Spatiotemporal dynamics assessment of snow cover to infer snowline elevation mobility in the mountainous regions

Choubin, Bahram; Alamdarloo, Esmail Heydari; Mosavi, Amir; Hosseini, Farzaneh Sajedi; Ahmad, Sajjad; Goodarzi, Massoud; Shamshirband, Shahaboddin

doi:10.1016/j.coldregions.2019.102870

Cited by 15 publications

(5 citation statements)

References 68 publications

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“…3 c) is of primary significance to avalanches. Different aspects have different influences on accumulating and melting of the snow because of the meteorological parameters, such as temperature, precipitation, wind velocity, and sunshine hours 63 , 64 , 72 . Curvature : Convex areas produce an increase in the rate of total motion of the snow cover downhill (a combination of creep and slide).…”

Section: Methodsmentioning

confidence: 99%

Mass wasting susceptibility assessment of snow avalanches using machine learning models

Choubin

Borji

Hosseini

et al. 2020

Sci Rep

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Snow avalanche is among the most harmful natural hazards with major socioeconomic and environmental destruction in the cold and mountainous regions. The devastating propagation and accumulation of the snow avalanche debris and mass wasting of surface rocks and vegetation particles threaten human life, transportation networks, built environments, ecosystems, and water resources. Susceptibility assessment of snow avalanche hazardous areas is of utmost importance for mitigation and development of land-use policies. This research evaluates the performance of the well-known machine learning methods, i.e., generalized additive model (GAM), multivariate adaptive regression spline (MARS), boosted regression trees (BRT), and support vector machine (SVM), in modeling the mass wasting hazard induced by snow avalanches. The key features are identified by the recursive feature elimination (RFE) method and used for the model calibration. The results indicated a good performance of the modeling process (Accuracy > 0.88, Kappa > 0.76, Precision > 0.84, Recall > 0.86, and AUC > 0.89), which the SVM model highlighted superior performance than others. Sensitivity analysis demonstrated that the topographic position index (TPI) and distance to stream (DTS) were the most important variables which had more contribution in producing the susceptibility maps.

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Section: Methodsmentioning

confidence: 99%

Mass wasting susceptibility assessment of snow avalanches using machine learning models

Choubin

Borji

Hosseini

et al. 2020

Sci Rep

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“…The employment of remote sensing technology has emerged as a significant approach for conducting cryosphere research [26][27][28][29][30]. Using satellite remote sensing, it is possible to extract and assess the snowline in regions that are challenging to reach due to their rugged topography and severe weather conditions [31,32]. MODIS snow cover products are extensively utilized for monitoring seasonal (or transient) snowline altitude over a large area due to their high temporal resolution [5,16,[33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

“…The Google Earth Engine (GEE) is a cloud computing platform that facilitates access to powerful computing resources for analyzing large geospatial datasets [39,40]. The potential of GEE for snow mapping and monitoring of snowlines has been demonstrated in several studies [32,41,42]. Further research is necessary to develop an effective approach for estimating continuous spatial SLA-EMS using Landsat imagery over a long-term period.…”

Section: Introductionmentioning

confidence: 99%

Landsat Satellites Observed Dynamics of Snowline Altitude at the End of the Melting Season, Himalayas, 1991–2022

et al. 2023

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Studying the dynamics of snowline altitude at the end of the melting season (SLA-EMS) is beneficial in predicting future trends of glaciers and non-seasonal snow cover and in comprehending regional and global climate change. This study investigates the spatiotemporal variation characteristics of SLA-EMS in nine glacier areas of the Himalayas, utilizing Landsat images from 1991 to 2022. The potential correlations between SLA-EMS, alterations in temperature, and variations in precipitation across the Himalayas region glacier are also being analyzed. The results obtained are summarized below: (1) the Landsat-extracted SLA-EMS exhibits a strong agreement with the minimum snow coverage at the end of the melting season derived from Sentinel-2, achieving an overall accuracy (OA) of 92.6% and a kappa coefficient of 0.85. The SLA-EMS can be accurately obtained by using this model. (2) In the last 30 years, the SLA-EMS in the study areas showed an upward trend, with the rising rate ranging from 0.4 m·a−1 to 9.4 m·a−1. Among them, the SLA-EMS of Longbasaba rose fastest, and that of Namunani rose slowest. (3) The SLA-EMS in different regions of the Himalayas in a W-E direction have different sensitivity to precipitation and temperature. However, almost all of them show a positive correlation with temperature and a negative correlation with precipitation.

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“…It is noted that due to the scarcity of observation stations in most mountainous areas over the world, it is difficult to adequately quantify the spatiotemporal variability of snow cover in the mountainous regions purely by station data (Woo and Thorne, 2006; Pu et al, 2007). The snow products of the Moderate Resolution Imaging Spectroradiometer (MODIS) have been widely used to describe the temporal and spatial distribution of snow in mountain areas (Hall and Riggs, 2007; Liang et al, 2008; Choubin et al, 2019), showing that the altitude is an indispensable and important factor influencing the temporal and spatial distribution of snow phenology (Redpath et al, 2019). For example, Li et al (2018) analysed the temporal and spatial distribution and the trend characteristics of the snow cover fraction (SCF) in seven upstream river basins on the Qinghai‐Tibet Plateau, finding that the distribution of snow cover is highly dependent on elevations, with a higher SCF and a later onset of snow melt at the higher elevation zones than at the lowers.…”

Section: Introductionmentioning

confidence: 99%

Elevation‐dependent response of snow phenology to climate change from a remote sensing perspective: A case survey in the central Tianshan mountains from 2000 to 2019

Wang

Zhang

Xiao

et al. 2021

Intl Journal of Climatology

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Alpine snow is an important water resource in arid/semi‐arid regions and sensitive to climate change. However, the response of snow phenology to climate change in different elevations remains unclear in mountain areas because of limited observation stations. In this study, the vertical difference of snow phenology and its response to climate change in high mountains are explored by using multi‐source remote sensing data from 2000 to 2019, taking the north slope of Central Tianshan Mountains as the study area. The results show that: (1) The temporal changes of snow cover days (SCD), snow cover onset date (SCOD), and snow cover end date (SCED) in different altitudes are various and contribute to the general change trends of the extended SCD, the advanced SCOD, and the advanced SCED from the hydrological year 2000 to 2018; (2) The snow phenology is significantly related to the changed temperature and/or precipitation in most altitudes, except for the SCD and SCOD in high altitudes, where the large temporal changes of temperature and precipitation lead to the complicated correlations in these altitudes; (3) The altitude threshold of 3600 m is identified to separate the relative importance of temperature and precipitation for SCD and SCOD, where the temperature shows a higher importance than precipitation below the altitude threshold, and neither temperature nor precipitation shows constant higher importance above the altitude threshold. As for SCED, the temperature is consistently more important than precipitation in most altitudes.

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Spatiotemporal dynamics assessment of snow cover to infer snowline elevation mobility in the mountainous regions

Cited by 15 publications

References 68 publications

Mass wasting susceptibility assessment of snow avalanches using machine learning models

Mass wasting susceptibility assessment of snow avalanches using machine learning models

Landsat Satellites Observed Dynamics of Snowline Altitude at the End of the Melting Season, Himalayas, 1991–2022

Elevation‐dependent response of snow phenology to climate change from a remote sensing perspective: A case survey in the central Tianshan mountains from 2000 to 2019

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