Estimation of the total sub-debris ablation from point-scale ablation data on a debris-covered glacier

Abstract

Glaciological ablation is computed from point-scale data at a few ablation stakes that are usually regressed as a function of elevation and averaged over the area-elevation distribution of a glacier. This method is contingent on a tight control of elevation on local ablation. However, in debris-covered glaciers, systematic and random spatial variations of debris thickness modify the ablation rates. We propose and test a method to compute sub-debris ablation where stake data are interpolated as a function of debris-thickness alone and averaged over the debris-thickness distribution at different parts of the glacier. We apply this method on Satopanth Glacier located in Central Himalaya utilising ~1000 ablation measurements obtained from a network of up to 56 stakes during 2015–2017. The estimated mean sub-debris ablation ranges between 1.5±0.2 to 1.7±0.3 cm d−1. We show that the debris-thickness-dependent regression describes the spatial variability of the sub-debris ablation better than the elevation dependent regression. The uncertainties in ablation estimates due to the corresponding uncertainties in the measurement of ablation and debris-thickness distribution, and those due to interpolation procedures are estimated using Monte Carlo methods. Possible biases due to a finite number of stakes used are also investigated.

Understanding the interrelationships among mass balance, meteorology, discharge and surface velocity on Chhota Shigri Glacier over 2002–2019 using in situ measurements

“…d −1 for clean ice). A quadratic polynomial function was fitted on all the point ablation data from different years to understand the altitude dependency of ablation on the glacier following standard glaciological practice (Kaser and others, 2003; Shah and others, 2019). The polynomial function analysis provides a better description of the mass-balance variation and altitude/debris cover dependency (Figs S4–S8).…”

Volume-area scaling for debris-covered glaciers

“…It also varies with time due to stochastic and systematic changes in headwall-erosion rates (Banerjee and Wani, 2018). Additional sources of debris due to rockfall, debris avalanche and degradation of lateral moraines (Nakawo and others, 1986) add further noise to the debris-thickness distribution (Shah and others, 2019). Complex mass-balance processes like avalanche activity (Laha and others, 2017), and stochastic increase in local ablation due to thermokarst features like supraglacial ponds and ice-cliffs (Sakai and others, 2000), may modify the mass-balance profile.…”

Seasonally stable temperature gradients through supraglacial debris in the Everest region of Nepal, Central Himalaya

“…4), and it is not clear which variable is more important in controlling the duration of sub-debris melt. Previous work on Satopanth Glacier in the Central Himalaya has demonstrated that debris thickness is more important than glacier elevation in controlling the magnitude of sub-debris melt (Shah and others, 2019). The shorter ablation season at higher elevation sites could be due to more effective heating over thinner debris layers (Steiner and Pellicciotti, 2016).…”

“…Previous studies have suggested that, in the absence of snowfall and phase changes within the debris, quasi-linear temperature profiles can be expected over the core ablation season in the Himalaya (Nicholson and Benn, 2013), which potentially simplifies the calculation of annual ablation from debris-covered glaciers. Furthermore, sub-debris melt appears to be more strongly controlled by debris thickness rather than glacier elevation (Shah and others, 2019). A step forward in understanding debris-covered glacier mass balance lies in observing how the conductive heat flux varies across the entire vertical debris profile, from the surface to the debris–ice interface.…”