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

Water storage and supply reservoirs are highly dynamic systems with complex three-dimensional (3d) flow characteristics that can be modeled with computationally demanding numerical simulation software. Such numerical models are vital to predict and plan efforts to maintain the functionality of reservoirs (e.g., drinking water supply, irrigation, or hydropower; Woolway et al., 2021;Zarfl et al., 2015). Still, modeling complex 3d hydrodynamics is a great challenge because many processes and factors, such as thermal stratification, may alter hydrodynam ics in a reservoir (Kerimoglu & Rinke, 2013;Li et al., 2010;Zhang et al., 2020). Thermal stratification occurs, for example, in monomictic, dimictic, or polymictic lakes and reservoirs with generally small flow velocities and

In this study the numerical model SSIIM 2 is used to simulate the flow situation and the suspended sediment transport within the Schwarzenbach reservoir in Germany. Hydrodynamic simulations are carried out to assess the influence of wind forces and different discretization schemes on the calculated flow field. A hydraulic plausibility check is performed based on stationary ADCP measurements to assess the performance of the model. Both the consideration of the wind and the plausibility check using an ADCP are hardly used in large reservoirs so far. The simulation results show a complex flow field with two large (re-)circulation zones in the middle of the reservoir, whereby the temporal development of the simulated and measured velocities have comparable characteristics. Moreover, morphodynamic simulations are performed to compute the suspended sediment transport. The results show that the settling behavior of the sediments is mainly influenced by the inflow discharge and the operation level of the reservoir.

Long-term predictions of reservoir sedimentation require an objective consideration of the preceding catchment processes. In this study, we apply a complex modeling chain to predict sedimentation processes in the Banja reservoir (Albania). The modeling chain consists of the water balance model WaSiM, the soil erosion and sediment transport model combination RUSLE-SEDD, and the 3d hydro-morphodynamic reservoir model SSIIM2 to accurately represent all relevant physical processes. Furthermore, an ensemble of climate models is used to analyze future scenarios. Although the capabilities of each model enable us to obtain satisfying results, the propagation of uncertainties in the modeling chain cannot be neglected. Hence, approximate model parameter uncertainties are quantified with the First-Order Second-Moment (FOSM) method. Another source of uncertainty for long-term predictions is the spread of climate projections. Thus, we compared both sources of uncertainties and found that the uncertainties generated by climate projections are 408% (for runoff), 539% (for sediment yield), and 272% (for bed elevation in the reservoir) larger than the model parameter uncertainties. We conclude that (i) FOSM is a suitable method for quantifying approximate parameter uncertainties in a complex modeling chain, (ii) the model parameter uncertainties are smaller than the spread of climate projections, and (iii) these uncertainties are of the same order of magnitude as the change signal for the investigated low-emission scenario. Thus, the proposed method might support modelers to communicate different sources of uncertainty in complex modeling chains, including climate impact models.

Purpose The sediment supply to rivers, lakes, and reservoirs has a great influence on hydro-morphological processes. For instance, long-term predictions of bathymetric change for modeling climate change scenarios require an objective calculation procedure of sediment load as a function of catchment characteristics and hydro-climatic parameters. Thus, the overarching objective of this study is to develop viable and objective sediment load assessment methods in data-sparse regions. Methods This study uses the Revised Universal Soil Loss Equation (RUSLE) and the SEdiment Delivery Distributed (SEDD) model to predict soil erosion and sediment transport in data-sparse catchments. The novel algorithmic methods build on free datasets, such as satellite and reanalysis data. Novelty stems from the usage of freely available datasets and the introduction of a seasonal snow memory into the RUSLE. In particular, the methods account for non-erosive snowfall, its accumulation over months as a function of temperature, and erosive snowmelt months after the snow fell. Results Model accuracy parameters in the form of Pearson’s r and Nash–Sutcliffe efficiency indicate that data interpolation with climate reanalysis and satellite imagery enables viable sediment load predictions in data-sparse regions. The accuracy of the model chain further improves when snow memory is added to the RUSLE. Non-erosivity of snowfall makes the most significant increase in model accuracy. Conclusion The novel snow memory methods represent a major improvement for estimating suspended sediment loads with the empirical RUSLE. Thus, the influence of snow processes on soil erosion and sediment load should be considered in any analysis of mountainous catchments.

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<p>Hydro-morphodynamic models are increasingly popular for predicting sedimentation processes in reservoirs. To leverage the accuracy of such models, their boundary conditions have to be defined as precise as possible. While hydrological models provide efficient routines to establish inflow hydrographs at the model boundaries, the determination of the sediment input is challenging and involves large uncertainties. This study identifies prominent parameters that influence the sediment input into a reservoir, and therefore, expected sedimentation rates. For this purpose, erosion and transport processes in the catchment area of the Banja Reservoir (Albania) are analyzed.</p><p>The Banja Reservoir is located on the Devoll River in the Southeast of Albania and has a storage capacity of 400 Million m³. The catchment area has a size of 2,900 km² and lies in a mountainous region. The climate is characterized by dry and hot summers and humid winters. There are significant differences in precipitation patterns in the catchment due to topographical conditions and with increasing distance from the coast in the West of the reservoir. Because snowfall is frequent in winter, the runoff regime of the Devoll River and its tributaries is driven by precipitation and snowmelt.</p><p>To calculate the sediment input at the inflow boundaries of the reservoir, a comprehensive analysis in combination with hydrological modelling of the catchment is indispensable. This study applies the Revised Universal Soil Loss Equation (RUSLE) model coupled with the SEdiment Delivery Distributed (SEDD) model, as an integrated approach that bridges interdisciplinary expertise in geomorphology and hydrology. Since measured precipitation data neither fulfils minimum requirements in terms of spatio-temporal resolution nor in terms of time series length, the ERA5 reanalysis dataset is used as input data. The coupled model is calibrated with suspended sediment data measured at a monitoring station upstream of the reservoir over a 2–years period. The model enables to approximate the monthly or annual sediment load for any point in the river network. Thus, the sediment load into the reservoir can be assessed for every major tributary, even in areas with limited data availability. In addition, a high spatial resolution (25 m x 25 m) of the model enables the identification of areas that cause particularly high sediment loads.</p><p>The optimized coupled model predicts sediment loads that are in good agreement with sediment loads measured at the monitoring station (Nash-Sutcliffe efficiency: NSE<sub>annual</sub> = 0.96; NSE<sub>monthly</sub> = 0.81). Consequently, climate reanalysis datasets are a viable alternative in regions with data scarcity. Furthermore, the spatial representation of the results suggests that the sediment load into the reservoir mainly originates from steep and sparsely vegetated or agricultural areas close to the river network. Intensive rainfall additionally fosters erosion, which is why erosion rates are higher in the Western part of the catchment area.</p>