NPG Nonlinear Processes in Geophysics NPG Nonlin. Processes Geophys. 1607-7946 Copernicus Publications Göttingen, Germany 10.5194/npg-11-383-2004 Streamflow disaggregation: a nonlinear deterministic approach Sivakumar B. 1 Wallender W. W. 2 1 Puente C. E. 3 1 Islam M. N. 4 5 Department of Land, Air and Water Resources, University of California, Davis, USA Department of Biological and Agricultural Engineering, University of California, Davis, USA Center for Computational Science and Engineering, University of California, Davis, USA Water Resources Division, Broward County, Fort Lauderdale, Florida, USA formerly at: Department of Land, Air and Water Resources, University of California, Davis, USA 08 09 2004 11 3 383 392 Copyright: © 2004 B. Sivakumar et al. 2004 This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Generic License. To view a copy of this licence, visit https://creativecommons.org/licenses/by-nc-sa/2.5/ This article is available from https://npg.copernicus.org/articles/11/383/2004/npg-11-383-2004.html The full text article is available as a PDF file from https://npg.copernicus.org/articles/11/383/2004/npg-11-383-2004.pdf

This study introduces a nonlinear deterministic approach for streamflow disaggregation. According to this approach, the streamflow transformation process from one scale to another is treated as a nonlinear deterministic process, rather than a stochastic process as generally assumed. The approach follows two important steps: (1) reconstruction of the scalar (streamflow) series in a multi-dimensional phase-space for representing the transformation dynamics; and (2) use of a local approximation (nearest neighbor) method for disaggregation. The approach is employed for streamflow disaggregation in the Mississippi River basin, USA. Data of successively doubled resolutions between daily and 16 days (i.e. daily, 2-day, 4-day, 8-day, and 16-day) are studied, and disaggregations are attempted only between successive resolutions (i.e. 2-day to daily, 4-day to 2-day, 8-day to 4-day, and 16-day to 8-day). Comparisons between the disaggregated values and the actual values reveal excellent agreements for all the cases studied, indicating the suitability of the approach for streamflow disaggregation. A further insight into the results reveals that the best results are, in general, achieved for low embedding dimensions (2 or 3) and small number of neighbors (less than 50), suggesting possible presence of nonlinear determinism in the underlying transformation process. A decrease in accuracy with increasing disaggregation scale is also observed, a possible implication of the existence of a scaling regime in streamflow.