Abstract
The self-adjustment of an alluvial channel is a complicated process with various factors influencing the stability and transformation of channel patterns. A cusp catastrophe model for the alluvial channel regime is established by selecting suitable parameters to quantify the channel pattern and stability. The channel patterns can be identified by such a model in a direct way with a quantified index, which is a 2D projection of the cusp catastrophe surface, and the discriminant function is obtained from the model to distinguish the river state. Predictions based on this model are consistent with the field observations involving about 150 natural rivers of small or medium sizes. This new approach enables us to classify the channel pattern and determine a river stability state, and it paves the way toward a better understanding of the regime of natural rivers to assist decision-making in river management.
Highlights
The self-adjustment of an alluvial channel is a complicated process with various factors influencing the stability and transformation of channel patterns
This paper presents a cusp catastrophe model forfunction alluvial channel suitableand parameters to determine the river channel and the channel stability
Is a cusp catastrophe surface in a translated 2D dimensional coordinate, and the discriminant functions are obtained from the model to distinguish channel stability
Summary
The self-adjustment of an alluvial channel is a complicated process with various factors influencing the stability and transformation of channel patterns. There are just a few attempts to incorporate these variables into a predictive model of channel pattern [10]; some researchers statistically analyzed large and complex geospatial data sets and developed predictive models of channel patterns [10,15,16] The diversity of these methods and the selection criteria for one or another is influenced by different reasons, for example, objectives and scale of work (site, network, and watershed), time requirements and data, statistical and programmer expertise, or the complexity of the area of study and its analysis and interpretation. The results indicate that this approach can be used to judge river patterns and states when the control factors can be obtained in the field, to study the regime of natural rivers, and to give some suggestions to river management
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