Spatio-temporal analysis of snow cover and effect of terrain attributes in the Upper Ganga River Basin, central Himalaya

Meetei, Prikash N.; Ahluwalia, Rajeev Saran; Rai, Sunny; Khobragade, S. D.; Sarangi, Sanat; Goel, Manmohan Kumar; Kumar, S. V.

doi:10.1080/10106049.2020.1762764

Cited by 7 publications

(3 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CRU data was developed by the climate research unit of the University of East Anglia at the NERC collaborative center using the angular distance weighting method at 0.5 o resolution. In this study, we acquired the CRU data [(https://crudata.uea.ac.uk/cru/data/hrg/ 2022)] between 1991 and 2021 Harris et al, 2021). The climatic data were used to estimate the influence of changing climate on SCA.…”

Section: Climate Research Unit Data (Rainfall and Temperature)mentioning

confidence: 99%

Trend of snow cover under the influence of climate change using Google Earth Engine platform: A case study of Astore (Western Himalayas) and Shigar (Karakoram region)

Moazzam

Rahman²,

Lee³

et al. 2022

Front. Environ. Sci.

View full text Add to dashboard Cite

Freshwater resources present in the Upper Indus Basin (UIB) supply water to Pakistan’s irrigation. Half of the annual water discharge in the UIB is contributed from the glacier and snow-fed basins in the Hindu Kush, Karakoram, and Himalaya (HKH) region, and it is currently under the threat of climate change. Therefore, it is very necessary to study and monitor the spatiotemporal changes in the snow cover area (SCA) and its response to climate variables to efficiently manage water reservoirs. Thus, keeping this problem in mind, we conducted this study using the Google Earth Engine Application Programming Interface (GEE API) for Astore and Shigar. We used mean annual Landsat data between 1991 and 2021 to derive the SCA using the normalized difference snow index (NDSI). ASTER GDEM data was used to extract the elevation and analyze the distribution of SCA on different elevation zones. Eventually, we used the climate research unit (CRU) data (rainfall and temperature) to analyze them with SCA. The results revealed that SCA in Astore and Shigar has an increasing trend with a rate of 11.16 km2/year and 4.27 km2/year, respectively. Mean annual precipitation and temperature also confirmed the increasing trend of SCA because mean annual precipitation is increasing and temperature is decreasing in both regions. SCA and elevation analysis revealed that SCA is decreasing on foothills while increasing at the valley top. This is because temperature is increasing and precipitation is decreasing from zone 1 to zone 3 and vice versa for zone 4. It is found that Astore and Shigar in UIB have an increasing trend of SCA and are not affected by global warming. Therefore, it is necessary to conduct studies on large scale to efficiently evaluate the impact of climate change on SCA.

show abstract

Section: Climate Research Unit Data (Rainfall and Temperature)mentioning

confidence: 99%

Trend of snow cover under the influence of climate change using Google Earth Engine platform: A case study of Astore (Western Himalayas) and Shigar (Karakoram region)

Moazzam

Rahman²,

Lee³

et al. 2022

Front. Environ. Sci.

View full text Add to dashboard Cite

show abstract

“…Most of the previous studies 51 – 54 analyzed the variation of snow parameters such as snow cover, snow depth and snow water equivalent on the factors of elevation, slope and aspect. Comprehensive evaluation of the influence of geographical factors on snow cover is of great significance for determining snow cover activities 47 .…”

Section: Discussionmentioning

confidence: 99%

Study of snow cover/depth evolution characteristics in Tianshan region of China based on geographical partition

Wang

Xie

Zhang

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Based on the digital elevation data, snow depth and snow cover remote sensing data, this paper divides six snow evolution areas and geographical partitions, extracts the geographical partitions of each evolution area and obtains the geographical characteristics of the evolution area for analysis. The results show that: (1) From 2003 to 2017, the average snow area decreased at a rate of − 0.004, and the average snow depth increased at a rate of 0.03. (2) The snow in the middle altitude hill with shady gentle slope area is the most obvious in the seasonal evolution, and the percentage of this region in the seasonal snow evolution area is 5.46%, the snow depth in the middle altitude hill with sunny and gentle slopes area increased and decreased significantly in the past 15 years, and the percentage of this region in the SD significant changes evolution area was 6.32%. The snow in the low relief middle altitude mountain with shady and moderate slope area not only shows obvious seasonal evolution, but also increases and decreases significantly in snow depth. And the percentage of this region in the seasonal snow significant evolution area is 5.82%. (3) The geographical partitions with the largest area in all evolution areas is the middle altitude hill with sunny and gentle slopes area (4.75%). (4) The geographical partition with the largest variation of snow depth in Tianshan region is the low relief middle altitude mountain with shady and moderate slope area (12.02 cm). (5) The snow accumulation and melting are obvious in the range of 1000–3500 m above altitude, different geomorphology types lead to obvious differences in snow characteristics. The snow melting is most obvious in the gentle slope area of the low topographic relief geomorphology types, and the snow accumulation is most obvious in the steep slope area of the middle relief geomorphology types.

show abstract

“…<insert Figure 3> Based on the climatic conditions of the study area, there are four seasons, namely pre-monsoon, monsoon, post-monsoon and winter. Considering snow dynamics, a year can be classified into snow ablation/melt period and snow accumulation period (Meetei et al, 2020). The time period from April to September with higher air temperature is usually considered as the ablation/melt period and the time period from October to the following March with snowfall events and lower air temperatures is considered as the accumulation period in the Himalayan region, resulting in a SCA peak around March (Jain et al, 2009;Tiwari et al, 2015;Bothale et al, 2015;Ahluwalia et al, 2016).…”

Section: Changes In Snow Cover Area In Ugrb and Its Sub-basinsmentioning

confidence: 99%

Assessing the snow cover dynamics and its relationship with different hydro-climatic characteristics in Upper Ganges river basin and its sub-basins

Thapa

Zhang

et al. 2021

Science of The Total Environment

View full text Add to dashboard Cite

Spatio-temporal analysis of snow cover and effect of terrain attributes in the Upper Ganga River Basin, central Himalaya

Cited by 7 publications

References 49 publications

Trend of snow cover under the influence of climate change using Google Earth Engine platform: A case study of Astore (Western Himalayas) and Shigar (Karakoram region)

Trend of snow cover under the influence of climate change using Google Earth Engine platform: A case study of Astore (Western Himalayas) and Shigar (Karakoram region)

Study of snow cover/depth evolution characteristics in Tianshan region of China based on geographical partition

Assessing the snow cover dynamics and its relationship with different hydro-climatic characteristics in Upper Ganges river basin and its sub-basins

Contact Info

Product

Resources

About