Extraction of snow melting duration and its spatiotemporal variations in the Tibetan Plateau based on MODIS product

Jin, Haoyu; Chen, Xiaohong; Zhong, Rong; Wu, Pan; Ju, Qin; Zhang, Juan; Yao, Tingyue

doi:10.1016/j.asr.2022.04.018

Cited by 3 publications

(3 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Meanwhile, spatial variations in snow cover within the Tibetan Plateau remain important to consider. Jin et al [31] and Wang et al [29] accounted for spatial variations, but they did not delve into the specific basins within the Tibetan Plateau. Moreover, the influence of climate on snow cover was overlooked in studies of Pu et al [28], You et al [17], and Yang et al [43].…”

Section: Discussionmentioning

confidence: 99%

“…Li et al also found that the snow cover decreased by 1.1% between 2001 and 2014 on the Tibetan Plateau [30]. Jin et al discovered that high snow cover days were primarily concentrated in the Nyainqentanglha Mountains, Karakoram Mountains, and Himalayas regions from 2003 to 2014 using long time-series data of snow cover area on the Tibetan Plateau [31]. Although numerous studies have contributed to understanding the snow cover changes on the Tibetan Plateau, they have often overlooked variations in snow cover within the plateau's internal basins, thereby limiting a comprehensive understanding of snow changes across the entire Tibetan Plateau.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Spatiotemporal Variations in Snow Cover on the Tibetan Plateau from 2003 to 2020

Pu,

Zhou,

Sun

et al. 2024

Water

View full text Add to dashboard Cite

The variations in snow cover on the Tibetan Plateau play a pivotal role in comprehending climate change patterns and governing hydrological processes within the region. This study leverages daily snow cover data and the NASA Digital Elevation Model (DEM) from 2003 to 2020 to analyze spatiotemporal snow cover days and assess their responsiveness to climatic shifts by integrating meteorological data. The results reveal significant spatial heterogeneity in snow cover across the Plateau, with a slight decreasing trend in annual average snow cover duration. Snow cover is predominantly observed during the spring and winter seasons, constituting approximately 32% of the total snow cover days annually. The onset and cessation of snow cover occur within a range of 120–220 days. Additionally, an increasing trend in snow cover duration below 5000 m altitude was observed, in addition to a decreasing trend above 5000 m altitude. Sub-basin analysis delineates the Tarim River Basin as exhibiting the lengthiest average annual snow cover duration of 83 days, while the Yellow River Basin records the shortest duration of 31 days. The decreasing trend in snow cover duration closely aligns with climate warming trends, characterized by a warming rate of 0.17 ± 0.54 °C per decade, coupled with a concurrent increase in precipitation at a rate of 3.09 ± 3.81 mm per year. Temperature exerts a more pronounced influence on annual snow cover duration variation compared to precipitation, as evidenced by a strong negative correlation (CC = −0.67). This study significantly augments the comprehension of hydrological cycle dynamics on the Tibetan Plateau, furnishing essential insights for informed decision-making in water resource management and ecological conservation efforts.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Variations in Snow Cover on the Tibetan Plateau from 2003 to 2020

Pu,

Zhou,

Sun

et al. 2024

Water

View full text Add to dashboard Cite

show abstract

“…Snow cover is an important component of the cryosphere and has significant effects on regional water balance, local weather, atmospheric circulation, and surface hydrological processes [ 1 , 2 , 3 , 4 ]. Previous studies have shown that small changes in snow cover in mountain areas may have great thermal and dynamical influences on regional and even global circulation systems [ 5 , 6 , 7 , 8 ], which is an essential factor of energy balance.…”

Section: Introductionmentioning

confidence: 99%

An Improved Spatiotemporal Data Fusion Method for Snow-Covered Mountain Areas Using Snow Index and Elevation Information

Gao

Liu

et al. 2022

Sensors

View full text Add to dashboard Cite

Remote sensing images with high spatial and temporal resolution in snow-covered areas are important for forecasting avalanches and studying the local weather. However, it is difficult to obtain images with high spatial and temporal resolution by a single sensor due to the limitations of technology and atmospheric conditions. The enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) can fill in the time-series gap of remote sensing images, and it is widely used in spatiotemporal fusion. However, this method cannot accurately predict the change when there is a change in surface types. For example, a snow-covered surface will be revealed as the snow melts, or the surface will be covered with snow as snow falls. These sudden changes in surface type may not be predicted by this method. Thus, this study develops an improved spatiotemporal method ESTARFM (iESTARFM) for the snow-covered mountain areas in Nepal by introducing NDSI and DEM information to simulate the snow-covered change to improve the accuracy of selecting similar pixels. Firstly, the change in snow cover is simulated according to NDSI and DEM. Then, similar pixels are selected according to the change in snow cover. Finally, NDSI is added to calculate the weights to predict the pixels at the target time. Experimental results show that iESTARFM can reduce the bright abnormal patches in the land area compared to ESTARFM. For spectral accuracy, iESTARFM performs better than ESTARFM with the root mean square error (RMSE) being reduced by 0.017, the correlation coefficient (r) being increased by 0.013, and the Structural Similarity Index Measure (SSIM) being increased by 0.013. For spatial accuracy, iESTARFM can generate clearer textures, with Robert’s edge (Edge) being reduced by 0.026. These results indicate that iESTARFM can obtain higher prediction results and maintain more spatial details, which can be used to generate dense time series images for snow-covered mountain areas.

show abstract

Climate change-derived environmental and physical factors influencing the socioeconomic development in the Himalayan region

Thapliyal,

Kimothi,

Dumka

et al. 2024

Environmental Research

View full text Add to dashboard Cite

Extraction of snow melting duration and its spatiotemporal variations in the Tibetan Plateau based on MODIS product

Cited by 3 publications

References 38 publications

Spatiotemporal Variations in Snow Cover on the Tibetan Plateau from 2003 to 2020

Spatiotemporal Variations in Snow Cover on the Tibetan Plateau from 2003 to 2020

An Improved Spatiotemporal Data Fusion Method for Snow-Covered Mountain Areas Using Snow Index and Elevation Information

Climate change-derived environmental and physical factors influencing the socioeconomic development in the Himalayan region

Contact Info

Product

Resources

About