دکتر محمد لطفی

The impact of climate change as the most important factor affecting agriculture, especially rainfed cultivation has challenged the management of these resources. This study tries to investigate the effect of climate change on the yield and length of the dryland wheat phenological stages in western Iran. For this purpose, two downscaling models, SDSM and LarsWG, were used to simulate the climate over the next 30 years. To model the performance and phonological stages, two models of AquaCrop and DSSAT in the base period and the future period were used, considering the three RCP climate scenarios of 2.6, 4.5, and 8.5. The results showed that the AquaCrop model has better performance and less error than DSSAT. So that the value of the coefficient of determination between observed and simulated data in the base period with AquaCrop model in Kermanshah, Sanandaj, and Ilam stations are 0.86, 0.64, and 0.89, respectively; and RMSE coefficient values are 198.6, 274.6 and 192 kg/ha, respectively. While, in the DSSAT model, the coefficient of determination is 0.90, 0.11, and 0.82, respectively, and the RMSE coefficient is 219.9, 288.1, and 238 kg/ha, respectively. The general results show that in LarsWG downscale model with AquaCrop and DSSAT agronomic model, the lowest yields are allocated to Kermanshah, Sanandaj, and Ilam in 8.5, 4.5, and 8.5 scenarios, respectively, and the highest yields are obtained in 2.6, 2.6, and 4.5 scenarios; which indicates a decrease in performance in the scenario of rising temperature and rising carbon dioxide. However, in the SDSM downscale model, the highest yield of dryland wheat is mainly in scenarios 4.5 and 8.5, and the lowest yield will be in scenario 2.6, which is different from the results of the LarsWG model. According to these results, it can be stated that the type of downscale model and crop model can be effective in the obtained results.

Rising global warming is one of the greatest challenges facing humanity in the 21st century. Therefore, it is very important to predict the maximum temperatures in order to know the amount of changes and, as a result, to take the necessary measures to adapt and moderate the adverse effects caused by it. Therefore, in this study, maximum temperatures were predicted in three provinces of Kurdistan, Kermanshah and Ilam in the west of the country. For this purpose, the data of two global models HadGEM2 and CanESM2 were used under three scenarios: RCP2.6, RCP4.5 and RCP8.5, as well as two microscale models of LARS-WG and SDSM, and changes in maximum temperatures on a monthly and annual basis in the future 2050-2021) compared to the base period (2018-1989) were examined in 17 meteorological stations. MAE, MSE, RMSE and R2 indices were used to calibrate and validate SDSM and LARS-WG models. The results showed that both models have a high ability to simulate the maximum temperatures of the study area. However, the SDSM model is more accurate than the LARS-WG model, with the lowest and highest accuracy for Bijar and Tazehabad stations with RMSE of 0.02 and 0.18, respectively. The results of maximum temperature forecasting also showed that according to both models, the maximum temperature in the future period will increase compared to the base period, which is the average of the studied models between 0.8 to 1.9 degrees Celsius in the region. Will be studied. The highest rate is estimated based on the RCP8.5 scenario. Spatially, the most changes are related to the northern and eastern areas of the study area and the least changes are related to the western areas of the study area.

It is important to prediction of precipitation changes to know how much it will change in the future and to consider the necessary measures to mitigate the adverse effects of climate change. Therefore, in this study, precipitation changes in the west of the Iran were predicted. For this purpose, the data of CanESM2 model were used under three scenarios of RCP2.6, RCP4.5 and RCP8.5 using the SDSM downscaling model and was investigated the changes of precipitation in the period (2021-2050) compared to the basic period (1989-2018). The MAE, MSE, RMSE and R2 indicators were also used to ensure the calibrasion and validation of the SDSM model. The results indicate the appropriate accuracy of this model in the study area. The results of a monthly survey of precipitation at the study station showed that precipitation will increase during the period (2021-2050) in most areas from October to December, and in some areas in addition to January to April. Based on this, it is expected that the amount of precipitation in the next period compared to the basic period will increase between 0.05 and 15% on average in the study area. Most of the changes are related to the northern and eastern areas of the study area and the least changes are related to the southern areas of the study area. Also, the highest and lowest changes are predicted based on RCP8.5 and RCP2.6 scenarios. Due to the complexity of the precipitation process, there are always uncertainties in the field of precipitation forecasting, and it is necessary to use the results of the models in this field with caution and to increase the accuracy of forecasting from different GCM models and microscale methods. Different used. Also, considering the increase and intensity of rainfall and mountainousness of the study area, the necessary strategies to deal with floods and its management should be considered.

Considering the significant effect of chilling and frost phenomenon on agricultural production in Iran, the aim of this study is projection of minimum temperature in three provinces of Kurdistan, Kermanshah and Ilam, Western of Iran. For this purpose, the data of 17 meteorological stations during the baseline period of (1989-2018) were collected. Then, the HadGEM2 and CanESM2 climate models outputs were statistically downscaled using LARS-WG and SDSM weather generators under three climate change scenarios of RCP2.6, RCP4.5 and RCP8.5 during future period (2021–2050) and compared with observed data. The performance of the two weather generators, were compared using MSE, RMSE, MAE and R2 indices. The results indicated the good accuracy of both statistical models in simulating the minimum temperature in the study area; however the SDSM model performed better than the LARS-WG. The projected changes of minimum temperature compared to the baseline period revealed a significant increase varying between 0.6 – 1.5 oC in study stations. The most significant change was observed in northern parts of study region especially in the Saqhez and Zarineh stations. The findings of this study can be used in frost risk management and agroclimatic planning in the region.