The determination of flow state remains an important challenge in non-perennial stream catchments. To identify periods of flow and no-flow, previous studies deployed temperature sensors on streambed surfaces and interpreted the resulting time series data using a moving standard deviation approach. However, this technique requires the specification of multiple, subjective constraints. To identify suitable alternative approaches, we tested six time-frequency analysis methods from three categories: (a) Fourier transform, (b) wavelet transform, and (c) empirical mode decomposition. We compared each of the methods abilities to discern periods of flow from synthetic and field data of streambed temperature time series data. When tested using a synthetically generated data set, the efficacy of methods ranged from moderate to high, with 86%–99% accuracy. When applied to a field data set, greater variability in performance was observed, with 66%–90% accuracy. This accuracy represents a sound ability to determine the percentage of time for which a stream flows and does not flow. However, in the presence of a noisy signal, determining the number of specific flow events as well as correctly identifying timing of activation and cessation remains a challenge that most methods struggled with; this has implications for understanding eco-hydrological functioning. Differences observed between methods included variations in the ease of implementation and evaluation of results, as well as computational requirements and the ability to handle discontinuous time series data. Based on these results, we suggest five areas for future research to improve the general understanding of time-frequency analysis techniques amongst practicing hydrologists.
Bibliographical noteFunding Information:
The authors acknowledge the NCGRT NCRIS Super Science project for data for the Kangarilla field site. We thank Karina Guti?rrez-Jurado for processing the data. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Constructive comments from the editors, Ty Ferr?, and three additional anonymous reviewers improved this manuscript.
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- signal processing
- time series