Incorporating moisture content in surface energy balance modeling of a debris-covered glacier

Giese, Alexandra; Boone, Aaron; Wagnon, Patrick; Hawley, R. L.

doi:10.5194/tc-14-1555-2020

Cited by 18 publications

(25 citation statements)

References 58 publications

(93 reference statements)

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“…Only data from the last decade were used, to align approximately with the availability of Landsat 8 thermal imagery (2013-present). Following these criteria, the six datasets selected were from: Baltoro Glacier (Groos et al, 2017), Satopanth Glacier (Shah et al, 2019), Lirung Glacier (McCarthy et al, 2017, Ngozumpa Glacier (Nicholson and Mertes, 2017;Nicholson, 2018), Changri Nup Glacier (Giese, 2019), and Hailuogou Glacier (Zhang et al, 2011) (Figure 2; Supplementary Table S1).…”

Section: Deriving Debris Thickness Distributionmentioning

confidence: 99%

“…On the southern of the two lobes comprising Changri Nup Glacier, there is a thick ridge of debris in the glacier's midline (Figure 5D). The debris that emerges as part of this surface ridge is eroded from a large rocky spur that generates many rockfalls (Giese, 2019). On Ngozumpa Glacier, there is an isolated area of thick debris cover near the upglacier limit of debris cover, on the northern edge of the northwestern branch (Figure 5C).…”

Section: The Relationship Between Glaciological Characteristics and Debris Thicknessmentioning

confidence: 99%

“…Glacier outlines are provided by GAMDAM(Sakai, 2019) and debris cover outlines are provided byScherler et al (2018). Note: Giese (2019), consistent withMiles et al (2018) andGiese et al (2020), defines the extent of Changri Nup within the southern of the two lobes indicated in this figure and considers the northern lobe to be a separate glacier named Changri Shar. However, for the purposes of this study the outline provided by GAMDAM(Sakai, 2019) is used, which includes both the northern and southern lobe.…”

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confidence: 96%

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Quantifying Patterns of Supraglacial Debris Thickness and Their Glaciological Controls in High Mountain Asia

et al. 2021

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Mapping patterns of supraglacial debris thickness and understanding their controls are important for quantifying the energy balance and melt of debris-covered glaciers and building process understanding into predictive models. Here, we find empirical relationships between measured debris thickness and satellite-derived surface temperature in the form of a rational curve and a linear relationship consistently outperform two different exponential relationships, for five glaciers in High Mountain Asia (HMA). Across these five glaciers, we demonstrate the covariance of velocity and elevation, and of slope and aspect using principal component analysis, and we show that the former two variables provide stronger predictors of debris thickness distribution than the latter two. Although the relationship between debris thickness and slope/aspect varies between glaciers, thicker debris occurs at lower elevations, where ice flow is slower, in the majority of cases. We also find the first empirical evidence for a statistical correlation between curvature and debris thickness, with thicker debris on concave slopes in some settings and convex slopes in others. Finally, debris thickness and surface temperature data are collated for the five glaciers, and supplemented with data from one more, to produce an empirical relationship, which we apply to all glaciers across the entire HMA region. This rational curve: 1) for the six glaciers studied has a similar accuracy to but greater precision than that of an exponential relationship widely quoted in the literature; and 2) produces qualitatively similar debris thickness distributions to those that exist in the literature for three other glaciers. Despite the encouraging results, they should be treated with caution given our relationship is extrapolated using data from only six glaciers and validated only qualitatively. More (freely available) data on debris thickness distribution of HMA glaciers are required.

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Section: Deriving Debris Thickness Distributionmentioning

confidence: 99%

Section: The Relationship Between Glaciological Characteristics and Debris Thicknessmentioning

confidence: 99%

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Quantifying Patterns of Supraglacial Debris Thickness and Their Glaciological Controls in High Mountain Asia

et al. 2021

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“…Of the parameters used in the SEBM (see Appendix Table A1), any could potentially factor into model uncertainty. However, the ten in Table 3 stand out as relatively less certain and/or more likely to elicit model sensitivity (Braithwaite, 1995;Moore, 1983;Klok and Oerlemans, 2004;Immerzeel et al, 2014;Shea et al, 2015;Giese et al, 2020;Johnson and Rupper, 2020). The albedos of bare ice and fresh snow directly affect the absorption of energy by the surface, while the temperature lapse rate and temperature at which precipitation phase transitions affect whether precipitation falls as rain or snow.…”

Section: Melt Uncertainty From Model Parameter Valuesmentioning

confidence: 99%

Indus River Basin Glacier Melt at the Subbasin Scale

Giese

Rupper²,

Keeler³

et al. 2022

Front. Earth Sci.

