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

Abstract

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.

Controls on the relative melt rates of debris-covered glacier surfaces

“…We apply models of surface energy balance for debris-covered ice (Steiner et al 2021), ice cliffs (Buri et al 2016), and supraglacial ponds (Miles et al 2018), and also model the energy balance of bare glacier ice (Reid et al 2012) as a reference (see appendix). We model these distinct surface types for periods where the WorldClim 2.1 (Fick and Hijmans 2017) monthly maximum air temperature is above 0 • C at the station's location, in order to compare only periods that are definitely within the local ablation season.…”

“…Sub-debris melt is calculated at the point scale with the model presented in detail in Steiner et al (2021).…”

An 11-year record of wintertime snow-surface energy balance and sublimation at 4863 m a.s.l. on the Chhota Shigri Glacier moraine (western Himalaya, India)

“…Glaciers in the Pamir and Kunlun Mountains are an extreme continental type, with cold temperature and low annual precipitation (Zhu et al, 2020;Li et al, 2019); thus their SEB characteristics are expected to behave differently than the majority of HK glaciers which are an alpine type, with relatively higher precipitation and temperature. In the HK region, a few SEB experiments have been carried out recently, most of them being in the central Himalaya in Nepal, yet at a smaller temporal range, from a month to a few seasons/years (Rounce et al, 2015;Steiner et al, 2018Steiner et al, , 2021Acharya and Kayastha, 2019;Litt et al, 2019;Matthews et al, 2020). SEB studies in the Indian Himalaya are few.…”

Challenges in Understanding the Variability of the Cryosphere in the Himalaya and Its Impact on Regional Water Resources

“…Also, the theoretical and physical understanding of various complex snow and glacier processes, such as influence of debris cover, long-term dynamic change, has improved significantly (Reid and Brock, 2010;Shea et al, 2015;Carenzo et al, 2016). Various surface melt and mass balance models have been implemented in the Himalayan region, such as hydrological models (Bhutiyani, 1999;Immerzeel et al, 2012), temperature-index model (Azam et al, 2014a), enhanced temperature-index model (Litt et al, 2019), albedo model (Brun et al, 2015), surface energy balance (SEB) model (Azam et al, 2014b), distributed SEB model (Arndt et al, 2021;Steiner et al, 2021;Srivastava and Azam, 2022), glacier dynamics model [Open Global Glacier Model (OGGM); Maussion et al, 2019], glacier evolution model [Python Glacier Evolution Model (PyGEM); Rounce et al, 2020]. However, the majority of the modeling studies use temperature-index models (Azam et al, 2021), with some modification for better representation (e.g., Pellicciotti et al, 2005).…”