مدل‌سازی، تحلیل و پیش بینی پدیده ی خشکسالی در ایران

پدیده ی خشکسالی مختص ناحیه ای خاص نبوده و مناطق مختلف جهان از آن متاثر می باشد، یکی از این مناطق، ایران در جنوب غرب آسیا می‌باشد که در چند سال اخیر از این پدیده رنج می برد. هدف پژوهش حاضر مدل‌سازی، تحلیل و پیش بینی خشکسالی در ایران می‌باشد. برای این کار ابتدا پارامترهای اقلیمی: بارش، دما، ساعات آفتابی، حداقل رطوبت نسبی و سرعت باد در بازه ی زمانی 29 ساله (2018-1990) در 30 ایستگاه ایران مورد استفاده قرار گرفت. برای مدل سازی، شاخص فازی t.i.b.i ابتدا چهار شاخص (set, spi, seb, mczi) با استفاده منطق فازی در نرم افزار matlab فازی‌سازی شدند و در نهایت برای پیش بینی از مدل شبکه ی عصبی مصنوعی تطبیقی anfis بهره گرفته شد. یافته های پژوهش نشان داد شاخص فازی نوین t.i.b.i طبقات خشکسالی، چهار شاخص مذکور را با دقت بالا در خود منعکس کرد. از بین 5 پارامتر اقلیمی مورد استفاده در این پژوهش، پارامتر دما و بارش در نوسان شدت خشکسالی بیش‌ترین تاثیر را داشت. شدت خشکسالی براساس مدل‌سازی صورت گرفته در مقیاس 6 ماهه بیش تر از 12 ماهه بود، بیش ترین درصد رخداد خشکسالی در ایستگاه بندرعباس با مقدار (24.30) در مقیاس 12 ماهه و کم ترین آن در ایستگاه شهرکرد با مقدار درصد فراوانی خشکسالی (0.36) درصد در مقیاس 6 ماهه اتفاق افتاده است. پیش بینی خشکسالی شاخص فازی t.i.b.i بر اساس مدل anfis ایستگاه‌های بندرعباس، بوشهر و زاهدان به ترتیب با مقدار شاخص t.i.b.i (0.62،‌ 0.96 و 0.97) در نیمه جنوبی ایران بیش تر در معرض خشکسالی قرار گرفتند. براساس نتایج کلی پژوهش در هر دو مقیاس 6 و 12 ماهه مناطق نیمه جنوبی ایران از شدت بیش تر خشکسالی برخوردار شد که نیازمند مدیریت دقیق و کارآمد در مدیریت منایع آبی در این مناطق می‌باشد.

Modelling, Analysis, and Prediction of Drought Phenomenon in Iran

1Introduction                   Drought is one of the most important natural disasters affecting agriculture and water resources, and its abundance is extremely high in arid and semiarid regions (Shamsenya et al., 2008: 165). Drought is a natural phenomenon that has a complex process due to the interactions of various meteorological factors and occurs in all climatic conditions and in all regions of the planet (Samandianfard & Asadi, 2017). According to the domestic and foreign studies, many researchers have conducted research on drought monitoring and prediction, but the research that can show the drought phenomenon more accurately with the future vision is not takenhas not been conducted if both do not cover the issue adequately. According to the researchers, this study was conducted to model, monitor and predict drought with the new method in Iran in this study.2MethodologyIn this study, drought modelling in Iran was carried out using climatic data of rainfall, temperature, sunshine, relative humidity and wind speed monthly (for 6 and 12 months scale) for the period of 29 years (19902018). At 30 stations using the new TIBI architecture model, a fuzzy set of four indicators (SET, SPI, SEB, and MCZI) valid in the World Meteorological Organization was used. For modelling the new TIBI index, the climatic data were first normalized, then four indices (SET,  SPI, SEB, and MCZI) were calculated separately and the fuzzy modelling of the four indices was performed in the Matlab software and eventually to prioritize the droughtaffected areas, the multivariate decisionmaking model, TOPSIS was used.3ResultsIn order to investigate the effect of drought fluctuations in drought conditions of stations, it is possible to determine the changes in the indicators (SET, SPI, SEB, and MCZI) in the TIBI index analysis. Considering the large number of stations studied, For better understanding, only the drought series diagrams were presented at Bojnourd station on two 6 and 12 month scales (Figures 7 and 8),, (in the mentioned figures, the red arrow shows the drought margin at a 6month scale with a value of 0.44 and greater, and a value of 0.76 and greater within the 12month scale. The analysis of these forms shows that at the 6year and 12month scale at Bojnourd station, the amount of evapotranspiration was similar in drought conditions, which decreased from April 1994 to February 1999, and after this month an increase was observed if the impact of rainfall on a 6month scale is weaker than the 12month scale. It means that from May 1993 to November 1997, an increasing trend followed by the same pattern, and the indicators (SET, SPI, SEB, and MCZI) affect the TIBI index and show some trends, indicating that the new TIBI fuzzy index reflects the four indicators well. The T.I.B.I index at the 6month scale shows a sharper shape than the scale 12.Prioritization of the stations involved in drought in Iran was analyzed using the TOPSIS model. The results of the TOPSIS model implementation using the degree of importance of the criteria derived from the entropy method indicate that, in terms of drought, more and fewer places are involved with drought by combining the two 6 and 12month scale. According to the TOPSIS multivariate decisionmaking model, it was determined that the three stations most affected by drought based on the TOPSIS model were Bandar Abbas, Ahvaz and Bushehr, respectively, in the south and southwest regions of Iran with priority points of score (1, 0.78, and 0.62 respectively), and the three stations of Gorgan, Shahrekord and Orumieh in the northern and western parts of Iran with the scores of 0.026, 0.033 and 0.035 had lower priorities for drought response, respectively (Table 6) and (Figure 11).4Discussion and conclusionDrought is a natural disaster that is gradually evolving under the influence of climatic anomalies over a long period of time. In recent years, various parts of the Middle East have faced drought, including those regions of Iran in Southwest Asia. In this study, drought phenomenon was assessed at 6 and 12 months using the new fuzzy index T.I.B.I. The results of the research showed that the total frequency of droughts in the 12month scale was more than 6 months but the severity of a 6monthold drought is more than 12 months old. On a 12month scale, the number of drought repetitions is more than 6 months. Drought persistence was higher at 12month scale, droughts were shorter at shortterm and affected by temperature parameter. However, the intensity of drought over a long period of time had a slower response to rainfall changes. The highest percentage of drought incidence in scale of 6 months; Bandar Abbas, Bushehr, Ahvaz and Zahedan stations in the southern half of the study area respectively with the of drought (16.62, 11.24, 14.13 and 62.6 and the lowest in the 6month scale were Urmia and Ardebil stations, with the percentages of 1.10 and 1.88, Ilam and Yasuj with the drought frequency of 1.61 and 2.01, Rasht and Gorgan, with a high percentage of drought frequency (1.26 and 0.87) in the northern and western part of Iran. The highest percentage of drought occurrence in scale 12; Bandar Abbas and Bushehr stations respectively with drought frequency of 24.30 and 14.83, Ahvaz with drought severity of 18.47, Kerman with 6.74 percent of drought frequencies in the south and southwest of Iran and the lowest in the 6month scale; stations of Birjand (1.70), Bojnurd (66.6), Urmia (1.17), and Tabriz (66.2) in the northwest of Iran, Rasht (0.58), Sari (0.78) in the northern part of Iran.