Volume 10, Issue 2 (9-2023)
Journal of Spatial Analysis Environmental Hazards 2023, 10(2): 149-166 |
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Farhood S, Khoorani A, Eftekharian A. Detecting Trends in Extreme Temperature and Precipitation events with Different Return Periods in Iran. Journal of Spatial Analysis Environmental Hazards 2023; 10 (2) :149-166
URL: http://jsaeh.khu.ac.ir/article-1-3407-en.html
URL: http://jsaeh.khu.ac.ir/article-1-3407-en.html
1- Master's student in Meteorology, Department of Geography, Hormozgan University, Iran
2- Associate Professor, Department of Geographical Sciences, Hormozgan University, Iran ,khoorani@hormozgan.ac.ir
3- Assistant Professor, Department of Statistics, Hormozgan University, Iran
2- Associate Professor, Department of Geographical Sciences, Hormozgan University, Iran ,
3- Assistant Professor, Department of Statistics, Hormozgan University, Iran
Abstract: (2011 Views)
Introduction
In recent years, research on climate change has increased due to its economic and social importance and the damages of increasing extreme events. In most studies related to climate change, detecting potential trends in the long-term average of climate variables have been proposed, while studying the spatio-temporal variability of extreme events is also important. Expert Team on Climate Change Detection and Indices (ETCCDI) has proposed several climate indices for daily temperature and precipitation data in order to determine climate variability and changes based on R package.
Various methods have been presented to investigate changes and trends in precipitation and temperature time series, which are divided into two statistical categories, parametric and non-parametric. The most common non-parametric method is the Mann-Kendall trend test. One of the main issues of this research is the estimation of each index value in different return periods. The return period is the reverse of probability, and it is the number of years between the occurrence of two similar events (Kamri and Nouri, 2015). Accordingly, choosing the best probability distribution function is of particular importance in meteorology and hydrology.
Despite of the enormous previous studies, there is no comprehensive research on the estimation of extreme indices values for different return periods. Accordingly, this study focuses on two main goals: First, the changes in temperature and rainfall intensity are analyzed by analyzing the findings obtained from extreme climate indices (15 indices) and then (second) estimating the values of the indicators for three different return periods (50, 200 and 500 years).
Data and methods
In this research, the daily data of maximum, minimum and total annual precipitation of 49 synoptic stations for 1991-2020 were used to analyze 15 extreme indices of precipitation and temperature. Namely, FD, Number of frost days: Annual count of days when TN (daily minimum temperature) < 0oC; SU, Number of summer days: Annual count of days when TX (daily maximum temperature) > 25oC, ID, Number of icing days: Annual count of days when TX (daily maximum temperature) < 0oC; TXx, Monthly maximum value of daily maximum temperature; TNx, Monthly maximum value of daily minimum temperature; TXn, Monthly minimum value of daily maximum temperature; TNn, Monthly minimum value of daily minimum temperature; DTR, Daily temperature range: Monthly mean difference between TX and TN; Rx1day, Monthly maximum 1-day precipitation; Rx5day, Monthly maximum consecutive 5-day precipitation; SDII Simple precipitation intensity index; R10mm Annual count of days when PRCP≥ 10mm; R20mm Annual count of days when PRCP≥ 20mm; CDD. Maximum length of dry spell, maximum number of consecutive days with RR < 1mm; CWD. Maximum length of wet spell, maximum number of consecutive days with RR ≥ 1mm. Finally, the trends of indices were estimated using the non-parametric Mann-Kendall test and the values of these indicators were estimated for 50, 200 and 500 years return periods.
In order to calculate values of each indicator for a given return period, the annual time series and its probability of occurrence are estimated and the most appropriate statistical distribution function that can be fitted on the data is selected from among twelve functions. In this estimation, EASY-FIT (a hydrology software), which supports a higher range of distribution functions, is used. The intended significance level for 500, 200 and 50 years return periods were 0.998, 0.995 and 0.98, respectively. The functions used in this research include: Lognormal (3P), Lognormal, Normal, Log-Pearson 3, Gamma (3P), Gumbel, Pearson 5 (3P), Log-Gamma, Inv. Gaussian, Pearson 6 (4P), Pearson 6, Gamma. Kolmogorov–Smirnov test is used to assess the goodness of fit of the estimation from three return periods.
