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.

中文翻译：

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从河床地表温度时间序列中识别非常年水流事件的时频信号处理方法的比较

流量状态的确定仍然是非多年生河流集水区的一个重要挑战。为了识别流动和无流动的时期，之前的研究在河床表面部署了温度传感器，并使用移动标准偏差方法解释了由此产生的时间序列数据。但是，该技术需要指定多个主观约束。为了确定合适的替代方法，我们测试了来自三类的六种时频分析方法：（a）傅立叶变换，（b）小波变换，和（c）经验模式分解。我们比较了每种方法从河床温度时间序列数据的合成数据和现场数据中识别流量周期的能力。当使用合成生成的数据集进行测试时，方法的有效性从中等到高不等，准确率为 86%–99%。当应用于现场数据集时，观察到更大的性能变化，准确率为 66%–90%。这种准确度代表了确定水流流动和不流动的时间百分比的良好能力。然而，在存在噪声信号的情况下，确定特定流量事件的数量以及正确识别激活和停止的时间仍然是大多数方法所面临的挑战；这对理解生态水文功能有影响。观察到的方法之间的差异包括实施和评估结果的难易程度，以及计算要求和处理不连续时间序列数据的能力。基于这些结果，