Abstract
Finding significant trends in hydroclimate time series has been deemed an essential task in numerous studies. Despite the existence of various trend detection methods, statistical significance is mostly examined for linear trends and related to the meaningfulness of the found trends. We wish to draw attention to a more general definition of meaningful trends by cross-referencing not only linear but also smoothing techniques. We apply linear regression (LR) and two smoothing techniques based on regularized minimal-energy tensor-product B-splines (RMTB) to the trend detection of standardized precipitation index (SPI) series over Taiwan. LR and both RMTB-based methods identify an overall upward (wetting) trend in the SPI series across the time scales in Taiwan from 1960 to 2019. However, if dividing the entire time series into the earlier (1960–1989) and later (1990–2019) sub-series, we find that some downward (drying) trends at varied time scales migrate from southcentral–southwestern to eastern regions. Among these significant trends, we have more confidence in the recent drying trend over eastern Taiwan since all the methods show trend patterns in highest similarity. We also argue that LR should be used with great caution, unless linearity in data series and independence and normality in residuals can be assured.
Highlights
Global warming, regardless of the contribution from human activities or natural causes, has intensified hydroclimate variability [1]
Lref. [4] applied the MK test to examine trends in precipitation and temperature at yearly, seasonal, and monthly time scales over the northwestern region of Bangladesh from 1980–2008; they indicated that their trend analysis results should be important to the planning of climate adaptation strategies in preparation for future climate conditions
This study aims at discussing whether statistically significant trends are truly meaningful through cross-referencing selected trend detection methods based on linear and smoothing techniques
Summary
Regardless of the contribution from human activities or natural causes, has intensified hydroclimate variability [1]. [10] evaluated trend and change points in annual and seasonal rainfall series using the MK and Pettitt tests in the Niger Central Hydrological Area; they derived the rainfall variability index and precipitation concentration index for a better illustration of rainfall variability. [9] lproposed a framework that quantifies how climate change and human activities influence hydrological drought; in their framework, they used the MK and Pettitt tests to examine trend and change points in hydrological variable series (e.g., precipitation, potential evapotranspiration, and runoff) over a semiarid basin in northern China Ref. Apart from these classical methods, some new methods for trend detection have been developed as well: ref. Apart from these classical methods, some new methods for trend detection have been developed as well: ref. [11] developed and used the innovative trend analysis method in conjunction with the MK test and Sen’s slope estimate for trend analysis on rainfall in the Upper Wabe Shebelle River Basin in Ethiopia
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