آنالیز روند تغییر اقلیم با استفاده از شاخص های حدی داده های بلند مدت بارش و دما در جنوب شرق ایران ‎

فصلنامه علمی برنامه ریزی منطقه ای

مقاله 8، دوره 11، شماره 44، اسفند 1400، صفحه 119-134   

نوع مقاله: مقاله پژوهشی

شناسه دیجیتال (DOI): 10.30495/jzpm.2022.4059

نویسندگان

سید حسن علوی نیا^{* 1}؛ مهدی زارعی²

¹استادیار گروه مرتع و آبخیزداری، دانشکده منابع طبیعی و علوم زمین، دانشگاه کاشان، کاشان، ایران.

²استادیار مرکز پژوهشی علوم جغرافیایی و مطالعات اجتماعی، دانشگاه حکیم سبزواری، سبزوار، ایران.

تاریخ دریافت: 09 دی 1398،  تاریخ بازنگری: 10 اردیبهشت 1399،  تاریخ پذیرش: 11 اردیبهشت 1399 

چکیده

کاربرد نمایه­های اقلیمی معرفی­­شده توسط تیم متخصصین شناسایی تغییر اقلیم و نمایه­ها یکی از پرکاربردترین شیوه­های تشخیص تغییر اقلیم می‌باشد. در این پژوهش به­منظور تشخیص وقوع یا عدم وقوع تغییر اقلیم از داده­های روزانه دما و بارش ایستگاههای سینوپتیک زابل، زاهدان و ایرانشهر طی دوره آماری 2015-1966 استفاده شد. بدین منظور 8 شاخص اقلیمی مرتبط با بارش و 2 شاخص مربوط به دما استفاده و آزمون من-کندال نیز برای تعیین روند و روش تخمین­گر شیب سن برای تعیین بزرگی روند بکار گرفته شد. نتایج تحقیق حاکی از روند افزایشی غیر معنی­دار در میزان بیشینه دمای بیشینه روزانه در ماه و میزان کمینه دمای کمینه روزانه در ماه می‌باشد. نمایه­های مربوط به بارش نیز در سرتاسر استان کاهشی و بدون روند می‌باشند. هم‌چنین، نمایه روزهای خشک متوالی در ایستگاه ایرانشهر کاهشی و بدون روند اما در دو ایستگاه دیگر افزایشی و بدون روند می‌باشد. افزون بر این نیز نمایه روزهای مرطوب متوالی در ایستگاه ایرانشهر دارای روند کاهشی معنی­دار و در زابل و زاهدان کاهشی غیر معنی­دار می‌باشد. نمایه­های بیشینه بارش یک روزه و پنج روزه نیز کاهشی می‌باشند که در مورد بیشینه بارش پنج روزه در ایستگاه زاهدان هیچ­گونه روندی مشاهده نمی‌شود. به­طورکلی می­توان بیان کرد که شاخص­های اقلیمی مربوط به دما و بارش در منطقه مطالعاتی به ترتیب رو به افزایش و کاهش می‌باشد. درمجموع با توجه به نبود روند معنی­دار در اکثریت قریب به اتفاق شاخص­ها می­توان نتیجه گرفت که تغییرات و نوسانات رخ­داده در منطقه مطالعاتی ارتباطی با پدیده تغییر اقلیم ندارد، هرچند که پی بردن به این تغییرات می­تواند کمک بسیار شایانی به تصمیم­گیری مدیران و برنامه­ریزان شهری و منطقه­ای، به خصوص در رابطه با امور مرتبط با توسعه کلان­شهرها و کشاورزی و غیره کند.

کلیدواژه‌ها

تغییر اقلیم؛ شاخص‌های اقلیمی؛ من-کندال؛ روند؛ جنوب شرق ایران

عنوان مقاله [English]

climate change trends analysis using by extreme indices of long term rainfall and temperature in South East of Iran

نویسندگان [English]

Seyed Hassan Alavinia¹؛ Mahdi Zarei²

¹Assistant Professor of Faculty of Natural resources and Earth Sciences, University of Kashan, Kashan, Iran

²Assistant Professor of Research center of social studies geographical science, Hakim Sabzevari University, Sabbzevar, Iran.

