Detecting the signals of hydrologic variability, including both periodicities and trends, at multi-timescales with evaluation of their statistical significance is one of the primary goals in hydrology studies, but it is also a challenging task. While the discrete wavelet spectrum (DWS) has been widely used to meet this purpose, it has limitations in practical applications, especially with the difficulty in detecting the spatial heterogeneity in hydrologic variability. In this study, a uniform DWS method is developed by determining the spectral values of the reference DWS, which are used as a uniform criterion for significance evaluation to identify hydrologic signals and compare their spatial heterogeneity. Application of the uniform DWS method to three annual runoff (and the corresponding precipitation) time series in the Yarlung Tsangpo River basin indicates that the significance of the runoff variability increases from down- to up-streams. It is shown that the uncertainty becomes large when the variability at small timescales is considered in short hydrologic time series. It is also demonstrated that hydrologic variability at long timescales usually tends to have a non-monotonic trend and that a monotonic trend would misrepresent the true underlying behavior. The uniform DWS method proposed here, by identifying the periodicities, non-monotonic and monotonic trend with significance evaluation, has the potential for a much wider use in hydrologic and climate sciences.

在多个时间尺度上检测水文变异性的信号，包括周期性和趋势，并对其统计意义进行评估，这是水文学研究的主要目标之一，但这也是一项艰巨的任务。尽管离散小波谱（DWS）已广泛用于满足此目的，但它在实际应用中存在局限性，尤其是在检测水文变异性的空间异质性方面存在困难。在这项研究中，通过确定参考DWS的光谱值来开发统一的DWS方法，将其用作重要性评估的统一标准，以识别水文信号并比较其空间异质性。将均匀DWS方法应用于雅鲁藏布江流域的三个年度径流（及相应的降水）时间序列，表明径流变异性的重要性从下游增加到上游。结果表明，在较短的水文时间序列中考虑小时间尺度的变异性时，不确定性会变大。还证明了长时间范围内的水文变异通常倾向于具有非单调趋势，并且单调趋势会歪曲真实的基本行为。本文提出的统一DWS方法通过识别周期性，非单调和单调趋势并进行显着性评估，具有在水文和气候科学中广泛应用的潜力。