Conclusions

Describing and characterizing the inputs and outputs discharge time series is of greatest importance. This paper utilizes the information and complexity theory due to its ability to deliver useful information about the hydrological processes that occur within a system. The hourly discharge 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. Furthermore, new extension for the information and complexity theory to be customized for flood assessment was proposed. Several interesting issues was revealed and learned from this research are summarized below.
Firstly, considering the analyses by means of the information and complexity theory, it is vital to define an appropriate word length professionally since word pattern play an important role in describing system states and the hidden regimes and structures. Secondly for low frequency analysis, by means of increasing aggregation lengths, it was detected the presence of two scaling regimes one for the short aggregation lengths and the other one for long ranges that may reflect the long memory characteristics of river flow fluctuations. Alternatively, in the case of high frequency analyses, it was confirmed that river fluctuations have extra sub daily (hourly) regime that was captured by streamflow data obtained by FAT. Moreover, the power spectral density analysis confirms our findings. In conclusion, this work reveals the proficiency of information and complexity metrics to be customized for streamflow analyses.