HR: 10:40h
AN: NG22A-02    [Abstracts]
TI: Hydrologic System Complexity and Classification: A Simple Nonlinear Data Reconstruction Approach
AU: * Sivakumar, B
EM: sbellie@ucdavis.edu
AF: University of California, Davis, Department of Land, Air and Water Resources, Davis, CA 95616 United States
AB: Recent technological advances, such as powerful computers, remote sensors, geographic information systems and worldwide networking facilities, have brought hydrologic research to a whole new level. They have facilitated extensive data collection, better data sharing, formulation of sophisticated methods, and development of complex models. Despite these obvious advances, however, there are serious concerns about their use in practice and also criticisms about our approach to hydrologic modeling. For example: (1) these developments naturally lead to more complex models than that may actually be needed; (2) we are certainly collecting more and more data, but not necessarily all the relevant data; (3) despite their complexity, these models do not perform sufficiently well, even for the situations they are developed for; and (4) since the models are often developed for specific situations, `translation' of the results to other situations is difficult. Recent studies have addressed these concerns in different forms, such as dominant processes, thresholds, model integration and simplification. A common aspect in these studies is that they recognize the need for a common "classification system" in hydrology, so that an appropriate identification as to the model and data requirements can be made. The present study explores this classification issue further using a simple nonlinear data reconstruction approach. The reconstruction involves representation of the multi-dimensional hydrologic system using only an available single-variable data series representing the system, through a delay coordinate procedure. The `extent of complexity' of the system is identified by the `region of attraction of trajectories' in the reconstructed space, which is then used to classify the system as potentially low-, medium- or high-dimensional. A host of river-related data, representing different geographic and climatic regions, and temporal scales, are studied. Yielding `attractors' that range from `very clear' ones to `very blur' ones depending on data, the results indicate the potential of this reconstruction concept for studying hydrologic system complexity and classification.
DE: 1800 HYDROLOGY
DE: 1847 Modeling
DE: 1860 Streamflow
DE: 4410 Bifurcations and attractors
DE: 4420 Chaos (7805)
SC: Nonlinear Geophysics [NG]
MN: Fall Meeting 2005