چکیده [English]

Applying climatic indices introduced by the Expert Team on Climate Change Detection and Indices is one of the most widely used methods to detect climate change. In present research, daily temperature and precipitation data of Zabol, Zahedan and Iranshahr synoptic stations were applied during the period 1966-2015 to detect the occurrence or absence of climate change. To this end, 8 precipitation and 2 temperature related indices were used, Mann-Kendall and slope estimator methods were applied to determine trend and magnitude of trend, respectively. Results suggested non-significant increasing trend in monthly maximum value of daily maximum temperature and daily minimum temperature. The studied indices related to precipitation are also decreasing without trend significant in all over the province. Consecutive dry days index of is decreasing without specific trend at Iranshahr station, but it increasing without specific trend at other two stations. In addition, Consecutive wet days index has a significant decreasing trend at Iranshahr station while it has non-significant decreasing trend at Zabol and Zahedan stations. The 1-day and 5-day maximum precipitation amount indices are decreased that is no trend observed for the 5-day maximum precipitation at Zahedan station. It can be stated that the climatic indices related to temperature and precipitation are increasing and decreasing, respectively in the study area. Overall, it can be concluded that the happened changes and fluctuations in the study area are not related to climate change phenomena due to the lack of significant trends in the majority of used indices. However, understanding these changes can greatly help decision makers and urban and regional planners, especially in matters related to metropolitan development and agriculture, and so on.
Extended Abstract
Introduction
Regarding the detection of climate change, the long-term series trend of climate parameters such as precipitation and temperature need to be studied. A major part of climate change studies has been conducted through analysis of precipitation, temperature, pressure, humidity time series and their positive or negative trends. Researches indicated that, atmospheric parameters are strongly influenced by the global warming, greenhouse gases, surface phenomena (ocean and land temperature increase), urbanization and urban heat island (Ben.Gai et al., 2001; You et al. 2011). Analysis of time series is an appropriate method used for mathematical modeling, prediction of future events, trend detection of climatic data and missing data reconstruction. Generally, it is said that the trend in the climate indices time series may result from a normal gradual change, climate change or human activities effects (Brooks and Carruthers, 1953). It should be noted that, confirming the existence of a significant trend in time series related to the precipitation or temperature (for example extreme climate indices) cannot be solely a decisive reason for the existence and occurrence of climate change in a region, but it reinforces the assumption of the event, because there are many parameters associated with the control and operation of atmospheric systems (Serrano et al. 1999: 2894). The Mann-Kendall test is one of the most common and widely used nonparametric methods for time series analysis, and it is used to identify the trend changes. This method is widely used to analyze hydrological and meteorological time series trend. The Mann–Kendall test is used for trend analysis in ETCCDI workshops.
Methodology
Expert Team on Climate Change Detection and Indices (ETCCDI) introduced 27 climate indices to study the climatic parameters (Peterson et al. 2001), consisting of 16 indices for temperature and 11 indices for precipitation. In the present study, climate indices including RX1day, RX5day, PRCPTOT, CWD, CDD, R20, R10, and R95p were used for precipitation, and TXx, TNn were used for temperature, according to the objective of this research. All of these indices were calculated by the RClimDex software package. The indices were calculated for three synoptic station in the area, and then the time series was attained associated with each index.  The Mann-Kendall test was used at 90, 95, and 99 ⁒of confidence level for time series indices at 10, 5, and 1 ⁒level of significance, respectively, and according to the Mann-Kendall test Z-statistic, the ascending and descending trend of each index were determined over time. Subsequently, the gradient of trend line was determined by Sen’s slope estimator. Also, the graph of climatic anomalies of indices was drawn compared to the long -term average for these synoptic stations over the time. The Sistan and Baluchestan province is licated in Southeast of Iran, between 25° 04ʹ to 31° 29ʹ north latitude and 58° 55ʹ to 63° 20ʹ east longitude with 178431 KM² area. In this study, to achieve precise results, daily data on temperature and precipitation collected from three synoptic stations including Zabol, Zahedan and Iranshahr in Sistan and Baluchestan was used during a 50-year period (1966 -2015).
Result and Discusion
The results indicate that the indices refer to the precipitation including PRCPTOT and R95p have a decrease trend during statistical period and the largest positive and negative anomaly the PRCPTOT index were in 1982 and 2001, respectively, compared to the long-term average, also about R95p occurs in 2007 and 2001. The anomalies related to rainfall intensity including RX1day, RX5day, R10 and R20 also have a decreasing trend. Regarding the temperature indices, it should be noted that temperature has been increasing over the years studied. The TNn anomaly is increasing with a relatively steep incline. This means that the cold days are decreasing during the desired years and generally the minimum temperatures have been followed by an increasing trend. In contrast, the anomaly of TXx has been almost constant trend and does not show much variation. The trend of PRCPTOT, RX1day, R10 and R95p are non-significant decreasing trend at all three stations. Regarding the CWD index, the results showed a decrease in all station, with a significant decrease at 95% confidence level at Iranshahr station and non-significant at the other two stations. In addition, the CDD index increased in Zabol and Zahedan and showed a non-significant decrease in Iranshahr. About the trend of temperature indices, it was found that the trend of TNn was non-significant increasing in Zahedan and Iranshar and there was no trend in Zabol. Also, the TXx index increased without trend in Zabol and Zahedan stations and there is a decrease trend at the 90% significant level in Iranshahr.
Conclusion
The present study investigated the occurrence of climate change in Sistan and Baluchestan province using daily temperature and precipitation. For this purpose, was used of 8 indices for precipitation and 2 indices for temperature from ETCCDI. The result showed that the indices under study have changes and fluctuations, but in the vast majority of cases the changes were short-term climate fluctuations and trends are not significant during the time. The study indicates that overall the amount of rainfall decreased in the whole region, especially in the northern part of the province, but the intensity of precipitation decreased in the central and southern regions more than in other areas. As for the temperature the whole region shows an increase in temperature. Occurrence of changes in the region causes dramatic changes in increasing energy and water demand as well as changes in the region’s water resources. Therefore, the results of this study and research like this can provide valuable help and guidance for planners, decision makers and policy makers in adopting strategies to cope with these changes, both in term of risk management and access to renewable and low-cost energy.

