Extended Abstract
Introduction
   Flooding as a natural hazard usually takes place in many parts of the world and could be a serious threat to the population and environment of the occurring places. So, analyzing the flooding sensitivity is essential for preventing and reducing future hazardous events in each watershed. Therefore, the following objectives are considered in this study: (a) Determining the sensitivity to flooding of sub-watershed based on some morphometric parameters. (b) Calculating the flood peak discharge in each sub-watershed using Rational method. (c) Investigating the relationship between the flooding sub-watershed rank with respect to morphometric parameters and the estimated rank based on the Rational method.
Materials & Methods
    In this study, extracting drainage network and 33 sub-watershed in Kebar-Fordo watershed located in Qom province with an area of ​​128372 hectares were performed by employing Arc Hydro tool in Arc-GIS environment. Then, six different morphometric parameters which affect flood occurrence were calculated. After that, flood sensitivity maps were prepared based on each morphometric parameters while each sub-watershed rank was determined. Finally the total rank of each watershed was estimated by averaging the whole ranks. Due to the lack of adequate observed flood peak discharge values, Rational method was applied to calculate the maximum flood discharge in each sub-watershed. Then Spearman correlation test in SPSS was used to calculate the correlation between the morphometric variable ranks and flood sensitivity of the Rational method.
Results & Discussion
   In this study, the main stream length ranking shows that five sub-watersheds 1, 5, 15, 20, and 28 are more susceptible to flooding. The watershed slope ranking indicate that sub-watersheds 20, 22, 25, 27, 28, 30, 31, 32, and 33 are more sensitive to flooding. Based on the roughness number, nine sub-watersheds have a flood sensitivity ranking of more than 3. The total basin relief parameter, which presents the height difference between the highest point and the outlet of the watershed, determines the runoff potential of a basin. The total roughness in sub-watersheds 31, 32, and 33 is higher than 3, which is evidence of flooding in these sub-watersheds. The mean elevation rank also indicates that watersheds 18, 20, 28, 29, 30, 32, and 33 are prone to flooding with a rank greater than 3. The basin perimeter is one of the effective parameters in runoff production. In this study, sub-watersheds 20, 15, 5, 1, and 28 have flood sensitivity ranks greater than 3. The flood susceptibility map of the studied area based on the average rank of the total morphometric parameters shows that the areas with high, medium, low and very low susceptibility classes include 0.49%, 47.79%, 42.87% and 8.85% of the area, respectively. This map shows that sub-watersheds 32, 31 and 33 are the most susceptible areas to flooding. The rank of slope, roughness number, and total basin relief in sub-watershed 32, is higher than 4, which shows that higher elevations and also greater slope lead to less surface infiltration, more overland flow, and therefore higher peak runoff in this sub-watershed. The calculation of the maximum discharge based on Rational method indicates that the flood ranking which is more than 3, could be seen only in sub-watershed 20 whereas, the values less than 3 could be observed in the rest of the sub-watersheds. Also, the Spearman correlation test shows that the relationship between the flood sensitivity rank of Rational method with the parameters of the perimeter and the stream length is significant at the 99% confidence level and the correlation coefficients are 0.898 and 0.784, while its relationship with the parameters of mean elevation, roughness number, total basin relief and slope is not significant. Also, the correlation coefficient between the flood sensitivity ranks of Rational method and the average flood rank of the morphometric parameters is 0.601 which is significant at the confidence level and indicates a positive relationship between these ranks.
Conclusion
   This research could be conducted by considering the effect of other parameters, such as land use, flood management practices in each drainage basin, and hydraulic structures along the major streams and rivers. The present study demonstrated that morphometric analysis could be used at different scales to help decision makers for understanding the spatial distribution of flood risk and formulating flood control strategies to minimize its negative impacts on residents and infrastructure, and also, proposed a model for continuously updating the flood mitigation plan for the study area.