Journal of Spatial Analysis Environmental Hazards

fa آشکارسازی تغییرات رخدادهای حدی دما و بارش با دوره های بازگشت مختلف در ایران Detecting Trends in Extreme Temperature and Precipitation events with Different Return Periods in Iran تخصصي Special پژوهشي Research <div style="text-align: justify;"> در بیشتر مطالعات مرتبط با تغییراقلیم، آشکارسازی روندهای پتانسیلی در متوسط طولانی مدت متغیرهای اقلیمی مطرح شده است، در حالیکه مطالعه تغییرپذیری فضایی- زمانی رویدادهای حدّی نیز دارای اهمیت است. در این پژوهش دادههای روزانهی دمای حداکثر، دمای حداقل و مجموع بارش 49 ایستگاه سینوپتیک با 30 سال دورهی آماری(2020-1991) برای تجزیه و تحلیل شاخصهای حدّی مورد استفاده قرار گرفته و 15 شاخص حدّی بارش و دما در محیط برنامه R استخراج گردید. درنهایت با استفاده از آزمون ناپارامتریک من-کندال روندهای این شاخص‌ها محاسبه شد.  مقادیر این شاخصها برای دورههای بازگشت 50، 200 و 500 ساله برآورد گردید و نقشههای آن مورد تحلیل و ارزیابی قرار گرفت. نتایج حاکی از آن است که روند شاخصهای بارش به جز شاخص طول دوره خشک (CDD) کاهشی است در حالیکه روند شاخص‌های حدّی دما به جز دو شاخص روزهای با دمای حداکثر و حداقل روزانه زیر صفر درجه، افزایشی است. از نظر فضایی شاخص‌های گرم در نواحی شمال غربی، شاخصهای سرد در نیمه جنوبی کشور دارای روند افزایشی بوده و حاشیه دریای خزر، دریای عمان و خلیج فارس و مناطق کوهپایه‌ای زاگرس تاثیرپذیرترین مناطق در نتیجه افزایش روند بودهاند. همچنین مقادیر شاخصها برای دوره بازگشتهای 50، 200 و 500 ساله برآورد شدند. در نتیجه بررسی های انجام شده، شمال غرب کشور بیشترین مقادیر شاخصها را در دوره بازگشتهای مختلف در شاخصهای دما دارد. افزایش مقادیر شاخصهای R10 ، R20 ، RX1day و RX5day  در دوره بازگشتهای مختلف بیشتر در رشته کوه زاگرس و البرز بوده است و شاخصهای CWD، CDD و SDII در حاشیه دریاهای خزر و خلیج فارس بیشترین مقادیر را داراست. </div> <div style="text-align: justify;">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.</div> تغییر اقلیم, دوره بازگشت, رویداد‌های حدّی, من-کندال, ایران climate change, return period, extreme events, Mann-Kendal, Iran. 149 166 http://jsaeh.khu.ac.ir/browse.php?a_code=A-10-310-3&slc_lang=fa&sid=1 Seddigheh Farhood صدیقه فرهود s.farhood.stu@hormozgan.ac.ir 100319475328460011446 100319475328460011446 No Master's student in Meteorology, Department of Geography, Hormozgan University, Iran Asadollah Khoorani اسدا.. خورانی khoorani@hormozgan.ac.ir 100319475328460011447 100319475328460011447 Yes Associate Professor, Department of Geographical Sciences, Hormozgan University, Iran Abbas Eftekharian عباس افتخاریان eftekharian@hormozgan.ac.ir 100319475328460011448 100319475328460011448 No Assistant Professor, Department of Statistics, Hormozgan University, Iran