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Beckers, Felix [Author]; Heredia, Andrés [Author]; Noack, Markus [Author]; Nowak, Wolfgang [Author]; Wieprecht, Silke [Author]; Oladyshkin, Sergey [Author]; 1 Institute for Modelling Hydraulic and Environmental Systems, Department of Hydraulic Engineering and Water Resources Management University of Stuttgart Stuttgart Germany [Author]; 2 Institute for Modelling Hydraulic and Environmental Systems, Department of Stochastic Simulation and Safety Research for Hydrosystems, SC SimTech University of Stuttgart Stuttgart Germany [Author]

Bayesian Calibration and Validation of a Large‐Scale and Time‐Demanding Sediment Transport Model

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  • Media type: E-Article
  • Title: Bayesian Calibration and Validation of a Large‐Scale and Time‐Demanding Sediment Transport Model
  • Contributor: Beckers, Felix [Author]; Heredia, Andrés [Author]; Noack, Markus [Author]; Nowak, Wolfgang [Author]; Wieprecht, Silke [Author]; Oladyshkin, Sergey [Author]; 1 Institute for Modelling Hydraulic and Environmental Systems, Department of Hydraulic Engineering and Water Resources Management University of Stuttgart Stuttgart Germany [Author]; 2 Institute for Modelling Hydraulic and Environmental Systems, Department of Stochastic Simulation and Safety Research for Hydrosystems, SC SimTech University of Stuttgart Stuttgart Germany [Author]
  • Published: GEO-LEOe-docs (FID GEO), 2020-07-24
  • Language: English
  • DOI: https://doi.org/10.1029/2019WR026966
  • Keywords: Lower River Salzach ; uncertainty analysis ; sediment transport modeling ; arbitrary polynomial chaos expansion ; surrogate model ; Bayesian updating
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  • Description: This study suggests a stochastic Bayesian approach for calibrating and validating morphodynamic sediment transport models and for quantifying parametric uncertainties in order to alleviate limitations of conventional (manual, deterministic) calibration procedures. The applicability of our method is shown for a large‐scale (11.0 km) and time‐demanding (9.14 hr for the period 2002–2013) 2‐D morphodynamic sediment transport model of the Lower River Salzach and for three most sensitive input parameters (critical Shields parameter, grain roughness, and grain size distribution). Since Bayesian methods require a significant number of simulation runs, this work proposes to construct a surrogate model, here with the arbitrary polynomial chaos technique. The surrogate model is constructed from a limited set of runs (n=20) of the full complex sediment transport model. Then, Monte Carlo‐based techniques for Bayesian calibration are used with the surrogate model (105 realizations in 4 hr). The results demonstrate that following Bayesian principles and iterative Bayesian updating of the surrogate model (10 iterations) enables to identify the most probable ranges of the three calibration parameters. Model verification based on the maximum a posteriori parameter combination indicates that the surrogate model accurately replicates the morphodynamic behavior of the sediment transport model for both calibration (RMSE = 0.31 m) and validation (RMSE = 0.42 m). Furthermore, it is shown that the surrogate model is highly effective in lowering the total computational time for Bayesian calibration, validation, and uncertainty analysis. As a whole, this provides more realistic calibration and validation of morphodynamic sediment transport models with quantified uncertainty in less time compared to conventional calibration procedures. ; Key Points: We reduce a time‐demanding sediment transport model with a surrogate technique based on the arbitrary polynomial chaos expansion (aPC). Bayesian model calibration and validation in a fraction ...
  • Access State: Open Access