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. The study employs a popular nonlinear dynamic method for identification of the level of complexity of streamflow and for its classification. The correlation dimension method, which has its base on data reconstruction and nearest neighbor concepts, is applied to monthly streamflow time series from a large network of 117 gaging stations across 11 states in the western United States (US). The dimensionality of the time series 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 within certain regions of the western US, but there are also strong exceptions.
Highlights
As in most other fields of science and engineering, growth in the field of hydrology during the past century has been unprecedented, largely driven by the invention of powerful computers, measurement devices, remote sensors, geographic information systems (GIS), digital elevation models (DEM), and networking facilities
The study argues, through highlighting the relevance of complexity and nonlinearity in hydrologic systems, that system complexity is an appropriate basis for the classification framework and nonlinear dynamic concepts constitute a suitable methodology for assessing system complexity
Hydrologic models play a crucial role in the assessment of water resources availability and decisions on water planning and management
Summary
As in most other fields of science and engineering, growth in the field of hydrology during the past century has been unprecedented, largely driven by the invention of powerful computers, measurement devices, remote sensors, geographic information systems (GIS), digital elevation models (DEM), and networking facilities. Snelder et al, 2005; Sivakumar et al, 2007; see the other articles in the current special issue “Catchment Classification and PUB” for some latest studies), with an aim to streamline catchments into different groups and sub-groups on the basis of their salient characteristics (e.g. data and process complexity) and to provide directions to model developers on the level of model complexity to invoke. These attempts are only preliminary and research in this direction is still in a state of infancy.
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