Document Type : Research Article
Authors
1 Ms of Water Resources Engineering, Urmia University
2 Associate Professor of Water Engineering Department, Urmia University Urmia - IRAN.
3 Department of Natural Resources, Urmia University
4 PhD in Water Resources Engineering, East Azarbijan regional water company, Iran,
Abstract
Snow cover is a critical component of the hydrological cycle in broad mountainous regions, performing as a crucial reservoir for drinkable and agricultural water. Precisely evaluating the extent of snow cover within watersheds is a fundamental aspect of snow hydrology, significantly impacting water resource management and climate research. This assessment is crucial for forecasting water availability, comprehending climatic trends, and ensuring a sustainable water supply for these areas. In this study, we estimate the snow cover levels of the Urmia Lake basin in Iran using MODIS imagery. Initially, snow cover variations were examined through depth and the number of snow days at nine synoptic stations over a 20-year period (1999-2019). To extract snow cover and snow line levels from MOD10A1 and MOD02H satellite images on a monthly and daily basis, unsupervised algorithms, supervised classification, and the Normalised Snow Index (NDSI) were employed. The year 2016 was identified as a typical meteorological year concerning basin rainfall and temperature variations. The efficacy of the selected algorithms was assessed by applying them in the normal year of 2016. The results were analysed using the Kappa coefficient index and overall accuracy. The Kappa coefficients for the three detection algorithms ranged from 0.94 to 0.98. While the Maximum Likelihood method exhibited higher Kappa coefficients, no significant differences were noted among these classification methods. These maps facilitate basin hydrological modelling, runoff estimations, actual evapotranspiration, water balance, and snow.
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