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Numerical Simulation 2019/2020: Choosing the right model in applied hydraulics, 11 Jan 2020, Marlin Shlewet, Alaa Abusamra CALIBRATED ROUGHNESS VALUES FOR DIFFERENT FLOW SCENARIOS Marlin, Shlewet Mailing address [email protected] Alaa, Abusamra Mailing address [email protected] SUB TITLE CASE STUDY: RIVER RHINE SECTION RUHRORT- WESEL, 1D MODELLING KEY WORDS Manning coefficient, Validation, Calibrition, Water Level, Unsteady Simulation, Cross Section, Boundary Conditions, Flood Period, Dry Period. ABSTRACT Roughness coefficient is the main important parameter in numerical surface water models, where flow discharge, water level and velocity are calculated from surface water eequations depending on it. This parameter´s value is non-measurable directely from the observation water field, but related to different other factors. The relevant concerned question in this paper is how can we define the exact value which can approximate our simulated model to the real case with enough accuracy?. In this present case study, 1D hydrodynamic model using HEC-ras software has been setted-up -to section of River Rhine approximately 34.1 km length, consists of 69 cross- sections from Ruhrort in the South to Wesel in the North in Germany- using observation time series flow data and H/Q relationship of a flood return period 10 years. The model is used to calibrate the optimized roughness value using evaluation coefficients method , comparing with the observation water level values down stream the river, after running the unsteady flow simulation for three different flow discharge scenarios (high,mean and low flow discharge) and using different manning values between the range 25-40 for each. Also validation for the calibrated manning value of each scenario is implemented. As a result, there is a focus point to find out rather if the flow discharge conditions have an impact on the choosen roughness coefficient value to model the unsteady simulation and how could this impact be explained. This study could be as pre-step to prepare a well-flood model for management plaining or other flow condition. 1. INTRODUCTION The one-dimensional (1D) hydrodynamic model has been used for river engineering and mapping flood risks which will help engineers to take precautionary measures to minimize flood damage. To obtained a realible and accureate results from the application of hydraulic models modeler must identify errors and inconsistency in the input data parameters1. To do so calibration is plays an important role to adjust a model’s parameters such as roughness to reproduce the suimulation results to an acceptable accuracy. The overall target of this task is to analyse potential flooding in the Lower Rhine from Ruhrort (the up-stream guage in South) to Wesel (the down-stream guage in North). Using 1-D hydraulic analysis model in Hec Ras to simulate and identify where and when the flooding may occure. This paper particulary investigate the dependency of the Manning’s roughness coefficient on the flow water condition by simulating defferent numerical unsteady model to check and calibrate Manning’s roughness coefficient for three diferent flow discharge scenarios, high flow during flooding, mean flow during normal conditions and low flow during driest conditions. Generally roughness decreas with increased stage and flow. However the Numerical Simulation 2019/2020: Choosing the right model in applied hydraulics, 11 Jan 2020, Marlin Shlewet, Alaa Abusamra findings of this paper showes different behavire for Rhine rever as the n values increase with increased stage due to effect of the vegitation which make the banks of the river are rougher than the channel. 2. 2. FIRST CALIBRATION SCENARIO FOR THE MANNING`S VALUE 2.1 Create and Run 1-D Model Simulation with Flood Flow Scenario 2.1.1 Geometry& Hotstart Model The geometry of the river is represented by defining topographic available data the coordinates (xyz) of the cross sections along the river and roughness coefficient parameter which is specified as a Manning´s value-n from a range values in 1-D HEC-ras model so it is associated with high degree of uncertainties. It represents the friction of water flow with the river bed and bank and depends on some direct factors like slope and bed type(high roughness associated with low n value)3. The concerned aim of this study is the roughness parameter to be calibrated to minimize errors in the simulation results. Thus , three simulations will be run for this scenario by estimating for each a uniform Manning roughness coefficient of 0.04, 0.029 and 0.025 respectively and that for all sections of the river and embankments, in order to do calibrartion. Hence, the available time series flow discharge of the studied flood event and the given rating curve are considered as boundary conditions up and down stream respectively. Result of HOTSTART simulation (unsteady simulation with steady state for 24h) is defined as initial conditions for the unsteady simulations. 