The optimization and extension of existing gauging networks is a challenging task, which can be done under consideration of many different aspects. One possibility is to maximize the obtained information on regional hydrological characteristics by new gauges compared to existing ones. For this, information theory approaches are most suitable. Here, the principle of maximum entropy is applied to calculate the probability of non-similarity of catchments to determine locations of new gauges according to the catchment characteristics that are most relevant for the hydrological conditions. The realization in an interactive application, provided online, makes use easy for practitioners and scientists. Goodness-of-fit measures are applied to investigate the explanatory power of the model and the contribution of each characteristic to the model, which gives information on the most influential properties of the catchment The relevance of the proposed approach is proven by comparing hydrological signatures between similar and non-similar catchment.

现有测量网络的优化和扩展是一项艰巨的任务，可以在考虑许多不同方面的情况下完成。一种可能性是与现有的新尺度相比，通过新的尺度最大化所获得的区域水文特征信息。为此，信息论方法是最合适的。在此，最大熵原理用于计算集水区非相似性的概率，从而根据与水文条件最相关的集水区特征确定新水位的位置。在线提供的交互式应用程序中的实现使从业人员和科学家易于使用。拟合优度度量用于调查模型的解释力以及每个特征对模型的贡献，