Discussion

This study shows the effectiveness of streamflow analysis using the information and complexity theory to detect different discharge patterns that may describe some hydrological processes during low and high frequency scales as well as during flood assessment. Indeed, the selection of suitable metrics to quantify streamflow patterns at different temporal scales is of paramount importance. Interesting issues were emerged from this research and discussed below.

The role of word length and word number in characterizing streamflow patterns

The importance of word length and word number, to the best of our knowledge, was not highlighted sufficiently in the literature. Pachepsky et al., (2016), indicated that investigating the role of word length efficiency to improve the information and complexity metrics would open new avenue for further explorations. Thus, the important question here that we can ask ourselves is what is the recommended word length that should be selected to process the symbolic strings method professionally. In fact, there is no direct rule available to advise an ideal word length that generates the finest results in information and complexity theory. However, since this work investigates the classification and characterization of various streamflow patterns during different scales, we suggested two types of word length (two and four characters). In the case of low and high frequency scales, streamflow patterns are mainly affected by the intensity of various rainfall events, hence, it is vital to examine the influence of different intense events on the discharge data. In this regard, we considered the hydrograph separation method proposed by (Raghunath, 2006) depicted in Fig. 8 to separate the hydrograph into runoff flow and baseflow. As a result, in the case of the Gōno River watershed, the number of N days after peak for the streamflow to get rid of a rainfall inputs are 4 days approximately and hence, we used 4 characters as a word length. Thus, in the case of AL=24 hours, it means that each 24 hours of streamflow records (i.e. 1-day discharge data) were grouped together, and then averaged to form a character, and correspondingly the word pattern is formed from 4 days to describe the state of a system for each 4 successive days.