Number of Required Observation Data for Rainfall Forecasting According to the Climate Conditions

More accurate forecasting of monthly rainfall is significantly important in drought forecasting in agriculture, irrigation schedule, water resources management, and crop pattern design. In this paper, ability of time series models in forecasting the rainfall according to the climate conditions is estimated. For this purpose, rainfall data of four different climates in Iran was selected. Using the data amounts of rainfall were forecasted by time series models for one next year. In first method number of observation data for model calibration were 60, and then this increase to the 120 and 588 data. Time series models have found a widespread application in many practical sciences. In addition, rainfall forecasting is done by some methods such as time series models, satellite imagery, and artificial neural networks. However, according to the deficit data in most rainfall forecasting, number of required observation data always been questioned. Therefore, this paper attempts to present number of required observation data according to the climate conditions. By comparing R2 of the models, it was determined that time series models are better appropriate to rainfall forecasting in semi-arid climate. Numbers of required observation data for forecasting of one next year were 60 rainfall data in semi-arid climate.