Figure 2 Map of soil texture of the Kulsi River Basin
Related Figures (13)
The momentum equation can be written in the form as where ‘A’ is the cross-sectional area, ‘Q’ is the rate of flow, “sq is the slope of the bed, ‘s,’is the friction slope, ‘g’ is the acceleration due to gravity, ‘y’ is the depth of flow, ‘? is the time coordinate, and ‘x’ is the space coordinate. where P is the wetted perimeter. Taking partial derivative of both side of Eq. 17, Wetted perimeter (P) for a parabolic channel section and its partial derivative with respect to x can be written ac Fig.6 LULC map of Kulsi River Basin Fig. 7 Inflow and lateral inflow hydrographs for Kulsi River Basin Table 1 Details of the sub-basins of the study area Fig. 8 Finite difference box for the solution of the diffusive wave equation showing the finite difference equations ( Source: Applied hydrology by Ven Te Chow, 1988) Fig. 10 Computed outflow hydrographs at different dis- tances at the Kulsi River using DWRF®M for the parabolic channel Computed outflow hydrograph at different distances using DWFRM for parabolic channel Fig.9 Flowchart of the DWFRM model Fig. 11 Computed outflow hydrograph at different distances at the Kulsi River using DWFRM for the rectangular channel Table 2. Comparison of the performance of the proposed model with the observed data at the outlet of the Kulsi River Basin a rectangular channel section. It can also be seen from the Table 2 that the time of occurrence of the peak flow predicted by the proposed model (DWFRM) (Whether the channel sec- tion is considered as parabolic or rectangular) is that of 24 h prior to the observed value of 29 days. However, as per the peak flow is concern, the DWFRM with the channel section considered as parabolic has given better result (peak flow error is equal to 20.78%). In other words, the peak flow predicted by the proposed model with parabolic channel section is close to the observed peak flow value. Again, from the total volume error criteria, it is seen that the proposed model with rectangu- ar channel section has given better result. The proposed model is found satisfactory irrespective of the grid size. waves. But, the prediction of flood hydrograph is difficult in the ungauged basin. Flood routing in an ungauged basin is complicated because of the non-availability of hydrometeor- ological data. In the present study, daily rainfall data for the month of June 2010 were collected from seven rain gauge stations near the study area (Kulsi River Basin). ArcGIS 10.1 software was used to delineate the Kulsi River Basin. The study area was divided into nine sub-basins. The col- lected rainfall data were distributed over the study area using the interpolation method in ArcGIS software. The inflow and lateral inflow hydrographs are the input parameters of hydraulic flood routing models, but in many situations, the non-availability of such field measured data is a major problem in the ungauged basin. In this study, the inflow and lateral flow of each sub-basin were evaluated using the SCS-CN method. The curve number for each sub-basin was calculated considering the LULC map and hydrological clas- sification of the study area. A diffusive wave routing model (DWFRM) was developed to predict the outflow hydro- graph at the outlet of the Kulsi River Basin. The developed Fig. 12 Comparison of outflow hydrographs for the parabolic and rectangular channel using DWFRM with the observed data at the Outlet of the Kulsi River Basin
