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.