Reservoirs are a common way to store and retain water serving for a multitude of purposes like storage of drinking and irrigation water, recreation, flood protection, navigation, and hydropower production, and have been built since centuries. Today, few reservoirs serve only one purpose, which requires management of present demands and interests. Since each reservoir project will cause negative impacts alongside desired advantages both on a local, regional and global scale, it is even more urgent to develop a common management framework in an attempt to mitigate negative impacts, incorporate different demands and make them visible within the discourse in order to avoid conflicts from early on. The scientific publications on reservoirs are manifold, yet a comprehensive and integrative holistic tool about management of this infrastructure is not available. Therefore, a comprehensive and integrated conceptual tool was developed and proposed by the authors of this paper that can contribute to the sustainable management of existing reservoirs. The tool presented herein is based on the results from the interdisciplinary CHARM (CHAllenges of Reservoir Management) project as well as the condensed outcome of relevant literature to aid and enhance knowledge of reservoir management. The incorporated results are based on field, laboratory and empirical social research. The project CHARM focused on five different aspects related to existing reservoirs in southern Germany (Schwarzenbachtalsperre, Franconian Lake District), namely: sedimentation of reservoirs, biostabilisation of fine sediments, toxic cyanobacteria(l) (blooms), greenhouse gas emissions from reservoirs and social contestation, respectively consent. These five research foci contributed to the topics and setup of a conceptual tool, put together by the research consortium via delphi questioning, which can be found alongside this publication to provide insights for experts and laymen. Conceptualising and analysing the management in combination with quantitative and qualitative data in one descriptive tool presents a novelty for the case studies and area of research. The distribution within the scientific community and interested public will possibly make a positive contribution to the goal of sustainable water resources management in the future.

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.

Three‐dimensional (3d) numerical models are state‐of‐the‐art for investigating complex hydrodynamic flow patterns in reservoirs and lakes. Such full‐complexity models are computationally demanding and their calibration is challenging regarding time, subjective decision‐making, and measurement data availability. In addition, physically unrealistic model assumptions or combinations of calibration parameters may remain undetected and lead to overfitting. In this study, we investigate if and how so‐called Bayesian calibration aids in characterizing faulty model setups driven by measurement data and calibration parameter combinations. Bayesian calibration builds on recent developments in machine learning and uses a Gaussian process emulator as a surrogate model, which runs considerably faster than a 3d numerical model. We Bayesian‐calibrate a Delft3D‐FLOW model of a pump‐storage reservoir as a function of the background horizontal eddy viscosity and diffusivity, and initial water temperature profile. We consider three scenarios with varying degrees of faulty assumptions and different uses of flow velocity and water temperature measurements. One of the scenarios forces completely unrealistic, rapid lake stratification and still yields similarly good calibration accuracy as more correct scenarios regarding global statistics, such as the root‐mean‐square error. An uncertainty assessment resulting from the Bayesian calibration indicates that the completely unrealistic scenario forces fast lake stratification through highly uncertain mixing‐related model parameters. Thus, Bayesian calibration describes the quality of calibration and correctness of model assumptions through geometric characteristics of posterior distributions. For instance, most likely calibration parameter values (posterior distribution maxima) at the calibration range limit or with widespread uncertainty characterize poor model assumptions and calibration.

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.

This study presents an experimental approach to investigate cohesive reservoir sediments. It is shown, how adjacent sediment cores can be extracted from reservoir beds with a Frahm Sediment Sampler. The cores are subsequently used for detailed investigations in a hydraulic laboratory. In a first step, related cores are identified based on their bulk density profiles. One part of the related cores is used to analyze the sediment properties over depth by means of potential stability parameters. The other part is used to determine the depth-dependent erosion stability in an erosion flume (SETEG-system). In the SETEG-system, a photogrammetric method is applied to measure the erosion rates of pre-defined sediment layers at different exposed shear stresses. Subsequently, the critical shear stress can be derived, which leads to an objective evaluation and allows a systematic approach. Finally, both results are combined to investigate possible correlations between the evaluated depth-dependent stability parameters and the measured erosion stability. The approach is presented on sediment cores from the case study “Kleiner Brombachsee”, a reservoir that is located in Middle Franconia, Germany.