We describe a regional parameter estimation scheme for the unified land model developed using a set of 220 river basins (102–104 km2) distributed across the conterminous United States. We evaluate predictive relationships between geographically varying catchment features and the model's soil parameters using principal components analysis. In addition to commonly used catchment descriptors (meteorological, geomorphic, and land-cover characteristics), we used satellite remote-sensing products and the United States Geologic Survey Geospatial Attributes of Gages for Evaluating Streamflow (GAGES-II) database. In a series of regionalization experiments, we contrast the more conventional procedure of using locally optimized parameters as predictands, with an approach that searches for zonally representative parameter values, using limited additional simulations. Parameters were evaluated through hydrologic model simulations in which daily flows were compared with observations over a 20 year period. We show that the penalty in streamflow prediction skill for using zonal parameters at a given basin (i.e., locally) is comparatively smaller than the penalty for using local parameters zonally. Regionalizations using zonal parameters and local catchment descriptors had the best model performance for both training and validation periods. Finally, we investigate the potential for transferring parameters globally by repeating the regionalization using only catchment attributes derived from globally available data and show that for the United States, model performance is only slightly worse than when U.S.-specific data area used.