According to past studies, the simulation of runoff events with high hydraulic risk has posed many challenges for policymakers, environmentalists and engineers around the world. Using 1-D modelling to predict flood risk from different return period events or multiple land use and climate change scenarios are common(Horritt & Bates, 2002).
It is noticeable that the use of the Digital Elevation Model (DEM) in the creation of flood models have reached an important role of the topographic and hydrological analysis of basin data, since it represents a series of elevations in the basin at regularly spaced intervals. This removes the assumption that the basin or area is a flat surface without contours(Heimhuber et al., 2015).
In case study on flood risk and flood prediction using GIS and the model of hydrodynamic presented the possibility of using DEM controlled in a GIS and translated into MIKE21. In the study, different scenarios were checked out, and results were translated into the GIS environment for flood visualization and analysis during a 100-year flood return period(Ntajal et al., 2017). However, Jagadish Prasad Patraa, Rakesh Kumara and Pankaj Manib pointed out that there was no real way to calibrate the simulations from the modelling output, as flood and stage data for the floods were rarely recorded and compared between the MIKE21 and MIKE1 results, the first being an improvement of the last one(Prasad, Kumar, & Mani, 2016).
In a research conducted by Sarawut Jamrussri and Yuji Toda on the hydraulic models and GIS for the study of the Mae Klong River in Thailand. Flow frequency analysis was used in the creation of a flood risk map. The study also showed that the simulation results were correctly presented in GIS and DTM format, using contour and height data from the river point. Sarawut Jamrussri and Yuji Toda conclude their study by suggesting that more studies be done in large basins, dividing them into sub-basins and introducing the network link to integrate them to have a general view of the basin. Runoff from floodplains, fluvial canals and artificial structures are important factors in the study of the prediction of runoff flow patterns, the researchers added. rainwater in upstream areas and not stable(Jamrussri & Toda, 2017).
2.1 Analysing methods
HMS uses a project name as the identifier for a hydrological model. A HMS project must have the following components before it can be executed: a basin model, a climate model and control specifications. The characteristics of the basin model and basin were created as a lower map file imported into the HMS from the data derived from HEC-HMS for model simulation(Oleyiblo & Li, 2010). The observed rainfall and discharge data were used to create the climate model using the User Indicator Weight Method (UIWM), and then the control specification template was created. The control specifications determine the time model for the simulation; its characteristics are: a start date and time, a date and time of completion, and a calculation time step. To operate the system, the basin model, the climate model and the control specifications were combined. The historical data observed from precipitation stations representing each sub-basin and one measuring station in the river basin and precipitation stations representing each sub-basin and one measuring station in the basin were used to calibrate the model. check. One-time step per hour was used for the simulation according to the time interval of the observed data(Hashemyan et al., 2015).
Figure 1. Model representation of the Ravine Lan Couline watershed in HEC-HMS.
The HEC-HMS model for flood simulation uses a graphical interface to build the semi-distributed basin model. For each sub-basin of the main basin, the hydrological model is forced using a unique hietograph. First, using the kriging method, spatial rainfall distributions were generated from the time values recorded in the rain gauge station located at the top of the river. Then, for each sub-basin, rainfall series per hour were calculated. The Soil Conservation Service curve number (SCS-CN) method was used to calculate the runoff volumes in the precipitation-runoff model(Hashemyan et al., 2015).
The HEC-RAS model was implemented using the cross sections to provide channel width and bed elevations. These sections were extended on both sides of the channel using DEM derived from LiDAR to provide a floodplain topography. The section was then described by 5-10 points on each side of the canal coinciding with significant topographic features such as slope failures. The elevation profile of the bed and examples of cross sections used in the HEC-RAS model are given in following figure.
The boundary conditions of the model are a dynamic discharge imposed at the upstream end of the section and an elevation of the surface of the water imposed downstream. end, both provided by scene recorders and a nominal section in the case of imposed discharge. Although the use of free surface elevation measured at the downstream end means that boundary conditions and validation data (travel time) are not completely independent, the effect of this situation has been judged low. Floodwave propagation time remains a good source of calibration data, as they are highly dependent on the calibration of the model and are not significantly affected by the downstream boundary conditions. The expected flood range was then calculated by re-projection of the water levels of the 19 cross sections in the high-resolution DEM.
In a research conducted by Fowze the HEC-RAS model when presented with the appropriate hydraulic and geometric data, calculates water-surface profiles. The original reference for the method to determining the roughness coefficient in reaches is Cowan method because it includes several factors control the roughness coefficient. Then HEC-Geo RAS extension was used to preparing and inputting geometric information about the reach that these data are include flow path, left and right bank and cross sections that in the form of new data layers was entered to HEC-RAS model.