Integration of Snowmelt Runoff Model (SRM) with GIS and Remote Sensing for Operational Forecasting in the Kırkgöze Watershed, Turkey

Accurate snowmelt runoff prediction is critical for water resource management in mountainous regions where seasonal snowpack constitutes the dominant water supply. This study demonstrates operational application of the degree-day based Snowmelt Runoff Model (SRM) integrated with Geographic Information Systems (GIS) and multi-platform remote sensing for discharge forecasting in the Kirkgoze Basin (242.7 km², 1823-3140 m elevation), Eastern Anatolia, Turkey. Three automatic weather stations spanning 872-m elevation gradient provided meteorological forcing, while MODIS MOD10A2 8-day composite products supplied operational snow cover observations validated against Landsat-5/7 (30-m resolution, 87.3% agreement, Kappa=0.73) and synthetic aperture radar imagery (RADARSAT-1 C-band, ALOS-PALSAR L-band). Uncalibrated model performance was modest (R²=0.384, volumetric difference=29.78%), demonstrating necessity of site-specific calibration. Systematic adjustment of snowmelt and rainfall runoff coefficients yielded excellent calibrated performance for 2009 melt season: R²=0.8606, correlation coefficient R=0.927, Nash-Sutcliffe Efficiency=0.854, volumetric difference=3.35%. Enhanced temperature lapse rate (0.75°C/100m vs. standard 0.65°C/100m) reflected severe continental climate. Multiple linear regression analysis identified temperature, snow-covered area, snow water equivalent, and calibrated runoff coefficients as significant discharge predictors (R²=0.881). Results confirm SRM's operational feasibility for seasonal forecasting and flood warning in data-scarce snow-dominated basins, with modest requirements (daily temperature, precipitation, satellite snow cover) aligning with operational monitoring capabilities. The methodology provides transferable framework for regional water resource management in climatically-vulnerable mountain environments where snowmelt supports agriculture, hydropower, and municipal supply.