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

Classification has been considered as a fundamental step towards improved science and management data. introducing methods which describe the underlying dynamics of runoff could be a promising way for catchment classification. in this respect chaos theory and correlation dimension was applied to test its ability to construct a concept to introduce a catchment classification framework in this study. the correlation dimension, as an indicator, was calculated for the daily river flow of sixty grouping stations in different catchments in iran, ranging in size from 8 to 36500 (km2). the results confirmed that applying this indicator to catchments in varied ranges, from low to high complexity, can also be classified. the results showed that iran’s catchments can be classified into four groups based on the complexity degree of runoff 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 unidentiable as group 4.the spatial pattern classification of iran’s catchments indicates that catchments with different climate characteristics which are located at a far distance from each other might yield similar responses along with the same level of complexity.