Prioritization of flood source areas in Darongar Dam basin using hydrological modeling

Floods caused by rainstorm are one of the most important natural hazards in most watersheds of Iran and damage various sectors every year. Therefore, planning and flood control can reduce potential hazards. Flood control in flood source areas, is one measure to reduce floods and this is usually done most effectively through hydrological modeling. So far, different methods and models have been used to determine flood-prone areas, but the efficiency of these methods in different climatic and environmental conditions is unknown and requires further research. Therefore, the purpose of this study is to evaluate the efficiency of ModClark model in HEC-HMS software and determining flood source areas in Darungar dam watershed in Dargaz city of Khorasan Razavi province.

In this research, spatial distributed hydrological ModClark model in the HEC-HMS was used. For this purpose, rainfall and runoff data of regional stations, digital elevation model (DEM) of the watershed, land use map, vegetation, and hydrological soil group maps were obtained from the regional offices.Tthe model inputs were extracted with ArcGIS software and HEC-GeoHMS . In this research, gridded curve number method for rainfall loss, ModClark method for rainfall-runoff, recession method for base flow and Lag method used for flood routing. In the next step, sensitivity analysis, calibration and validation of the model were performed using six rainfall-runoff events. Then the design rainfall in the return period of 10, 25 and 50 years were entered into the model. Then the contribution of each sub watershed in flood hydrograph in watershed outlet was determined by using Unit Flood Response method and Successive Single Sub watershed Elimination. Finally priority of each sub watershed in the flood peak discharge and flood volume at the main outlet, were determined.

The results of sensitivity analysis for the selected parameters revealed that the parameters related to the curve number (curve number map and CNratio) had the highest sensitivity and the storage coefficient had the lowest sensitivity. Then the model was calibrated with sensitive parameters. The mean values of NSE, RMSE and PBIAS were 0.90, 0.27 and -3.36% for calibration events and 0.862, 0.35 and -7.71% for validation events, respectively. Thus, the efficiency of the model for flood prediction was confirmed. The prioritization results showed that for the return period of 25 years, sub watersheds of 53, 9 and 56 had the highest participation and sub watersheds of 44, 69 and 43 had the lowest participation in the outlet peak flow. In the 50 years return period, sub watersheds of 53, 66 and 37 had the highest participation and sub watersheds of 2, 43 and 44 had the lowest participation in the peak flow. Prioritization of sub watersheds based on flood volume criterion, shows more consistent results, so that the first three priorities in different return periods belong the sub watersheds of 53, 66 and 37. Despite some differences in prioritization by these two criteria, the spatial distribution of different degrees of flood risk is almost similar. Based on peak flow criterion, most of these sub watershed are located in the Center and based on volume criterion, they are mostly located in the Northwest of the watershed. Southern sub watershed in both criteria are classified as low-risk and safe sub watershed.

The overall study results for flood source areas, without considering the area of each sub watershed, indicated that in addition to the geological factors and vegetation, there is a direct relationship between the contribution to peak flood of sub watershed and their slope. The results of the sub watershed prioritization reveal that in the short return period, most sub watershed had little contribution in the outlet peak flow, but by increasing return period, sub watershed prioritization becomes more stable. Based on the results, it was concluded that size and location of the sub watersheds does not affect their contribution to flood peak and volume. The results of this study can be used in planning flood control operations in the study area. Since there are many methods to prioritize sub-basins in terms of flooding, it is recommended that prioritization be conducted with other methods and their results to introduce the best compare models.