Flood Hydrograph Analysis Through Employing Physical Attributes Using Two and Multiple Variables Regression Factor and Cluster Analysis

Since direct experimental evidence is not available, this must be verified through a modeling approach, provided adequate data be available. Many statistical methods are used to study the relation between independent and dependent variables.This research was carried out at the western part of Jazmurian basin tlocated in the southeast of Iran. In this paperused ten physical characteristics such as area (A), perimeter (Pr), average elevation of basin (av.e), average slope (av.s), gravelious coefficient (G), length of main stream (L), pure slope of main stream (P), length of output to one point equivalent center of basin (Lc), Time of concentration (Tc) and lag time(Tl) as independent variables and nine hydrograph component such as Qp, Q25, Q50, Q75, Tp, T25, T50, T75 and Tb as dependent variables.We investigate flood hydrograph through the physical attributes using two and multiple variables regression factor and cluster analysis.With the data of twelve hydrometric stations. Normality test was done using Kolmograph- Smironov. After using four mentioned methods and with the use of modeling, the relations between dependent and independent variables weres defined. The evaluation of hydrologic model behavior and performance is commonly made and reported through comparisons of simulated and observed variables. Frequently, comparisons are made between simulated and measured stream flow at the catchments outlet. Significant models have correlation coefficient bigger than 0.325 at 0.01 significant level and higher than 0.250 at 0.05 significant levels. Three criteria such as root mean square error (RMSE), relative error (RE) and coefficient of efficiency (CE) were used for selecting the ultimate models. The results revealed that with the use of physical characteristics of the basin we can determine the synthetic hydrograph. The results also showed that the two- variable models have higher efficiency in estimating the discharge variables of the simulated hydrographs. After the cluster analysis for group in which are more station s, it results in more significance of the model than one whose group included less stations.