Snow parameters play a very important role in hydrological cycle, local weather pattern, and climate change, eventually influencing total runoff, river flows, and water availability through the amount and timing of snow melt. In this paper, the spatiotemporal variations of snow parameters in Dibang basin, Arunachal Pradesh, India, for a period of 10 years (2006–2015) were analyzed using Spatially Distributed Snowmelt Runoff Model (SDSRM) coupled with remote sensing and GIS techniques. SDSRM was selected due of its capability to simulate snow parameters in scarcely/un-gauged basin where observed data are not available. Moderate Resolution Imaging Spectroradiometer (MODIS) albedo at 500 m resolution from Terra satellite was used to obtain daily snow albedo for the study area. The analysis of 10 years showed that maximum average snow density occurred in the month of June and minimum in the month of December; maximum average snow depth in January and minimum in July; maximum average snow water equivalent (SWE) in January and minimum in July and August; maximum average degree-day factor (DDF) in June and minimum in December; maximum average snowmelt depth in July and minimum in the month of January. Considering the yearly maximum and minimum average, snow density ranged from 404.84 to 522.84 kg/m3 with an average of 461.76 kg/m3; snow depth ranged from 0.01 to 0.68 m with an average of 0.24 m; SWE ranged from 0.01 to 0.29 m with an average of 0.11 m; DDF ranged from 0.45 to 0.58 cm/ °C-day with the average of 0.51 cm/ °C-day; and snowmelt depth ranged from 0.02 to 0.11 m/day with the average of 0.06 m/day. From the yearly temporal analysis, only snow density showed a slight decreasing trend, while the other snow parameters like snow depth, SWE, DDF, and snowmelt depth had a slight increasing trend. Based on yearly temporal analysis of each month, March and November showed an increasing trend for all the snow parameters out of which snow density and DDF showed a significant increasing trend in the month of March, while a slight decreasing trend was observed in the month of August, September, and October. The modeled SWE was compared with the observed SWE, and it was found that they were in good agreement with each other. The other snow parameters were also found to be within comparable ranges reported by other studies. Thus, this study demonstrated the capability of SDSRM to simulate snow parameters over an alpine watershed in Eastern Himalaya and can be recommended for use in un-gauged/scarcely gauged basins over the Himalayan region.

中文翻译：

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使用空间分布融雪径流模型 (SDSRM) 的 MODIS 数据雪参数的时空变化：阿鲁纳恰尔邦迪邦盆地的案例研究

雪参数在水文循环、当地天气模式和气候变化中起着非常重要的作用，最终通过雪融化的数量和时间影响总径流、河流流量和可用水量。在本文中，使用空间分布融雪径流模型（SDSRM）结合遥感和 GIS 技术分析了印度阿鲁纳恰尔邦迪邦盆地雪参数的时空变化，为期 10 年（2006-2015）。选择 SDSRM 是因为它能够在没有观测数据的几乎/未测量的盆地中模拟雪参数。使用来自 Terra 卫星的 500 m 分辨率的中分辨率成像光谱仪 (MODIS) 反照率来获得研究区的每日雪反照率。10年的分析表明，平均积雪密度最大出现在6月份，最小出现在12月份；1 月平均积雪深度最大，7 月最小；1 月平均雪水当量 (SWE) 最大，7 月和 8 月最小；6 月平均度日因子 (DDF) 最大，12 月最小；7 月平均融雪深度最大，1 月最小。考虑到年最大和最小平均值，积雪密度在 404.84 至 522.84 kg/m3 之间，平均为 461.76 kg/m3；积雪深度0.01～0.68 m，平均0.24 m；SWE 范围为 0.01 至 0.29 m，平均为 0.11 m；DDF 范围为 0.45 至 0.58 cm/°C-day，平均为 0.51 cm/°C-day；融雪深度为0.02～0.11 m/day，平均为0.06 m/day。从逐年时间分析来看，只有积雪密度呈小幅下降趋势，而其他积雪参数如积雪深度、SWE、DDF和融雪深度均呈小幅上升趋势。从各月的逐年时间分析来看，3月和11月各降雪参数均呈上升趋势，其中3月降雪密度和DDF呈显着上升趋势，3月呈小幅下降趋势。八月、九月和十月。将建模的 SWE 与观察到的 SWE 进行比较，发现它们相互吻合。其他雪参数也被发现在其他研究报告的可比范围内。因此，