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author:

Wang, Qianfeng (Wang, Qianfeng.) [1] (Scholars:王前锋) | Tang, Jia (Tang, Jia.) [2] | Zeng, Jingyu (Zeng, Jingyu.) [3] | Leng, Song (Leng, Song.) [4] | Shui, Wei (Shui, Wei.) [5] (Scholars:税伟)

Indexed by:

Scopus SCIE

Abstract:

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.

Keyword:

Change point Hebei Mann-Kendall Maximum likelihood approach Precipitation Trend

Community:

  • [ 1 ] [Wang, Qianfeng]Fuzhou Univ, Coll Environm & Resource, Fujian Prov Key Lab Remote Sensing Soil Eros & Di, Fuzhou 350116, Fujian, Peoples R China
  • [ 2 ] [Tang, Jia]Fuzhou Univ, Coll Environm & Resource, Fujian Prov Key Lab Remote Sensing Soil Eros & Di, Fuzhou 350116, Fujian, Peoples R China
  • [ 3 ] [Zeng, Jingyu]Fuzhou Univ, Coll Environm & Resource, Fujian Prov Key Lab Remote Sensing Soil Eros & Di, Fuzhou 350116, Fujian, Peoples R China
  • [ 4 ] [Shui, Wei]Fuzhou Univ, Coll Environm & Resource, Fujian Prov Key Lab Remote Sensing Soil Eros & Di, Fuzhou 350116, Fujian, Peoples R China
  • [ 5 ] [Wang, Qianfeng]Minist Educ China, Key Lab Spatial Data Min & Informat Sharing, Fuzhou 350116, Fujian, Peoples R China
  • [ 6 ] [Tang, Jia]Minist Educ China, Key Lab Spatial Data Min & Informat Sharing, Fuzhou 350116, Fujian, Peoples R China
  • [ 7 ] [Zeng, Jingyu]Minist Educ China, Key Lab Spatial Data Min & Informat Sharing, Fuzhou 350116, Fujian, Peoples R China
  • [ 8 ] [Shui, Wei]Minist Educ China, Key Lab Spatial Data Min & Informat Sharing, Fuzhou 350116, Fujian, Peoples R China
  • [ 9 ] [Leng, Song]Univ Technol Sydney, Climate Change Cluster, Broadway, NSW 2007, Australia

Reprint 's Address:

  • 税伟

    [Shui, Wei]Fuzhou Univ, Coll Environm & Resource, Fujian Prov Key Lab Remote Sensing Soil Eros & Di, Fuzhou 350116, Fujian, Peoples R China;;[Shui, Wei]Minist Educ China, Key Lab Spatial Data Min & Informat Sharing, Fuzhou 350116, Fujian, Peoples R China

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Source :

ARABIAN JOURNAL OF GEOSCIENCES

ISSN: 1866-7511

Year: 2019

Issue: 23

Volume: 12

1 . 3 2 7

JCR@2019

1 . 8 2 7

JCR@2020

JCR Journal Grade:4

CAS Journal Grade:4

Cited Count:

WoS CC Cited Count: 8

SCOPUS Cited Count: 8

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 2

Online/Total:129/10389995
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