Hydrometric data is important for water resources management and decision support. Hydrometric network provides direct informative data and therefore deserves careful consideration on its design and evaluation. Among most widely-used methods for hydrometric network design, information theory based methods have reached great attention and application. This study focuses on the hydrometric network (stream flow and precipitation) design. In addition, the estimation on basic measures from information theory have not been enough discussed. Herein, we compare binning estimation and copula entropy. It is found that while entropy measures exhibited similar results from different methods, the ranking results were different especially for stations in the middle of the ranking.