Forecasting monthly streamflow dynamics in the western United States: a nonlinear dynamical approach

An attempt is made in the present study to understand the dynamics of monthly streamflow in the western United States from a nonlinear dynamical perspective. For this purpose, streamflow data observed at 79 stations across 11 states in the western United States are analyzed. A nonlinear prediction method with a local approximation approach is employed. The method uses the concept of phase-space reconstruction to represent the underlying dynamics, i.e. reconstruction of the single-dimensional (or variable) streamflow series in a multi-dimensional phase-space. The analysis is carried out by grouping the 79 stations under three categories, on the basis of the magnitude of (mean) streamflow, as: (1) low-flow stations, having mean streamflow values less than 2.832 m3/s; (2) high-flow stations, having mean streamflow values more than 28.32 m3/s; and (3) medium-flow stations, having mean streamflow values between 2.832 and 28.32 m3/s. Detailed analyses and results are presented only for three stations, selected to represent the low-flow, medium-flow, and high-flow categories, respectively. The results indicate that the local approximation prediction approach yields, in general, reasonably good predictions of streamflow dynamics irrespective of the flow regime, but the predictions for the low-flow stations are found to be relatively better than that obtained for the medium-flow and high-flow stations. These results seem to suggest the possible presence of chaotic behavior in the monthly streamflow dynamics in the western United States. Studies to verify and support the present results are underway.