Results
The results indicate that while the trend of precipitation indices except for the Maximum length of dry spell (CDD) is decreasing, the trend of temperature indices was increasing, except for two indices of the days with daily maximum and minimum temperatures below zero degrees. From a spatial perspective, hot indices in the northwestern regions, cold indices in the southern half of the country shows an increasing trend, and the Caspian Sea, Oman Sea, Persian Gulf coastal regions, and the Zagros foothills are the most affected areas as a result of the increasing trends. Also, the index values were estimated for 50, 200 and 500 years return periods. As a result of the investigations, for temperature indices the north-west of the country has the highest values by different return periods. The increase in the values of R10, R20, RX1day and RX5day indices in the different return periods was more in the Zagros and Alborz mountain ranges, and the CWD, CDD and SDII indices have the highest values in the Caspian Sea and Persian Gulf Coastal areas.
In recent years, research on climate change has increased due to its economic and social importance and the damages of increasing extreme events. In most studies related to climate change, detecting potential trends in the long-term average of climate variables have been proposed, while studying the spatio-temporal variability of extreme events is also important. Expert Team on Climate Change Detection and Indices (ETCCDI) has proposed several climate indices for daily temperature and precipitation data in order to determine climate variability and changes based on R package.
Various methods have been presented to investigate changes and trends in precipitation and temperature time series, which are divided into two statistical categories, parametric and non-parametric. The most common non-parametric method is the Mann-Kendall trend test. One of the main issues of this research is the estimation of each index value in different return periods. The return period is the reverse of probability, and it is the number of years between the occurrence of two similar events (Kamri and Nouri, 2015). Accordingly, choosing the best probability distribution function is of particular importance in meteorology and hydrology.
Despite of the enormous previous studies, there is no comprehensive research on the estimation of extreme indices values for different return periods. Accordingly, this study focuses on two main goals: First, the changes in temperature and rainfall intensity are analyzed by analyzing the findings obtained from extreme climate indices (15 indices) and then (second) estimating the values of the indicators for three different return periods (50, 200 and 500 years).
Data and methods
In this research, the daily data of maximum, minimum and total annual precipitation of 49 synoptic stations for 1991-2020 were used to analyze 15 extreme indices of precipitation and temperature. Namely, FD, Number of frost days: Annual count of days when TN (daily minimum temperature) < 0oC; SU, Number of summer days: Annual count of days when TX (daily maximum temperature) > 25oC, ID, Number of icing days: Annual count of days when TX (daily maximum temperature) < 0oC; TXx, Monthly maximum value of daily maximum temperature; TNx, Monthly maximum value of daily minimum temperature; TXn, Monthly minimum value of daily maximum temperature; TNn, Monthly minimum value of daily minimum temperature; DTR, Daily temperature range: Monthly mean difference between TX and TN; Rx1day, Monthly maximum 1-day precipitation; Rx5day, Monthly maximum consecutive 5-day precipitation; SDII Simple precipitation intensity index; R10mm Annual count of days when PRCP≥ 10mm; R20mm Annual count of days when PRCP≥ 20mm; CDD. Maximum length of dry spell, maximum number of consecutive days with RR < 1mm; CWD. Maximum length of wet spell, maximum number of consecutive days with RR ≥ 1mm. Finally, the trends of indices were estimated using the non-parametric Mann-Kendall test and the values of these indicators were estimated for 50, 200 and 500 years return periods.
In order to calculate values of each indicator for a given return period, the annual time series and its probability of occurrence are estimated and the most appropriate statistical distribution function that can be fitted on the data is selected from among twelve functions. In this estimation, EASY-FIT (a hydrology software), which supports a higher range of distribution functions, is used. The intended significance level for 500, 200 and 50 years return periods were 0.998, 0.995 and 0.98, respectively. The functions used in this research include: Lognormal (3P), Lognormal, Normal, Log-Pearson 3, Gamma (3P), Gumbel, Pearson 5 (3P), Log-Gamma, Inv. Gaussian, Pearson 6 (4P), Pearson 6, Gamma. Kolmogorov–Smirnov test is used to assess the goodness of fit of the estimation from three return periods.
