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Entropy 2012, 14(7), 1274-1284; doi:10.3390/e14071274
Article

Discrete Wavelet Entropy Aided Detection of Abrupt Change: A Case Study in the Haihe River Basin, China

1,2
, 1,2,*  and 3
1 Key Laboratory of Water Cycle & Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China 2 Center for Water Resources Research, Chinese Academy of Sciences, Beijing 100101, China 3 General Institute of Water Resources and Hydropower Planning and Design, Ministry of Water Resources, Beijing 100120, China
* Author to whom correspondence should be addressed.
Received: 10 May 2012 / Revised: 5 July 2012 / Accepted: 11 July 2012 / Published: 17 July 2012
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Abstract

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.
Keywords: climate change; temperature; time series analysis; abrupt change; wavelet; information entropy; Haihe River Basin climate change; temperature; time series analysis; abrupt change; wavelet; information entropy; Haihe River Basin
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Sang, Y.-F.; Wang, Z.-G.; Li, Z.-L. Discrete Wavelet Entropy Aided Detection of Abrupt Change: A Case Study in the Haihe River Basin, China. Entropy 2012, 14, 1274-1284.

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