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<p>Snowfall and snow cover area (SCA) are critical for maintaining glaciers' health and regulating river discharge in the Himalayas. This study analyzed seasonal SCA dynamics in the Pindari and Kafni glacier valleys (Kumaon Himalaya), combining field and remote sensing observations acquired from Landsat satellite imageries during the accumulation period (November – December and January-April) for the last two decades, 2008–2009, 2015–2016, and 2021–2022. We employed the Normalized Difference Snow Index (NDSI) and the recently developed Snow Water Index (SWI) to delineate and compare SCA across these adjacent basins. Results were validated and incorporated with field-based observations and high-resolution Google Earth imagery. The overall accuracy of NDSI and SWI was 70% and 72%, respectively. SWI may offer a superior approach to NDSI in effectively handling cloudy images for water and snow cover analysis. Specifically, year-wise SCA exhibited an increasing trend: 1.3 times higher was quantified in the year 2021–22 and 1.2 times in 2015–16 as compared to the SCA estimation in 2008–09. Both NDSI and SWI analyses revealed minimum SCA in December and maximum SCA in April, with a remarkable exception of 2021–22, which showed minimum SCA (14.49%) in April and maximum SCA (20.52%) in January. These findings underscore an increasing sensitivity of SCA to climate warming in recent years, leading to rapid snow melting. In comparison to our results with other neighboring regions of the Indian and Nepal Himalaya, our results indicate an overall increase in SCA and snow mass trend, while the snowpack is melting rapidly, due to substantial heterogeneity, atmospheric dynamics, and Rain-on-snow (ROS). Our results also suggest a subtle increasing trend in SCA; a highly possible shift in water phenology may not compensate for the increasing water demand, particularly during the lean melt season, which deserves further investigation to continue long-term <i>in-situ</i> monitoring of SCA estimation for the accurate and more reliable results. The outcome of the present research work provides an extensive understanding of the present state of SCA estimation in the central Himalayan region. The study grasps the significant value for the fields of glaciology, hydrology, climatology, and cryospheric science.</p>

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Snow cover analysis using NDSI and SWI indices in Pindari-Kafni Glacier valleys, Kumaon Himalaya

  • 期刊论文

  • 发表日期：

    2025年11月18日

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  • Pankaj Chauhan,
  • Ram L. Ray,
  • Supriti Samanta,
  • Dharmaveer Singh,
  • Rajib Shaw,
  • Nirmal Kumar

摘要

Snowfall and snow cover area (SCA) are critical for maintaining glaciers' health and regulating river discharge in the Himalayas. This study analyzed seasonal SCA dynamics in the Pindari and Kafni glacier valleys (Kumaon Himalaya), combining field and remote sensing observations acquired from Landsat satellite imageries during the accumulation period (November – December and January-April) for the last two decades, 2008–2009, 2015–2016, and 2021–2022. We employed the Normalized Difference Snow Index (NDSI) and the recently developed Snow Water Index (SWI) to delineate and compare SCA across these adjacent basins. Results were validated and incorporated with field-based observations and high-resolution Google Earth imagery. The overall accuracy of NDSI and SWI was 70% and 72%, respectively. SWI may offer a superior approach to NDSI in effectively handling cloudy images for water and snow cover analysis. Specifically, year-wise SCA exhibited an increasing trend: 1.3 times higher was quantified in the year 2021–22 and 1.2 times in 2015–16 as compared to the SCA estimation in 2008–09. Both NDSI and SWI analyses revealed minimum SCA in December and maximum SCA in April, with a remarkable exception of 2021–22, which showed minimum SCA (14.49%) in April and maximum SCA (20.52%) in January. These findings underscore an increasing sensitivity of SCA to climate warming in recent years, leading to rapid snow melting. In comparison to our results with other neighboring regions of the Indian and Nepal Himalaya, our results indicate an overall increase in SCA and snow mass trend, while the snowpack is melting rapidly, due to substantial heterogeneity, atmospheric dynamics, and Rain-on-snow (ROS). Our results also suggest a subtle increasing trend in SCA; a highly possible shift in water phenology may not compensate for the increasing water demand, particularly during the lean melt season, which deserves further investigation to continue long-term in-situ monitoring of SCA estimation for the accurate and more reliable results. The outcome of the present research work provides an extensive understanding of the present state of SCA estimation in the central Himalayan region. The study grasps the significant value for the fields of glaciology, hydrology, climatology, and cryospheric science.