Hydrologic system complexity and nonlinear dynamic concepts for a catchment classification framework

摘要

The absence of a generic modeling framework in hydrology has long beenrecognized. With our current practice of developing more and more complexmodels for specific individual situations, there is an increasing emphasisand urgency on this issue. There have been some attempts to provideguidelines for a catchment classification framework, but research in thisarea is still in a state of infancy. To move forward on this classificationframework, identification of an appropriate basis and development of asuitable methodology for its representation are vital. The present studyargues that hydrologic system complexity is an appropriate basis for thisclassification framework and nonlinear dynamic concepts constitute asuitable methodology. The study employs a popular nonlinear dynamic methodfor identification of the level of complexity of streamflow and for itsclassification. The correlation dimension method, which has its base on datareconstruction and nearest neighbor concepts, is applied to monthlystreamflow time series from a large network of 117 gaging stations across 11states in the western United States (US). The dimensionality of the timeseries forms the basis for identification of system complexity and,accordingly, streamflows are classified into four major categories:low-dimensional, medium-dimensional, high-dimensional, and unidentifiable.The dimension estimates show some "homogeneity" in flow complexity withincertain regions of the western US, but there are also strong exceptions.