Results
The results indicate that while the trend of precipitation indices except for the Maximum length of dry spell (CDD) is decreasing, the trend of temperature indices was increasing, except for two indices of the days with daily maximum and minimum temperatures below zero degrees. From a spatial perspective, hot indices in the northwestern regions, cold indices in the southern half of the country shows an increasing trend, and the Caspian Sea, Oman Sea, Persian Gulf coastal regions, and the Zagros foothills are the most affected areas as a result of the increasing trends. Also, the index values were estimated for 50, 200 and 500 years return periods. As a result of the investigations, for temperature indices the north-west of the country has the highest values by different return periods. The increase in the values of R10, R20, RX1day and RX5day indices in the different return periods was more in the Zagros and Alborz mountain ranges, and the CWD, CDD and SDII indices have the highest values in the Caspian Sea and Persian Gulf Coastal areas.
Type of Study: Research |
Subject:
Special
Received: 2023/12/7 | Accepted: 2023/09/1 | Published: 2023/09/1
Received: 2023/12/7 | Accepted: 2023/09/1 | Published: 2023/09/1
References
1. اسدی، اشرف و علی حیدری. 1390. تحلیل تغییرات سریهای دما و بارش یراز طی دوره 2005-1951. مجله جغرافیا و برنامهریزی محیطی، 22(41):152-137.
2. تقوی، فرحناز و حسین محمدی. 1384. روند شاخصهای حدّی دما و بارش در تهران. پژوهشهای جغرافیایی، 53: 172-151.
3. حمیدیانپور، محسن؛ محمد سلیقه و غلامعباس فلاحقالهری. 1392. کاربرد انواع روشهای درونیابی به منظور پایش و تحلیل فضایی خشکسالی(مورد: استان خراسان رضوی). جغرافیا و توسعه، 30: 57-70.
4. درخشنده، علی؛ خورانی، اسداله و رضازاده، مریم. 1402. روندیابی بارش در ایران بر اساس دادههای MERRA2. فیزیک زمین و فضا. انتشار آنلاین. doi: 10.22059/jesphys.2023.350125.1007465
5. رحیمزاده، فاطمه؛ اکرم هدایت دزفولی و آرزو پوراصغریان. 1390. ارزیابی روند و جهش نمایههای حدّی دما و بارش استان هرمزگان. جغرافیا و توسعه ، 21: 116-97.
6. زرین، آذر و عباسعلی داداشی رودباری.1400. پیشنگری دورههای خشک و مرطوب متوالی در ایران مبتنی بر برونداد همادی مدلهای تصحیح شده اریبی CMIP6. فیزیک زمین و فضا، 3: 561-578.
7. زرین، آذر؛ عباسعلی داداشی رودباری و نرگس صالحآبادی. 1399. بررسی بیهنجاری و روند دمای ایران در پهنههای مختلف اقلیمی با استفاده از مدلهای جفتشده پروژه مقایسه متقابل مرحله ششم(CMIP6). ژئوفیزیک ایران، 15: 35-54.
8. عساکره، حسین. 1391. توزیع فراوانی تغییر بارشهای شدید در شهر زنجان. مجله جغرافیا و برنامهریزی محیطی، 23(45): 66-51.
9. قصابفیض، مصطفی و حسین اسلامی.1396. ارزیابی روند تغییرات بارندگی با روش من-کندال و رگرسیون خطی در استان خوزستان. فصلنامه علمی و تخصصی مهندسی آب، پاییز 96.
10. کمری، حمزه و آذر نوری. 1395. ارزیابی و برآورد دوره بازگشت بارندگی با استفاده از دادههای بارندگی سالانه(مطالعه موردی: کرمانشاه). فصلنامه پژوهش در علوم، مهندسی و فناوری، 4: 25-35.
11. ماوی، اچ. اس. 1994. مقدمهای بر هواشناسی کشاورزی. ترجمه محمدرضا اردکانی، محمدرضا حاجسیدهادی، حسن نطقی طاهری (1383). تهران، قلمستان هنر.
12. محمدی، بختیار. 1390. تحلیل روند بارش سالانه ایران. جغرافیا و برنامهریزی محیطی، 3: 106-95.
13. محمدی، حسین؛ قاسم عزیزی، فرامرز خوش اخلاق و فیروز رنجبر. 1396. تحلیل روند شاخصهای حدّی بارش روزانه در ایران. مجله پژوهش های جغرافیای طبیعی، 1: 21 .
