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Spatial and temporal rainfall variability and its controlling factors under an arid climate condition: case of Gabes Catchment, Southern Tunisia.

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Title: Spatial and temporal rainfall variability and its controlling factors under an arid climate condition: case of Gabes Catchment, Southern Tunisia.
Authors: Jemai, Sabrine1 (AUTHOR) jemai.sabrine@gmail.com, Kallel, Amjad2 (AUTHOR), Agoubi, Belgacem3 (AUTHOR), Abida, Habib4 (AUTHOR)
Source: Environment, Development & Sustainability. Apr2022, Vol. 24 Issue 4, p5496-5513. 18p.
Subject Terms: *Hierarchical clustering (Cluster analysis), *Cluster analysis (Statistics), *Principal components analysis, *Multiple regression analysis, *Hydrologic cycle
Geographic Terms: Tunisia
Abstract: Precipitation is the principal component of the hydrologic cycle. This study examines the spatial and temporal rainfall variability in Gabes Catchment (southeastern Tunisia) by analyzing annual precipitation data in nine stations during the period extending from 1977 to 2015. Several multivariate statistical tools, essentially principal component analysis (PCA), hierarchical clustering analysis and multiple linear regression, are used to characterize spatial variability of rainfall and identify its major controlling factors. PCA resulted in four principal components explaining 70% of the total variance. Stations were clustered into three different groups based on topography, the proximity to the Mediterranean Sea, continentality and seasonality. Hierarchical clustering, applying Ward method, classified variables into two groups. A multiple regression model including 13 variables was developed, representing a suitable tool for predicting precipitation of the different stations spread throughout Gabes Catchment. The proposed model displays acceptable efficiency with an absolute prediction error of approximately 87%. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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An: 155889240
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Spatial and temporal rainfall variability and its controlling factors under an arid climate condition: case of Gabes Catchment, Southern Tunisia.
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  Data: <searchLink fieldCode="JN" term="%22Environment%2C+Development+%26+Sustainability%22">Environment, Development & Sustainability</searchLink>. Apr2022, Vol. 24 Issue 4, p5496-5513. 18p.
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  Data: *<searchLink fieldCode="DE" term="%22Hierarchical+clustering+%28Cluster+analysis%29%22">Hierarchical clustering (Cluster analysis)</searchLink><br />*<searchLink fieldCode="DE" term="%22Cluster+analysis+%28Statistics%29%22">Cluster analysis (Statistics)</searchLink><br />*<searchLink fieldCode="DE" term="%22Principal+components+analysis%22">Principal components analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Multiple+regression+analysis%22">Multiple regression analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Hydrologic+cycle%22">Hydrologic cycle</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Tunisia%22">Tunisia</searchLink>
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  Label: Abstract
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  Data: Precipitation is the principal component of the hydrologic cycle. This study examines the spatial and temporal rainfall variability in Gabes Catchment (southeastern Tunisia) by analyzing annual precipitation data in nine stations during the period extending from 1977 to 2015. Several multivariate statistical tools, essentially principal component analysis (PCA), hierarchical clustering analysis and multiple linear regression, are used to characterize spatial variability of rainfall and identify its major controlling factors. PCA resulted in four principal components explaining 70% of the total variance. Stations were clustered into three different groups based on topography, the proximity to the Mediterranean Sea, continentality and seasonality. Hierarchical clustering, applying Ward method, classified variables into two groups. A multiple regression model including 13 variables was developed, representing a suitable tool for predicting precipitation of the different stations spread throughout Gabes Catchment. The proposed model displays acceptable efficiency with an absolute prediction error of approximately 87%. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=155889240
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1007/s10668-021-01668-7
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      – Code: eng
        Text: English
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        PageCount: 18
        StartPage: 5496
    Subjects:
      – SubjectFull: Hierarchical clustering (Cluster analysis)
        Type: general
      – SubjectFull: Cluster analysis (Statistics)
        Type: general
      – SubjectFull: Principal components analysis
        Type: general
      – SubjectFull: Multiple regression analysis
        Type: general
      – SubjectFull: Hydrologic cycle
        Type: general
      – SubjectFull: Tunisia
        Type: general
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      – TitleFull: Spatial and temporal rainfall variability and its controlling factors under an arid climate condition: case of Gabes Catchment, Southern Tunisia.
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            NameFull: Jemai, Sabrine
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            NameFull: Kallel, Amjad
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            NameFull: Agoubi, Belgacem
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            NameFull: Abida, Habib
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            – D: 01
              M: 04
              Text: Apr2022
              Type: published
              Y: 2022
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              Value: 24
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              Value: 4
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            – TitleFull: Environment, Development & Sustainability
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