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Hydrological data, such as precipitation, is fundamental for planning, designing, developing, and managing water resource projects as well as for hydrologic research. An optimal raingauge network leads to more accurate estimates of mean or point precipitation at any site over the watershed. Some studies in the past have suggested increasing gauge network density for reducing the estimation error. However, more stations mean more cost of installation and monitoring. This study proposes an approach on the basis of kriging and entropy theory to determine an optimal network design in the city of Shanghai, China. Unlike the past studies using kriging interpolation and entropy theory for network design, the approach developed in the current study not only used the kriging method as an interpolator to determine rainfall data at ungauged locations but also incorporated the minimum kriging standard error (KSE) and maximum net information (NI) content. The approach would thus lead to an optimal network and would enable the reduction of kriging standard error of precipitation estimates throughout the watershed and achieve an optimum rainfall information. This study also proposed an NI-KSE-based criterion which is dependent on a single-objective optimization. To evaluate the final optimal gauge network, areal average rainfall was estimated and its accuracy was compared with that obtained with the existing rain gauge network.A kriging and entropy theory-based approach is developed for gauge network design.The method considers both minimum estimating error andoptimum rainfall information.The best combination is verified to be effectiveto achieve final goals.