Figure 6 is here.
Fig. 6 Information-Complexity diagram for the streamflow data according to different aggregation length.
To give a conceptual understanding about the high frequency findings, metric entropy and fluctuation complexity according to different aggregation lengths are displayed in Fig. 7 (a, b). Since the investigated period is relatively short (i.e. the hourly data from 2016-01 to 2016-06), the results, will not be sufficiently informative, especially, for the long ranges. However, the comparison manifests some interesting outcomes. First, the computed metric entropy (Fig. 7a), shows that there is a notable variation between the streamflow data records obtained by Ozekiyama, and FAT (since both of these stations are located at the same site). More importantly, the estimated information content by means of FAT has higher values compared to RC, particularly, for AL \(\leq\) 4 hours suggesting that there is an additional scaling regime occurs during sub-daily scales (i.e. few hours), and hence the FAT is capable to capture the streamflow fluctuations that occur during hourly scales. Apparently, both high and low frequencies confirm that the information contents (i.e. metric entropy) at small aggregation lengths have consistent slope as streamflow computed by means of RC approach (Fig. 7a, and 7c). Alternatively, the fluctuation complexity estimates (Fig. 7b), demonstrates that there is remarkable difference between FAT and Ozekiyama estimates, therefore, it is advised to consider high resolution streamflow records to accurately investigate the hidden phenomena that cannot be observed by conventional discharge calculation methods.