تحلیل مکانی و زمانی روند تغییرات سالانة کمینه و بیشینه‌ی دما در ایران مبتنی بر روش رگرسیون چندک

نوع مقاله : مقاله پژوهشی

نویسندگان

1 1- دانش‌آموخته کارشناسی ارشد مهندسی منابع آب، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گلستان

2 دانشیار گروه مهندسی آب، دانشگاه علوم کشاورزی و منابع طبیعی گرگان

3 استادیار گروه مهندسی آب، دانشگاه علوم کشاورزی و منابع طبیعی گرگان

4 4- دانشجوی دکترای علوم و مهندسی آب- منابع آب، دانشگاه ارومیه، آذربایجان غربی

چکیده

دمای هوا یکی از متغیرهای مهم آب و هواشناسی است و تغییرات شدید در متغیرهای دمایی، موجب افزایش احتمال وقوع پدیده‌های حدی نظیر خشکسالی، بارش‌های سنگین و طوفان می‌شود. روش رگرسیون چندک این توانایی را دارد که با بررسی روند چندک‌های مختلف توزیع، تغییرات در سطوح مختلف پارامتر را در طول زمان مشخص کند. در این پژوهش، تغییرات زمانی و مکانی از کمینه و بیشینة دما در پهنه‌ی جغرافیایی ایران بررسی قرار گرفت. روش رگرسیون چندک بر روی چندک‌های مختلف از سری زمانی داده‌های کمینه و بیشینة دمای روزانة 102 ایستگاه هواشناسی در دوره 30 ساله (1396-1367) اجرا گردید و نتایج آن با استفاده از روش‌های مختلف درون‌یابی در محیط GIS به منظور انتخاب بهترین روش درون‌یابی پهنه‌بندی شد. نتایج پهنه بندی مکانی شیب‌های چندک موردنظر با استفاده از روش‌های مختلف درون‌یابی نشان داد که روش درون‌یابی بیزین کریجینگ تجربی دارای کمترین مقدار RMSE می‌باشد. همچنین نتایج نشان داد که روش رگرسیون چندک، روندهای افزایشی معنی‌دار با شیب‌های متفاوتی را برای متغیرهای کمینه و بیشینة دما در چندک‌های مختلف و برای بخش‌های مختلف از ایران در طول 30 سال نشان داده است؛ بیش‌ترین روندهای افزایشی برای مقادیر بسیار پایین از کمینة دما در نیمه‌ی غربی، مقادیر میانه در نیمه‌ی شرقی و مقادیر بسیار بالا در نیمه‌ی غربی، شرق و بخش مرکزی ایران بوده است. در مقابل، بیش‌ترین روندهای افزایشی برای مقادیر بسیار پایین از بیشینة دما در شمال غربی، مقادیر میانه در نیمه‌ی شرقی، غرب و بخش مرکزی، و مقادیر بسیار بالا در نیمه‌ی شمالی ایران دیده شده است. و به طور کلی می‌توان بیان کرد که دمای ایران در اثر تغییر اقلیم افزایش یافته و روش رگرسیون چندک برای بررسی و کنترل دماهای بسیار بالا و بسیار پایین که در مطالعات خطر آب‌و‌هوایی اهمیت بیش‌تری نسبت به دمای میانگین دارند، مفید می‌باشد.

کلیدواژه‌ها


عنوان مقاله [English]

Spatial and Temporal Analysis of Annual Minimum and Maximum Temperature Trend in Iran based on Quantile Regression Method

نویسندگان [English]

  • Sedighe Bararkhanpour 1
  • Khalil Ghorbani 2
  • Meysam Salarijazi 3
  • Laleh Rezaei ghaleh 4
1 Department of Water Engineering, College of Soil and Water Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Iran, Gorgan
2 Associate Professor of Water Engineering Department, Gorgan University of Agricultural Sciences and Natural Resources
3 Assistant Professor of Water Engineering Department, Gorgan University of Agricultural Sciences and Natural Resources
4 Ph.D. in Water Sciences & Engineering, Urmia University
چکیده [English]

