Evaluation of information transfer and data transfer models of rain-gauge network design based on information entropy

Abstract

Rainfall is one of the most fundamental components of the water cycle and is one of the fundamental inputs of hydrological models. A well-designed network can not only depict the regional precipitation characteristics, but also economically yield maximum needed rainfall information. In regions where either there is limited data or data is not available, it is a common challenge to add stations. The entropy theory-based information transfer model and geostatistical interpolation techniques are two solutions to meet the challenge. In this study, we used a representative rain gauge network to do the network design. Two models, based on information transfer and data transfer, were compared for network design. Other rain gauges in the study area were used as reference (“true values”) for assessing the model. Results showed that the information transfer model estimated transinformation between station pairs better than did the data transfer model. Different representative gauges were evaluated separately by the directional information transfer index (DIT). The candidate gauges selected with least information redundancy were similar for both information transfer and data transfer models. Though both models captured some least information-redundant areas, other areas may be bypassed because of model errors or estimation errors.