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The calibration of parameters in complex systems usually requires a large computational effort. Moreover, it becomes harder to perform the calibration when non-linear systems underlie the physical process, and the direction to follow in order to optimize an objective function changes depending on the situation. In the context of shallow water equations (SWE), the calibration of parameters, such as the roughness coefficient or the gauge curve for the outlet boundary condition, is often required. In this work, the SWE are used to simulate an open channel flow with lateral gates. Due to the uncertainty in the mathematical modeling that these lateral discharges may introduce into the simulation, the work is focused on the calibration of discharge coefficients. Thus, the calibration is performed by two different approaches. On the one hand, a classical Monte Carlo method is used. On the other hand, the development and application of an adjoint formulation to evaluate the gradient is presented. This is then used in a gradient-based optimizer and is compared with the stochastic approach. The advantages and disadvantages are illustrated and discussed through different test cases.

Calibration of the 1D shallow water equations: a comparison of Monte Carlo and gradient-based optimization methods