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

Abstract

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.

Validación De Las Ecuaciones Empíricas De Transporte De Sedimentos Por Arrastre Y Suspensión Empleando Mediciones en Campo

“…Respecto a los resultados en cuanto a la comparación del caudal líquido y sólidos estimados con las mediciones y ecuaciones Beckers, F., Heredia, A., Noack, M., Nowak, W., Wieprecht, S., & Oladyshkin, S. (2020) en su respectivo estudio recomiendan realizar también un análisis bajo un enfoque bayesiano estocástico para calibrar y validar modelos morfodinámicos de transporte de sedimentos y para cuantificar incertidumbres paramétricas con el fin de aliviar las limitaciones de los procedimientos de calibración. Otro aspecto a considerar en esta investigación son los procesos de transporte de sedimentos desde las laderas hasta las cuencas profundas pues así los señalan Manta, K., Rousakis, G., Anastasakis, G., Lykousis, V., Sakellariou, D., & Panagiotopoulos, I. P. (2019), dichos procesos deberían evaluarse no sólo a nivel de tramos de ríos sino a nivel de toda una cuenca, sin embargo, para el caso de estudio considerado se requiere un sistema de medición de sedimentos a nivel diario.…”

Surrogate-based Bayesian comparison of computationally expensive models: application to microbially induced calcite precipitation

“…The suggested BaPC framework has shown promising results for computationally demanding models (e.g. [6,43,54]) and further details are shown in [53]. Alternatively, other Bayesian strategies can be found in [55].…”

“…Here, we restrict the number of Bayesian updates to ten due to the high computational demand and previous experience (see e.g. [6]), so that P…”

Fuzzy map comparisons enable objective hydro‐morphodynamic model validation

“…For example, if the numerical model overestimates erosion, the Shields parameter is increased to emulate a higher resistance of grains against hydraulic forces. The outcome is a potentially overcalibrated hydro-morphodynamic model that needs to be validated with a second observation dataset in order to determine the model's accuracy (Beckers et al, 2020;Mosselman & Le, 2016). Both calibration and validation of hydro-morphodynamic models can be carried out using spatially explicit topographic data acquired at two different points in time.…”