کلیدواژه‌ها [English]

Climate Change, Extremes Indices, Mann-Kendall, Trend, Southeast of Iran

مراجع

1-      Mahmoudi, P., Tavousi, T., Shabab moghadam, A.M, (2016). Evaluation of the trend of abundant changes in sultry days in the southern half of Iran, Regional Planning, Vol. 7, No. 26, Summer 2017, pp. 68-55.

2-      Ramazanipour, M. (2019). Predict the Impact of Climatic Change on the Agro-climatic Indexes and Rice Yield Case study: North of Iran. Regional Planning, 8(32), 70-80.

3-      Razie, T., Danesh kar arasteh, P., Saghafian, B, (2007). “Study of temporal and spatial pattern of meteorological droughts in Sistan and Baluchestan province, Agricultural Scientific Journal. Volume. 30 No. 1.

4-      Geographical Organization of the Armed Forces (2005). Atlas Guide of Iran's Provinces, Geographical Organization of the Armed Forces.

5-      Feizi, V., Farajzadeh, M., Nourozi, R, (2010). Study of Climate Change in Sistan and Baluchestan Province by Man-Kendall Method", Proceedings of the Fourth International Congress of Geographers of the Islamic World (ICIWG 2010), Zahedan, Iran.

6-      Ben-Gai, T., Bitan, A., Manes, A., & Alpert, P. (2001). Climatic variations in the moisture and instability patterns of the atmospheric boundary layer on the East Mediterranean coastal plain of Israel. Boundary-layer meteorology, 100(2), 363-371.

7-      Brooks, C. E. P., & Carruthers, N. (1953). Handbook of statistical methods in meteorology. Handbook of statistical methods in meteorology.

8-      Gan, T. Y. (1998). Hydroclimatic trends and possible climatic warming in the Canadian Prairies. Water resources research, 34(11), 3009-3015.

9-      Kendall, M. G. (1975). Rank correlation measures. Charles Griffin, London, 202, 15.

10-  Pasquini, A. I., Lecomte, K. L., Piovano, E. L., & Depetris, P. J. (2006). Recent rainfall and runoff variability in central Argentina. Quaternary International, 158(1), 127-139.

11-  Peterson, T. C. (2005). Climate change indices. WMO bulletin, 54(2), 83-86.

12-  Peterson, T., Folland, C., Gruza, G., Hogg, W., Mokssit, A., & Plummer, N. (2001). Report on the activities of the working group on climate change detection and related rapporteurs. Geneva: World Meteorological Organization.

13-  Serrano, A., García, J., Mateos, V. L., Cancillo, M. L., & Garrido, J. (1999). Monthly modes of variation of precipitation over the Iberian Peninsula. Journal of Climate, 12(9), 2894-2919.

14-  Shahid, S., & Hazarika, M. K. (2010). Groundwater drought in the northwestern districts of Bangladesh. Water resources management, 24(10), 1989-2006.

15-  You, Q., Kang, S., Aguilar, E., Pepin, N., Flügel, W. A., Yan, Y., ... & Huang, J. (2011). Changes in daily climate extremes in China and their connection to the large scale atmospheric circulation during 1961–2003. Climate Dynamics, 36(11-12), 2399-2417.

16-  Xu, Z. X., Takeuchi, K., & Ishidaira, H. (2003). Monotonic trend and step changes in Japanese precipitation. Journal of hydrology, 279(1-4), 144-150.

17-  Zhang, X., Alexander, L., Hegerl, G. C., Jones, P., Tank, A. K., Peterson, T. C., ... & Zwiers, F. W. (2011). Indices for monitoring changes in extremes based on daily temperature and precipitation data. Wiley Interdisciplinary Reviews: Climate Change, 2(6), 851-870.