Developing an Algorithm for Evaluating the Impact of Climate Change on Snow Cover Area Using Remote Sensing Data

Climate change and its intensifying impact on the hydrological cycle have been acknowledged in recent years. Considering the importance of available snow to the water resources recharge, especially in semiarid regions, evaluating climate change impacts on snow cover area (SCA) has gained significant attention. In this study, a methodology is presented which is capable of evaluating the impact of climate change on SCA. To collect historical data, MODIS snow products are used. To evaluate the impact of climate change on SCA, the turning points of SCA time series are detected using the Standard Normal Homogeneity Test; and the trend of historical data is analyzed using Mann-Kendall and Sen’s slope tests. To project the future SCA (2021-2099) under climate change considering precipitation and temperature as inputs an Artificial Neural Network-based model is developed. The proposed methodology is tested in sub-basins in Iran. Analyzing the present SCA (2000-2020) the results showed that the SCA’s turning points are only detected in two sub-basins of Tashk-Bakhtegan-Maharloo in 2007 and Gavkhooni in 2008, respectively. Moreover, a significant decreasing trend of SCA is detected during winter in the majority of sub-basins. According to the results, most sub-basins will experience a significant reduction in their future SCA under all climate change scenarios. In other words, compared to the historical SCA, the average future SCA will increase by 100 percent in RCP 4.5 scenario while decreasing by 20 percent in RCP 8.5 scenario.