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Spatial analysis and investigation of Tele-connection patterns with drought in central Iran | ||
| فیزیک زمین و فضا | ||
| مقاله 6، دوره 42، شماره 4، دی 1395، صفحه 49-61 اصل مقاله (1.36 M) | ||
| شناسه دیجیتال (DOI): 10.22059/jesphys.2016.58915 | ||
| نویسندگان | ||
| مهران فاطمی1؛ کمال امیدوار* 2؛ ابراهیم مسگری3؛ مهدی نارنگی فرد4 | ||
| 1کارشناس مدیریت بحران | ||
| 2عضو هیات علمی دانشگاه یزد | ||
| 3کارمند دانشگاه پیام نور اردبیل | ||
| 4دانشگاه یزد | ||
| چکیده | ||
| This study, adopting an environmental approach to atmospheric circulation, estimates drought periods over a 20-year period (1992-2011) in 21 synoptic stations in Kerman, Yazd and Isfahan provinces, which share a long-term statistical period, using Standardized Precipitation Index (SPI). The data pertinent to 18 teleconnection patterns were derived and compared with climatic data of provinces under study and the relationship between major droughts in the region and these patterns was evaluated in accordance with correlation methods and multivariate regression model. According to the results, a total of 37.42% of annual SPI variation in Isfahan province, 51.09 % of SPI variation in Kerman province and 42.17% of SPI variation in Yazd province can be explained by the above patterns. Finally, the multivariate Scandinavia pattern (SCA) in Isfahan, East Atlantic (EA) pattern in Kerman and Tropical Southern Atlantic (TSA) pattern in Yazd were found to be the most effective patterns in explaining annual SPI changes in central Iran. | ||
| کلیدواژه‌ها | ||
| Tele-connection؛ Standardized Precipitation Index؛ Kriging؛ Correlation and Regression Models؛ Central Iran | ||
| عنوان مقاله [English] | ||
| Spatial analysis and investigation of Tele-connection patterns with drought in central Iran | ||
| نویسندگان [English] | ||
| mehran fatemi1؛ Kamal Omidvar2؛ | ||
| چکیده [English] | ||
| Drought, as a normal, recurrent climatic condition with complicated mechanism, is one of the least known natural disasters that may occur in any climatic condition. Considering the recent drought events in the Iran and the deterioration of water crisis, especially in the area under study, the necessity of further research in this area is felt. Also, the evaluation and monitoring of drought with reliable indices as well as its relationship with large-scale atmospheric patterns is the first step in the mitigation and management of this natural phenomenon. Thus, this study set out to explore the relationship between dry and wet years in Isfahan, Kerman and Yazd provinces and large-scale northern hemisphere atmospheric circulation patterns (teleconnection) using a variety of methods. The results illustrated the relationship of these patterns with drought in central Iran. In the present study, Standard Precipitation Index (SPI) was used as a measure for the severity of the drought. Using statistical methods such as correlation and linear multiple regression models, the most effective patterns and the mechanism of their effect was determined. Of a total of 18 teleconnection patterns with respect to their existence and severity of activity during different seasons, it was found that EPNP, SOI and MEI patterns in Isfahan and NAO, EA, EAWR, TNA and TSA patterns in Kerman had the strongest impact at a significant level of 0.01, but none of these patterns reached the level of significance in Yazd province. Moreover, EAWR, SOI, PDO and TNI patterns in Isfahan, NAO, EA, EPNP, EAWR, POL, SOI, MEI, NOI, PDO, TNA and TSA patterns in Kerman and NAO, EA, EAWR and TNA in Yazd province were significant at significance level of 0.05. As discussed earlier, there are regional differences in this regard, but the impact of teleconnection patterns on the severity of droughts in stations was not symmetrical. In other words, some patterns were more noticeable and active under drought conditions. The results of this study are in line with the findings of Khosravi (2004), in which MEL, NOI, NP, PDO and POL patterns had the most significant correlation with the SPI in Sistan and Baluchistan province. The overlapping of SPI time series and NAO pattern represents the absolute subordination of dry year and wet year frequency of this pattern in Kerman station (in the period 1999-2013), Isfahan station (in the period 2002-2011) and Yazd station (in the period 2008-2011). Moreover, the severe drought of 2010 was accompanied with an unusually low NAO index. The variation of Drought Severity Index in Isfahan, Kerman and Yazd stations with respect to TNA index revealed that in Kerman province (in the period 1996-2006), Isfahan province (in the period 1996-2004) and Yazd province (in the period 1994-1998) the greatest harmony between pattern oscillations and intensity of droughts was observed. The year 2010 was considered as an unusual year in all the stations when TNA took a radically different direction and drought severity deteriorated with pattern intensification. Overall, on an annual basis, approximately 37.42, 51.09 and 42.17% of SPI variation in Isfahan, Kerman and Yazd provinces can be explained by the models respectively. To determine the patterns that had the greatest effect on the severity of the dry and wet years selected by the model, the stepwise regression models were used. According to the results, the multivariate Scandinavia (SCA) pattern in Isfahan province, Eastern Atlantic (EA) pattern in Kerman province and the Tropical South Atlantic (TSA) in Yazd province in central Iran were the most effective patterns that explained annual SPI variation. As reported by Khosravi (2005), in the winter drought patterns, North Pacific (NP) pattern plays a more significant role, explaining 60% of variation in winter drought severity. The droughts events in central Iran are linked to droughts in northern Scandinavia, West Africa and the Azores. Thus, a deeper understanding of regional droughts and its relationship with large-scale atmospheric patterns can help adopt appropriate measures to efficiently handle natural and water resources. | ||
| کلیدواژه‌ها [English] | ||
| Tele-connection, Standardized Precipitation Index, Kriging, Correlation and Regression Models, Central Iran | ||
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