Magnetic resonance sounding (MRS) provides quantitative hydrogeological information on hydrostratigraphy and hydraulic parameters of subsurface (e.g. flow and storage property of aquifers) that can be integrated in distributed hydrologic models. The hydraulic parameters are typically obtained by pumping tests. In this study, we propose an MRS integration method based on optimizing MRS estimates of aquifer hydraulic parameters through hydrologic model calibration. The proposed MRS integration method was applied in the 73 km2 Carrizal Catchment in Spain, characterized by a shallow unconfined aquifer with an unknown aquifer bottom. 12 MRS survey results were inverted with Samovar 11.3, schematized and integrated in the transient, distributed, coupled, hydrologic, MARMITES-MODFLOW model. As the aquifer bottom was unknown, the aquifer was schematized into one unconfined layer of uniform thickness. For that layer, MRS estimators of specific yield and transmissivity/hydraulic conductivity were calculated as weighted averages of the inverted MRS layers. The MRS integration with hydrologic model was carried out by introducing multipliers of specific yield and transmissivity/hydraulic conductivity that were optimized during transient model calibration using 11 time-series piezometric observation points. The optimized multipliers were 1.0 for specific yield and 3.5*10-9 for hydraulic conductivity. These multipliers were used, and can be used in future MRS investigations in the Carrizal Catchment (and/or adjacent area with similar hydrogeological conditions), to convert MRS survey results into aquifer hydraulic parameters. The proposed method of MRS data integration in the hydrologic model of Carrizal Catchment not only allowed us to calibrate the model but also to confirm the functional capability of MRS in quantitative groundwater assessment. Most importantly however, it demonstrated that if pumping tests are not available, the use of MRS integrated in distributed coupled hydrological models, or even in standalone groundwater models, provides a valuable aquifer parameterization alternative.