Snow recharge is an important dominant hydrological process in the high altitude mountainous karstic aquifer systems. In general, widely used karst-dedicated hydrological models (e.g., KarstMod, Varkarst) do not include a snow routine in the model structure to avoid increasing the number of model parameters while representing the complex hydrological process. As a result, recharge process is not represented well, which questions the optimality of the results that can be obtained under available datasets. This study presents a novel pre-processing method –called SCA routine– to compensate for the missing snow routine in karst models. The proposed pre-processing method is driven by temperature, precipitation, and satellite-based snow observation datasets. The method classifies the precipitation input into three physical phases (rain, snow, and mixed) based on the temperature datasets to distribute each phase over the catchment using satellite-driven Snow-Covered Area (SCA) products. By the proposed method, the spring discharge simulations are regulated well in time and magnitude. To examine the added utility of the SCA routine, the SCA-included simulations are compared to the model performances with no routine and the classical Degree-Day method as a benchmark. To test the efficiency of our proposed method, we used a karst hydrological model (KarstMod) to simulate the karst spring discharge in a well-observed semi-arid snow-dominated karstic aquifer (Central Taurus, Turkey). Our results confirmed that the KarstMod model coupled by SCA routine ensures better model performance with a value of NSE = 0.784 than those of the classical Degree-day method (NSE = 0.760) and the model with no routine (NSE = 0.306), thus providing a physically more realistic parameter set.