Employing nonlinear dynamic concepts for catchment classification using runoff response of catchments

Classication is considered as a fundamental step towards improved science and management data. Introducing methods that describe the underlying dynamics of runo could be a promising way for catchment classication. In this respect, chaos theory and correlation dimension were applied to test its ability to construct a concept to introduce a catchment classication framework in this study. The correlation dimension, as an indicator, was calculated for the daily river ow of sixty grouping stations in dierent catchments in Iran, ranging in size from 8 to 36500 km2. The results conrmed that applying this indicator to catchments in varied ranges, from low to high complexity, could also be classied. The results showed that Iranʹs catchments could be classied into four groups based on the complexity degree of runo time series. The group is categorized as follows: low dimension (D2 < = 4) as Group 1, medium dimension (D2 = 5) as Group 2, high dimension (D2 = > 6) as Group 3, and unidentiable as Group 4. The spatial pattern classication of Iranʹs catchments indicates that catchments with dierent climate characteristics, which are located at a far distance from each other, might yield similar responses along with the same level of complexity