14. نکوئیان، صابر؛ افشین هنربخش، سید جواد ساداتینژاد و روحاله فتاحی. 1390. انتخاب بهترین توزیع آماری با پارامترهای مختلف با دو روش آزمون کلموگروف-اسمیرنوف و آزمون مربع کای با استفاده از نرمافزار FFA در برآورد سیلاب(مورد: حوزه آبخیز کارون شمالی). پنجمین کنفرانس سراسری آبخیزداری و مدیریت منابع آب و خاک.
15. Abaurrea, J., Cebrián, A.C. (2001). Trend and Variability Analysis of Rainfall Series and their Extreme Events. In: India, M.B., Bonillo, D.L. (eds) Detecting and Modelling Regional Climate Change. Springer, Berlin, Heidelberg. [DOI:10.1007/978-3-662-04313-4_17]
16. Alizadeh-Choobari, O, and M. Najafi. 2017. Trend and Changes in air temperature and precipitation over different regions of Iran. Journal of the earth and space physics.
17. Mondal, A.; V, Lakshami,and H, Hashemi. 2018. Intercomparison of trend analysis of multisatelite monthly precipitation products and gauge measurements for river basins of india. Journal of Hydrology, 565(2018) 779-790.
18. Nikulin, G.; E. Kjellstrom, U. Hansson, G. Strandberg, and A. Ullerstig. 2009. Evaluation and future projections of temperature, precipitation and wind extremes over Europe in an ensemble of regional climate simulations. Rossby Centre, Swedish Meteorological and Hydrological Institute, Sweden.
19. Rahimi, M and S. Fatemi. 2019. Mean Versus Extreme Precipitation Trends in Iran over the Period 1960-2017. Pure Appl. Geophys, Springer Nature Switzerland AG, [DOI:10.1007/s00024-019-02165-9.]
20. Rahimi, N,; N. Mohammadian, A. Rezai Vanashi, and K. Whan. 2018. Trend in indices of extreme temperature and precipitation in iran over the period 1960-2014. Open journal of ecology, 8: 396-415.
21. Tian, Q.; and S.Yang. 2016. Regiornal Climatic Response to global warming: Trends in temperature and precipitation in the Yellow, Yangtze and Pearl River Basins the 1950s. Quaternary International xxx (2016) 1-11.
22. Vaghefi, S.A., Keykhai, M., Jahanbakhshi, F. et al. (2019). The future of extreme climate in Iran. Sci Rep 9, 1464. [DOI:10.1038/s41598-018-38071-8]
23. Zhang, X,; F. Yang. 2004. RclimDex (1.0) User Manual, Climate Research Branch Environment Canada Downsview, Ontario Canada.
24. Zia Hashemi, M.; Shamsedin, A.Y,; Melviile, B. W. 2011. Comparison of SDSM and LARS-WG for simulation and downscaling of extreme precipitation events in a watershed. Stoch Environ Res Risk Assess, 25: 475
25. Asadi, A., & Heydari, A. (2011). Analysis of Temperature and Precipitation changes of Shiraz during the Period of 1951-2005. Geography and Environmental Planning, 22(1), 137-152.
26. Mohammadi, H., Taghavi, F. Extreame temperature and precipitation (2007). -. GEOGRAPHICAL RESEARCH QUARTERLY, 38(1), -.
27. Hamidiyanpour, M., Saligeh, M., Falah Ghlhari, Gh. (2014). Applaying Types of Interpolation Methods for Spatial Analysis and Monitoring of SPI Drought Case study: Khorasan Razavi Province Geography and Development, 11(30), 57-70. doi: 10.22111/gdij.2014.242
28. Rahimzadeh, F., Dezfouli, A. H., Asgharian, A. (2011). Evaluation of trends and jumps in extreme temperature and precipitation indices in Hormozgan province. Geography and Development, 9(21), 97-116. doi: 10.22111/gdij.2011.583
29. Zarrin, A., & Dadashi-Roudbari, A. A. (2021). Projected consecutive dry and wet days in Iran based on CMIP6 bias‐corrected multi‐model ensemble. Journal of the Earth and Space Physics, 47(3), 561-578. doi: 10.22059/jesphys.2021.319270.1007295
30. Zarrin, A., dadashi-rodbari, A., & Salehabadi, N. (2021). Projected temperature anomalies and trends in different climate zones in Iran based on CMIP6. Iranian Journal of Geophysics, 15(1), 35-54. doi: 10.30499/ijg.2020.249997.1292
31. Asakereh, H. (2012). Frequency Distribution Change of Extreme precipitation in Zanjan City. Geography and Environmental Planning, 23(1), 51-66.
