Browsing by Author "Mouris, Kilian"

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Open Access
Assessment of uncertainties in a complex modeling chain for predicting reservoir sedimentation under changing climate
(2023) Pesci, María Herminia; Mouris, Kilian; Haun, Stefan; Förster, Kristian
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.
Open Access
Bayesian calibration points to misconceptions in three‐dimensional hydrodynamic reservoir modeling
(2023) Schwindt, Sebastian; Callau Medrano, Sergio; Mouris, Kilian; Beckers, Felix; Haun, Stefan; Nowak, Wolfgang; Wieprecht, Silke; Oladyshkin, Sergey
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.
Open Access
A holistic approach to assess the impact of global change on reservoir sedimentation
(Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2024) Mouris, Kilian; Wieprecht, Silke (Prof. Dr.-Ing.)
Open Access
An interdisciplinary model chain quantifies the footprint of global change on reservoir sedimentation
(2023) Mouris, Kilian; Schwindt, Sebastian; Pesci, María Herminia; Wieprecht, Silke; Haun, Stefan
Global change alters hydro-climatic conditions, affects land use, and contributes to more frequent droughts and floods. Large artificial reservoirs may effectively alleviate hydro-climatic extremes, but their storage capacities are threatened by sedimentation processes, which in turn are exacerbated by land use change. Envisioning strategies for sustainable reservoir management requires interdisciplinary model chains to emulate key processes driving sedimentation under global change scenarios. Therefore, we introduce a model chain for the long-term prediction of complex three-dimensional (3d) reservoir sedimentation considering concurrent catchment, hydro-climatic, and land-use conditions. Applied to a mountainous Mediterranean catchment, the model chain predicts increased sediment production and decreased discharge for high and medium emission pathways. Increased winter precipitation, accompanied by a transition from snowfall to rainfall, is projected to aggravate reduced summer precipitation, emphasizing a growing need for reservoirs. Additionally, higher winter precipitation proliferates sediment production and reservoir sedimentation. Land use change can outweigh the increased reservoir sedimentation originating from hydro-climatic change, which highlights the significance of localized actions to reduce sediment production. Finally, a 3d hydro-morphodynamic model provides insights into interactions between global change and reservoir sedimentation with spatially explicit information on future sedimentation patterns facilitating the implementation of management strategies.
Open Access
Introducing seasonal snow memory into the RUSLE
(2022) Mouris, Kilian; Schwindt, Sebastian; Haun, Stefan; Morales Oreamuno, Maria Fernanda; Wieprecht, Silke
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.
Open Access
Stability criteria for Bayesian calibration of reservoir sedimentation models
(2023) Mouris, Kilian; Acuna Espinoza, Eduardo; Schwindt, Sebastian; Mohammadi, Farid; Haun, Stefan; Wieprecht, Silke; Oladyshkin, Sergey
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.