Spatial-diurnal variability of snow/glacier melt runoff in glacier regime river valley: Central Himalaya, India

Ahluwalia, Rajeev Saran; Rai, Sunny; Meetei, Prikash N.; Kumar, S. V.; Sarangi, Sanat; Chauhan, Pankaj; Karakoti, Indira

doi:10.1016/j.quaint.2021.01.003

Cited by 9 publications

(4 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Snow cover, a critical component of the cryosphere and hydrological cycle, is important for providing valuable surface water resources, both globally and regionally [1][2][3][4]. Environmental factors, including elevation, temperature, radiation, humidity, wind speed, and others, have variable impacts on snow melting across diverse spatiotemporal scales [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Investigating the Spatial, Proximity, and Multiscale Effects of Influencing Factors in the Snowmelt Process in the Manas River Basin Using a Novel Zonal Spatial Panel Model

Li,

Liu,

Xiao

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

It is essential to investigate the influences of environmental elements on snow cover to understand the mechanism of the snowmelt process. These elements, as influencing factors, have spatial heterogeneity, which results in significant differences and uncertainties in the extent and range of their effects at different scales. However, little research has been conducted on the spatial interaction and mechanisms of these factors at multiple scales. This study selected the Manas River basin in the Tianshan Mountains as the study area. The study period is 2015–2020. The snow cover status index is calculated based on available Landsat8-OLS/TIRS data; influencing factors are collected from multiple datasets. Their relationships are explored using a novel zonal spatial panel model, fully considering the spatial, proximity, and scale effects. The findings are as follows: (1) There is a robust spatial interaction and proximity effect between snowmelt and various factors, and such effects display distinct spatial heterogeneity. The elevation (ELE), slope (SLP), land surface temperature (LST), and normalized digital vegetation index (NDVI) showed significant overall dominant effects on the snow melting process. The influencing factors with apparent proximity effects are LST, ELE, SLP, NDVI, and air temperature (TEMP), and their influence ranges are different. (2) The relative importance and significance rank of dominant influencing factors vary under different partition schemes and scales. As the scale decreases, the significance of terrain- and vegetation-related factors increases, whereas the significance of temperature- and elevation-related factors decreases, and the number of dominant factors also decreases. (3) The influencing factors represent distinct characteristics among each zone at the optimally partitioned scale we defined. The overall influencing pattern demonstrates a characteristic of being globally dictated by elevation and temperature, with local terrain factors, vegetation, and wind speed modifying this pattern. Our study provides practical data support and a theoretical basis for deepening our understanding of the influence mechanism of the snow melting process.

show abstract

Section: Introductionmentioning

confidence: 99%

Investigating the Spatial, Proximity, and Multiscale Effects of Influencing Factors in the Snowmelt Process in the Manas River Basin Using a Novel Zonal Spatial Panel Model

Li,

Liu,

Xiao

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The latter reflects the response of glaciers to climate change and controls changes in the runoff and glacier [33]. Currently, various methods and techniques, including ground observations, remote-sensing technology, and model predictions, are widely employed for monitoring and analyzing GMB and glacier runoff [34]. These methods provide valuable data on the spatiotemporal variations of glacier runoff, enabling a better understanding of the relationship between them.…”

Section: Introductionmentioning

confidence: 99%

The Influence of Glacier Mass Balance on River Runoff in the Typical Alpine Basin

Yang,

Du,

et al. 2023

Water

View full text Add to dashboard Cite

Quantifying the effects of alpine GMB (Glacier Mass Balance) on river runoff is an important content of climate change. Uncertainty exists in GMB monitoring when applying remote-sensing technology. There are several reasons for these uncertainties, such as terrain deviation co-registration among different topographic data, the mismatch between GSE (Glacier Surface Elevation) from satellite monitoring and the GMB that comprises the physical glacier properties, the driving factors of GMB, and the response patterns of the runoff within the basin. This paper proposed a method based on the ridge line co-registration of DEMs (Digital Elevation Models), and the Tailan River basin, which is a typical glacier melt runoff recharge basin located in the southern Tianshan Mountains, was selected. Abnormal values in GSE changes were removed using ice thickness data, and the GSE results were optimized based on the regularity of the GSE change with altitude to estimate the GMB. The driving factors of the GMB and the response characteristics of the runoff in the basin were also explored. The results showed that the accuracy of the optimized GSE results across different periods has improved by more than 25%. The mean annual thinning value of GSE in the basin from 2000 to 2022 was −0.25 ± 0.02 m·a−1, corresponding to a GMB value of −0.30 ± 0.02 m w.e.a−1, indicating a consistent GMB loss state. Combined with climate data, the glaciers in the basin were impacted by rising temperatures, and the smallest increase in annual precipitation in the basin was insufficient to compensate for the GMB loss. Moreover, in the past 22 years, glacier meltwater accounts for 46.15% of the total runoff in the Tailan River basin.

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

Spatial-diurnal variability of snow/glacier melt runoff in glacier regime river valley: Central Himalaya, India

Cited by 9 publications

References 39 publications

Investigating the Spatial, Proximity, and Multiscale Effects of Influencing Factors in the Snowmelt Process in the Manas River Basin Using a Novel Zonal Spatial Panel Model

Investigating the Spatial, Proximity, and Multiscale Effects of Influencing Factors in the Snowmelt Process in the Manas River Basin Using a Novel Zonal Spatial Panel Model

The Influence of Glacier Mass Balance on River Runoff in the Typical Alpine Basin

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

Contact Info

Product

Resources

About