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. |
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| 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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| 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] |
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| ISSN: | 1387585X |
| DOI: | 10.1007/s10668-021-01668-7 |
