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This study introduces a nonlinear determinis- tic approach for streamflow disaggregation. According to this approach, the streamflow transformation process from one scale to another is treated as a nonlinear determinis- tic process, rather than a stochastic process as generally as- sumed. The approach follows two important steps: (1) re- construction of the scalar (streamflow) series in a multi- dimensional phase-space for representing the transforma- tion dynamics; and (2) use of a local approximation (near- est 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 re- veal 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 increas- ing disaggregation scale is also observed, a possible implica- tion of the existence of a scaling regime in streamflow.

Streamflow disaggregation: a nonlinear deterministic approach