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Principles

Numerical models in water resources engineering approximate the motion of fluids through iterative solutions of the Navier-Stokes equations and their statistical approximation with the Reynolds-averaged Navier-Stokes equations. The role of numerical models is becoming more and more important where models can be distinguished regarding their simplification hypotheses (e.g., for dimensions or fluid characteristics). Purely hydrodynamic models simulate the motion of water and have high accuracy for simulating flow phenomena, but major challenges remain for morphodynamic modeling. While one-dimensional (1d cross-section-averaged) modeling is slowly abandoned for its incapacity to account for complex flow phenomena in natural rivers, two-dimensional (2d) and three-dimensional (3d) models are becoming more and more popular. Still, there are challenges in model choices and understanding numerical models. In this context, Mosselman & Le (2016) highlight five widespread and common problems in the creation and interpretation of numerical models. These five mistakes are:

  1. Preparation: One-dimensional (1d), two-dimensional (2d), and three-dimensional (3d) models require similar input data (flow series, stage-discharge relation, roughness, digital elevation model, grain sizes). What varies are the computation (3d > 2d > 1d) and the calibration (1d > 2d > 3d) efforts.

  2. Grid setup: The model boundaries need to be at an adequate distance to the area of interest. An inflow boundary should only be along the permanently wetted riverbed and the most upstream 1-2% of the modeled channel bed should have a non-erosive constraint assigned to the cells. Otherwise, the model may be unstable because of locally very high velocity and erosion rates close to the inflow boundary.

  3. Model setup: Read and understand how turbulence closures are implemented in the model to set the model parameters used for the turbulence closure realistically and yield a stable model.

  4. Model validation/post-processing: Wrong confidence in poorly validated numerical models: Every model requires validation data, which involves exhausting and labor-intensive fieldwork.

  5. Model interpretation: The direction of sediment transport and water flow vectors mostly differ.

This chapter introduces open-access and open-source software with extensive tutorials on pre-processing (geo) spatially explicit data, setting up model control files, running models, and post-processing. Tutorials are available in this eBook for the following software:

Calibration and Validation

A numerical model may provide good data, which is not meaningful unless a model is calibrated and validated. There are three possibilities to do so.

  1. Numerical calibration assesses the stability of the simulation itself. The parameters affected are for example the CFL (Courant-Friedrichs-Lewy) condition or other hydraulic parameters. A numerical calibration can be time-consuming and requires expert knowledge to judge the validity of parameters.

  2. Hydraulic calibration (and validation), which compares modeled water surface levels, flow velocities, or bed shear stress with observation data.

  3. Morphological calibration and validation compare simulated with observed terrain change rates (not applicable here because it was not applied in the model).

This eBook provides hints for model calibration (parameters) in the TELEMAC sections on hydrodynamics and morphodynamics.

What to do with Numerical Model Results?

Once the model is calibrated, it can be used to simulate flood hydrographs to assess the stability of river engineering features and the river landscape or inundation area. Moreover, the habitat quality of rivers for target fish species can be assessed, for example, as a function of water depth, flow velocity, and grain size (and other parameters). There is even special software to perform these tasks, such as CASiMiR (commercial) or River Architect.

References
  1. Mosselman, E., & Le, T. B. (2016). Five common mistakes in fluvial morphodynamic modeling. Advances in Water Resources, 93, 15–20. 10.1016/j.advwatres.2015.07.025