Local trend analysis method of hydrological time series based on piecewise linear representation and hypothesis test
Local trend analysis method of hydrological time series based on piecewise linear representation and hypothesis test
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DOI：
10.1016/j.jclepro.2022.130695
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发表时间：
2022-02-01
影响因子：
11.1
通讯作者：
Shen，Teng
中科院分区：
文献类型：
--
作者：
Xie，Yangyang;Liu，Saiyan;Shen，Teng
分析水文时间序列在气候变化和人类活动影响下的非平稳性具有重要意义。与以往的研究相比，本研究更关注局部趋势而不是整体趋势和均值变化，提出了一种水文时间序列非平稳分析的局部趋势分析方法--PLRHT方法。在PLRHT方法中，分段线性表示算法和赤池信息准则确定的拟合局部趋势线被用作水文时间序列的基线分量。两个相邻拟合的局部趋势线之间的差异包括斜率和截距差异，它们共同决定了两个相应子序列的平均值差异。此外，基于斜率和截距的差异，定义了水文时间序列的3个变点（拐点、突变点和双变量点），并基于Monte Carlo实验进行了假设检验。最后，以人工水文时间序列和实测水文时间序列为例，将PLRHT方法与其他5种非平稳性分析的代表性方法（游程检验、秩和检验、Brown-Forsythe检验、T检验和启发式分割）进行了应用效果比较。结果表明，与5种非平稳性分析方法相比，PLRHT方法不仅能更准确地检测出人工和实测水文时间序列的变点，而且能定量分析趋势和突变对这些水文时间序列均值变化的贡献。此外，局部趋势分析可以对实测水文时间序列的非平稳性进行更深入的因果分析。因此，局部趋势分析有助于了解变化环境下水文过程的变化特征和驱动力，PLRHT方法是揭示局部均值变化与局部趋势关系的一种可行的局部趋势分析方法。
It is important to analyze the nonstationarity of hydrological time series under the influence of climate change and human activities. Compared with previous studies, this study focuses more on local trends rather than overall trend and mean value change, and presents a local trend analysis method, namely PLRHT method, for the nonstationary analysis of hydrological time series. In the PLRHT method, the fitted local trend lines determined by the piecewise linear representation algorithm and Akaike information criterion are used as the baseline component of hydrological time series. The difference between two adjacent fitted local trend lines includes the slope and intercept differences, which jointly determine the mean value difference of two corresponding subseries. In addition, based on the slope and intercept differences, three change points of the hydrological time series (inflection point, break point and bivariate point) are defined and their significance is tested by hypothesis test based on Monte Carlo experiments. Finally, the application effect of the PLRHT method is compared with five other representative methods of non-stationarity analysis (runs test, rank sum test, Brown-Forsythe test, T test and heuristic segmentation) by using artificial and observed hydrological time series as examples. The results show that, compared with the five nonstationarity analysis methods, the PLRHT method could not only detect more accurate change points of the artificial and observed hydrological time series, but also quantitatively analyze the contributions of trends and abrupt changes to mean value variations of these hydrological time series. Besides, the local trend analysis enables a deeper causality analysis for the nonstationarity of the observed hydrological time series. Therefore, local trend analysis could help understanding the variation characteristics and driving forces of hydrological processes in the changing environment, and the PLRHT method is a feasible local trend analysis method to reveal the relationship between local mean value changes and local trends.