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Extreme, short-duration fluctuations caused by storage hydropower plant discharges or 'hydropeaking' occur when hydropower is used to cover the peak electrical loading conditions of a power network. The overall effects of hydropeaking can result in serious disturbances to the hydrologic regime, river morphology and the ecological condition of a river. In this study a transient, fuzzy logic based two-dimensional fish habitat model was used to investigate the stranding risk to juvenile European grayling (Thymallus thymallus) corresponding to different river morphologies. The stranding risk was simulated using two 24 hour discharge hydrographs in two alpine gravel bed river reaches. Both reaches were in close proximity to the hydropower plant outlet and were chosen due to their starkly contrasting morphological features. Spatially distributed stranding risk was determined based on a multi-step procedure which took into account the stationary habitat suitability, critical dewatering rates and flow depths. Although the number of reaches used in the investigation was limited in scope, clear distinctions with respect to the stranding risk were found. The reach with wider, flatter cross sections had a larger amount of stranding risk areas as compared to the reach with a steeply incised channel form. Stranding risk was found to be related to a specific set of changes in the discharge than to a particular rate of change or magnitude of the flow fluctuations. The temporal distribution of stranding risk was found to be almost identical for both reaches.

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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.