loading page

Comparing methods of identifying non-stationary, non-linear processes from stream temperature time series data
  • Daniel Partington,
  • Margaret Shanafield,
  • Chris Turnadge
Daniel Partington
Flinders University
Author Profile
Margaret Shanafield
Flinders University

Corresponding Author:mshanafield@yahoo.com

Author Profile
Chris Turnadge
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Author Profile


The determination of flow state remains an important challenge in non-perennial river catchments. Previous studies interpreted stream channel temperature time series data using the moving standard deviation method to identify the timing and duration of flow. However, the performance of this technique requires the user to specify multiple subjective constraints. We implemented six variations of time-frequency analysis from three categories: (1) Fourier transform methods, (2) wavelet transform methods, and (3) Empirical Mode Decomposition methods. We evaluated and compared their ability to discern periods of flow from synthetic and field data of stream temperature time series data. Overall, all methods performed reasonably well, with performance of 63–99 % success in matching flow and no-flow periods. Greater variability in performance was observed when evaluating field data. Differences between methods include the ease of implementation and evaluation of results, computational needs, and ability to handle discontinuous data. We suggest five primary areas for future research to improve the general understanding of these time-frequency analysis techniques.