Regional detection of multiple change points and workable application for precipitation by maximum likelihood approach

  • Qianfeng Wang
  • Jia Tang
  • Jingyu Zeng
  • Song Leng
  • Wei ShuiEmail author
Original Paper


Precipitation is an essential part of the hydrological cycle. Objective change-point detection plays an important role in the research on extreme climate events and risk assessment in the context of global climate change. The study can automatically identify and extract multiple change points over a lengthy time series and calculate trends over several segmentations by uniting the maximum likelihood approach and the nonparametric Mann-Kendall trend test which is also compared with ordinary least squares (OLS). The observed station precipitation data were compiled over Hebei Province in China from 1961 to 2014. Temporal-spatial characteristics were also investigated by using several indices: the number of change points, standard deviation, timing of change points, minimum and maximum trends for segmentations, and the generalization trend. Obvious change points were generally around 1974, 1981, and 1998, and occurred at few stations before 1974 and after 2000, indicating that precipitation was relatively stable in the study area during the periods 1961–1974 and 2000–2014; the minimum trend for segmentations decreased over all stations; the maximum trend for segmentations increased over all stations except Leting; and generalization trend weakened abrupt changes in specific time sections among the multiple segmentations. Change-point detection followed by trend analysis can detect an obvious increasing or decreasing trend over certain parts of the time series and the proposed method can serve as a management tool with proper measures to deal with climate change. The results for both segmentations and generalization can provide a workable reference for managing regional water resources and implementing strategies to mitigate meteorological risks.


Precipitation Change point Maximum likelihood approach Mann-Kendall Trend; Hebei 



We would like to thank Guangyu Li for providing helpful support.

Funding information

This research received financial support from the National Natural Science Foundation of China (No. 41601562), also sponsored by China Scholarship Council, the Research Project for Young Teachers of Fujian Province (No. JAT160085), and the Scientific Research Foundation of Fuzhou University (No. XRC-1536).


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Copyright information

© Saudi Society for Geosciences 2019

Authors and Affiliations

  1. 1.College of Environment and Resource, Fujian Provincial Key Laboratory of Remote Sensing of Soil Erosion and Disaster PreventionFuzhou UniversityFuzhouChina
  2. 2.Key Lab of Spatial Data Mining & Information SharingMinistry of Education of ChinaFuzhouChina
  3. 3.Climate Change ClusterUniversity of Technology SydneyBroadwayAustralia

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