Mass- and Energy-Balance Modeling and Sublimation Losses on Dokriani Bamak and Chhota Shigri Glaciers in Himalaya Since 1979

Abstract

Available surface energy balance (SEB) studies on the Himalayan glaciers generally investigate the melt-governing energy fluxes at a point-scale. Further, the annual glacier-wide mass balance (Ba) reconstructions have often been performed using temperature-index (T-index) models. In the present study, a mass- and energy-balance model is used to simulate the Ba on Dokriani Bamak Glacier (DBG, central Himalaya) and Chhota Shigri Glacier (CSG, western Himalaya) using the bias-corrected ERA5 data from 1979 to 2020. The model is calibrated using in-situ Ba and validated against available in-situ altitudinal and geodetic mass balances. DBG and CSG show mean Ba of −0.27 ± 0.32 and −0.31 ± 0.38 m w.e. a−1 (meter water equivalent per year), respectively, from 1979 to 2020. Glacier-wide net shortwave radiation dominates the SEB followed by longwave net radiation, latent heat flux, and sensible heat flux. The losses through sublimation are around 22% on DBG and 20% on CSG to the total ablation with a strong spatial and temporal variability. Modeled Ba is highly sensitive to snow albedo —with sensitivities of 0.29 and 0.37 m w.e. a−1 for 10% change in the calibrated value—on DBG and CSG, respectively. The sensitivity of the modeled mean Ba to 1°C change in air temperature and 10% change in precipitation, respectively is higher on DBG (−0.50 m w.e. a−1°C−1, 0.23 m w.e. a−1) than the CSG (−0.30 m w.e. a−1°C−1, 0.13 m w.e. a−1). This study provides insights into the regional variations in mass-wastage governing SEB fluxes at a glacier-wide scale, which is helpful for understanding the glacier–climate interactions in the Himalaya and stresses an inclusion of sublimation scheme in T-index models.

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)

“…Recently, Singh et al (2020) conducted a SEB experiment on a moraine surface with ephemeral snow cover near the Pindari Glacier in Uttarakhand using 2-year data from a weather station. Glacierwide applications of SEB remain rare to date in the HK region (Srivastava and Azam, 2022).…”

“…Stigter et al (2018) showed that sublimation loss on the central Himalayan Yala Glacier in Nepal is larger than 20 % of winter snowfall. Srivastava and Azam (2022) studied the glacier-wide SEB on the Chhota Shigri and Dokriani glaciers in the Indian Himalaya and estimated a mass loss through sublimation of up to 20 % of the total annual ablation, with strong spatial and temporal variability. Sublimation contribution is observed to be up to 66 % of the total mass loss on the Purogangri Ice Cap of the northcentral Tibetan Plateau (Huintjes et al, 2015b).…”

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).…”

Evaluation of ERA5 and Dynamical Downscaling for Surface Energy Balance Modeling at Mountain Glaciers in Western Canada

“…As part of the ERA5 framework, ERA5-Land reanalysis was created at even higher grid spacing (9 km) by forcing the land component of the ERA5 (Muñoz Sabater et al, 2021). Since the releases of both ERA5 and ERA5-Land, several studies have successfully applied these datasets for mass balance modeling at individual glaciers, inducing several glaciers in Central and High Mountain Asia (Kronenberg et al, 2022;Srivastava and Azam, 2022;Arndt et al, 2021;Azam and Srivastava, 2020) and two glaciers in Western Canada (Mukherjee et al, 2022). Despite the high spatial resolution, especially in ERA5-Land, all of these studies used some form of bias corrections and/or statistical downscaling to resolve the scale mismatch between the reanalysis grids and individual sites.…”