Multiannual observations and modelling of seasonal thermal profiles through supraglacial debris in the Central Himalaya

Rowan, Ann V.; Nicholson, Lindsey; Collier, Emily; Quincey, Duncan J.; Gibson, Morgan; Wagnon, Patrick; Rounce, David; Thompson, Sarah S.; King, Owen; Watson, C. Scott; Irvine‐Fynn, Tristram; Glasser, Neil F.

doi:10.5194/tc-2017-239

Cited by 3 publications

(3 citation statements)

References 30 publications

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“…The smooth down-glacier increase in debris thickness, and the corresponding decline of the surface ablation rate as discussed above, provide only a first- order description of the debris effects (Benn and Lehmkuhl, 2000; Scherler and others, 2011b; Banerjee and Shankar, 2013). The role of several other complicating factors, e.g., the presence of numerous thermokarst ephemeral ponds and cliffs that increase local melt rate (Reynolds, 2000; Sakai and others, 2000; Miles and others, 2017), vertical and horizontal variations of the thermal properties of debris (Nicholson and Benn, 2013; Rowan and others, 2017), the random short-scale spatial variation of debris thickness (Mihalcea and others, 2006; Zhang and others, 2011; Nicholson and Mertes, 2017; Rounce and others, 2018) and the accumulation contribution from avalanches (Laha and others, 2017) need to be quantified for accurate surface mass-balance estimates on any typical debris-covered Himalayan glacier. The standard glaciological mass-balance measurement protocol (Kaser and others, 2003) may not be designed to handle the above issues.…”

Section: Introductionmentioning

confidence: 99%

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

Shah

Banerjee²,

Nainwal

et al. 2019

J. Glaciol.

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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.

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Section: Introductionmentioning

confidence: 99%

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

Shah

Banerjee²,

Nainwal

et al. 2019

J. Glaciol.

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“…However, several researchers have observed reduced melting and explained a reduction in heat transfer from thick debris (Pratap et al, 2015;Patel et al, 2016;Sharma et al, 2016;Nicholson et al, 2018). The heat transfer from debris surface to debris ice interface zone largely depends upon the thermal characteristics of the debris pack (Mihalcea et al, 2006;Lambrecht et al, 2011;Rowan et al, 2017) and atmospheric conditions (Collier et al, 2015). The thermal characteristics of the debris pack mainly depends on debris composition, its thickness, and its moisture content and is explained in terms of thermal resistance and conductivity.…”

Section: Introductionmentioning

confidence: 99%

Influence of Supraglacial Debris Thickness on Thermal Resistance of the Glaciers of Chandra Basin, Western Himalaya

et al. 2021

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A large number of glaciers in the Hindu-Kush Himalaya are covered with debris in the lower part of the ablation zone, which is continuously expanding due to enhanced glacier mass loss. The supraglacial debris transported over the melting glacier surface acts as an insulating barrier between the ice and atmospheric conditions and has a strong influence on the spatial distribution of surface ice melt. We conducted in-situ field measurements of point-wise ablation rate, supraglacial debris thickness, and debris temperature to examine the thermal resistivity of the debris pack and its influence on ablation over three glaciers (Bara Shigri, Batal, and Kunzam) in Chandra Basin of Western Himalaya during 2016–2017. Satellite-based supraglacial debris cover assessment shows an overall debris covered area of 15% for Chandra basin. The field data revealed that the debris thickness varied between 0.5 and 326 cm, following a spatially distributed pattern in the Chandra basin. The studied glaciers have up to 90% debris cover within the ablation area, and together represent ∼33.5% of the total debris-covered area in the basin. The supraglacial debris surface temperature and near-surface air temperature shows a significant correlation (r = > 0.88, p = < 0.05), which reflects the effective control of energy balance over the debris surface. The thermal resistivity measurements revealed low resistance (0.009 ± 0.01 m2°C W−1) under thin debris pack and high resistance (0.55 ± 0.09 m2°C W−1) under thick debris. Our study revealed that the increased thickness of supraglacial debris significantly retards the glacier ablation due to its high thermal resistivity.

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“…100m surface elevation contours are plotted with thin blue lines, with thicker lines highlighting 4000, 4500, 5000, 5500, and 6000 m contour lines. (Reynolds, 2000;Sakai and others, 2000;Miles and others, 1993), vertical and horizontal variation of the thermal properties of debris (Nicholson and Benn, 2013;Rowan and others, 2018), random short-scale spatial variation of debris thickness (Mihalcea and others, 2006;Zhang and others, 2011; Mertes, 2017; Rounce and others, 2018), and the accumulation contribution from avalanches (Laha and others, 2017) need to be quantified for accurate surface mass-balance estimates on any typical debris-covered Himalayan glacier. The standard glaciological mass-balance measurement protocol (Kaser and others, 2003) may not be designed to handle some of the above issues.…”

Section: Introductionmentioning

confidence: 99%

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

Singh¹,

Banerjee²,

Nainwal³

et al. 2019

Preprint

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This is the preprint of an article that is under review in the Journal of Glaciology. The abstract is as follows: Glaciological mass balance is computed from point-scale field data at a few ablation stakes that are regressed as a function of elevation, and averaged over the area-elevation distribution of the glacier. This method is contingent on a tight control of elevation on local ablation. On debris-covered glaciers, systematic and random spatial variations of debris thickness modify ablation rates. A method that takes into account the debris-thickness variability in extrapolating point-scale ablation data may be more accurate on these glaciers. We propose and test a methGlaciological mass balance is computed from point-scale field data at a few ablation stakes that are regressed as a function of elevation, and averaged over the area-elevation distribution of the glacier. This method is contingent on a tight control of elevation on local ablation. On debris-covered glaciers, systematic and random spatial variations of debris thickness modify ablation rates. A method that takes into account the debris-thickness variability in extrapolating point-scale ablation data may be more accurate on these glaciers. We propose and test a method where stake data are interpolated as a function of debris-thickness alone, and averaged over the observed debris-thickness distribution at different parts of the glacier. We apply this method to compute sub-debris ablation rate on Satopanth Glacier (Central Himalaya) utilising about a thousand ablation measurements at a network of up to 56 stakes during 2015--2017. We compare our results with that from the standard glaciological method. The uncertainties in both the estimates due to the corresponding uncertainties in measurement of ablation and debris-thickness distribution, and that due to interpolation procedures are estimated using Monte Carlo methods. Possible biases due to finite number of stakes used are investigated, and net specific balance of Satopanth glacier is computed.od where stake data are interpolated as a function of debris-thickness alone, and averaged over the observed debris-thickness distribution at different parts of the glacier. We apply this method to compute sub-debris ablation rate on Satopanth Glacier (Central Himalaya) utilising about a thousand ablation measurements at a network of up to 56 stakes during 2015--2017. We compare our results with that from the standard glaciological method. The uncertainties in both the estimates due to the corresponding uncertainties in measurement of ablation and debris-thickness distribution, and that due to interpolation procedures are estimated using Monte Carlo methods. Possible biases due to finite number of stakes used are investigated, and net specific balance of Satopanth glacier is computed.

show abstract

Multiannual observations and modelling of seasonal thermal profiles through supraglacial debris in the Central Himalaya

Cited by 3 publications

References 30 publications

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

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

Influence of Supraglacial Debris Thickness on Thermal Resistance of the Glaciers of Chandra Basin, Western Himalaya

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

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