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Detection of abrupt change is a key issue for understanding the facts and trends of climate change, but it is also a difficult task in practice. The Mann-Kendall (MK) test is commonly used for treating the issue, while the results are usually affected by the correlation and seasonal characters and sample size of series. This paper proposes a discrete wavelet entropy-aided approach for abrupt change detection, with the temperature analyses in the Haihe River Basin (HRB) as an example. The results show some obviously abrupt temperature changes in the study area in the 1960s-1990s. The MK test results do not reflect those abrupt temperature changes after the 1980s. Comparatively, the proposed approach can detect all main abrupt temperature changes in HRB, so it is more effective than the MK test. Differing from the MK test which only considers series' value order or the conventional entropy which mainly considers series' statistical random characters, the proposed approach is to describe the complexity and disorderliness of series using wavelet entropy theories, and it can fairly consider series' composition and characteristics under different scales, so the results can more accurately reflect not only the abrupt changes, but also the complexity variation of a series over time. However, since it is based on the entropy theories, the series analyzed must have big sample size enough and the sampling rates being smaller than the concerned scale for the accurate computation of entropy values. [ABSTRACT FROM AUTHOR]

Abstract: Evapotranspiration is an important flux term in the water cycle that integrates atmospheric demands and surface conditions. Thus, analysis of the temporal variation of reference evapotranspiration (ET0) will help us to understand climate change and its effect on hydrology. In this paper, we present an analysis of the monthly ET0 at 15 stations during 1961–2005 in the Taoer River basin in China, calculated by the FAO Penman–Monteith method. We derived the growing season and annual total ET0 time series and using the Mann–Kendall method, moving t test and Morlet wavelet conducted comprehensive time series analysis to characterize significance test, abrupt change and period in the ET0 data sets. The results present that: (i) in terms of the seasonal cycle, monthly ET0 reaches its peak in May and growing season ET0 accounts for 60.7% of annual total. Long term growing season ET0 fluctuates in accordance with annual ET0, both having turning points in 1982 and 1993; (ii) with respect to the long term persistence, the trends for growing season and annual ET0 show the same spatial patterns: high positive values in the west study area in the upper reach and negative values in the Southeast study area in the lower reach. The spatial distribution of annual tendencies suggests an influence from the altitude and latitude; (iii) for individual station, abrupt changes in annual ET0 are more pronounced than those in growing season ET0. The timings for the abrupt changes in ET0 series at individual stations are consistent with those in regional ET0. Abrupt changes detected in the early 1980s are all increasing changes, while those in earlier 1990 are all decreasing changes; and (iv) based on the Morlet wavelet analysis, there exist significant periods of 1, 3 and 7.3 years in annual ET0 series and significant 1 year period and 7.3 years period in growing season ET0 series. Maximum air temperature, mean air temperature, relative humidity and bright sunshine hours are main climate variables responsible for the periodicity in ET0. [Copyright &y& Elsevier]