation of ground penetrating radar (GPR) data for artificial neural network (ANN) network training decreased the root mean squared error (RMSE) by 11 m (24–13 m) and the mean thickness error of the five transverse sections by 7 m (13–6 m)
The model results were compared with the GPR-derived ice thicknesses on five transverse cross sections located between 4400 and 4900 m a.s.l. on Chhota Shigri Glacier

Summary

Introduction

Himalayan glaciers have been showing wastage over the last few decades (Brun and others, 2017; Azam and others, 2018; Bolch and others, 2019); their dynamics are gradually adjusting (Azam and others, 2012; Dehecq and others, 2019) to the current mass distribution and hypsometry. Volume– area scaling is essentially a transposition because unknown parameters such as glacier depth are derived from glacier area (Lliboutry, 1987; Gantayat and others, 2014) This method has generally been applied at the regional scale (Radić and Hock, 2011; Bliss and others, 2013; Frey and others, 2014) and can be erroneous when applied to individual glaciers for which the scaling parameter have not been well-established (Agrawal and Tayal, 2013; Bahr and others, 2015). Another shortcoming of the volume–area scaling method is that it yields no useful information on subglacial topography, which is a necessary boundary condition for glacier dynamics models (Clarke and others, 2009).

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Conclusion