عنوان مقاله [English]
نویسندگان [English]چکیده [English]
Programminng and prioritizing are two important factors in basin management that needs different data as like as yield value and its fluctuations. The amount of average yield with different return periods is needed for different purposes as: Estimating river yield, planning for the operation of small and large dams, downstream farms management and other water resources and watershed management projects. Based on available references, there isn’t any comprehensive surveying on this matter. Results of this paper can be applied directly for estimating unguaged sites yields. In this study, hydrometric stations data were collected and analyzed and then the stations which had sufficient data (in regards of both quality and quantity) in the common period were selected. Annual average yields of these stations were calculated. Basins homogenizing was done based on yields and other effective parameters. Furthermore, a number of years in all stations were put away from regional analyzing for evaluating achieved equations. In addition to regional analyzing of annual average yields, probability analysis was also done; the best statistical distribution was fitted and yeild discharge with 2 to 100 years return periods were estimated. By applying basin’s area, probable yield regional equations with 2 years return period were extracted for homogeneous regions.Then based on regional growth curve (Dimensionless ratio of yields with different return periods devided by yield with 2 ears return period) yield values in the stations which didn’t participate in the calculation, were achieved and evaluated with observed yield values. So in each region appropriate equations were determined. As the results of this study shows, in this region homogenizing for yield estimation in unguaged sites was not an appropriate method using regional growth curve, but it can estimate yields with an acceptable accuracy. On the other hand, results shows that regression equations based on median were more accurate in comparison with mean.