عنوان مقاله [English]
Introduction: In the vast country, Iran, which has been widely used in the latitude and diverse altitudes, we see the effects of climate change. One of the most fundamental factors in the structure of the planet is climate, and undoubtedly nature, man and all the preventive life of the widespread influence of the climate. Knowledge of climatic zones and influential elements in each region based on climatic indicators and elements has long been the focus of many scientists. Perspectives are less common in literature and climatological texts; This is while environmental planning and analytical analysis is a systematic attempt to look to the long-term future in the field of climatology; One of the most important challenges today and in the future is the issue of rising temperatures. In general, the study of scientific studies and reports shows that the climate and temperature pattern is changing and this issue needs to be studied and paid more attention to the importance of water resources and agriculture in the study area and tried to study the climatic conditions of the province Sistan and Baluchestan is covered.
Materials and Methods: The study area in the present study is the southeastern part of Iran, Sistan and Baluchestan province. In this study, meteorological data from synoptic stations include daily precipitation, minimum temperature, maximum temperature and sundial for the period 1987-1920. The mentioned data have been obtained from the Statistics and Information Center of the Meteorological Organization of the country. -LARS WG is a generator of meteorological data. The LARS - WG model was designed by a scientist from the Rotamstead Center for Agricultural Studies in the United Kingdom. Using data monitored to study the climate behavior of stations in the statistical period, as well as daily network data of future total circulation models, Modeled future courses daily. The core of this model is the use of the Markov chain, which has been used repeatedly. Performance of LARS - WG model Using error measurement indices and ensuring the suitability of the model, future period data were generated using general turnover model data. In this study, statistical tests were used to evaluate the performance accuracy of the LARS-WG model using NSE, MSE and R2 criteria. In this study, the maximum temperature in the base period and future periods based on two scenarios, RCP2.6 (commitment of countries to reduce greenhouse gases) and RCP8.5 (if not adhering to reducing greenhouse gases) was followed by Using maximum daily temperature data of six synoptic stations in the southeastern part of Iran based on the threshold of the 95th percentile, among the available data, heat waves greater than or equal to three days were identified for each station. Since air temperature determines potential evapotranspiration, the ionp relationship is based on the average total annual rainfall relative to the average evapotranspiration.
Results: To compare the simulated and observational monthly averages, the distribution chart related to the mean of each of the variables in the whole period under study in the southeastern part of Iran was drawn and the correlation coefficient of each was calculated. The distribution diagram of minimum temperature, maximum temperature, precipitation related to the southeastern part of Iran is shown in Figure (3). The observed and generated distribution diagrams show the minimum temperature, maximum temperature, precipitation and sundial of the southeastern zone in the period 1987-2019. As can be seen in the above figures, the results show high correlation coefficients. In general, in the southeastern part of Iran, the correlation coefficients between modeling and observational values in the period under review are significant at least at the level of 1%. Becomes. The least changes are related to the coastal station and the most changes are related to the land stations. Changes in the mean annual temperature difference of the southeastern part of the country during the statistical period with a long-term average of 30 years were investigated; In this case, temperature data show a positive trend in the region and in general, temperature changes in the southeastern part of Iran are evident and these changes can be named as an indicator of climate change.
Conclusion: The HadCM2 model shows that the average annual temperature is 22 degrees Celsius much lower than the observed average, with a difference of -2, and has good accuracy for predicting future climate change. The seasonal average temperature increases in six stations and the amount of rainfall in the stations increases towards winter and spring; Most of the heat waves belong to Zabol station and as we go from north to south, the number of these waves has decreased. The spatial displacement of climatic zones in three consecutive periods indicates an increase in climatic drought coefficient and the expansion of climatic territory. Finally, the results show the trend of increasing temperature and decreasing rainfall in the coming decades in the southeastern regions of Iran.
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