Phase-space analysis of daily streamflow: characterization and prediction |
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| Institution: | 1. The Cincinnati Earth System Science Program, Department of Civil and Environmental Engineering, University of Cincinnati, PO Box 210071, Cincinnati, OH 45221-0071, USA;2. Department of Civil Engineering, Texas A&M University, College Station TX 77843, USA;1. University of Wisconsin-Madison, WI 53706, USA;2. University of North Carolina at Charlotte, NC 28223, USA;3. Wuhan University, Hubei 430072, China;4. Jishou University, Hunan 416000, China;5. Business College of Beijing Union University, Beijing 100101, China;1. Laboratory of Physical Chemistry of Materials, Department of Physics, Faculty of Sciences, Tunisia;2. Department, College of Engineering – Prince Sattam Bin Abdulaziz University, 655, AlKharj 11942, Saudi Arabia;3. Institute Néel, CNRS–University J. Fourier, BP166, 38042 Grenoble, France;1. Hydrologic Science & Engineering Program, Integrated GroundWater Modeling Center (IGWMC), Department of Geology and Geological Engineering, Colorado School of Mines, Golden, CO 80401, United States;2. Department of Geotechnical Engineering and Geosciences, Universitat Politècnica de Catalunya, UPC-BarcelonaTech, 08034 Barcelona, Spain;1. Department of Biomedical Engineering, Engineering Faculty, Bülent Ecevit University, 67100 Zonguldak, Turkey;2. Department of Electrical and Electronics Engineering, Engineering Faculty, Bülent Ecevit University, 67100 Zonguldak, Turkey |
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| Abstract: | This paper describes a methodology, based on dynamical systems theory, to model and predict streamflow at the daily scale. The model is constructed by developing a multidimensional phase-space map from observed streamflow signals. Predictions are made by examining trajectories on the reconstructed phase space. Prediction accuracy is used as a diagnostic tool to characterize the nature, which ranges from low-order deterministic to stochastic, of streamflow signals. To demonstrate the utility of this diagnostic tool, the proposed method is first applied to a time series with known characteristics. The paper shows that the proposed phase-space model can be used to make a tentative distinction between a noisy signal and a deterministic chaotic signal.The proposed phase-space model is then applied to daily streamflow records for 28 selected stations from the Continental United States covering basin areas between 31 and 35 079 km2. Based on the analyses of these 28 streamflow time series and 13 artificially generated signals with known characteristics, no direct relationship between the nature of underling stream flow characteristics and basin area has been found. In addition, there does not appear to be any physical threshold (in terms of basin area, average flow rate and yield) that controls the change in streamflow dynamics at the daily scale. These results suggest that the daily streamflow signals span a wide dynamical range between deterministic chaos and periodic signal contaminated with additive noise. |
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