Deciphering Glacier Velocity Dynamics in North Sikkim, India (1990–2022) through Geospatial Investigation

The glaciers of North Sikkim, located in the Central Himalaya, serve as crucial indicators of climate change, shedding light on cryospheric processes and hydrological impacts. This study utilizes feature tracking-based remote sensing techniques to analyze glacier velocity trends from 1990 to 2022, examining 679 glaciers categorized as clean glaciers (CG), debris-covered glaciers (DG), glaciers associated with lakes (GL), rock glaciers (RG), and smaller glaciers (SG, <0.5 km²). Landsat imagery reveals velocity values ranging from -20.17 m/year to 21.99 m/year, with a mean velocity of 0.49 m/year. spatial-temporal analysis identifies three velocity phases for the glaciers like moderate variability (1990–2000), stabilization (2001–2010), and acceleration (2011–2022). The latest phase shows a sharp rise in mean velocity to 0.75 m/year, correlating with increased warming trends. Smaller glaciers exhibited the highest climate sensitivity, with extreme velocities reaching 21.9 m/year, while debris-covered glaciers showed periodic accelerations exceeding 10 m/year in 1996 and 2022. Spatial analysis highlights the influence of glacier size and elevation, with higher-altitude glaciers (>5000 m) moving faster due to steeper gradients.

Class-wise analysis reveals distinct behaviors. Clean glaciers remained stable (-0.5 to 1.5 m/year), while lake-associated glaciers experienced significant velocity surges, peaking at 19.4 m/year in 2021–2022 due to hydrological influences. Debris-covered glaciers recorded the highest mean velocity (0.66 m/year), whereas rock glaciers were the most stable (-0.08 m/year). High-velocity glaciers clustered near the Teesta basin, suggesting localized drivers like increased precipitation and glacial lake expansion. This study provides crucial insights into Himalayan glacier dynamics, informing hydrological modeling, disaster risk assessment, and water resource management. Future research should incorporate climate datasets and advanced modeling to predict glacier responses under varying climate scenarios, ensuring effective cryospheric monitoring amid rapid environmental change.