Introduction Temperature is one of the most important meteorological variables and any change in temperature variable causes changes in the occurrence of extreme phenomena such as drought, heavy rainfall, and storms that will cause irreparable damage in various social, economic, and agricultural sectors. Therefore, it is important to study the trend of these climatic variables in order to achieve methods for controlling and managing damages. Methods based on the mean or median of the data are generally used in studies related to trend investigation, since mean is a measure of central tendency, if studied alone may not provide information about trend variation in different parts of meteorological and hydrological data distribution, especially distribution tails. While extreme weather events often result from extreme values of climatic parameters. For this purpose, to study trend variation in the different data ranges, the quantile regression method was proposed, which has no limitations of previous parametric and nonparametric methods and has the ability to study trend variation and Show changes in different quantiles or different values of a climatic parameter. Therefore, the purpose of this study is to investigate the trend of temporal and spatial changes of minimum and maximum temperature on an annual scale using the quantile regression method in the geographical area of Iran.

Materials and methods The study area in the present study is the geographical area of Iran, which due to its location in the middle latitudes of 30 degrees, most of its area is covered by arid and semi-arid climates. In order to analyze a trend, maximum and minimum daily temperature data of 102 meteorological stations with a statistical period of 30 years (1988-2017) were obtained from the Meteorological Organization. After preparing the data, the annual time series was formed from the minimum and maximum temperature for this period of 30 years. Then the quantile regression method was used to analyze the trend variation in different quantiles of minimum and maximum temperature and the estimated slopes for the whole country were zoned using different interpolation methods in the GIS environment after that the Bayesian kriging interpolation method was selected for interpolation and the results were analyzed.

Results and discussion The results showed that the quantile regression method showed different trends for the minimum and maximum temperature variables in different quantiles and for different parts of Iran during the year. In general, both temperature variables had an increasing trend in all studied quantiles for all parts of Iran; Lower quantiles of the minimum temperature have an increasing trend in most parts of Iran and the most increasing trend slopes have been observed in the western half of the country, and about 63% of the area of Iran had a positive slope of 5-10%. While in the median quantile, the trend variation is more severe and all regions of Iran have a significant increasing trend that has been significant in most regions. in general, about 73% of the regions have a slope of 5-10%, which is visible in the western half, northeast, and southeastern parts and about 24% of the areas have a slope of 10-15% which is seen in eastern Iran. However, upper quantiles of minimum temperature that indicate high-temperature values also have a positive and significant trend in most parts of Iran, which in general 69% of the regions have a trend slope of 2-5%, which is located in the eastern half, north and south of the country, while 29% of Iran's area has a slope of 5-10%, which is mainly located in the western half and parts of the east and center of the country. However, in the study of the lower quantiles of the maximum temperature, the trend variation was more than the minimum temperature and there were significant increasing trends in most parts of Iran that 47% of the area had a slope of 2-5% which is located in the eastern half of Iran, and also 43% and 10% of the area of Iran had a slope of 5-10 and 10-15 %, respectively, which were observed in the western half of the country, but the number of increasing slopes was higher in the west. The median quantiles of the maximum temperature have a slope of 5-10% in 73% of the area, and 24% of the areas have a slope of 10-15%, which was significant in all cases. However, for the upper quantiles of the maximum temperature, trend variation was not significant, so that 64% of the area had a slope of 2-5% in the southern half and 36% of the areas had a slope of 5-10% in the northern half of Iran.

Conclusion The most increasing trends for low values of minimum temperature were in the western half, median values in the eastern half, and high values in the western half, east and central part of Iran. In contrast, the highest upward trends for low values of maximum temperature are obtained in the northwest, median values in the eastern, western, and central half, and high values in the northern half of Iran. trend slopes for both minimum and maximum temperature have been higher in the median quantile and in general, it can be inferred that the temperature in Iran has increased due to climate change and the quantile regression method is useful to study and control very high and very low temperatures that are more important than the average temperature in climate risk studies.

کلیدواژه‌ها [English]

  • Temperature
  • Quantile Regression
  • Temporal and Spatial Trend
  • GIS
  • Iran
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