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

Abstract

Abstract. The absence of a generic modeling framework in hydrology has long been recognized. With our current practice of developing more and more complex models for specific individual situations, there is an increasing emphasis and urgency on this issue. There have been some attempts to provide guidelines for a catchment classification framework, but research in this area is still in a state of infancy. To move forward on this classification framework, identification of an appropriate basis and development of a suitable methodology for its representation are vital. The present study argues that hydrologic system complexity is an appropriate basis for this classification framework and nonlinear dynamic concepts constitute a suitable methodology. Discussing the utility of hydrologic data in describing the complexity of the underlying system, the study also offers a three-step procedure for a classification framework: (1) detection of possible patterns and determination of complexity levels of hydrologic systems; (2) classification of hydrologic systems into groups and sub-groups based on patterns and complexity; and (3) verification of the classification framework through establishing relationships between the data patterns/complexity and the catchment/process properties. The framework is expected to lead to a much more effective and efficient procedure for identifying the appropriate structure and complexity of models for hydrologic systems and, thus, save significant time, data collection, and computational requirements.