2026-09-24T19:15:21Zhttps://api.repository.cam.ac.uk/server/oai/requestoai:www.repository.cam.ac.uk:1810/3117552024-06-26T14:00:14Zcom_1810_183634com_1810_256064col_1810_214795
Quantifying Supraglacial Debris Thickness and the Glaciological Controls on its Spatial Distribution in High Mountain Asia
10.17863/CAM.58844
Boxall, Karla
As debris-covered glaciers (DCGs) become a more prominent feature of the shrinking
mountain cryosphere (Shukla et al., 2009; Thakuri et al., 2014; Gaddam et al. 2016; Tielidze
et al., 2019), there is an increasing need to understand how they will respond to climate
change. In particular, there is a requirement to improve our understanding of both the
current and future spatial distribution of supraglacial debris thickness. This knowledge is
indispensible because the mass balance response of DCGs is dependent on the distribution
of its debris thickness, given that debris cover can either enhance or inhibit ablation
depending on its thickness (Østrem, 1959; Nakawo and Young, 1981; Mattson et al., 1993;
Nicholson and Benn, 2006). This knowledge is particularly applicable in the cryosphere of
High Mountain Asia (HMA) where ~18% of the total ice mass is stored under a debris mantle
(Nuimura et al., 2012; Bolch et al., 2012; Kraajienbrink et al., 2017) and 1.4 billion people
rely on glacial runoff (Immerzeel et al., 2010; Kamp et al., 2011; Shukla and Qadir, 2016).
To improve our understanding of the current spatial distribution of debris thickness in HMA,
this study aims to improve the empirical calculation of debris thickness from surface
temperature. This study undertakes the first systematic comparison of different forms of
the empirical relationship between debris thickness and surface temperature (linear,
rational curve and two types of exponential curve) to determine which derives the most
accurate debris thickness distribution for six different glaciers (Baltoro Glacier, Satopanth
Glacier, Lirung Glacier, Ngozumpa Glacier, Changri Nup Glacier and Hailuogou Glacier). This
method is only successful when the in situ debris thickness data is well distributed. When
well-distributed data is provided, the rational curve and the linear relationship consistently
perform best. Tentatively, it is suggested that the rational curve performs best for glaciers
with a thinner debris cover, whilst a linear relationship performs best for glaciers with a
thicker debris cover.
Data is collated from the six glaciers to produce an empirical relationship applicable to
multiple glaciers over the HMA region, including those with no in situ debris thickness data
where the glacial-scale method of derivation cannot be undertaken. The novel application
of the rational curve to a dataset collated from multiple glaciers, deemed to be
representative of the region, produced a regional scale debris thickness distribution with a
similar accuracy, but a greater precision than the current regional scale derivation of debris
thickness that uses an exponential form of the relationship (Kraaijenbrink et al., 2017).
To improve our understanding of the future spatial distribution of debris thickness in HMA,
this study quantifies the influence of glaciological characteristics (elevation, slope, aspect,
curvature, velocity) on debris thickness spatial variability. This will allow for more accurate
predictions concerning the evolution of debris thickness distribution in the future. This
study quantifies the interplay between the controlling variables by demonstrating the
covariance of velocity and elevation and of slope and aspect, in addition to revealing the
dominance of velocity and elevation over slope and aspect in explaining the distribution of
debris thickness. In the majority of cases, thicker debris is expected at low elevations, on
slowly flowing ice. However, the relationship between debris thickness and slope/aspect
varies. On Satopanth Glacier, thicker debris occurs on flatter, W-facing slopes, but on
Ngozumpa, Changri Nup and Hailuogou Glaciers, thicker debris occurs on steeper, E-facing
slopes. The first empirical evidence of the influence of curvature was found on Hailuogou
Glacier, where thick debris occurs on concave slopes. The percentage of debris thickness
variability explained by these factors alone varies between 1% and 50% for the different
glaciers. It is suggested that the variation in the relationship between debris thickness and
slope/aspect, in addition to the variation in the proportion of debris thickness variability
explained, could be explained by the varying strength of the influence of rockfall and the
melt out of englacial debris, neither of which are explicitly accounted for in this study.
Overall, this study improves the understanding of the current and future distribution of the
spatial distribution of debris thickness, by (i) assessing which form of the surface
temperature/debris thickness relationship produces the most accurate and precise debris
thickness distribution, on six individual glaciers, (ii) improving the empirical derivation of
debris thickness at the regional scale and (iii) quantifying the interplay and dominance of
glaciological characteristics in controlling the spatial distribution of debris thickness.
Understanding both the current and future distribution of supraglacial debris thickness is
essential for better understanding the future response of DCGs to climate change.
University of Cambridge
2020-06
Thesis
masters
MPhil
en
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cryosphere
glaciers
glaciological