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High Mountain Asia (HMA) hosts the largest mass of ice outside the Polar Regions and provides water to large downstream communities. Glacier change has been highly diverse across the region over the last decades, with glaciers in the Pamirs experiencing near-neutral mass balance while fast rates of mass loss are observed in the Southeastern Tibetan Plateau (STP). In a previous modelling study in the STP, we found that precipitation phase changes associated with climate warming were a major accelerator of glacier losses, but this mechanism of mass loss acceleration has yet to be explored across the rest of HMA. High-elevation measurements of precipitation phase are rare, especially along glacierized elevations, which lie at the solid-mixed-liquid boundary during summer months. Therefore, accurate representation of precipitation phase in models is essential for quantifying glacier accumulation rates and projecting change.Here we use downscaled and bias-corrected ERA5-Land reanalysis to show the high sensitivity of the solid precipitation ratio to the choice of parameterisation and its impacts on the seasonality of simulated snowfall accumulation at three glacierized catchments covering distinctive climates in HMA (Kyzylsu in the Northern Pamirs, Trakarding-Trambau in the Nepalese Himalayas and Parlung No.4 in the STP). We then apply a land-surface model at high spatio-temporal resolution (100m, hourly) to show the effect of the precipitation phase parametrization on the simulated glacier-mass balance, snowpack dynamics and catchment hydrology. Our results highlight the need to better constrain precipitation phase in glaciological studies and to consider the uncertainty associated with the choice of parameterisation., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

Melt from supraglacial ice cliffs is an important contributor to the mass loss of debris-covered glaciers. However, ice cliff contribution is difficult to quantify as they are highly dynamic features, and the paucity of observations of melt rates and their variability leads to large modelling uncertainties. We quantify monsoon season melt and 3D evolution of four ice cliffs over two debris-covered glaciers in High Mountain Asia (Langtang Glacier, Nepal, and 24K Glacier, China) at very high resolution using terrestrial photogrammetry applied to imagery captured from time-lapse cameras installed on lateral moraines. We derive weekly flow-corrected digital elevation models (DEMs) of the glacier surface with a maximum vertical bias of ±0.2 m for Langtang Glacier and ±0.05 m for 24K Glacier and use change detection to determine distributed melt rates at the surfaces of the ice cliffs throughout the study period. We compare the measured melt patterns with those derived from a 3D energy balance model to derive the contribution of the main energy fluxes. We find that ice cliff melt varies considerably throughout the melt season, with maximum melt rates of 5 to 8 cm d−1, and their average melt rates are 11–14 (Langtang) and 4.5 (24K) times higher than the surrounding debris-covered ice. Our results highlight the influence of redistributed supraglacial debris on cliff melt. At both sites, ice cliff albedo is influenced by the presence of thin debris at the ice cliff surface, which is largely controlled on 24K Glacier by liquid precipitation events that wash away this debris. Slightly thicker or patchy debris reduces melt by 1–3 cm d−1 at all sites. Ultimately, our observations show a strong spatio-temporal variability in cliff area at each site, which is controlled by supraglacial streams and ponds and englacial cavities that promote debris slope destabilisation and the lateral expansion of the cliffs. These findings highlight the need to better represent processes of debris redistribution in ice cliff models, to in turn improve estimates of ice cliff contribution to glacier melt and the long-term geomorphological evolution of debris-covered glacier surfaces., The Cryosphere, 16 (11), ISSN:1994-0416, ISSN:1994-0424

