Water resources management has been a critical component of sustainable resources planning. One of the most commonly used data in water resources management is streamflow measurements. Daily streamflow time series collected at a stream gage provide information on the temporal variation in water quantity where the gage is located. However, streamflow information is often needed at ungaged catchments especially when the stream gage network is not dense. One conventional approach to estimate streamflow at an ungaged catchment is to transfer streamflow measurements from the spatially closest stream gage, commonly referred to as the donor or reference gage using the drainage-area ratio method. Recently, the correlation between daily streamflow time series is proposed as an alternative to distance for reference stream gage selection. The Map Correlation Method (MCM) enables development of a map that demonstrates the spatial distribution of correlation coefficients between daily streamflow time series at a selected stream gage and all other locations within a selected study area. Although utility of the map correlation method has been demonstrated in various studies, due to its geostatistical analysis procedure it is time-consuming and hard to implement for practical purposes such as installed capacity selection of run-of-river hydropower plants during their feasibility studies. In this study, an easy-to-use GIS-based tool, called MCM_GIS is developed to apply the MCM in estimating daily time series of streamflow. MCM_GIS provides a user-friendly working environment and flexibility in choosing between two types of interpolation models, kriging and inverse distance weighting. The main motivation of this study is to increase practical application of the MCM by integrating it to the GIS environment. MCM_GIS can also carry out the leave-one-out cross-validation scheme to monitor the overall performance of the estimation. The tool is demonstrated on a case study carried out in Western Black Sea Region, Turkey. ESRI's ArcGIS for Desktop product along with a Python script is utilized. The outcomes of inverse distance weighting and ordinary kriging are compared. Results of GIS-based MCM are in good agreement with the observed hydrographs.