The Analysis of the Trend and the Cycles of Annual Precipitation Characteristics of Zanjan
Principal Component Analysis (PCA) is an optimum mathematical method to decrease variables into some limited components in order to justify the highest variance of primary variables. In this study some statistical characteristics of annual precipitation of Zanjan city including sum of annual precipi...
| Główni autorzy: | , |
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| Format: | Artykuł |
| Język: | fas |
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University of Tabriz
2013-12-01
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| Seria: | نشریه جغرافیا و برنامهریزی |
| Hasła przedmiotowe: | |
| Dostęp online: | https://geoplanning.tabrizu.ac.ir/article_572_33a35efd24f406f3a5ac35385f4788ff.pdf |
| _version_ | 1827314160194551808 |
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| author | Hossein Asakareh Ali Bayat |
| author_facet | Hossein Asakareh Ali Bayat |
| author_sort | Hossein Asakareh |
| collection | DOAJ |
| description | Principal Component Analysis (PCA) is an optimum mathematical method to decrease variables into some limited components in order to justify the highest variance of primary variables. In this study some statistical characteristics of annual precipitation of Zanjan city including sum of annual precipitation, number of rainy days, extreme daily precipitation in a year, the ratio of extreme precipitation to the sum of annual precipitation and some characteristics such as Standard Deviation (SD), Skewness (Sk), Kurtosis (Ku), Absolute Mean Deviation (AMD) and Mean Absolute Interannual Variability (MAIV) were was calculated from monthly precipitation for each year, and were introduced principal component analysis technique. The results show that 95% percent of annual precipitation variations can be explained through 4 components. The first component which indicates the highest data variance (42.6%), represents annual precipitation and absolute variability indices including SD, AMD and MAIV. The second component represents the shape of frequency distribution indices (Sk, Ku), the third component represents extreme precipitations and finally the fourth component represents the number of rainy days. The analysis of the trend of components scores show that first and fourth components scores have a significant decreasing and increasing trend, respectively. Round a lines show a precipitation decrease during the period under study from one hand and having uniform temporal distribution on the other hand. |
| first_indexed | 2024-04-24T22:30:10Z |
| format | Article |
| id | doaj.art-1e152110faa34cbf9b83b17c36b4432c |
| institution | Directory Open Access Journal |
| issn | 2008-8078 2717-3534 |
| language | fas |
| last_indexed | 2024-04-24T22:30:10Z |
| publishDate | 2013-12-01 |
| publisher | University of Tabriz |
| record_format | Article |
| series | نشریه جغرافیا و برنامهریزی |
| spelling | doaj.art-1e152110faa34cbf9b83b17c36b4432c2024-03-19T21:52:05ZfasUniversity of Tabrizنشریه جغرافیا و برنامهریزی2008-80782717-35342013-12-011745121142572The Analysis of the Trend and the Cycles of Annual Precipitation Characteristics of ZanjanHossein Asakareh0Ali Bayat1Zanjan University of ClimatologyClimatology, University of ZanjanPrincipal Component Analysis (PCA) is an optimum mathematical method to decrease variables into some limited components in order to justify the highest variance of primary variables. In this study some statistical characteristics of annual precipitation of Zanjan city including sum of annual precipitation, number of rainy days, extreme daily precipitation in a year, the ratio of extreme precipitation to the sum of annual precipitation and some characteristics such as Standard Deviation (SD), Skewness (Sk), Kurtosis (Ku), Absolute Mean Deviation (AMD) and Mean Absolute Interannual Variability (MAIV) were was calculated from monthly precipitation for each year, and were introduced principal component analysis technique. The results show that 95% percent of annual precipitation variations can be explained through 4 components. The first component which indicates the highest data variance (42.6%), represents annual precipitation and absolute variability indices including SD, AMD and MAIV. The second component represents the shape of frequency distribution indices (Sk, Ku), the third component represents extreme precipitations and finally the fourth component represents the number of rainy days. The analysis of the trend of components scores show that first and fourth components scores have a significant decreasing and increasing trend, respectively. Round a lines show a precipitation decrease during the period under study from one hand and having uniform temporal distribution on the other hand.https://geoplanning.tabrizu.ac.ir/article_572_33a35efd24f406f3a5ac35385f4788ff.pdfprincipal component analysistrendprecipitationzanjan city |
| spellingShingle | Hossein Asakareh Ali Bayat The Analysis of the Trend and the Cycles of Annual Precipitation Characteristics of Zanjan نشریه جغرافیا و برنامهریزی principal component analysis trend precipitation zanjan city |
| title | The Analysis of the Trend and the Cycles of Annual Precipitation Characteristics of Zanjan |
| title_full | The Analysis of the Trend and the Cycles of Annual Precipitation Characteristics of Zanjan |
| title_fullStr | The Analysis of the Trend and the Cycles of Annual Precipitation Characteristics of Zanjan |
| title_full_unstemmed | The Analysis of the Trend and the Cycles of Annual Precipitation Characteristics of Zanjan |
| title_short | The Analysis of the Trend and the Cycles of Annual Precipitation Characteristics of Zanjan |
| title_sort | analysis of the trend and the cycles of annual precipitation characteristics of zanjan |
| topic | principal component analysis trend precipitation zanjan city |
| url | https://geoplanning.tabrizu.ac.ir/article_572_33a35efd24f406f3a5ac35385f4788ff.pdf |
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