AbstractThis study investigated multivariate relationships between critical erosion thresholds of reservoir sediments and their physicochemical and biological characteristics to unravel the effect of ...sedimentological parameters on fine sediment erosion. We collected 22 sediment cores from the deposits of two reservoirs located in southern Germany, Grosser Brombachsee (GBS), and Schwarzenbachtalsperre (SBT). An erosion flume and an advanced photogrammetric method were used to quantify critical erosion thresholds for a succession of vertical layers over sediment depth. The functional relationships between the critical erosion thresholds and a collection of sediment parameters including bulk density, sediment composition, percentiles, cation exchange capacity, organic content, extracellular polymeric substances (EPS proteins and carbohydrates), and chlorophyll-a were examined. The clay-dominated sediments of the GBS with comparatively low total organic carbon and sand content were, on average, 10 times more stable than the sandy sediments of the SBT. Consequently, for the clay-dominated sediments, strong positive correlations were found between the erosion thresholds and clay content. In contrast, the sandy sediment layers experienced strong positive correlations with the sand content and percentiles. The bulk density was mainly positively correlated, and the total organic carbon content was mainly negatively correlated, with the erosion thresholds. Furthermore, EPS and chlorophyll-a were not good indicators for the erosion thresholds, suggesting an ambiguous influence of biology. Generally, the strength of the relations decreased for sediment layers deeper than 10 cm. Overall, our results underline the need to investigate the influence of sediment characteristics on fine sediment erodibility from varying natural environments.
The description of complex river environments requires interdisciplinary approaches to collect and manage manifold data types and sources. Deriving comprehensive knowledge from complex data sources ...is challenging and necessitates not only knowledge of environmental science but also statistics and Software engineering. This study introduces a relational database framed in an application called River Analyst for creating and managing river data with open-source standards (Python3 and Django). We conceptualize data models of river environments, which describe sediment characteristics and hydraulics related to hyporheic exchange. River Analyst enabled us to derive novel insights for restoring rivers affected by so-called riverbed clogging, notably, fine sediment infiltration in the hyporheic zone. The database analysis reveals that clogging is not a dominant control process when the fraction of fine sediment exceeds 50%–55%. In conclusion, the new Software holds promise for data-informed advancements in augmenting knowledge to restore ecologically functional hydro-environments.
•A database app in Django enables scalable, centralized management of fluvial data.•Database scheme enhances data analysis through linked parametrical data.•Open-source framework can leverage data-driven decisions for river restoration.
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.
Plain Language Summary
Software tools for replicating a real‐world element, such as an artificial lake, need to account for many unknown parameters to create a physically sound conceptual computer model. Still, simplification assumptions are necessary to break down the complex reality into parameters that are easier to calculate. But the simplified parameters take on different values for each model and require specific adjustments. To perform these adjustments, a past event is typically reproduced with the conceptual model and different simplification parameter combinations. The simplification parameter combinations leading to the best possible replication of the past event are assumed to be valid to use the conceptual model for predictions of future events. Alas, many potentially false combinations can replicate a past event with very good results. Thus, a conceptual computer model can be overly adjusted regarding a particular phenomenon, such as heat transfer. Also, the number of possible adjustment tests is limited due to the long computing time of a conceptual model. For these reasons, we use a fast, simplified statistical model of a more complex conceptual model and machine learning for the adjustment process. We find that the statistic uncertainty increases with decreasing physical correctness of simplification parameter combinations.
Key Points
Bayesian calibration efficiently and objectively fits constrained, case‐specific model parameters and identifies remaining uncertainties
Post‐calibration uncertainty assessments help identify incorrect parameter combinations and constraints
More constrained calibration leads to lower uncertainty, which is not detected by global statistics
Abstract Reservoir sedimentation poses a significant challenge to water resource management. Improving the lifespan and productivity of reservoirs requires appropriate sediment management strategies, ...among which flushing operations have become more prevalent in practice. Numerical modeling offers a cost-effective approach to assessing the performance of different flushing operations. However, calibrating highly parametrized morphological models remains a complex task due to inherent uncertainties associated with sediment transport processes and model parameters. Traditional calibration methods require laborious manual adjustments and expert knowledge, hindering calibration accuracy and efficiency and becoming impractical when dealing with several uncertain parameters. A solution is to use optimization techniques that enable an objective evaluation of the model behavior by expediting the calibration procedure and reducing the issue of subjectivity. In this paper, we investigate bed level changes as a result of a flushing event in the Bodendorf reservoir in Austria by using a three-dimensional numerical model coupled with an optimization algorithm for automatic calibration. Three different sediment transport formulae (Meyer-Peter and Müller, van Rijn, and Wu) are employed and modified during the calibration, along with the roughness parameter, active layer thickness, volume fraction of sediments in bed, and the hiding-exposure parameter. The simulated bed levels compared to the measurements are assessed by several statistical metrics in different cross-sections. According to the goodness-of-fit indicators, the models using the formulae of van Rijn and Wu outperform the model calculated by the Meyer-Peter and Müller formula regarding bed patterns and the volume of flushed sediments.
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.
► A 3D RANS model was used to compute the suspended sediment distribution in a reservoir. ► The study includes 3D measurements of the sediment distribution using a LISST-SL device. ► The measurements ...were used to validate the numerical model. ► The morphological bed level changes during an entire operation year were simulated. ► The simulations showed good agreement with the measured deposition pattern.
The three-dimensional numerical model SSIIM was used to compute suspended sediment distribution and deposition pattern in a hydropower reservoir. The study also included three-dimensional measurements of suspended sediments in the reservoir using the LISST-SL instrument. The measurement device is based on a laser-diffraction method and measures concentrations and grain size distributions instantly. It was applied to 25 locations in the reservoir where vertical profiles were taken. The measurements and computed results were compared and reasonable agreement was found. In addition, computed bed elevation changes were compared with measured values in the conducted study. The results of the numerical model agree well with the bed levels taken by echo sounding.
The numerical model SSIIM solves the Reynolds-averaged Navier–Stokes equations in three dimensions and uses an adaptive grid which moves in accordance to changes in the water and bed levels. The suspended sediment transport is calculated by solving the convection–diffusion equation and the bed load transport by an empirical formula. The used implicit free-water surface algorithm provides the possibility of using large time step sizes, which makes a simulation of an operation year on a desktop PC possible.
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.
A fully three-dimensional numerical model for reservoir flushing has been tested against field measurements for the Angostura reservoir in Costa Rica. The numerical program solves the ...Reynolds-averaged Navier–Stokes (RANS) equations in three-dimensions and uses for discretization the finite-volume method together with a second-order upwind scheme. The used grid is unstructured and non-orthogonal, made of a mixture of hexahedral and tetrahedral cells. In addition to the bathymetry data of the prototype, the model uses grain size distributions of the bed, discharge rates and water levels during the flushing. Simulated bed level changes during the flushing are presented in this study as well as the computed amount of eroded sediments. Where the amount of flushed out sediments show reasonable agreement, differences in the developed flushing channel simulated by the model and compared to the prototype were observed. However, the presented study shows that due to the increasing development of three-dimensional RANS models, the simulation of a reservoir flushing in a prototype becomes feasible.
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