Drought Early Warning Methods over Iran
Drought is one of the atmospheric events that causes great casualties. In this study, the main goal of this research is long rang prediction of drought as large scale climatic signals data were used. Large scale climatic signals are among parameters that are used in analysis of seasonal and annual v...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | fas |
| Published: |
University of Sistan and Baluchestan
2008-03-01
|
| Series: | جغرافیا و توسعه |
| Subjects: | |
| Online Access: | https://gdij.usb.ac.ir/article_1616_a18c931155cf0c1b0c07cf4b20463f14.pdf |
| _version_ | 1797805130818519040 |
|---|---|
| author | abdolah Sedaghat Kerdar ebrahim Fatahi |
| author_facet | abdolah Sedaghat Kerdar ebrahim Fatahi |
| author_sort | abdolah Sedaghat Kerdar |
| collection | DOAJ |
| description | Drought is one of the atmospheric events that causes great casualties. In this study, the main goal of this research is long rang prediction of drought as large scale climatic signals data were used. Large scale climatic signals are among parameters that are used in analysis of seasonal and annual variations of temperature and precipitation. In this research, data of monthly Southern Oscillation Index (SOI), Northern Atlantic Oscillation (NAO) index and Nino indices like NINO4, NINO3, NINO3.4 and NINO1+2 with ENSO phenomenon were used. All data concerning the above signals for the period 1960-2000 were received from National Center of Environmental Prediction (NCEP). To determine the most important effective signals on precipitation in different regions of the country, multi-regression method was used. Results of such a regression analysis showed that the most important signals causing precipitation are NINO1+2, and NINO3 indices. In this research, using Artificial Neural Network (ANN) method, prediction of precipitation in simultaneous lead time periods of three and six months was done. Comparison of ANN model results with observed data showed that the wet periods correspond with warm phases of ENSO and negative NAO, whereas cold phases ENSO and positive NAO associate with drought years in Iran. |
| first_indexed | 2024-03-13T05:47:25Z |
| format | Article |
| id | doaj.art-057b547892e8489281c6bf94d0001680 |
| institution | Directory Open Access Journal |
| issn | 1735-0735 2676-7791 |
| language | fas |
| last_indexed | 2024-03-13T05:47:25Z |
| publishDate | 2008-03-01 |
| publisher | University of Sistan and Baluchestan |
| record_format | Article |
| series | جغرافیا و توسعه |
| spelling | doaj.art-057b547892e8489281c6bf94d00016802023-06-13T20:06:39ZfasUniversity of Sistan and Baluchestanجغرافیا و توسعه1735-07352676-77912008-03-01611597610.22111/gdij.2008.16161616Drought Early Warning Methods over Iranabdolah Sedaghat Kerdarebrahim FatahiDrought is one of the atmospheric events that causes great casualties. In this study, the main goal of this research is long rang prediction of drought as large scale climatic signals data were used. Large scale climatic signals are among parameters that are used in analysis of seasonal and annual variations of temperature and precipitation. In this research, data of monthly Southern Oscillation Index (SOI), Northern Atlantic Oscillation (NAO) index and Nino indices like NINO4, NINO3, NINO3.4 and NINO1+2 with ENSO phenomenon were used. All data concerning the above signals for the period 1960-2000 were received from National Center of Environmental Prediction (NCEP). To determine the most important effective signals on precipitation in different regions of the country, multi-regression method was used. Results of such a regression analysis showed that the most important signals causing precipitation are NINO1+2, and NINO3 indices. In this research, using Artificial Neural Network (ANN) method, prediction of precipitation in simultaneous lead time periods of three and six months was done. Comparison of ANN model results with observed data showed that the wet periods correspond with warm phases of ENSO and negative NAO, whereas cold phases ENSO and positive NAO associate with drought years in Iran.https://gdij.usb.ac.ir/article_1616_a18c931155cf0c1b0c07cf4b20463f14.pdfartificial neural networkclimatic signalsdroughtmulti-regression methodnorthern atlantic oscillation (nao)southern oscillation index (soi) |
| spellingShingle | abdolah Sedaghat Kerdar ebrahim Fatahi Drought Early Warning Methods over Iran جغرافیا و توسعه artificial neural network climatic signals drought multi-regression method northern atlantic oscillation (nao) southern oscillation index (soi) |
| title | Drought Early Warning Methods over Iran |
| title_full | Drought Early Warning Methods over Iran |
| title_fullStr | Drought Early Warning Methods over Iran |
| title_full_unstemmed | Drought Early Warning Methods over Iran |
| title_short | Drought Early Warning Methods over Iran |
| title_sort | drought early warning methods over iran |
| topic | artificial neural network climatic signals drought multi-regression method northern atlantic oscillation (nao) southern oscillation index (soi) |
| url | https://gdij.usb.ac.ir/article_1616_a18c931155cf0c1b0c07cf4b20463f14.pdf |
| work_keys_str_mv | AT abdolahsedaghatkerdar droughtearlywarningmethodsoveriran AT ebrahimfatahi droughtearlywarningmethodsoveriran |