A variety of computational techniques have been developed to efficiently and effectively draw realizations from the parameter space of watershed models for parameter estimation. However, to date, it has not been clearly understood how these techniques should be evaluated side by side. The main goal of this study is to develop and demonstrate a computational procedure for evaluating parameter sampling techniques. The analysis hinges on the evaluation of (1) efficiency in minimizing objective functions at the lowest required realizations; (2) effectiveness in drawing samples that adequately represent watershed characteristics based on automatic calibration results, and (3) effectiveness in enhancing the identifiability of the effective parameter space. The proposed procedure was applied to evaluate the performance of six commonly implemented sampling techniques for multisite, multiresponse parameter estimation of a river basin scale model in the Eagle Creek Watershed, Indiana, in the Midwestern United States. Results show that a particular technique surpassed all other methods in convergence speed and behavioral statistics. In addition, solutions derived using that technique were distributed closely in relatively small regions of the whole domain space, which enhanced the efficiency of the parameter search process.