Sub-seasonal variability of supraglacial ice cliff melt rates and associated processes from time-lapse photogrammetry

Abstract

Abstract. Melt from supraglacial ice cliffs is an important contributor to the mass loss of debris-covered glaciers. However, ice cliff contribution is difficult to quantify as they are highly dynamic features, and the paucity of observations of melt rates and their variability leads to large modelling uncertainties. We quantify monsoon season melt and 3D evolution of four ice cliffs over two debris-covered glaciers in High Mountain Asia (Langtang Glacier, Nepal, and 24K Glacier, China) at very high resolution using terrestrial photogrammetry applied to imagery captured from time-lapse cameras installed on lateral moraines. We derive weekly flow-corrected digital elevation models (DEMs) of the glacier surface with a maximum vertical bias of ±0.2 m for Langtang Glacier and ±0.05 m for 24K Glacier and use change detection to determine distributed melt rates at the surfaces of the ice cliffs throughout the study period. We compare the measured melt patterns with those derived from a 3D energy balance model to derive the contribution of the main energy fluxes. We find that ice cliff melt varies considerably throughout the melt season, with maximum melt rates of 5 to 8 cm d−1, and their average melt rates are 11–14 (Langtang) and 4.5 (24K) times higher than the surrounding debris-covered ice. Our results highlight the influence of redistributed supraglacial debris on cliff melt. At both sites, ice cliff albedo is influenced by the presence of thin debris at the ice cliff surface, which is largely controlled on 24K Glacier by liquid precipitation events that wash away this debris. Slightly thicker or patchy debris reduces melt by 1–3 cm d−1 at all sites. Ultimately, our observations show a strong spatio-temporal variability in cliff area at each site, which is controlled by supraglacial streams and ponds and englacial cavities that promote debris slope destabilisation and the lateral expansion of the cliffs. These findings highlight the need to better represent processes of debris redistribution in ice cliff models, to in turn improve estimates of ice cliff contribution to glacier melt and the long-term geomorphological evolution of debris-covered glacier surfaces.

Thinning and surface mass balance patterns of two neighboring debris-covered glaciers in southeastern Tibetan Plateau

“…CC BY 4.0 License. (Immerzeel et al, 2014;Brun et al, 2018;Mölg et al, 2019;Anderson et al, 2021a;Kneib et al, 2021Kneib et al, , 2022Mishra et al, 2021;Sato et al, 2021). These studies confirm the potential for investigating glacier-or sub-glacierscale domains based on high-resolution data, which can provide detailed observations of local processes (e.g., Westoby et al, 2020).…”

“…The in-situ ice thickness measurements were used to correct the 24K Glacier distributed ice thickness dataset from Farinotti et al ( 2019) (Fig. S2; Kneib et al, 2022). No in-situ ice thickness measurements exist for 23K Glacier; therefore, we directly used the consensus ice thickness from Farinotti et al (2019) for this glacier.…”

“…The outlines of the 'hotspots' area were obtained by merging two-phase outlines (Burn et al, 2018), and the total hotspots area for each zone was established by summing the area of these features. In this way, we can effectively measure surface change in these hotspots zones, thereby providing us with an upper-bound estimate of the contribution of ice cliffs and ponds to the surface mass balance (Mishra et al, 2021;Kneib et al, 2022). We assumed the uncertainty of areas density of ice cliffs and supraglacial ponds to be equal to 0.5%.…”

“…Ice cliffs and supraglacial ponds are common features of debris-covered glaciers (Sakai et al, 1998(Sakai et al, , 2000, they are directly exposed to incoming radiations and therefore act as melt 'hotspots' (Sakai, 1998(Sakai, , 2002Reid and Brock, 2014;Juen et al, 2014;Steiner et al, 2015;Buri et al, 2016;Miles et al, 2016;Miles et al, 2018;Buri et al, 2021). The areas influenced by cliffs and ponds are characterized by high thinning and melt rates (~6-10 times) relative to surrounding debris-covered ice in the southeastern Tibetan Plateau , as revealed by the differencing of high-resolution DEMs and the results of energy-balance modelling (Thompson et al, 2016;Brun et al, 2018;Miles et al, 2018Buri et al, 2016Buri et al, , 2021Kneib et al, 2022;Sato et al, 2021;Mishra et al, 2021). There is a debate on the so-called 'debris cover anomaly' phenomenon since some research considers that the insulating effect of debris cover has a larger effect on total thinning than the enhanced ice ablation due to exposed ice cliffs and supraglacial ponds (e.g., Hambrey et al, 2008;Vincent et al, 2016;Brun et al, 2018;Anderson et al, 2021a).…”

“…Since ice cliffs and supraglacial ponds tend to have a relatively small area and be very dynamic (Miles et al, 2017;, it is challenging to accurately track their evolution over time and quantify their contribution to melt (Mishra et al, 2021;Kneib et al, 2022). Because of these challenges, studies which have investigated their spatial distribution, dynamics, and melt contribution have typically utilized unpiloted aerial vehicle (UAV) or very high-resolution optical satellite data https://doi.org/10.5194/tc-2022-231 Preprint.…”