. This study proposes a framework to estimate the uncertainty of hydrodynamic models on floodplains. The traditional floodplain resistance formula of Pasche (1984) (based on Lindner, 1982) used for river modelling as well as the approaches of Baptist et al. (2007), Jarvela (2004), and Battiato and Rubol (2014) have been considered for carrying out an uncertainty analysis (UA). The analysis was performed by means of three different methods: traditional Monte Carlo (MC), First-Order Second-Moment (FOSM) and Metamodelling. Using a two-dimensional hydrodynamic model, a 10 km reach of the River Rhine was simulated. The model was calibrated with water level measurements under steady flow conditions and then the analysis was carried out based on flow velocity results. The compared floodplain friction formulae produced qualitatively similar results, in which uncertainties in flow velocity were most significant on the floodplains. Among the tested resistance formulae the approach from Jarvela presented on average the smallest prediction intervals i.e. the most accurate results. It is important to keep in mind that UA results are not only dependent on the defined input parameters deviations, but also on the number of parameters considered in the analysis. In that sense, the approach from Battiato and Rubol is still attractive for it reduces the current analysis to a single parameter, the canopy permeability. The three UA methods compared gave similar results, which means that FOSM is the less expensive one. Nevertheless it should be used with caution as it is a first-order method (linear approximation). In studies involving dominant non-linear processes, one is advised to carry out further comparisons.

/pdf/uncertainty-analysis-of-floodplain-friction-in-hydrodynamic-q02yfffb7z.pdf

Uncertainty analysis of floodplain friction in hydrodynamic models

The predictive capability of a model is dependent on the parameter uncertainty involved in it. This study examines the effect of predictive uncertainty and parameter sensitivity in the application of the well-known HEC-RAS 1D hydrodynamic CSU (Colorado State University) scour prediction model. Correlation-based technique was used for carrying out the sensitivity analysis. Monte Carlo method was adopted for uncertainty quantification. The methodology suggested in the present study drastically improved the predictive capability of the model, by reducing the model error from 26.6% to 0.07%. In general, it improved the predictive capability of any scour model when tested on 19 datasets.

/pdf/parameter-uncertainty-in-hec-ras-1d-csu-scour-model-5d3nuz7y1g.pdf

Parameter Uncertainty in HEC-RAS 1D CSU Scour Model

Application of generalized likelihood uncertainty estimation approach in real time updating of flood forecasting model

Uncertainty analysis of water levels of sistan river and reliability analysis of flood control system

Introduction The roughness parameter in hydraulic modelling of natural river and channel flows is not measurable easily and accurate point determination of roughness coefficient, its spatial and temporal variations includes several uncertainties that acts as the main source of error and uncertainties in hydraulic modeling. These drawbacks restricts the applicability of hydraulic modelling in river engineering projects, flood control and management, re-habitation and river restoration. Due to the uncertainties in rating curve, stage, top water width, stream power, shear stress, Froude number and velocity. Because of these drawbacks, in the current study uncertainty analysis by Monte-Carlo Simulation (MCS) combined with HEC-RAS model. Methodology In the current study uncertainty analysis, by Monte-Carlo Simulation (MCS) combined with HEC-RAS model is used to study a 105 km reach of Karoon River from Mollasani to Farsiat as shown in Fig. 1. The model is calibrated and verified using two year daily data of river flow and stage levels in Ahvaz station at the middle of the river reach. A computational control module is developed and combined with computational core of HEC-RAS to perform MCS automatically and the flowchart of modeling strategy and uncertainty analysis is presented in Fig.2. The MCS approach is coupled with computational core of HEC-RAS model by developing a subprogram that create and modifies the input files of HEC-RAS, run it automatically based on random samples of n Manning, and extracting the results of HEC-RAS model in each execution for further analysis in an automatic procedure. By using probability distribution of Manning roughness, 3000 simulations performed and graphical and quantitative indices used to evaluate the uncertainties of model results. In order to refine proper MCS from non-proper ones, the NSE>0.75 index is used to objectively sample n Manning from uncertainty analysis. The uncertainty analysis of proper MCS evaluated by 5 and 95% uncertainty bounds. The uncertainty analysis of model results are evaluated based on the six parameters of water surface elevation, top width of water, flow velocity, Froude number, stream power and shear stress in 3000 runs of peak flow and mean flow discharges respectively and quantified by two indices of 95PPU and d-factor. Results and Discussion The calibration and verification results of the HEC-RAS model in Figs 3-4 shows that in the calibration data set the R2 and RMSE of model in discharge are 0.94 and 21 (m3/s); and 093 and 0.6 (m) for water stage respectively. These values in the verification stage were 0.94 and 25.2(m3/s) for discharge; and 0.91 and 0.1(m) for water stage respectively. The results in 105 km length of Karoon River reveals high level of uncertainties with d-factor greater than 1 up to 11 in peak discharge of 3000 and mean daily discharge of 457 m3/s. These results revealed that using conditional evaluations based on NSE>0.75 reduced the uncertainty of d-factor in results of rating curve, stage, top water width, stream power, shear stress, Froude number and velocity. The d-factor of water stage reduced from 2 to 0.07 in peak discharge, and from 0.96 to 0.02 in average flows. These uncertainty reductions in top width of water were 2.5 to 0.19 in peak discharge and 1.3 to 0.078 in average flows of Karoon river. The highest uncertainty of HEC-RAS model results observed in water velocity and Froude number with di-factor 10.85 and 7.44 in peak discharge respectively. This trend of uncertainty reduction observed for water velocity, Froude number, stream power and shear stress along the river, as provided in Tables 1-3. The spectral responses of hydraulic parameters in model result that presented in Figs 5-10, indicate that although the HEC-RAS model produced high uncertainty values, especially in the complex domain of Karoon river, but these uncertainties dos not deviates the hydraulic patters of river flow in the study reach. The peak and maximum values and the zones of high vales of parameters, show high level of uncertainty than the small or moderate hydraulic situations. These indicates the inherent uncertainty in model results that causes high extents of spectral responses for model simulations. The provided findings necessitates the accurate determination of roughness coefficient according to its spatial and dynamic variations along the river reach. Conclusion The uncertainty results revealed high level of latent uncertainties in HEC-RAS model results and probabilistic analysis of models results is required for river re-habitation and management practices of large rivers such as Karoon River to provide certain and reliable results. The presented methodology and framer in the current study that uses automatic control and automation of HEC-RAS runs, strengths the modeling capability of one dimensional river flows for probabilistic analysis and automatic calibration of this mode.

http://jhyd.iha.ir/article_129403_edd7b0f01904b37e0a3518be69f63f14.pdf

Bahman Fakoori

Papers

Uncertainty analysis of the HEC-RAS results in hydraulic simulation of Karoon River flow by Monte-Carlo approach