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Pakistan is the most glaciated country on the planet but faces increasing water scarcity due to the vulnerability of its primary water source, the Indus River, to changes in climate and demand. Glacier melt constitutes over one-third of the Indus River’s discharge, but the impacts of glacier shrinkage from anthropogenic climate change are not equal across all eleven subbasins of the Upper Indus. We present an exploration of glacier melt contribution to Indus River flow at the subbasin scale using a distributed surface energy and mass balance model run 2001–2013 and calibrated with geodetic mass balance data. We find that the northern subbasins, the three in the Karakoram Range, contribute more glacier meltwater than the other basins combined. While glacier melt discharge tends to be large where there are more glaciers, our modeling study reveals that glacier melt does not scale directly with glaciated area. The largest volume of glacier melt comes from the Gilgit/Hunza subbasin, whose glaciers are at lower elevations than the other Karakoram subbasins. Regional application of the model allows an assessment of the dominant drivers of melt and their spatial distributions. Melt energy in the Nubra/Shyok and neighboring Zaskar subbasins is dominated by radiative fluxes, while turbulent fluxes dominate the melt signal in the west and south. This study provides a theoretical exploration of the spatial patterns to glacier melt in the Upper Indus Basin, a critical foundation for understanding when glaciers melt, information that can inform projections of water supply and scarcity in Pakistan.

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“…This results in an increase in conductivity compared to a drier coarse-texture debris pack by a factor of 2 (Figure 4B). Such considerations are essential to further drive development of more complex models that are able to incorporate moisture (Collier et al, 2014;Giese et al, 2020), but also for routing times of melt water from the glacier as melt water does not immediately become available to discharge. Second, due to this available water in the pores and relatively dense debris texture, the debris pack is largely frozen below a depth of 20 cm in winter.…”

Section: Debris Propertiesmentioning

confidence: 99%

Distributed Melt on a Debris-Covered Glacier: Field Observations and Melt Modeling on the Lirung Glacier in the Himalaya

2021

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Debris-covered glaciers, especially in high-mountain Asia, have received increased attention in recent years. So far, few field-based observations of distributed mass loss exist and both the properties of the debris layer as well as the atmospheric drivers of melt below debris remain poorly understood. Using multi-year observations of on-glacier atmospheric data, debris properties and spatial surface elevation changes from repeat flights with an unmanned aerial vehicle (UAV), we quantify the necessary variables to compute melt for the Lirung Glacier in the Himalaya. By applying an energy balance model we reproduce observed mass loss during one monsoon season in 2013. We show that melt is especially sensitive to thermal conductivity and thickness of debris. Our observations show that previously used values in literature for the thermal conductivity through debris are valid but variability in space on a single glacier remains high. We also present a simple melt model, which is calibrated based on the results of energy balance model, that is only dependent on air temperature and debris thickness and is therefore applicable for larger scale studies. This simple melt model reproduces melt under thin debris (<0.5 m) well at an hourly resolution, but fails to represent melt under thicker debris accurately at this high temporal resolution. On the glacier scale and using only off-glacier forcing data we however are able to reproduce the total melt volume of a debris-covered tongue. This is a promising result for catchment scale studies, where quantifying melt from debris covered glaciers remains a challenge.

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Incorporating moisture content in surface energy balance modeling of a debris-covered glacier

Cited by 18 publications

References 58 publications

Quantifying Patterns of Supraglacial Debris Thickness and Their Glaciological Controls in High Mountain Asia

Quantifying Patterns of Supraglacial Debris Thickness and Their Glaciological Controls in High Mountain Asia

Indus River Basin Glacier Melt at the Subbasin Scale

Distributed Melt on a Debris-Covered Glacier: Field Observations and Melt Modeling on the Lirung Glacier in the Himalaya

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