Streamflow Patterns in a Mountain River at Low and High Frequency Scales
and Assessment of Flood events Using Information and Complexity Theory
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.