Comparing methods of identifying non-stationary, non-linear processes
from stream temperature time series data
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