بررسی توانمندی مدل sdsm در ریزمقیاس نمایی متغیر دما در استان خوزستان

تغییر اقلیم و افزایش دما از مسایل مهم زیست محیطی بشر به حساب می آیند در چند دهه اخیر افزایش دمای زمین باعث بر هم خوردن تعادل اقلیمی کره زمین شده و تغییرات اقلیمی گسترده‌ای را در اغلب نواحی کره زمین موجب گردیده است.در این پژوهش برای پیش بینی دما از مدل ریزمقیاس نمایی آماری sdsm استفاده کردیم و 7 ایستگاه سینوپتیک استان خوزستان، که دارای آمار اقلیمی 45 ساله (2005 - 1961) و 40 ساله (2005 - 1966) میلادی بودند، انتخاب گردید. خروجی های مدل اقلیمی مدلcanesm2 ، تحت سناریوهای rcp2.6و rcp8.5 استفاده شده است. داده های دوره پایه (2005-1961) میلادی است که از 30 سال اول داده‌ها (1990-1961) برای واسنجی و از 15 سال دوم (2005-1991) برای ارزیابی نحوی عملکرد مدل استفاده ‌شده است. معیارهای خطا و دقت ارزیابی شده است. مقایسه نتایج حاصل از تحلیل آماری برای هر دو مجموعه داده مشاهداتی و ریزمقیاس‌نمایی‌‌شده نشان می‌دهد که، مدل sdsm در ریزمقیاس‌نمایی دمای خروجی مدلcanesm2 به‌درستی عمل می‌کند. با بررسی میانگین دما و مقایسه آن با دوره پایه، به این نتیجه رسیدیم در دوره آینده، دما افزایش می یابد. پیش بینی بدبینانه و خوش بینانه را به ترتیب با سناریوهای rcp2.6 و rcp8.5 در دوره 2100-2006 را نشان می دهد که نسبت به دوره پایه به طور متوسط دمای میانگین استان 24/8oc به ترتیب 2/1 , 3/4 درجه سانتیگراد افزایش داشته اند که بیشترین افزایش دما در ایستگاه اهواز و شوشتر و کمترین دما در ایستگاه باغ ملک رخ می دهد .

performance analysis of sdsm in downscaling temperature in khuzestan province

introduction: by examining the trend of air temperature changes, it is possible to search for traces of climatic changes in the area of iran. temperature is one of the most important meteorological parameters that is used in many studies. this parameter is of special importance in climate change studies, as the increase in temperature is considered one of the most important human environmental issues. in this research, the purpose of the research is to look at the average temperature changes in the base and future period of khuzestan province. the evaluation of the model and the reproduction of climatic variables and the perspective of the future climatic conditions are examined, and this question is raised: is the sdsm model in khuzestan province highly accurate?materials and methods:the area studied in the current research is the synoptic stations of khuzestan province. in this study, meteorological data including the values of minimum temperature, maximum temperature and average temperature for the studied period have been used. in this research, we used sdsm statistical micro-scale exponential model for temperature prediction and 6 synoptic stations of khuzestan province, which had 45-year (1961-2005) and 40-year (1966-2005) climatic statistics, were selected. the outputs of the canesm2 climate model have been used under rcp2.6 and rcp8.5 scenarios. the data of the base period (1961-2005) were used for the first 30 years of data (1961-1990) for calibration and the second 15 years (1991-2005) for the syntactic evaluation of the model performance. error and accuracy measures are evaluated.results and discussion: mae, nrmse, rmse, mse and r2 were calculated based on the average values of the variables in each month. these values were obtained according to the daily temperature produced by the model and the observed values for calibration and validation data. the results showed that according to the nrmse, the error rate in temperature estimation is acceptable (less than 10%) and is almost the same in all stations. the results showed that according to the high correlation coefficient of 87%, the performance of the model is confirmed. finally, it indicates that the model has relatively good accuracy in estimating the climatic variable of temperature. in most stations, they overlap the most in the first months of the year, which is the reason for the accuracy of the model in the first months of the year. in the stations of ahvaz, bandar mahshahr, omidiye aghajari and bagh malek in the first seven months of the year, the highest overlap and accuracy are included, and in the last five months of the year, the average retrospective temperature in these stations is 2.4, 2.4, 2.6 respectively. and 2.7 degrees celsius shows the difference with the observational data. dezful, abadan and shushtar stations have the highest overlap and accuracy in the first three months of the year and july. in the rest of the months, the average retrospective temperature in these stations is 2.6, 2 and 2 degrees celsius, respectively, the difference with the data shows observations. the temperature has increased in all periods and for the rcp2.6 scenario, it increases more than the rcp8.5 scenario.