Evaluation of the Performance of ClimGen and LARS-WG Models in generating rainfall and temperature time series in rainfed research station of Sisab, Northern Khorasan

Researches in the field of risk assessment and water resource risk management and drought require long-term statistical climate data, while practically; the available collected data in synoptic stations are quite short. To enlarge the data length, we can use weather generators. Weather generators models generate weather data with similar statistical characteristics with actual data. In this paper, we have used LARG-WG and ClimGen models to generate 500 data sample for rainfall and temperature time series in the rainfed research station of Sisab in Northern Khorasan. For performance evaluation of these two models, we have used RMSE, MAE, and CD over the 30 years collected data and their corresponding generated data. Also, to compare the statistical similarity of the generated data with the collected data, we have used t-student, F, and X2 tests. With these tests, the similarity of 16 statistical characteristics of the generated data and the collected data has been investigated in the level of confidence 95%. The results showed that for this Station, LARS-WG model can better simulate rainfall data while ClimGen is a better choice for simulating maximum and minimum temperature data.