Evaluation of snow cover and seasonal streams using remote sensing techniques: Lake Urmia

Snow is an important component of the water cycle. Measurement and monitoring of snow are crucial for understanding climate and hydrological cycles, as well as for the sustainable management of water resources. However, due to climate change, the amount of snowfall is decreasing, snowmelt is accelerating, and the effects on water flow are changing. Snow serves as a vital source for both water supply and ecosystems/agricultural activities. Snowfall data can be utilized in water resources management and the development of climate models. Snow measurements are essential for water resources management, climate model development, and understanding the water cycle. However, ground-based snow measurements may have limitations in terms of time and spatial coverage. Therefore, remote sensing methods such as satellite imagery are employed for monitoring snow cover and estimating snow water equivalent. These technologies enable a more comprehensive assessment of changes in snow cover and snow water equivalent. In this study, snow cover and snow water equivalent in the Urmia Lake Basin were examined, and their quantity and distribution were analyzed. Furthermore, the impact of these variables on seasonal flow rates and the water level of the lake was investigated. The Urmia Lake Basin, located in northwestern Iran, is assumed to have significant natural reservoirs of snow due to its mountainous terrain. Considering the risk of Urmia Lake drying up, monitoring the amount of snow is important for water resources management. Remote sensing data and ground-based measurements were used to analyze snow cover and snow water equivalent in the study. The results demonstrate the contribution of snowfall to surface runoff and the water level of the lake. In the study, data from AMSR-E, GLDAS, and ERA5-land SWE products were compared for the period between 2007 and 2013. Statistical measures such as RMSE, R, and R 2 were used to reflect the data quality of each SWE product. These measurements indicated that ERA5-land SWE product had higher overall accuracy compared to the other SWE products. These results suggest that this product performs better in SWE estimation and can be preferred for SWE predictions. Subsequently, the relationship between ERA5 SWE and other hydroclimatic variables was examined. Based on the outputs of a multiple linear regression model, the SWE variable was identified as the parameter with the highest standardized absolute β value, indicating its greatest influence on the amount of outflow to the lake. In this model, the most important variables can be stated as SWE, Agricultural water use, and Rainfall.