Uncertainty analysis of the statistically downscaling Precipitation and Temptation on the Qorantalar
One consequence of a significant increase in the man-made greenhouse gases in recent decades has been a global rise in air temperature with the commensurate rise in the atmospheric heat energy, which in turn affects the hydrologic cycle. Thus a drastic change in the amount, distribution and timing o...
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| Format: | Article |
| Language: | fas |
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Marvdasht Branch, Islamic Azad University
2017-10-01
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| Series: | مهندسی منابع آب |
| Subjects: | |
| Online Access: | https://wej.marvdasht.iau.ir/article_2651_9a614e220ebfda88f8f47c95e6c09d4b.pdf |
| _version_ | 1827385514907402240 |
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| author | Mehdi Ahmadi Bagher Ghermez Cheshmeh Hoda Ghasemiyeh |
| author_facet | Mehdi Ahmadi Bagher Ghermez Cheshmeh Hoda Ghasemiyeh |
| author_sort | Mehdi Ahmadi |
| collection | DOAJ |
| description | One consequence of a significant increase in the man-made greenhouse gases in recent decades has been a global rise in air temperature with the commensurate rise in the atmospheric heat energy, which in turn affects the hydrologic cycle. Thus a drastic change in the amount, distribution and timing of the hydrologic events is logical. Therefore, preparation for the future water-related events dictates an implication of detailed studies on the prediction of the future rise in temperature and the resultant change in precipitation. The Atmosphere-Ocean General Circulation Model (AOGCM) is considered to be the most reliable software for the predicting the weather-related events. The statistical downscaling method (SDSM) and the Artificial Neural Networks (ANN) were tested to remove the uncertainty related to the AOGCM. Of a few software used for downscaling, SDSM was proved to be the most reliable for predicting the 2011-2040 changes in air temperatures and precipitation under the A2, B2 scenarios of the HadCM3 for the Qorantalar Watershed. Results indicated that there would be an increase of 7% and 6% in the precipitation amount, and 0.34 and 0.86 degrees Celsius in temperature using the A2 and B2 scenarios, respectively. |
| first_indexed | 2024-03-08T15:27:31Z |
| format | Article |
| id | doaj.art-791fc1057f1d4aacae3ea98c0d9d0fc6 |
| institution | Directory Open Access Journal |
| issn | 2008-6377 2423-7191 |
| language | fas |
| last_indexed | 2024-03-08T15:27:31Z |
| publishDate | 2017-10-01 |
| publisher | Marvdasht Branch, Islamic Azad University |
| record_format | Article |
| series | مهندسی منابع آب |
| spelling | doaj.art-791fc1057f1d4aacae3ea98c0d9d0fc62024-01-10T08:08:31ZfasMarvdasht Branch, Islamic Azad Universityمهندسی منابع آب2008-63772423-71912017-10-01103411242651Uncertainty analysis of the statistically downscaling Precipitation and Temptation on the QorantalarMehdi Ahmadi0Bagher Ghermez Cheshmeh1Hoda Ghasemiyeh2دانشجوی دکتری علوم و مهندسی آبخیزداری واحد علوم و تحقیقاتاستادیار، پژوهشکده حفاظت خاک و آبخیزداریاستادیار دانشگاه کاشانOne consequence of a significant increase in the man-made greenhouse gases in recent decades has been a global rise in air temperature with the commensurate rise in the atmospheric heat energy, which in turn affects the hydrologic cycle. Thus a drastic change in the amount, distribution and timing of the hydrologic events is logical. Therefore, preparation for the future water-related events dictates an implication of detailed studies on the prediction of the future rise in temperature and the resultant change in precipitation. The Atmosphere-Ocean General Circulation Model (AOGCM) is considered to be the most reliable software for the predicting the weather-related events. The statistical downscaling method (SDSM) and the Artificial Neural Networks (ANN) were tested to remove the uncertainty related to the AOGCM. Of a few software used for downscaling, SDSM was proved to be the most reliable for predicting the 2011-2040 changes in air temperatures and precipitation under the A2, B2 scenarios of the HadCM3 for the Qorantalar Watershed. Results indicated that there would be an increase of 7% and 6% in the precipitation amount, and 0.34 and 0.86 degrees Celsius in temperature using the A2 and B2 scenarios, respectively.https://wej.marvdasht.iau.ir/article_2651_9a614e220ebfda88f8f47c95e6c09d4b.pdfclimate change analysissdsmartificial neural networks |
| spellingShingle | Mehdi Ahmadi Bagher Ghermez Cheshmeh Hoda Ghasemiyeh Uncertainty analysis of the statistically downscaling Precipitation and Temptation on the Qorantalar مهندسی منابع آب climate change analysis sdsm artificial neural networks |
| title | Uncertainty analysis of the statistically downscaling Precipitation and Temptation on the Qorantalar |
| title_full | Uncertainty analysis of the statistically downscaling Precipitation and Temptation on the Qorantalar |
| title_fullStr | Uncertainty analysis of the statistically downscaling Precipitation and Temptation on the Qorantalar |
| title_full_unstemmed | Uncertainty analysis of the statistically downscaling Precipitation and Temptation on the Qorantalar |
| title_short | Uncertainty analysis of the statistically downscaling Precipitation and Temptation on the Qorantalar |
| title_sort | uncertainty analysis of the statistically downscaling precipitation and temptation on the qorantalar |
| topic | climate change analysis sdsm artificial neural networks |
| url | https://wej.marvdasht.iau.ir/article_2651_9a614e220ebfda88f8f47c95e6c09d4b.pdf |
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