11 Interfakultäre Einrichtungen
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/12
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Item Open Access Editorial - rapid, reproducible, and robust environmental modeling for decision support : worked examples and open-source software tools(2023) White, Jeremy T.; Fienen, Michael N.; Moore, Catherine R.; Guthke, AnneliItem Open Access Multivariate motion patterns and applications to rainfall radar data(2023) Fischer, Svenja; Oesting, Marco; Schnurr, AlexanderThe classification of movement in space is one of the key tasks in environmental science. Various geospatial data such as rainfall or other weather data, data on animal movement or landslide data require a quantitative analysis of the probable movement in space to obtain information on potential risks, ecological developments or changes in future. Usually, machine-learning tools are applied for this task, as these approaches are able to classify large amounts of data. Yet, machine-learning approaches also have some drawbacks, e.g. the often required large training sets and the fact that the algorithms are often hard to interpret. We propose a classification approach for spatial data based on ordinal patterns. Ordinal patterns have the advantage that they are easily applicable, even to small data sets, are robust in the presence of certain changes in the time series and deliver interpretative results. They therefore do not only offer an alternative to machine-learning in the case of small data sets but might also be used in pre-processing for a meaningful feature selection. In this work, we introduce the basic concept of multivariate ordinal patterns and the corresponding limit theorem. A simulation study based on bootstrap demonstrates the validity of the results. The approach is then applied to two real-life data sets, namely rainfall radar data and the movement of a leopard. Both applications emphasize the meaningfulness of the approach. Clearly, certain patterns related to the atmosphere and environment occur significantly often, indicating a strong dependence of the movement on the environment.Item Open Access A wealth of P-T-t information from metasediments in the HP-UHP terrane of the Pohorje Mountains, Slovenia, elucidates the evolution of the Eastern Alps(2023) Li, Botao; Massonne, Hans‐Joachim; Yuan, XiaopingContrasting views exist in regard of the evolution of metamorphic rocks in the southeastern Pohorje Mountains (Mts), located in the southeastern Eastern Alps. Major debated points are whether micaschists have experienced ultrahigh‐pressure metamorphism in the Late Cretaceous (Eo‐Alpine) and whether they were continuously exhumed or experienced a multiple subduction-exhumation process from that time on. Therefore, we studied micaschist sample 18Slo39 with two generations of garnet and phengitic muscovite from this area. Our detailed study of this rock included petrographic observations, chemical analyses of minerals with the electron microprobe, pseudosection modelling, conventional geothermometry, and monazite in‐situ U‐Th‐Pb dating using laser‐ablation inductively coupled plasma (ICP) mass spectrometry. The following results were obtained: The studied micaschist was subject to a peak pressure of 1.31 ± 0.14 GPa at 603 ± 26°C in Eo‐Alpine times: 90.62 ± 2.78 (2σ) Ma (Stage I). Contact metamorphism at pressure-temperature conditions of 0.66 ± 0.10 GPa and 577 ± 23°C was induced by the intrusion of the Pohorje pluton (Stage III). We determined an early Miocene age of 18.33 ± 0.43 (2σ) Ma for this intrusion. Based on this study and the previously reported data for a micaschist (16Slo12) taken in the vicinity of sample 18Slo39, a geodynamic model is proposed for the region of the Pohorje Mts considering Eo‐Alpine subduction of oceanic crust and European continental crust, of which the micaschist was part of. Another high‐pressure event in the Eocene (Stage II) was the result of intracontinental subduction because of transpression by the Periadriatic fault system that separates the Eastern Alps from the Southern Alps. This type of subduction gave rise to magma generation and ascent to form the Pohorje pluton, which caused contact metamorphism in its vicinity.Item Open Access Efficient wildland fire simulation via nonlinear model order reduction(2021) Black, Felix; Schulze, Philipp; Unger, BenjaminWe propose a new hyper-reduction method for a recently introduced nonlinear model reduction framework based on dynamically transformed basis functions and especially well-suited for transport-dominated systems. Furthermore, we discuss applying this new method to a wildland fire model whose dynamics feature traveling combustion waves and local ignition and is thus challenging for classical model reduction schemes based on linear subspaces. The new hyper-reduction framework allows us to construct parameter-dependent reduced-order models (ROMs) with efficient offline/online decomposition. The numerical experiments demonstrate that the ROMs obtained by the novel method outperform those obtained by a classical approach using the proper orthogonal decomposition and the discrete empirical interpolation method in terms of run time and accuracy.Item Open Access To bucket or not to bucket? : analyzing the performance and interpretability of hybrid hydrological models with dynamic parameterization(2024) Acuña Espinoza, Eduardo; Loritz, Ralf; Álvarez Chaves, Manuel; Bäuerle, Nicole; Ehret, UweHydrological hybrid models have been proposed as an option to combine the enhanced performance of deep learning methods with the interpretability of process-based models. Among the various hybrid methods available, the dynamic parameterization of conceptual models using long short-term memory (LSTM) networks has shown high potential. We explored this method further to evaluate specifically if the flexibility given by the dynamic parameterization overwrites the physical interpretability of the process-based part. We conducted our study using a subset of the CAMELS-GB dataset. First, we show that the hybrid model can reach state-of-the-art performance, comparable with LSTM, and surpassing the performance of conceptual models in the same area. We then modified the conceptual model structure to assess if the dynamic parameterization can compensate for structural deficiencies of the model. Our results demonstrated that the deep learning method can effectively compensate for these deficiencies. A model selection technique based purely on the performance to predict streamflow, for this type of hybrid model, is hence not advisable. In a second experiment, we demonstrated that if a well-tested model architecture is combined with an LSTM, the deep learning model can learn to operate the process-based model in a consistent manner, and untrained variables can be recovered. In conclusion, for our case study, we show that hybrid models cannot surpass the performance of data-driven methods, and the remaining advantage of such models is the access to untrained variables.Item Open Access Analyzing the generalization capabilities of a hybrid hydrological model for extrapolation to extreme events(2025) Acuña Espinoza, Eduardo; Loritz, Ralf; Kratzert, Frederik; Klotz, Daniel; Gauch, Martin; Álvarez Chaves, Manuel; Ehret, UweData-driven techniques have shown the potential to outperform process-based models in rainfall–runoff simulation. Recently, hybrid models, which combine data-driven methods with process-based approaches, have been proposed to leverage the strengths of both methodologies, aiming to enhance simulation accuracy while maintaining a certain interpretability. Expanding the set of test cases to evaluate hybrid models under different conditions, we test their generalization capabilities for extreme hydrological events, comparing their performance against long short-term memory (LSTM) networks and process-based models. Our results indicate that hybrid models show performance similar to that of the LSTM network for most cases. However, hybrid models reported slightly lower errors in the most extreme cases and were able to produce higher peak discharges.