32. Ghasabfeiz, M., & eslami, H. (2017). Variations Trend Evaluation of Rainfall Using Mann-Kendall and Linear Regression in Khuzestan Province. Water Engineering, 5(2), 113-121.
33. Kamari, H., and Nouri, A. 2016. Evaluation and estimation of rainfall return period using annual rainfall data (case study: Kermanshah). Journal of Research in Science, Engineering and Technology, 4: 25-35.
34. Mavi, H. S. (1994), Introduction to Agrometeorology. Translated by, Ardakani, M. R., Haj seyed Hadi, M. R., Taheri, H. N. Tehran. Ghalamestan-e-Honar.
35. Mohammadi, B. (2011). Trend Analysis of annual rainfall over Iran. Geography and Environmental Planning, 22(3), 95-106.
36. Mohammadi, H., Azizi, G., khoshahklagh, F., & Ranjbar, F. (2017). Analysis of Daily Precipitation Extreme Indices Trend in Iran. Physical Geography Research Quarterly, 49(1), 21-37. doi: 10.22059/jphgr.2017.61577
37. Nekoueian, S., Honrabakhsh, A., Sadati-nejad, A. J., Fatahi, R. 2012. Selection of the best statistical distribution with different parameters with two methods of Kolmogrof-Smirnov test and Chi-square test using FFA software in flood estimation (case: North Karun watershed).
38. 5th National Conference on Watershed Management and Soil and Water Resources Management.
39. Abaurrea, J., Cebrián, A.C. (2001). Trend and Variability Analysis of Rainfall Series and their Extreme Events. In: India, M.B., Bonillo, D.L. (eds) Detecting and Modelling Regional Climate Change. Springer, Berlin, Heidelberg. [DOI:10.1007/978-3-662-04313-4_17]
40. Alizadeh-Choobari, O, and M. Najafi. 2017. Trend and Changes in air temperature and precipitation over different regions of Iran. Journal of the earth and space physics.
41. Mondal, A.; V, Lakshami,and H, Hashemi. 2018. Intercomparison of trend analysis of multisatelite monthly precipitation products and gauge measurements for river basins of india. Journal of Hydrology, 565(2018) 779-790.
42. Nikulin, G.; E. Kjellstrom, U. Hansson, G. Strandberg, and A. Ullerstig. 2009. Evaluation and future projections of temperature, precipitation and wind extremes over Europe in an ensemble of regional climate simulations. Rossby Centre, Swedish Meteorological and Hydrological Institute, Sweden.
43. Rahimi, M and S. Fatemi. 2019. Mean Versus Extreme Precipitation Trends in Iran over the Period 1960-2017. Pure Appl. Geophys, Springer Nature Switzerland AG, [DOI:10.1007/s00024-019-02165-9.]
44. Rahimi, N,; N. Mohammadian, A. Rezai Vanashi, and K. Whan. 2018. Trend in indices of extreme temperature and precipitation in iran over the period 1960-2014. Open journal of ecology, 8: 396-415.
45. Tian, Q.; and S.Yang. 2016. Regiornal Climatic Response to global warming: Trends in temperature and precipitation in the Yellow, Yangtze and Pearl River Basins the 1950s. Quaternary International xxx (2016) 1-11.
46. Vaghefi, S.A., Keykhai, M., Jahanbakhshi, F. et al. (2019). The future of extreme climate in Iran. Sci Rep 9, 1464. [DOI:10.1038/s41598-018-38071-8]
47. Zhang, X,; F. Yang. 2004. RclimDex (1.0) User Manual, Climate Research Branch Environment Canada Downsview, Ontario Canada.
48. Zia Hashemi, M.; Shamsedin, A.Y,; Melviile, B. W. 2011. Comparison of SDSM and LARS-WG for simulation and downscaling of extreme precipitation events in a watershed. Stoch Environ Res Risk Assess, 25: 475.
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