Bayesian estimation of a 1D hydrodynamic model in a tidal river: Application to the Lower Seine River, France

TL;DR

This paper presents a Bayesian approach to calibrate a 1D hydrodynamic model for tidal rivers, specifically applied to the Lower Seine, to improve discharge estimation under complex, unsteady flow conditions influenced by tides.

Contribution

It introduces an automatic Bayesian calibration method for the Manning-Strickler friction coefficients in a 1D hydrodynamic model, accounting for uncertainties and complex flow dynamics.

Findings

Bayesian calibration improves parameter estimation accuracy.

The method effectively captures flow reversal and tidal effects.

Uncertainty quantification enhances model reliability.

Abstract

Monitoring water stage and discharge at hydrometric stations is essential for flood characterization and prediction. Continuous measurement is feasible for stage records, whereas discharge must be calculated, typically using a rating curve. Several methods have been developed, such as surface velocity measurement or rating curve methods. Nevertheless, hydrometric stations may be influenced by tidal fluctuations, leading to transient flow conditions that can disrupt the stage-discharge relation, complicating the accurate estimation of discharge. In the case of a quasi-unsteady flow, the dynamic can be managed by a relationship between water level, elevation, and discharge based on the Manning-Strickler formula and measurements of water level and slope of the waterline. However, in unsteady flow, when tidal effects are pronounced, these types of relationships and their variations have proven to be less effective. To capture the complex flow dynamics, including flow reversal, an approach via a 1D hydrodynamic model is proposed. Here, model estimation refers to estimating the posterior distribution of parameters and the structural error model.To set up the model, the cross-sectional geometry, friction coefficient, upstream discharge(s), and downstream water level are necessary. In hydrodynamic modeling, the friction coefficients, often represented through a set of Strickler coefficients, are the main calibration parameter, but manual calibration is difficult due to spatial variations in roughness combined with unsteady flow. Furthermore, understanding and quantifying uncertainties associated with data and the model are an important step in the calibration process. Therefore, automatic calibration of friction coefficients is proposed through Bayesian inference.In terms of numerical tools, the 1D hydrodynamic code used here is Mage, developed by INRAE, which solves the 1D Saint-Venant equations for river flows and transients. However, the proposed method is not specific to a given simulation code: it can be applied to any usual 1D hydrodynamic code. Bayesian calibration is performed using the software BaM! (Bayesian modeling: https://github.com/BaM-tools), which allows specifying prior information on model parameters (in this case, friction coefficients) to then estimate them with associated uncertainties, using observations themselves with their uncertainties (not only water levels but also gauging campaigns).