The selection of a powerful measure to characterize and describe the inputs and outputs of mountainous rivers is of prime importance. Information and complexity metrics have the ability to reveal invaluable information about the hydrological processes that occur within a system. In this work, the hourly streamflow records obtained from five gauging stations for a mountainous river were analyzed to quantify the different patterns and characterize system states at low and high frequencies using increasing aggregation lengths. In addition, we proposed a new extension for the information and complexity theory to be customized for flood assessment. Moreover, we clarified how a pattern (i.e. a word length) by means of information and complexity metrics can be suitably defined. Regarding the low frequency analyses, the information and complexity metrics showed that river discharge has two scaling regimes one of them may describe the river memory characteristics. Furthermore, for high frequency findings, an additional scaling regime that occurs within hourly scales captured by streamflow data obtained by a novel hydroacoustic system, which is one of the novel aspects of our work. Additionally, the power spectral density results match with our findings. This work reveals the performance of information and complexity metrics to be customized for analyzing streamflow patterns at different temporal scales.