Assessment of the combined efects of Manning roughness and DEM resolution for the water surface elevation prediction using the HEC‑RAS model: a case study of Moudjar River in the Northeast of Algeria - Said Kechida · Fares Laouacheria · Lotf Zeghadnia (My University, 2023)
Knowledge of variation of the water surface elevation in a river is very important for developing hydraulic models able to predict fooding and food hazard mapping. The roughness and DEMs resolution are factors that can afect the fow characteristics in a river. Manning's roughness plays a signifcant role because it is not a constant parameter and varies depending on the length of the river. DEM resolution is potentially the crucial element to give better food estimations. The objective of the present study is to assess the combined efects of Manning roughness and DEMs resolution to predict water surface elevation of the Moudjar River by using HEC-RAS 1D model. In this study, diferent results were obtained from various combinations of DEM resolution, and Manning roughness coefcients by applying the unsteady HEC-RAS (Hydrologic Engineering Centers- River Analysis System) 1D model on the Moudjar River in Algeria, to predict and quantify the performance regarding actual water surface elevation extent and the generated water surface limits. Data from February 02, 2019, to February 07, 2019, were used to determine predictive accuracy. The simulation results of the stage level of the river fow for diferent combinations of Manning’s roughness coefcient and DEM’s resolution show the closer values between the observed and simulated stage hydrograph, only in the case of the coarser DEM resolution (50 m) where the results are very distant and unacceptable. Statistical parameters such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Nash Sutclif (NSE) were used to verify the model's accuracy and predictive ability. The results obtained and the methodology applied to the Moudjar River can be used as a useful reference for HEC-RAS modelling and food predicting in Algerian rivers.