Currently, about 12–13 % of High Mountain Asia's glacier area is debris-covered, altering its surface mass balance. However, in regional-scale modelling approaches, debris-covered glaciers are typically treated as clean-ice glaciers, leading to a potential bias when modelling their future evolution. Here, we present a new approach for modelling debris area and thickness evolution, applicable from single glaciers to the global scale. We implement the module into the Global Glacier Evolution Model (GloGEMflow), a combined mass-balance ice-flow model. The module is initialized with both glacier-specific observations of the debris’ spatial distribution and estimates of debris thickness, accounts for the fact that debris can either enhance or reduce surface melt depending on thickness, and enables representing the spatio-temporal evolution of debris extent and thickness. We calibrate and evaluate the module on a select subset of glaciers, and apply the model using different climate scenarios to project the future evolution of all glaciers in High Mountain Asia until 2100. Compared to 2020, total glacier volume is expected to decrease by between 35 ± 15 % and 80 ±11 %, which is in line with projections in the literature. Depending on the scenario, the mean debris-cover fraction is expected to increase, while mean debris thickness is modelled to show only minor changes, albeit large local thickening is expected. To isolate the influence of explicitly accounting for supraglacial debris-cover, we re-compute glacier evolution without the debris-cover module. We show that glacier geometry, area, volume and flow velocity evolve differently, especially at the level of individual glaciers. This highlights the importance of accounting for debris-cover and its spatio-temporal evolution when projecting future glacier changes., The Cryosphere Discussions, ISSN:1994-0432, ISSN:1994-0440

The snow cover dynamics of High Mountain Asia are usually assessed at spatial resolutions of 250 m or greater, but this scale is too coarse to clearly represent the rugged topography common to the region. Higher‐resolution measurement of snow‐covered area often results in biased sampling due to cloud cover and deep shadows. We therefore develop a Normalized Difference Snow Index‐based workflow to delineate snow lines from Landsat Thematic Mapper/Enhanced Thematic Mapper+ imagery and apply it to the upper Langtang Valley in Nepal, processing 194 scenes spanning 1999 to 2013. For each scene, we determine the spatial distribution of snow line altitudes (SLAs) with respect to aspect and across six subcatchments. Our results show that the mean SLA exhibits distinct seasonal behavior based on aspect and subcatchment position. We find that SLA dynamics respond to spatial and seasonal trade‐offs in precipitation, temperature, and solar radiation, which act as primary controls. We identify two SLA spatial gradients, which we attribute to the effect of spatially variable precipitation. Our results also reveal that aspect‐related SLA differences vary seasonally and are influenced by solar radiation. In terms of seasonal dominant controls, we demonstrate that the snow line is controlled by snow precipitation in winter, melt in premonsoon, a combination of both in postmonsoon, and temperature in monsoon, explaining to a large extent the spatial and seasonal variability of the SLA in the upper Langtang Valley. We conclude that while SLA and snow‐covered area are complementary metrics, the SLA has a strong potential for understanding local‐scale snow cover dynamics and their controlling mechanisms.

The aerodynamic roughness length (z0) is an essential parameter in surface energy balance studies, but few literature values exist for debris-covered glaciers. We use microtopographic and aerodynamic methods to assess the spatial variability of z0 for Lirung Glacier, Nepal. We apply structure from motion to produce digital elevation models for three nested domains: five 1 m2 plots, a 21,300 m2 surface depression, and the lower 550,000 m2 of the debris-mantled tongue. Wind and temperature sensor towers were installed in the vicinity of the plots within the surface depression in October 2014. We calculate z0 according to a variety of transect-based microtopographic parameterizations for each plot, then develop a grid version of the algorithms by aggregating data from all transects. This grid approach is applied to the surface depression digital elevation model to characterize z0 spatial variability. The algorithms reproduce the same variability among transects and plots, but z0 estimates vary by an order of magnitude between algorithms. Across the study depression, results from different algorithms are strongly correlated. Using Monin-Obukov similarity theory, we derive z0 values from the meteorological data. Using different stability criteria, we derive median values of z0 between 0.03 m and 0.05 m, but with considerable uncertainty due to the glacier's complex topography. Considering estimates from these algorithms, results suggest that z0 varies across Lirung Glacier between ∼0.005 m (gravels) to ∼0.5 m (boulders). Future efforts should assess the importance of such variable z0 values in a distributed energy balance model.