عنوان مقاله [English]
This study compared three different snowmelt scenarios using a monthly water balance model in the Taleghan-Alamut Basin in north of Iran. Three scenarios were tested in this study: a temperature-based, a net radiation-based, and an energy balance-based. Remote sensing data were utilized to mitigate the challenges of modeling snowmelt in a basin with limited ground information. The calibration and validation processes were carried out in a two-stage method. First, snow modeling was conducted grid-based throughout the basin, and the model parameters were validated. Using snow cover observed by the MODIS sensor, the model dispcipancy between computed and observed snow accumulation was calculated by comparing the percentage of to the calculated snow storage in each cell of the basin. In the second stage, the other model parameters were calibrated as a lumpt hydrologic model across the basin. Ultimately, the net radiation-based and energy balance-based models showed superior performance compared to the temperature-based model. During the validation period, the Kling-Gupta efficiency metric for the temperature-based snowmelt model was 0.72, while for the net radiation-based and energy balance-based models were 0.78 and 0.86, respectively. Additionally, the correlation coefficient between MODIS snow cover data and snow storage calculated in the three models ranged from 0.62 for the energy balance-based model to 0.72 for the temperature-based model. According to the results, the proposed methodology is suitable for assessing snow budget and the snow hydrology in mountainous areas with limited data availability.