Function that has more consistent with the desired data will select as a probability distribution until can obtain hydrology variable value per each probability from it. Theoretically, there are different probability functions that were recorded and measurement tentative. This estimate can be expressed using the concept of event return period. This analysis provides this possible until the frequency value of events that are more than their observational value estimated during the period of data record.

Frequency analysis of floods and precipitation extreme values, the magnitude of this phenomenon and also their frequency give appropriate information for different analysis such as determination of risk criterions and reliability in the design of structures.

One of current methods in discharge estimation with different return periods is statistical distributions. In hydrology use of physical models not possible for future events forecasting and most use the single models that system describes based on mathematical terms and concepts. The information and data were recorded in the past will help to us until obtain some statistic parameters and then will forecast events that may occur in the future. For example, in the flood control projects calculated the annual mean flood damage and also design of structures dimension such as dam spillway conducts with regard to probability of floods occurrence and discharges related to them. Calculation of mean probability of occurrence or floods mean return periods can help to solve many problems. Introduction In hydrology cannot determinate time of phenomenon occurrence such as floods or discharge but can investigate previous events occurrence procedure and obtain their mean probability of occurrence.

Log Pearson type III with R.S.S (145.91) and with R.S.S (13.67) for annul peak discharge and Log Pearson type III with R.S.S (145.91) and with R.S.S (13.67) for annul maximum discharge in Jelogir Majin and Pole Zal stations respectively, also Pearson type III and then Log Pearson type III with R.S.S (7.55 & 7.8) and R.S.S (0.79 & 0.8) demonstrated the most fitting for annual mean discharge in Jelogir Majin and Pole Zal stations respectively.ġ. The best probability distribution selected according to SMADA output and R.S.S results. After outlier, homogeneity and adequacy test, data has analyzed by SMADA software and residual sum of squares (R.S.S). In this study in order to determine suitable probability distribution for estimating of annual discharge series with different return periods, annual peak, maximum and mean discharges with a 58 years time period had been collected from Karkheh River (Jelogir Majin and Pole Zal stations in upstream of Karkheh Dam) in Khuzestan province at Iran. The most common among these methods is the Probability Distribution method. Different methods are employed in applying probability distribution in hydrology.

Software smada