بررسی روند و پیش‌بینی میانگین حداکثر دمای شمال‌غرب ایران با استفاده از مدل‌های سری زمانی

در این تحقیق روند تغییرات میانگین حداکثر دما در 12 ایستگاه منتخب شمال غرب کشور با دوره آماری 24 ساله (1376-1399) بررسی گردید. ابتدا روند معنی دار برای سری های زمانی سالانه با بکارگیری آزمون ناپارامتری من-کندال در سطح اطمینان 95 و 99 درصد مورد ارزیابی قرار گرفت. نتایج نشان داد روند تغییرات زمانی میانگین حداکثر دمای هوا در همه ایستگاه های مورد مطالعه افزایشی بوده و بیشترین موارد معنی داری در ایستگاه های مراغه، و ارومیه مشاهده گردید. سپس میانگین حداکثر دمای ماهانه در این ایستگاه ها با استفاده از مدل سری های زمانی پیش بینی شد. بدین منظور، از سری مدل فصلی sarima(p,d,q)(p,d,q)ω استفاده شد. به منظور معرفی بهترین مدل از شاخص های ضریب همبستگی (r) و ضریب کارایی (ce) استفاده گردید. در نهایت بر اساس مدل های برازش یافته پیش بینی برای 8 سال آتی انجام شد. پیش بینی ها مشخص کرد که در منطقه مورد مطالعه در 8 سال آینده (1400-1407) میانگین حداکثر دمای هوا در محدوده 69/0 تا 39/4 درجه سانتی گراد به ویژه در ماه های زمستان افزایش خواهد یافت. افزایش دما در زمستان می تواند اثرات منفی شدیدی بر منابع آب، رژیم بارش ها و فعالیت های کشاورزی منطقه مورد مطالعه داشته باشد.

investigation of trend and prediction of maximum mean temperature in northwest iran using time series models

background and objectivesclimate change and its consequent impacts on the different phenomena of the earth are serious mankind concerns during recent years. climate change and global warming have very significant negative impacts on different resources including water and ice resources, forests, pastures, agricultural fields, industry, and finally human life. air temperature and precipitation variations are the primary effects of climate change on atmospheric elements. hence, the assessment of the atmospheric element for an instance temperature has critical importance. temperature rise caused by climate change has serious negative impacts on agricultural activities through increasing the evaporation and the possibility of droughts. because climatological elements have nonlinear behavior and they are not a function of a certain statistic distribution therefore a tendency for using non-parametric approaches, especially mann-kendall is growing. the complicated nature of physical processes and lack of adequate knowledge in the climate models have caused the creation of statistical models and their development for defining these processes. the application of these models for the reconstruction of past values and predicting future values has been called time series. the aim of the current research is to analyze the variation trend of mean maximum monthly temperature using the mann-kendall test, mean maximum monthly temperature with time series method, determine the proper pattern and prediction of temperature variations in the northwest of iran.methodologyin this research, the trend of mean maximum temperature variations in 12 selected stations in northwest iran over a 24-year (1997-2020) period was investigated. at first, the trend of variation data series was tested using the mann-kendall approach. then, the mean maximum monthly temperature was predicted using the time series model. minitab 17 software was applied in order for time series model development and prediction purposes. the total number of data for each set was 285 where 80% of them were considered for calibration and 20% for model validation. the performance of models was investigated based on model efficiency coefficient (ce) and correlation coefficient (r) indices. the ce varies between -∞ to 1 and the closer values to 1 indicate more accurate model performance. finally, temperature predictions were done for the following 8 years (2021-28) based on developed models.findingsthe obtained results of the application of the mann-kendall test for determining the mean maximum temperature trend in 12 studied stations in northwest iran clarified an increasing behavior for all stations. increasing trends in ahar and sarab stations were significant at the level of 95% and in the tabriz, maragh, miyaneh, khalkhal, urmia, khoy, and mahabad stations the significance level was 99%. regarding the basic assumptions in time series modeling, before starting model creation, the normal and static situation of the data series was tested. the obtained results of these tests also showed a linear increasing trend in the investigated stations. consequently, seasonal and non-seasonal differential process on initial series in the studied stations was conducted to model recognition through acf and pacf differential series graphs. the temperature variations along different seasons of the year in all stations proved more increase for all stations in the winter in comparison with other seasons. considering the 12th differential level due to seasonal characteristics of data, acf and pacf graphs of differential series were plotted and a correlation was observed between data in the first lag. to create a series model, the seasonal model of sarima(p,d,q)(p, d, q)ω was applied. after calibration and validation of the final models for studied stations, these models were applied for predicting 8 following years (2018-2026) and were compared with the basic period (1994-2017). according to the predictions, the mean maximum temperature in all stations shows an increase in comparison to the basic period. the highest increasing amount is for jolfa station with 4.39˚c and the lowest value was determined for parsabad station with 0.69 ˚c. the variations in temperature were assessed on a seasonal scale for 8 upcoming years. the comparisons of temperature variation for all stations in the different seasons showed increasing behaviors in all stations in winter in comparison with other seasons.conclusionthe mean maximum temperature in 12 studied stations was modeled by time series. high values for r and ce in these stations proved the high accuracy of this method for predicting air temperature. after model development and selection of the most proper model for studied stations, the prediction of temperature was performed for 8 following years for each station. the temperature variations in this duration were investigated seasonally and the results showed that the maximum temperature increase for all stations will occur in the winter. temperature increasing in winter months may cause negative impacts like change in precipitation pattern from snow to rain, early melting of region snow reservoirs, incomplete vernalization of seeds, and early start of the growing season with a risk of frost hazard for crops.