Stability criteria for Bayesian calibration of reservoir sedimentation models

Abstract Modeling reservoir sedimentation is particularly challenging due to the simultaneous simulation of shallow shores, tributary deltas, and deep waters. The upstream parts reservoirs, where deltaic avulsion erosion processes occur, compete with validity modeling assumptions used simulate deposition fine sediments in We investigate how complex numerical models can be calibrated accurately predict presence competing model simplifications identify importance calibration parameters for prioritization measurement campaigns. This study applies Bayesian calibration, a supervised learning technique using surrogate-assisted inversion Gaussian Process Emulator calibrate two-dimensional (2d) hydro-morphodynamic simulating Albania. Four were fitted obtain statistically best possible bed level changes between 2016 2019 through two differently constraining data scenarios. One scenario included measurements from entire half reservoir. Another only geospatially valid range model. Model accuracy parameters, evidence, variability four indicate that converges toward physically meaningful parameter combinations when nodes are approach also allowed comparison multiple found dry bulk density deposited most important factor calibration.