The impact of climate change on growing wheat on the slopes of Mount Sablan using the Lars -wg model (a case study of Meshkin Shahr and Sarab plains)

Today, climate issues threaten the security of the world, security which is considered necessary and vital in all fields and for all people. The phenomenon of climate change can affect the water requirement of crops by changing the amount of evaporation and transpiration of plants and the duration, intensity and time of rainfall. The studies related to climate change that have been conducted in Iran in recent years have focused more on climatic indicators and the effects of these changes on agricultural production have been given less attention. Therefore, assessing the effects of climate change on agriculture is an essential need. Due to the fact that Ardabil province is one of the poles of agriculture and animal husbandry and any change in the climate will endanger the lives of most of the residents of this region and will cause a change in the use of farms, pastures and the loss of agricultural production.kkkT Therefore, assessing the effects of climate

The study area is located on the slopes of Sabalan Mountain in Ardabil and East Azerbaijan provinces. Its geographical location is located at latitudes of 37 degrees and 44 minutes to 38 degrees 25 minutes and longitude 46 degrees and 22 minutes to 48 degrees 41 minutes. The minimum height of the area is 371 meters and its maximum is 4811 meters above sea level (Fig, 1). This study was conducted to influence climate change in wheat cultivation on slopes of Sabalan mountain. The important part of this research is based on statistics and information about meshkinshahr and sarab synoptic stations. In order to investigate the climate change conditions, a basic statistical period and a period as climate change should be determined. Therefore, statistical periods in this study from 1995 to 2015 and climate change period from 2016 to 2045 and 2046 were selected. Statistics about the studied stations were obtained from the Statistics and Information Bank of the National Meteorological Organization. The data taken from these statistics include: maximum and minimum temperature,daily rainfall and sunshine. to work with the LARS-WG model; First, the studied data should be sorted as Julius days. After collecting data in the Excel environment, it should be noted that there is no missing data, in case of missing, it must be encoded with -99. Aggregated data must be stored in a folder in nodpad with st extension.The address of folders stored in the N environment should be provided to the model from the option series, and the output data address should be given to the model. How to choose a scenario. First, in Excel environment, statistical data is placed in a column and in the next step, the generated data for each scenario is placed in the columns in front of each column of observational statistics, after preparing this step, the data is taken to the spss environment and correlation is taken between observational and production data. Production scenarios that are highly correlated with observational statistics are accepted as the studied scenario. CROPWAT software was used to estimate water requirement and effective precipitation. By entering minimum temperature, maximum temperature, relative humidity, wind speed and number of sunshine hours related to the plant, as well as the environment and region and its cultivation time, you can calculate the water requirement of the plant at different growth stages. Figure 2 shows the steps of data analysis in CROPWAT software.

In this study, first, the power of LARS-WG model for the basic statistical period of the years (1995-2015) was measured. The purpose of this assessment is whether the model has the ability to simulate for future periods. To do this, the tst file containing the results of comparing the statistical characteristics of the observed data with the simulated data was plotted as a diagram (Shapes, 2 and 3). The results showed that in Meshkinshahr station, the model has better efficiency for simulating maximum temperature and minimum temperature. The observed temperature and simulated temperature for 1995-2015 are similar and the diagrams are overlapping. Also, the deviation of production criteria is in the range of number one. This model is not effective in simulating the sunshine hour because it simulates the amount of sunshine hours in the first half of the year less than the actual size. In the case of rainfall, the model is better than April and May in other months. In Sarab station, the data were performed based on LARS-WG model by comparing the statistical period data and the produced data. To ensure the ability of computational data model, they were compared by model and observational data in the studied stations. Comparative results show the data of minimum temperature, maximum temperature, precipitation and sunshine in mirage synoptic stations for the base period. The capability of LARS-WG model in modeling minimum temperature, maximum temperature and radiation in these stations is completely in accordance with the observed data. The standard deviation rate is between 0.5 and 1. The results of this study show that in Meshkinshahr station, precipitation and temperature in the period 2016 to 2045 are -3.9 and +1.73, respectively, and these two variables in the period 2046 to 2065 are -6.67 and +1.80, respectively. In Sarab station, precipitation and temperature in the period 2016 to 2045 were +6.17 and -0.69, respectively, and in the statistical period of 2046 to 2065 were -12.91 and +0.37, respectively. As a result, rainfall in the coming periods is associated with a decrease in Sarab and Meshkinshahr stations, respectively. Temperatures are also rising at about 2 °C in Meshkinshahr station and the temperature increase is low in Sarab station. In comparison, Sarab plain has a wonderful state because by the period 2046 to 2065 the rainfall in this plain will decrease about 13% compared to the base period, in the same time period, wheat evapotranspiration will reach from 530 mm in the base period to 887 mm in the period 2046 to 2065. Wheat water requirement also increases from 422 mm in the base period to 810 mm, i.e. about 92%. Also, modeling shows that the average minimum temperature of this region decreases from -3 in the base period to -9.67 °C in January and from -4.3 to -8.23 in February. According to the modeling and with the decrease in rainfall in this region, wheat cultivation in this plain will face limitations in the future. However, the results of the models indicate that meshkinshahr plain is better in future periods than Sarab plain.