2.1.2 Set-up and Run the Flood Model Simulation The choosen period to simulate the unsteady model for the flood event is continued for two months from 1/10/98 to 30/11/98, so boundary conditions of a flow hydrograph for upstream is used for that period and taken from the available time series flow discharge of Ruhrort station for that period. For the downstream the same rating curve is used. To run this unsteady simulation, a suitable time step for numerical computations of 5 min has been chosed according to the Courant Friedrichs Lewy (CFL) by taking into account the distance between cross sections (500m ) to avoid instability. This simulation run 3 times for each n value. 3.2 Calibration with Results and Discussions In this study the calibration will be done for the roughness coefficient of Manning´s value for the river bed by using comparison period during the flood (high discharge values) from (30-Oct to 9-Nov)/98 and compare between the observation water levels downstream of Wesel station and the three different simulation data of water levels downstream resulting from changing Manning´s value (0.025, 0.029, 0.04). Down stream simulation data has chosen to be compared in order to involve all information from flood and upstream. This comparison to define the optimized manning´s value for this scenario will be evaluated by using different evaluation coefficients: error index - Root Mean Square Error (RMSE) n obs sim 2 √∑ (Vi −Vi ) RMSE = i=1 , (1) n - Percent bias (PBIAS) n obs sim 100∗∑ (Vi −Vi ) i=1 PBIAS = n , (2) obs ∑ Vi i=1 dimensionless evaluation - Nash-Sutcliffe efficiency (NSE) Numerical Simulation 2019/2020: Choosing the right model in applied hydraulics, 11 Jan 2020, Marlin Shlewet, Alaa Abusamra n obs sim 2 ∑ (Vi −Vi ) i=1 NSE = 1 − n , (3) obs obs 2 ∑ (Vi −Vmean) i=1 obs sim Where Vi is the observed water level at time i, Vi is the simuated water level at time i. n=0.04 n=0.029 n=0.025 Evaluation Coefficient K=25 K=34 K=40 RMSE 0.085 0.093 0.100 PBIAS 0.080 0.077 0.083 NSE 9.995E-01 9.994E-01 9.993E-01 Table 1: Result of Coef evaluation for water level downstream from calibration The best match for this scenario with the observed H values during flood discharge was with a K value of 25 (n=0.04) for the river Rhein, where the RMSE and PBIAS have shown the minimum value and the NSE has shown the best fit with observed plot as showen in figure 1. The interpretation for this result is because of that the high water level during flooding makes the friction between the water flow and the river bed on a higher elevation where normally the distribution of the vegetation and other rough things are existed intensively if we do a site visit, means big Manning´s value n (small Strickler´s value-K). Figure 1: The graph shows Water Level at Wesel for different n value with high flow simulation 3.3 Validation Validation is important to check the result from calibrartion of the chosen roughness-coe for the high flow scenario and determine whether the model provides reliable and accepted result for other similar events also. The chosen time of validation is another flooding period from 1/12/2002 to 31/01/2003, where an unsteady simulation is run with same conditions as before and calibrated for the period(01/01-11/01)2003, using the chosen Manning´s value n=0.04. Evaluation Coefficient RMSE PBIAS NSE n=0.04 - K=25 0.043 0.006 9.998E-01 Table 2: Result of Coef evaluation for water level downstream from validation for n=0.04 The evaluation of the selected flood period gives even better result of RMSE and PBIAS, and a perfect fit with observation data from NSE value. Model is validated with n=0.04 during flood period. Numerical Simulation 2019/2020: Choosing the right model in applied hydraulics, 11 Jan 2020, Marlin Shlewet, Alaa Abusamra Figure 2: the graph represent validation of Water Level at Wesel for n=0.04 for with high flow simulation. 4. SECOND CALIBRATION SCENARIO FOR THE MANNING`S VALUE 4.1 Create and Run 1-D Model Simulation with Mean Flow Scenario Aim of this scenario is to check wether the chosen Manning´s value n=0.04 from previous scenario is also could be an optimized value for mean flow discharge values. A period of mean discharge values is needed to be simulated which is already obviously existed on the same period of the previous two months have been simulated from 1/10/98 to 30/11/.

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