Complexity of hydrologic basins: A chaotic dynamics perspective

The objective of this study is to examine the dynamic complexity of hydrologic basins using techniques of phase space reconstruction. These non-parametric, model-free techniques which have their basis in the theory of chaotic dynamical systems are advantageous in resurrecting the features of multivariable dynamics solely from one-dimensional timeseries. Dynamic complexity is characterized by two measures directly interpretable as number of active degrees of freedom (dimensionality) and strength of nonlinearity. The analysis is conducted on daily streamflow observations of 408 basins from the Model Parameter Estimation Project (MOPEX) dataset spanning a wide range of climatic, topographic and land surface properties. The results show that the two complexity measures vary considerably across basins; however, their distributions are positively skewed, indicating that most basins exhibit low dimensionality and moderate nonlinearity. By examining how dynamic complexity relate to a set of 15 basin properties, it was found that dynamics dimensionality is primarily related to basin size; precisely larger basins exhibit lower dimensional dynamics. On the other hand, strength of nonlinearity is found to be regulated by the extent of vegetation in land cover. Specifically, basins with limited vegetation cover tend to have more linear dynamics. Furthermore, some observations regarding the presence of chaotic dynamics and the relationship between dynamic complexity and accuracy of prediction are presented. The results of this study have implications and potential relevance to catchment similarity and classification frameworks as well as model selection and extrapolation of model parameters to ungauged basins.

Automated summary

This study examines the dynamic complexity of hydrologic basins using phase space reconstruction techniques, finding that most basins exhibit low dimensionality and moderate nonlinearity. Dynamics dimensionality is primarily related to basin size, while strength of nonlinearity is linked to vegetation cover extent. The results have implications for catchment similarity, classification frameworks, model selection, and parameter extrapolation to ungauged basins.