Perspective of Rainfall Variations in Northwestern Iran using Climate Change Circulation Models under Climate Scenarios

Document Type : Research Article

Authors

• Shadieh Heydari Tashekaboud ¹
• Abbas Mofidi ²
• Akbar Heydari tashekaboud ²

¹ Faculty of Agriculture, University of Sari

² Ferdowsi University of Mashhad

10.22067/geo.v0i0.80780

Abstract

1 Introduction
Assessing the projection of precipitation changes as one of the most important climatic and hydrologic parameters will help to overcome the challenges of water resource managers and planners. The future projection of precipitation is very important for countries whose economies are based on agriculture. On the other hand, by prediction of precipitation, it is possible to cope with the drought and reduce the damage caused by it. Therefore, in this research, the projection of long-term precipitation changes in the northwest of Iran was investigated.
2 Materials and Methods
The data of 5 GCMs were under two RCP4.5 and RCP8.5 scenarios downscaled using the LARS-WG6. The precipitation changes were carried out at three different periods (2021-2040, 2051-2070, 2081-2100) and was compared to the base period (1980-1989). LARS-WG6 is a stochastic model using semi-empirical distribution to generate climatic data by statistical downscaling techniques. This model requires less input data than other climatic models because of the repeated calculations and also has more utility for simplicity and efficiency. The LARS-WG model, as a downscaling model, although having less complexity in the simulation process and in relation to input and output data, still has a high ability to predict climate change. The main reason of creating this model was to overcome the weaknesses of the Markov chain. The sixth version of this model (LARS-WG6) has been updated and released in 2018 for Fifth Report Data Mining (CIMP5). To implement the LARS-WG model, daily data on minimum temperature and maximum temperature and daily precipitation in the statistical period (1980–2010) were used as the basis for past climate change and for future climate simulation. To generate synthetic data, the model uses long-term daily station data as input for comparison. If the two data sets are matched, the model will be able to generate time series for future periods. In order to validate and ensure model robustness, the model was run for the baseline statistical period to generate a series of synthetic data in the baseline period. Then, in order to evaluate the performance of the model, the outputs were compared with observational data (meteorological station data) by means of statistical tests (T-test for estimating monthly mean precipitation and F-test for estimating monthly variance of precipitation), were compared. In addition, the performance of the LARS-WG model has been used to determine the coefficients of determination (R2), mean square error (RMSE), mean square error (MSE), and absolute mean error (MAE).
3 Results and Discussion
The results of downscaling model performance showed that there is no significant difference between measured and observed values with critical error of 0.05 in most months of the year and this model is a suitable method for simulating rainfall in the study area. Also, future precipitation results showed that according to the predictions of most models, especially EC-EARTH and MIROC5 during the period of 2021 to 2040, precipitation in the study area will decrease by 4.6, 0.9 and 9.4% respectively and the most changes will happen in the southeast and west part of study area especially in the Khalkhal, Khodabandeh and Orumiyeh stations. Based on the results of EC-EARTH and MIROC5 models, rainfall in both scenarios will increase by 2.5% and 12%, respectively, with the most significant changes in the southwestern regions of the study area, especially Mahabad and Sardasht stations. The results of rainfall changes over the period (2051-2070) also showed that according to GFDL-CM3, HadGEM2 and MPI-ESM models, precipitation decreased by 2.5, 0.3 and 11.8%, respectively. The most reduction in scenarios is based on the RCP8.5 scenario and the MPI-ESM model, which decreased from 16 to 130 mm in the study area, the lowest and highest being at Pars Abad and Sardasht stations, respectively. During this period, according to EC-EARTH and MIROC5 models, rainfall will increase in the study area that it's equal with regional rainfall average with 10.1% and 3% respectively. The results of rainfall changes over the period (2081–2100) also indicate that, under GFDL-CM3, MIROC5 and MPI-ESM models, and rainfall rates in the region decrease. The highest RCP8 scenario values 5.5 are 18.8%, 6.2% and 11.3%, respectively. According to the EC-EARTH and HadGEM2 models, rainfall during this period increased by 7.5% and 17.9%, respectively. Results show that the lowest and highest precipitation changes in this period are in the northeast and southwest of the study area. Overall, based on the results of the different models, precipitation will experience a slight increase over the period (2021-2040), with an average of 0.3% of the total model output. The area will be level. In the next two periods (2051-2070 and 2081-2000), the average precipitation is expected to decrease by 0.7% and 1.4%, respectively. According to the results, the most decreasing changes were in the eastern regions of the study area and the most incremental changes were in the southwestern region of the study area. The most decreasing and incremental changes are also more evident under the RCP8.5 scenario than the RCP4.5 scenario. The results show that the model accurately simulates rainfall in the studied stations. Pearson correlation coefficients between observational and simulated data are acceptable at the significant level of 0.01.
4 Conclusion
Climate change is one of the most important environmental challenges of human society in recent years due to the global warming, the crisis in water resources, the change of ecosystems, and the social and economic problems caused by these changes. It has attracted the attention of many scientific circles worldwide. Temperature and precipitation are among the most prominent climate variables in an area. For this purpose, the present study investigated the prospects of long-term precipitation changes in the northwest of the country by using the outputs of 5 atmospheric general circulation models under two scenarios RCP4.5 and RCP8.5. The LARS-WG6 model was used for microarray outputs. Therefore, the results of performance evaluation of LARS-WG model using different statistical tests and calibration indices showed that this model has good accuracy for simulation of precipitation in most of the studied months and stations. The results of the outputs of different global models showed that precipitation in future periods in the study area based on GFDL-CM3, HadGEM2 and MPI-ESM models will be lower than the base period. So, by using EC-EARTH and Miroc5 models, the rainfall amount will examine more than the value of the base period or close to it, respectively. Various results in the different applied models showed that long-term precipitation prospects will only be investigated using the GCM model, which will lead to many uncertainties too. It can be said that this trend is due to the complexity of the precipitation process and the abilities and characteristics which each of these models exhibits. Overall, according to the results, precipitation will increase in the near future (2021-2040) in the northwest of the country compared to the baseline period. But the prospect of precipitation in the near future shows that there is a decreasing trend among the five GCM models except for HadGEM2. Thus, predicting the probability of precipitation in the northwest of the country in the near future will be a relative increase in precipitation compared to the baseline period and a sharp decrease in precipitation in the near future. The results of this research can be used in the managing and planning of water resources, agriculture, energy and so on.
 

Keywords

• Precipitation
• Climate change
• GCMs
• LARS-WG6
• RCP

20.1001.1.23221682.1398.8.1.8.9

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