Prewhitening-Aided Innovative Trend Analysis Method for Trend Detection in Hydrometeorological Time Series

作者：Jingqun Huo, Ping Xie · 发表于：Water · 年份：2025 · DOI：10.3390/w17050731 · 被引用次数：3 · 研究领域：Cryospheric studies and observations、Arctic and Antarctic ice dynamics、Climate variability and models

Detection of trends in hydrometeorological time series is essential for understanding the complex variability of hydrometeorological data. Although different types of methods have been proposed, accurately identifying trends and their statistical significance is still challenging due to the complex characteristics of hydroclimatic data and the limitations of diverse methods. In this article, we propose a new trend detection approach, namely the prewhitening-aided innovative trend analysis (ITA). This method first corrects the significance test formula of the original ITA method, followed by a prewhitening method to eliminate serial autocorrelation and ensure independence. Results of Monte–Carlo experiments verified the superiority of the prewhitening-aided ITA method to the previous ITA methods. Moreover, serial correlations had significant impacts on the performance of diverse methods. Comparatively, the traditional ITA method kept high Type I errors and tended to overestimate the significance of trends. The four ITA methods, which were improved in previous studies, performed better than the traditional ones but could not overcome the influence of either positive or negative correlation characteristics of time series. The four prewhitening-aided ITA methods performed much better as they could effectively handle serial correlation. Among all the nine methods concerned in this study, the variance correction prewhitening-aided ITA (VCPWITA0) method performed the best. Detection...