Estimation of Irrigation Water Requirement using Probability Distribution Functions (PDF) Case study: (TriticumAestivum L.)

Imperfect knowledge due to unpredictable climatic conditions leads to uncertainty in decision making in agriculture. Therefore, a probabilistic analysis of the various possibilities besides increasing flexibility in decision-making can also enhance the reliability of decisions. Using Tabriz Synoptic station’s data, the values ofthe crop (TriticumAestivum L.) evapotranspiration and effective rainfall in mid to late season in the study area (Tabriz plain) were estimated. Analyzing the effective rainfall and crop evapotranspiration as probabilistic variables, a reliable method for estimating the irrigation water requirement was described using the “probability distributionfunctions”.By examining 12 different probability distribution function, it was found that the log normal distribution function achieves the best results for the monthly precipitation data with values greater than 30 mm, but there is no unique probability distribution function for evapotranspiration data.This method can also provide a general formula for calculations of irrigation water requirement with different probability levelsduringthe growing season. Results showed thatthe reported values of irrigation requirement in the NETWAT program that is atthe 50 % probability level andused in most of irrigation projects in Iran, were different the calculated values in this study.According to similar reports in different researches, the represented method is recommended for more precise estimation of irrigation water requirements (with different probabilities) especially in critical situations (with high evapotranspiration and low rainfall values) or for crops that are sensitive to water stress in 80% probabilitylevelor higher.