Contents lists available at ScienceDirect Cold Regions Science and Technology journal homepage: www.elsevier.com/locate/coldregions Spatiotemporal dynamics assessment of snow cover to infer snowline elevation mobility in the mountainous regions Bahram Choubin a , Esmail Heydari Alamdarloo b , Amir Mosavi c,d , Farzaneh Sajedi Hosseini b , Sajjad Ahmad e , Massoud Goodarzi f , Shahaboddin Shamshirband g,h, ⁎ a West Azarbaijan Agricultural and Natural Resources Research and Education Center, AREEO, Urmia, Iran b Department of Reclamation of Arid and Mountainous Regions, Faculty of Natural Resources, University of Tehran, Karaj, Iran c School of the Built Environment, Oxford Brookes University, Oxford, UK d Institute of Automation, Kando Kalman Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary e Department of Civil and Environmental Engineering and Construction, University of Nevada, Las Vegas, USA f Soil Conservation and Watershed Management Research Institute (SCWMRI), AREEO, Tehran, Iran g Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Viet Nam h Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Viet Nam ARTICLE INFO Keywords: MODIS Normalized difference snow index Snow cover Snowline elevation ABSTRACT Due to the complex physics of both snow and snowmelt, particularly in mountainous topographic regions, studying the dynamics and variations of snow cover (SC) has been a very challenging task, and therefore, its relationship with snowline elevation (SLE) mobility has not been well documented. The spatiotemporal dy- namics of SC in the Haraz Watershed, where streamflow is snowmelt-dominated, is of great importance, par- ticularly for monitoring ecosystem processes, irrigation practices, and water management in the region. In the current study, due to the lack of a ground-based station, the remotely sensed eight-day Moderate Resolution Imaging Spectroradiometer (MODIS) images were considered in order to assess the dynamics of SC through investigating the monthly-normalized difference snow index (NDSI) during 2001 to 2018. Additionally, the SLE mobility was inferred through representing and assessing three indices related to SC, including variability in the number of snowy pixels and variations of the minimum and mean elevation in snowy pixels over time. According to the results, generally, 99.49% of the study regions showed NDSI declines, and 56.85% of these pixels showed significant trends. Variations of SC frequency showed that 32% of the study area has a moderate to very high snow existence probability. The trend of minimum and average elevation in snowy pixels indicates that January and December had significant increases, meaning that SLE increases during that time and moves towards higher elevations. The rate of changes in the average elevation of snowy pixels indicated an increasing rate of SLE in the months of January, February, March, August, and December, respectively equal to 8.18, 0.69, 2.51, 22.59, and 5.82 m per year. Further, results indicated that the percentage of the significant decrease in SC is highest on the slope aspects of southeast, south, and southwest (respectively equal to 66.81%, 62.35%, and 62.35%), meaning that SLE increases faster on these slope aspects. 1. Introduction The variations of snow cover (SC) profoundly affect the chemical, biological, and geological processes of the earth (Wu and Kirtman, 2007). Furthermore, the accumulating and melting seasonal snow in mountainous regions are considered as the important components in the hydrological cycle (Bavay et al., 2009; Freudiger et al., 2017; Huss et al., 2017). The investigation of variations of SC is essential for the assessments of the avalanche, streamflow, flood, drought, water supply, hydropower generation, irrigation, weather, etc. (Robinson et al., 1993; Dahe et al., 2006; Choubin et al., 2019a). Furthermore, the variations of SC can be used for regional climate change modeling (Dahe et al., 2006), and snowmelt modeling (Fontaine et al., 2002). Spatiotemporal variations of SC via in-situ measurements have been studied around the world (Huang et al., 2016; Pedersen et al., 2016; Zhong et al., 2018; Xuejin et al., 2019). Different snow models have been developed based on such ground-measured data in order to study and predict SC variations, as essential indicators for various https://doi.org/10.1016/j.coldregions.2019.102870 Received 29 May 2019; Received in revised form 2 August 2019; Accepted 21 August 2019 ⁎ Corresponding author at: Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Viet Nam. E-mail address: shahaboddin.shamshirband@tdtu.edu.vn (S. Shamshirband). Cold Regions Science and Technology 167 (2019) 102870 Available online 22 August 2019 0165-232X/ © 2019 Elsevier B.V. All rights reserved. T