Improving Snowmelt Runoff Model (SRM) Performance Incorporating Remotely Sensed Data

Snow has a significant impact on the hydrological cycle, contributing to energy generation, meeting agricultural demands, and providing drinking water. Effective management of snowmelt runoff can help control and prevent potential risks. The purpose of the study is to evaluate the use of the remotely sensing data to improve the estimation accuracy of the snowmelt-runoff by using the Snowmelt-Runoff Model (SRM). To do this, a total of 1595 Tropical Rainfall Measuring Mission (TRMM) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images were prepared between 2014 and 2015 to acquire data on precipitation, minimum and maximum temperatures and Snow Cover Area (SCA). The accuracy of precipitation data was evaluated using Root Mean Squared Error (RMSE) and Root Mean Squared Log-Error (RMSLE) to ensure their reliability. Additionally, a sensitivity analysis of the SRM model’s coefficients, particularly for recession coefficient (K) and snow runoff (C_s), was conducted to understand their impact on the model’s performance. In this study, meteorological station data and satellite data from the years 2014 and 2015 were utilized for the validation and calibration stages, respectively. The model’s ability to estimate snowmelt runoff using remote sensing data was evaluated using both on-site stations and satellite data. In the calibration period, the snowmelt runoff estimation results were obtained with Nash-Sutcliffe Efficiency (NSE) index values of 0.72 and 0.70 for on-site stations and satellite data, respectively. In the validation period, the NSE index values were 0.60 and 0.93 for on-site stations and satellite data, respectively indicating improved performance when using satellite data to estimate the snowmelt runoff. The study’s findings show that remote sensing data enhances the performance of the SRM model for estimating the snowmelt-runoff.