Qi et al. (2026) Assessing the impact of ice thickness uncertainty on future glacier evolution in the Himalayas using a higher-order glacier flow model

Identification

• Journal: Journal of Hydrology
• Year: 2026
• Date: 2026-04-01
• Authors: Xiaoning Qi, Yuzhe Wang, Tong Zhang, Lei Wang, Baojuan Huai, Hongmin An, Weijun Sun
• DOI: 10.1016/j.jhydrol.2026.135450

Research Groups

• College of Geography and Environment, Shandong Normal University, Jinan, China
• State Key Laboratory of Earth Surface Processes and Hazards Risk Governance, Beijing Normal University, Beijing, China

Short Summary

This study quantifies the impact of initial ice thickness uncertainty on future glacier evolution in the Himalayas using a higher-order flowline model and seven global ice thickness datasets. It finds that by 2100, this uncertainty leads to a standard deviation of 1.5%–6.2% in projected volume loss, comparable to climate model resolution uncertainty, with greater divergence in the Central Himalayas and for smaller, lower-elevation glaciers.

Objective

• To investigate and quantify the influence of ice thickness uncertainty, derived from multiple global datasets, on modeled glacier changes (thickness, volume, and surface velocity) in the Himalayas using a higher-order glacier flow model.

Study Configuration

• Spatial Scale: 30 representative glaciers across the Western, Central, and Eastern Himalayas, covering an area of approximately 21,400 km² (total Himalayan glacier area).
• Temporal Scale: Future glacier evolution projections from 2020 to 2100, based on a committed climate scenario using average conditions from 1991 to 2020.

Methodology and Data

• Models used:
  • Higher-order flow-band glacier flow model: PoLIM (Polythermal Land Ice Model)
  • Mass balance model: Temperature-index model
  • Model initialization: Robin inverse method
• Data sources:
  • Glacier outlines: Randolph Glacier Inventory (RGI) v6.0 (cross-checked with v7.0)
  • Glacier centerlines: Open Global Glacier Model (OGGM)
  • Surface elevation: SRTM DEM (30 m spatial resolution)
  • Ice thickness products (7 datasets): HF, GlabTop2, OGGM, Fürst, GlabTop2-IITB, Farinotti-composite, Millan
  • Climate reanalysis: ERA5-Land (daily 2 m air temperature and precipitation, 0.1° × 0.1° spatial resolution)
  • Geodetic mass balance (for calibration/validation): Hugonnet et al. (2021) global glacier elevation change time series (2000–2020)
  • Glacier surface velocity (for initialization): ITS_LIVE Velocity Mosaic Version 2 (annual mean, 1985–2020, 120 m spatial resolution)

Main Results

• By 2100, Himalayan glaciers are projected to lose an average of 23.4 ± 3.7 m in thickness, 0.10 ± 0.02 km³ in volume, and experience a 0.67 ± 0.54 m/a reduction in surface velocity.
• Differences among initial ice thickness datasets lead to a standard deviation of 1.5%–6.2% in projected volume loss and an inter-model spread of up to 18.3%.
• The Central Himalayas exhibit the largest divergence in projected volume loss (standard deviation of 6.2%), followed by the Western Himalayas (4.9%), and the Eastern Himalayas (1.5%).
• Inter-dataset variability decreases with increasing glacier elevation and size; smaller (<1 km²) and lower-elevation glaciers are more sensitive to initial thickness differences.
• The Millan and Fürst datasets consistently define the upper and lower bounds of projected changes, respectively.
• The use of a higher-order ice dynamics model (PoLIM) amplifies the sensitivity of projections to initial ice thickness inputs due to its explicit resolution of longitudinal and transverse stresses.
• The magnitude of uncertainty from ice thickness datasets is comparable to that from climate model resolution.

Contributions

• Systematically quantifies the impact of multiple global ice thickness datasets on future glacier evolution in the Himalayas, addressing a gap in previous research.
• Utilizes a higher-order ice flow model (PoLIM) to assess this uncertainty, highlighting its amplified effect compared to simpler models.
• Provides new insights into the spatial (regional, elevational) and size-dependent variability of glacier response to initial ice thickness uncertainty.
• Emphasizes the critical need for improved ice thickness observations and the importance of incorporating multiple thickness datasets in regional glacier modeling for robust projections.

Funding

• National Natural Science Foundation of China (grants 42271134 and 41901088)
• Taishan Scholars Program of Shandong Province (No. tsqn202312158)

Citation

@article{Qi2026Assessing,
  author = {Qi, Xiaoning and Wang, Yuzhe and Zhang, Tong and Wang, Lei and Huai, Baojuan and An, Hongmin and Sun, Weijun},
  title = {Assessing the impact of ice thickness uncertainty on future glacier evolution in the Himalayas using a higher-order glacier flow model},
  journal = {Journal of Hydrology},
  year = {2026},
  doi = {10.1016/j.jhydrol.2026.135450},
  url = {https://doi.org/10.1016/j.jhydrol.2026.135450}
}