پژوهشنامه مدیریت حوزه آبخیز (Oct 2024)
Projection of Future Climatic Variables based on CMIP5 and CMIP6 Models in the Gedarchay Catchment (West Azarbaijan)
Abstract
Extended Abstract Background: The primary issue facing the Earth in this century is the increase in global temperatures and changes in climate variables due to industrialization and rising greenhouse gas emissions. Therefore, it is crucial to investigate temperature trends and climatic changes on both global and regional scales. While several general circulation models have been developed to predict future climate states, different and new methods have been invented to use the output of these models on regional and local scales due to the lack of optimal use of the output of these models caused by the limitation in spatial resolution on the local scale. The Gedarchay watershed is significant for its Gedarchay river basin and groundwater resources—especially in agriculture—, hence it has been the focus of various studies. However, no research has yet studied the impacts of climate change under SSP scenarios of the 6th report, which incorporate socio-economic factors. Thus, this study aims to analyze future changes in climate variables for the Gedarchay Naghadeh watershed under the RCP emission scenarios of the fifth report (CMIP5) and the SSP scenarios of the sixth report (CMIP6), integrating greenhouse gas emissions and socioeconomic activities. The findings could significantly inform future water resource policymaking and planning. Methods: This research utilized the SDSM microscale exponential model to analyze climatic variable changes in the Gedarchay Naghadeh watershed in northwestern Iran. The model's effectiveness was first assessed for climate variables, followed by predictions extending to 2100. Calibration and recalibration were performed using observational data from the Mahabad Synoptic Station and NCEP data. The model's performance was evaluated using correlation coefficients, mean absolute error, and mean square error. After confirming the model's reliability, outputs from the CanESM2 and CanESM5 models were studied for the periods 2031-2050 and 2081-2100 under the RCP 2.6, 4.5, 8.5 and SSP1-2.6, 2-4.5, 5-8.5 scenarios by the microscale SDSM statistical model. Results: The model's evaluation and recalibration were done using NCEP, CanESM2, and CanESM5 data to forecast and compare precipitation, maximum, and minimum temperatures for the Mahabad station across two periods 2031-2050 and 2081-2100, against a baseline. The accuracy of the SDSM model was assessed using average absolute error statistics, and the errors for precipitation, maximum, and minimum temperatures were 1.645, 0.029, and 0.031, respectively, with CanESM2; their values were 0.73, 1.10, and 1.89. Correlation coefficients were also calculated, yielding 0.998, 0.999, and 0.999 for the CanESM2 model and 0.999, 0.993, and 0.971 for the CanESM5 model. The mean squared errors were 2.240, 0.043, and 0.045 for CanESM2, and 0.89, 1.49, and 2.07 for CanESM5. Results indicate that the average maximum temperature is projected to rise by 0.93 °C from 2031 to 2050 under the RCP scenario but it remains stable from 2081 to 2100. Increases of 1.24 °C in 2031-2050 and 0.35 °C in 2081-2100 were anticipated under the SSP scenario. The average minimum temperature increases for the RCP scenario were 0.27 and 0.28 °C for the respective periods, and 0.46 and 0.43 °C for the SSP scenario. Rainfall is projected to rise by 0.59 and 0.38 mm in the RCP scenario during the two periods, compared to increases of 2.15 and 1.64 mm, respectively, under the SSP scenario. Conclusion: The evaluation of the SDSM model's accuracy in predicting precipitation, maximum temperature, and minimum temperature using R, MAE, and RMSE statistics indicates a strong alignment between predicted values and the base period. Results show an increase in precipitation and minimum temperature in both the near and distant futures, with a rise in maximum temperature in the near future and stability in the distant future. Given the significance of climate change and its impacts on agriculture, the environment, and water resources, it is essential for managers and planners to implement effective solutions. These include altering cultivation patterns, using drought-resistant crops, establishing early warning systems, training farmers in climate adaptation methods, and promoting renewable energy to mitigate climate change effects.
