Document Type : Research Paper
Authors
1
Dept. of Water Sciences and Engineering, Imam Khomeini International University
2
Department of Water Sciences and Engineering Imam Khomeini International University, Qazvin, Iran
10.22059/jwim.2025.397707.1240
Abstract
In this study, precipitation, minimum temperature, maximum temperature, and evapotranspiration data from the CNRM-CM6-1, GFDL-ESM4, ACCESS-CM2, and CanESM5 climate models were compared with Qazvin synoptic data for the base period 1986-2014 individually and ensemble. The results showed that evapotranspiration, minimum and maximum temperatures in the group model (combination of the aforementioned climate models using the weighted linear averaging method of the models) are associated with reasonable and appropriate estimates with coefficient of determination values of 0.95 and low RMSE values. The results also showed that running models in groups reduces errors. Using an ensemble model, precipitation data, minimum temperature, maximum temperature, and evapotranspiration were simulated under two scenarios, SSP2_4.5 and SSP5_8.5, for future periods, and the results showed that temperature and evapotranspiration will increase and precipitation will decrease in future periods. The maximum and minimum temperature changes compared to the base period in the period 2026-2050 for the SSP2_4.5 and SSP5_8.5 scenarios will be 1.9, 2.49, 2.98, and 3.31 degrees Celsius, respectively, and the precipitation changes for the SSP2_4.5 and SSP5_8.5 scenarios will be -37.82 and -11.24 mm, respectively. Using climatic parameters, wheat yield was evaluated using random forest, neural network, and ensemble model methods in the baseline period, and the results showed that the ensemble model reduced the error. Therefore, the ensemble model was used to simulate wheat yield in future periods, and the results showed that wheat yield would decrease in future periods. The yield changes in the period 2076-2100 will be -7.22 and -10.81 percent in the SSP2_4.5 and SSP5_8.5 scenarios, respectively.
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